---
title: "Scholar4AI: journal research rankings and trends"
description: "In the last 30 days (2026-09-27 to 2026-10-26), 2,971 papers were published in the 205 tracked finance, accounting, economics, management, operations research and AI journals (as indexed by OpenAlex). The most frequent keywords were “large language models” (89), “China” (79) and “Chinese listed companies” (56)."
url: https://scholar4ai.com/
markdown_url: https://scholar4ai.com/index.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T11:39:12+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# Scholar4AI

Journal Research Rankings: live rankings of research keywords, universities, institution web domains, countries and fields across recent papers in leading Elsevier, Springer Nature and IEEE journals in finance, accounting, economics, management, operations research and AI, refreshed four times a day from OpenAlex. Window: 2026-09-27 – 2026-10-26 (30 days).

- Papers (30 days): 2,971
- Papers (last 7 days): 269
- Journals: 205
- Institutions: 2,594
- Countries: 104
- Web domains: 2,425

## Key findings

- In the last 30 days (2026-09-27 to 2026-10-26), 2,971 papers were published in the 205 tracked finance, accounting, economics, management, operations research and AI journals (as indexed by OpenAlex).
- The most frequent keywords were “large language models” (89), “China” (79) and “Chinese listed companies” (56).
- The institutions with the most papers were Chinese Academy of Sciences (35), Xi'an Jiaotong University (34) and Tsinghua University (32).
- China was involved in 44% of the papers (1,305), followed by United States (347) and United Kingdom (194).
- Among institution web domains, .edu.cn appeared on 43% of the papers.
- The most-mentioned equations and models were Difference-in-differences (121), Instrumental variables (two-stage least squares) (101) and Logistic regression (logit) (97).
- The most-mentioned kinds of data were Macroeconomic and development indicators (59), Stock market indices (45) and China household and social surveys (26); the most-mentioned datasets were S&P 500 (31), World Development Indicators (World Bank) (30) and Eurostat (23).

## Categories

### Finance

- Papers: 491 in 46 journals
- Top keywords: “China” (27), “Chinese listed companies” (26) and “corporate governance” (23)
- Top institutions: Central University of Finance and Economics (11), Southwestern University of Finance and Economics (11) and University of International Business and Economics (9)
- Dashboard: https://scholar4ai.com/categories/finance.md

### Accounting

- Papers: 58 in 16 journals
- Top keywords: “corporate governance” (10), “Chinese listed companies” (5) and “management control” (4)
- Top institutions: Bowling Green State University (2), Tianjin University (2) and Elon University (2)
- Dashboard: https://scholar4ai.com/categories/accounting.md

### Economics

- Papers: 784 in 83 journals
- Top keywords: “China” (44), “monetary policy” (18) and “Chinese listed companies” (14)
- Top institutions: Centre National de la Recherche Scientifique (20), Peking University (12) and Centre for Economic Policy Research (12)
- Dashboard: https://scholar4ai.com/categories/economics.md

### Management

- Papers: 274 in 25 journals
- Top keywords: “Chinese listed companies” (10), “digital transformation” (10) and “dynamic capabilities” (10)
- Top institutions: BI Norwegian Business School (7), Lappeenranta-Lahti University of Technology (5) and University of Reading (5)
- Dashboard: https://scholar4ai.com/categories/management.md

### Operations Research

- Papers: 240 in 11 journals
- Top keywords: “mixed-integer linear programming” (12), “demand uncertainty” (8) and “supply chain coordination” (8)
- Top institutions: Beihang University (9), Southwest Jiaotong University (8) and Hong Kong Polytechnic University (7)
- Dashboard: https://scholar4ai.com/categories/operations-research.md

### AI

- Papers: 1,124 in 24 journals
- Top keywords: “large language models” (70), “graph neural networks” (38) and “multimodal fusion” (29)
- Top institutions: Chinese Academy of Sciences (30), Tsinghua University (25) and University of Electronic Science and Technology of China (22)
- Dashboard: https://scholar4ai.com/categories/ai.md


## Papers per day

*Papers per publication date*

| Date | Papers |
|---|---:|
| 2026-09-27 | 39 |
| 2026-09-28 | 109 |
| 2026-09-29 | 124 |
| 2026-09-30 | 146 |
| 2026-10-01 | 869 |
| 2026-10-02 | 47 |
| 2026-10-03 | 87 |
| 2026-10-04 | 21 |
| 2026-10-05 | 90 |
| 2026-10-06 | 95 |
| 2026-10-07 | 97 |
| 2026-10-08 | 93 |
| 2026-10-09 | 106 |
| 2026-10-10 | 86 |
| 2026-10-11 | 42 |
| 2026-10-12 | 84 |
| 2026-10-13 | 90 |
| 2026-10-14 | 95 |
| 2026-10-15 | 101 |
| 2026-10-16 | 87 |
| 2026-10-17 | 113 |
| 2026-10-18 | 19 |
| 2026-10-19 | 62 |
| 2026-10-20 | 54 |
| 2026-10-21 | 69 |
| 2026-10-22 | 51 |
| 2026-10-23 | 65 |
| 2026-10-24 | 30 |
| 2026-10-25 | 0 |
| 2026-10-26 | 0 |

## Growing over the year (Jul 2026–Sep 2026 vs Oct 2025–Jun 2026)

*Papers in Jul 2026–Sep 2026 beyond what the keyword's share in Oct 2025–Jun 2026 predicts*

| Keyword | Papers per month, Oct 25 – Oct 26* | Papers Jul 2026–Sep 2026 | vs. earlier share |
|---|---|---:|---:|
| absorptive capacity | 5, 6, 9, 9, 8, 9, 8, 8, 5, 17, 12, 15, 8 | 44 | +22 |
| AI governance | 12, 7, 9, 6, 7, 13, 12, 19, 11, 12, 25, 15, 5 | 52 | +20 |
| uncertainty estimation | 6, 2, 4, 9, 9, 11, 6, 9, 14, 14, 10, 17, 7 | 41 | +18 |
| mixture of experts | 7, 8, 8, 14, 6, 13, 13, 11, 14, 16, 19, 12, 10 | 47 | +16 |
| human-AI collaboration | 4, 7, 6, 7, 3, 6, 12, 6, 11, 10, 14, 12, 8 | 36 | +16 |

More: https://scholar4ai.com/trends.md



## Top research keywords (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Keyword | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 89 | 8 |
| 2 | [China](https://scholar4ai.com/keywords/china.md) | 79 | 5 |
| 3 | [Chinese listed companies](https://scholar4ai.com/keywords/chinese-listed-companies.md) | 56 | 1 |
| 4 | [corporate governance](https://scholar4ai.com/keywords/corporate-governance.md) | 49 | 4 |
| 5 | [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 39 | 1 |
| 6 | [artificial intelligence](https://scholar4ai.com/keywords/artificial-intelligence.md) | 36 | 0 |
| 7 | [emerging markets](https://scholar4ai.com/keywords/emerging-markets.md) | 35 | 4 |
| 8 | [information asymmetry](https://scholar4ai.com/keywords/information-asymmetry.md) | 31 | 1 |
| 9 | [digital transformation](https://scholar4ai.com/keywords/digital-transformation.md) | 30 | 2 |
| 9 | [multimodal fusion](https://scholar4ai.com/keywords/multimodal-fusion.md) | 30 | 0 |

## Top universities and research institutions (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Institution | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 35 | 5 |
| 2 | [Xi'an Jiaotong University](https://scholar4ai.com/institutions/xi-an-jiaotong-university.md) | 34 | 2 |
| 3 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 32 | 4 |
| 4 | [Beijing Institute of Technology](https://scholar4ai.com/institutions/beijing-institute-of-technology.md) | 31 | 1 |
| 5 | [Southwestern University of Finance and Economics](https://scholar4ai.com/institutions/southwestern-university-of-finance-and-economics.md) | 30 | 1 |
| 5 | [Wuhan University](https://scholar4ai.com/institutions/wuhan-university.md) | 30 | 1 |
| 7 | [Centre National de la Recherche Scientifique](https://scholar4ai.com/institutions/centre-national-de-la-recherche-scientifique.md) | 29 | 5 |
| 8 | [Beijing Jiaotong University](https://scholar4ai.com/institutions/beijing-jiaotong-university.md) | 26 | 1 |
| 8 | [University of Chinese Academy of Sciences](https://scholar4ai.com/institutions/university-of-chinese-academy-of-sciences.md) | 26 | 3 |
| 10 | [University of Electronic Science and Technology of China](https://scholar4ai.com/institutions/university-of-electronic-science-and-technology-of-china.md) | 25 | 1 |

## Top institution web domains (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Web domain | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [cas.cn](https://scholar4ai.com/domains/cas.cn.md) | 42 | 5 |
| 2 | [xjtu.edu.cn](https://scholar4ai.com/domains/xjtu.edu.cn.md) | 34 | 2 |
| 3 | [tsinghua.edu.cn](https://scholar4ai.com/domains/tsinghua.edu.cn.md) | 33 | 4 |
| 4 | [bit.edu.cn](https://scholar4ai.com/domains/bit.edu.cn.md) | 31 | 1 |
| 4 | [cnrs.fr](https://scholar4ai.com/domains/cnrs.fr.md) | 31 | 7 |
| 4 | [whu.edu.cn](https://scholar4ai.com/domains/whu.edu.cn.md) | 31 | 1 |
| 7 | [swufe.edu.cn](https://scholar4ai.com/domains/swufe.edu.cn.md) | 30 | 1 |
| 8 | [njtu.edu.cn](https://scholar4ai.com/domains/njtu.edu.cn.md) | 26 | 1 |
| 8 | [ucas.ac.cn](https://scholar4ai.com/domains/ucas.ac.cn.md) | 26 | 3 |
| 10 | [uestc.edu.cn](https://scholar4ai.com/domains/uestc.edu.cn.md) | 25 | 1 |

## Top countries (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Country | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | China (CN) | 1,305 | 87 |
| 2 | United States (US) | 347 | 37 |
| 3 | United Kingdom (GB) | 194 | 20 |
| 4 | Italy (IT) | 126 | 13 |
| 5 | Australia (AU) | 106 | 11 |
| 6 | Germany (DE) | 105 | 9 |
| 7 | India (IN) | 104 | 11 |
| 8 | France (FR) | 98 | 16 |
| 9 | South Korea (KR) | 86 | 7 |
| 10 | Spain (ES) | 80 | 8 |

## Research fields (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Field | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | Computer Science | 756 | 53 |
| 2 | Economics, Econometrics and Finance | 617 | 52 |
| 3 | Business, Management and Accounting | 539 | 51 |
| 4 | Engineering | 294 | 24 |
| 5 | Social Sciences | 268 | 21 |
| 6 | Decision Sciences | 154 | 24 |
| 7 | Environmental Science | 43 | 3 |
| 8 | Medicine | 40 | 4 |
| 9 | Energy | 32 | 9 |
| 9 | Psychology | 32 | 2 |

## Papers per journal (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Journal | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 249 | 14 |
| 2 | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 188 | 9 |
| 3 | [Finance research letters](https://scholar4ai.com/journals/finance-research-letters.md) | 127 | 3 |
| 4 | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 110 | 6 |
| 5 | [International Review of Economics & Finance](https://scholar4ai.com/journals/international-review-of-economics-finance.md) | 97 | 6 |
| 6 | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 90 | 0 |
| 7 | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 89 | 6 |
| 8 | [Economic Analysis and Policy](https://scholar4ai.com/journals/economic-analysis-and-policy.md) | 71 | 1 |
| 8 | [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | 71 | 1 |
| 10 | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 64 | 8 |

## Most-used equations and models (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Equation or model | Formula | Papers |
|---:|---|---|---:|
| 1 | [Difference-in-differences](https://scholar4ai.com/equations/difference-in-differences.md) | $Y_{it} = \alpha_i + \lambda_t + \delta\,(\mathit{Treat}_i \times \mathit{Post}_t) + \varepsilon_{it}$ | 121 |
| 2 | [Instrumental variables (two-stage least squares)](https://scholar4ai.com/equations/instrumental-variables-two-stage-least-squares.md) | $\hat\beta_{\mathrm{2SLS}} = (X^\top P_Z X)^{-1} X^\top P_Z\, y,\quad P_Z = Z (Z^\top Z)^{-1} Z^\top$ | 101 |
| 3 | [Logistic regression (logit)](https://scholar4ai.com/equations/logistic-regression-logit.md) | $\Pr(y = 1 \mid x) = \frac{1}{1 + e^{-x^\top\beta}}$ | 97 |
| 4 | [Ordinary least squares (OLS)](https://scholar4ai.com/equations/ordinary-least-squares-ols.md) | $\hat\beta = (X^\top X)^{-1} X^\top y$ | 93 |
| 5 | [Graph neural network (graph convolution)](https://scholar4ai.com/equations/graph-neural-network-graph-convolution.md) | $H^{(l+1)} = \sigma\big(\tilde D^{-1/2} \tilde A\, \tilde D^{-1/2} H^{(l)} W^{(l)}\big)$ | 75 |
| 6 | [Mediation model (indirect effect)](https://scholar4ai.com/equations/mediation-model-indirect-effect.md) | $M = \alpha_0 + a X + e_1,\quad Y = \beta_0 + c' X + b M + e_2,\quad \text{indirect effect} = a\,b$ | 60 |
| 7 | [Scaled dot-product attention (Transformer)](https://scholar4ai.com/equations/scaled-dot-product-attention-transformer.md) | $\mathrm{Attention}(Q, K, V) = \mathrm{softmax}\Big(\frac{Q K^\top}{\sqrt{d_k}}\Big) V$ | 56 |
| 8 | [Two-way fixed-effects panel regression](https://scholar4ai.com/equations/two-way-fixed-effects-panel-regression.md) | $y_{it} = \alpha_i + \lambda_t + x_{it}^\top\beta + \varepsilon_{it}$ | 55 |
| 9 | [Structural equation modeling (SEM)](https://scholar4ai.com/equations/structural-equation-modeling-sem.md) | $\eta = B\eta + \Gamma\xi + \zeta,\quad y = \Lambda_y\,\eta + \varepsilon$ | 50 |
| 10 | [Vector autoregression (VAR)](https://scholar4ai.com/equations/vector-autoregression-var.md) | $y_t = c + \sum_{i=1}^{p} A_i\, y_{t-i} + \varepsilon_t$ | 49 |

## Most-used data (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Data category | Most-mentioned datasets | Papers |
|---:|---|---|---:|
| 1 | [Macroeconomic and development indicators](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md) | World Development Indicators (World Bank) (30), Eurostat (23), Penn World Table (7), IMF data (IFS, WEO, DOTS) (7) … | 59 |
| 2 | [Stock market indices](https://scholar4ai.com/datasets/stock-market-indices.md) | S&P 500 (31), CSI 300 / Shanghai Composite (8), MSCI indices (5), Euro Stoxx / DAX (5) … | 45 |
| 3 | [China household and social surveys](https://scholar4ai.com/datasets/china-household-and-social-surveys.md) | China Household Finance Survey (CHFS) (11), China Family Panel Studies (CFPS) (9), China Health and Nutrition Survey (CHNS) (4), CHARLS (3) … | 26 |
| 4 | [China statistical yearbooks and regional indices](https://scholar4ai.com/datasets/china-statistical-yearbooks-and-regional-indices.md) | Marketization index (Fan Gang) (13), China Statistical Yearbook (3), National Bureau of Statistics of China (3), China City Statistical Yearbook (2) … | 21 |
| 5 | [Financial data platforms](https://scholar4ai.com/datasets/financial-data-platforms.md) | Refinitiv Datastream (5), WRDS (4), Refinitiv Eikon / LSEG Workspace (3), Yahoo Finance (3) … | 20 |
| 6 | [China financial and firm databases](https://scholar4ai.com/datasets/china-financial-and-firm-databases.md) | CSMAR (17), CNRDS (6), Wind (5) | 19 |
| 6 | [Corporate filings and text](https://scholar4ai.com/datasets/corporate-filings-and-text.md) | Earnings call transcripts (9), Loughran-McDonald dictionary (9), 10-K filings (4), XBRL filings (2) … | 19 |
| 8 | [European and other national household panels](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md) | Labour Force Surveys (5), SHARE (4), German Socio-Economic Panel (SOEP) (3), Understanding Society / BHPS (2) … | 17 |
| 9 | [Options and volatility data](https://scholar4ai.com/datasets/options-and-volatility-data.md) | VIX (16), OVX (crude oil volatility index) (2) | 16 |
| 10 | [Company financial statements](https://scholar4ai.com/datasets/company-financial-statements.md) | Compustat (10), Orbis (Bureau van Dijk) (3), Amadeus (Bureau van Dijk) (1), S&P Capital IQ (1) | 15 |


## Latest papers

*Latest papers*

| Date | Title | Journal | Authors | Affiliations |
|---|---|---|---|---|
| 2026-10-24 | [Digital competency, data science literacy, and fraud detection skills among generation Z accountants: Evidence from Peru](https://doi.org/10.1016/j.jaccedu.2026.101057) | [Journal of Accounting Education](https://scholar4ai.com/journals/journal-of-accounting-education.md) | Jorge Miguel Chávez-Díaz, Elsa Esther Choy Zevallos, Lázaro Miguel Ángel Cotrina Reyes et al. |  |
| 2026-10-24 | [PF-Mamba-UNet: An Anatomical-Prior-Guided Medical Image Segmentation Network Based on Mamba and Spatial-Channel Collaborative Attention Fusion](https://doi.org/10.1007/s12559-026-10667-7) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Leyi Xiao, Kangshun Liao, Baoxian Zhou et al. | Hainan University |
| 2026-10-24 | [Forging holistic nuclear governance: The unifying role of China's atomic energy law in risk regulation and industry promotion](https://doi.org/10.1016/j.enpol.2026.115681) | [Energy Policy](https://scholar4ai.com/journals/energy-policy.md) | BangDa HU |  |
| 2026-10-24 | [Framed norms: the effect of choice and belief information on tax compliance](https://doi.org/10.1007/s10797-026-10009-1) | [International Tax and Public Finance](https://scholar4ai.com/journals/international-tax-and-public-finance.md) | Federico Atzori, Vittorio Pelligra | University of Cagliari |
| 2026-10-24 | [University shocks and research productivity inequality: Evidence from the COVID-19 pandemic](https://doi.org/10.1016/j.jebo.2026.107802) | [Journal of Economic Behavior & Organization](https://scholar4ai.com/journals/journal-of-economic-behavior-organization.md) | Yuxiang Jiang, R. Liu, Dominik M. Rösch et al. | Southwestern University of Finance and Economics; University at Buffalo, State University of New York |
| 2026-10-24 | [Recent Developments at the CMA: 2025–26](https://doi.org/10.1007/s11151-026-10066-0) | [Review of Industrial Organization](https://scholar4ai.com/journals/review-of-industrial-organization.md) | Catherine Colebrook, Helen Ferguson, Jenny Haydock et al. |  |
| 2026-10-24 | [Coal subsidies and long-term contract pricing: a perspective from factor marketization reforms](https://doi.org/10.1016/j.enpol.2026.115678) | [Energy Policy](https://scholar4ai.com/journals/energy-policy.md) | Xiaofei Wang, Nan Liu, Kuijian Qu et al. | Shandong Institute of Business and Technology; Hunan Normal University; Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education) |
| 2026-10-24 | [The intensive margin of food access and its association with health outcomes: evidence of heterogeneous effects across U.S. Communities](https://doi.org/10.1016/j.foodpol.2026.103212) | [Food Policy](https://scholar4ai.com/journals/food-policy.md) | Qingwei Qiao, Syed Badruddoza, Modhurima Dey Amin et al. | Washington State University; Texas Tech University; Economic Research Service |
| 2026-10-24 | [Geopolitical risk and energy poverty in African net energy importers: Localized effects, structural vulnerability, and conditional resilience](https://doi.org/10.1016/j.enpol.2026.115680) | [Energy Policy](https://scholar4ai.com/journals/energy-policy.md) | Amine Kendri, Hafid El Hassani |  |
| 2026-10-24 | [From Big Data to Generative Artificial Intelligence: A Bibliometric Analysis of the Conceptual Structure and Thematic Evolution of Computational Social Science (2011–2025)](https://doi.org/10.1007/s10796-026-10830-3) | [Information Systems Frontiers](https://scholar4ai.com/journals/information-systems-frontiers.md) | Rukiye ÜNAL AYAZ, Hakan Özköse, Muhammet Berigel | Karadeniz Technical University |
| 2026-10-24 | [Fiscal information and stated willingness to pay taxes: evidence from a survey experiment](https://doi.org/10.1007/s10797-026-10007-3) | [International Tax and Public Finance](https://scholar4ai.com/journals/international-tax-and-public-finance.md) | José María Durán-Cabré, Alejandro Esteller Moré, Riccardo Secomandi | Barcelona School of Economics; Universitat de Barcelona; University of Ferrara |
| 2026-10-24 | [Oil import diversification and the macroeconomic transmission of oil supply shocks in the Eurozone](https://doi.org/10.1007/s00181-026-02986-3) | [Empirical Economics](https://scholar4ai.com/journals/empirical-economics.md) | Antonio J. Garzón, Mehmet Pinar | Universidad de Cádiz; Istanbul Bilgi University; Universidad de Sevilla |
| 2026-10-24 | [Evolutionary Game Analysis of Value Co-Creation in Green Innovation Chains Under Revenue-Sharing Contracts](https://doi.org/10.1007/s10614-026-11450-4) | [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | Rongjian Xie, Sifan Wang, Qianyao Yang et al. | Nanjing University of Information Science and Technology; Nanjing University of Science and Technology |
| 2026-10-24 | [Inequality matters: A systematic review of heterogeneity in integrated assessment models](https://doi.org/10.1016/j.ecolecon.2026.109280) | [Ecological Economics](https://scholar4ai.com/journals/ecological-economics.md) | Sara Pavesio, Jaime Nieto, Mercedes Molpeceres et al. | Universidad de Valladolid |
| 2026-10-24 | [A poorly targeted subsidy? The fiscal and social costs of EU VAT expenditures](https://doi.org/10.1007/s10797-026-10008-2) | [International Tax and Public Finance](https://scholar4ai.com/journals/international-tax-and-public-finance.md) | Michael Christl | Universidad Loyola Andalucía |

## Questions this page answers

### What are the most common topics in recent finance, accounting, economics, management, operations research and AI research?

In the last 30 days, the most frequent OpenAlex keywords in papers from the tracked finance, accounting, economics, management, operations research and AI journals were “large language models” (89), “China” (79), “Chinese listed companies” (56), “corporate governance” (49) and “graph neural networks” (39).

### Which universities and institutions publish the most finance, accounting, economics, management, operations research and AI research?

In the last 30 days, the institutions with the most papers in the tracked finance, accounting, economics, management, operations research and AI journals were Chinese Academy of Sciences (35), Xi'an Jiaotong University (34), Tsinghua University (32), Beijing Institute of Technology (31) and Southwestern University of Finance and Economics (30).

### Which countries publish the most finance, accounting, economics, management, operations research and AI research?

Counting papers with at least one author at an institution in the country, the leaders over the last 30 days were China (1,305), United States (347), United Kingdom (194), Italy (126) and Australia (106).

### Which equations and models do recent finance, accounting, economics, management, operations research and AI research papers use most?

Counting papers that mention them by name in the title, abstract, keywords or full text, the most frequent in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days were Difference-in-differences (121), Instrumental variables (two-stage least squares) (101), Logistic regression (logit) (97), Ordinary least squares (OLS) (93) and Graph neural network (graph convolution) (75).

### What data do recent finance, accounting, economics, management, operations research and AI research papers use?

Counting papers that mention a dataset by name, the kinds of data used most over the last 30 days were Macroeconomic and development indicators (59), Stock market indices (45), China household and social surveys (26), China statistical yearbooks and regional indices (21) and Financial data platforms (20); the most-mentioned datasets were S&P 500 (31), World Development Indicators (World Bank) (30), Eurostat (23), CSMAR (17) and VIX (16).

### How current is this data?

It is rebuilt from OpenAlex 4 times a day (00:00, 06:00, 12:00, 18:00, UTC+09:00); the last update was 2026-10-26 11:39 UTC+09:00. OpenAlex usually indexes new papers within a few days of publication.


---

---
title: "Finance research: top topics, institutions and countries"
description: "In the last 30 days (2026-09-27 to 2026-10-26), 491 papers were published in the 46 tracked finance journals (as indexed by OpenAlex). The most frequent keywords were “China” (27), “Chinese listed companies” (26) and “corporate governance” (23)."
url: https://scholar4ai.com/categories/finance
markdown_url: https://scholar4ai.com/categories/finance.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T11:39:12+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# Finance research rankings

Papers in the 46 tracked finance journals, window 2026-09-27 – 2026-10-26 (30 days). Keywords, institutions, web domains and countries are ranked by paper count.

- Papers (30 days): 491
- Papers (last 7 days): 33
- Journals: 46
- Institutions: 641
- Countries: 67
- Web domains: 626

## Key findings

- In the last 30 days (2026-09-27 to 2026-10-26), 491 papers were published in the 46 tracked finance journals (as indexed by OpenAlex).
- The most frequent keywords were “China” (27), “Chinese listed companies” (26) and “corporate governance” (23).
- The institutions with the most papers were Central University of Finance and Economics (11), Southwestern University of Finance and Economics (11) and University of International Business and Economics (9).
- China was involved in 43% of the papers (209), followed by United States (63) and United Kingdom (20).
- Among institution web domains, .edu.cn appeared on 42% of the papers.
- The most-mentioned equations and models were Difference-in-differences (43), Instrumental variables (two-stage least squares) (35) and Ordinary least squares (OLS) (35).
- The most-mentioned kinds of data were Stock market indices (25), China financial and firm databases (15) and Macroeconomic and development indicators (14); the most-mentioned datasets were S&P 500 (18), CSMAR (14) and VIX (12).

## Papers per day

*Papers per publication date*

| Date | Papers |
|---|---:|
| 2026-09-27 | 6 |
| 2026-09-28 | 24 |
| 2026-09-29 | 27 |
| 2026-09-30 | 24 |
| 2026-10-01 | 123 |
| 2026-10-02 | 7 |
| 2026-10-03 | 20 |
| 2026-10-04 | 6 |
| 2026-10-05 | 14 |
| 2026-10-06 | 22 |
| 2026-10-07 | 18 |
| 2026-10-08 | 20 |
| 2026-10-09 | 20 |
| 2026-10-10 | 18 |
| 2026-10-11 | 9 |
| 2026-10-12 | 15 |
| 2026-10-13 | 16 |
| 2026-10-14 | 16 |
| 2026-10-15 | 16 |
| 2026-10-16 | 12 |
| 2026-10-17 | 16 |
| 2026-10-18 | 2 |
| 2026-10-19 | 7 |
| 2026-10-20 | 6 |
| 2026-10-21 | 10 |
| 2026-10-22 | 7 |
| 2026-10-23 | 8 |
| 2026-10-24 | 2 |
| 2026-10-25 | 0 |
| 2026-10-26 | 0 |

## Growing over the year (Jul 2026–Sep 2026 vs Oct 2025–Jun 2026)

*Papers in Jul 2026–Sep 2026 beyond what the keyword's share in Oct 2025–Jun 2026 predicts*

| Keyword | Papers per month, Oct 25 – Oct 26* | Papers Jul 2026–Sep 2026 | vs. earlier share |
|---|---|---:|---:|
| [artificial intelligence](https://scholar4ai.com/keywords/artificial-intelligence.md) | 4, 5, 12, 4, 3, 8, 8, 4, 6, 10, 12, 9, 8 | 31 | +14 |
| corporate finance | 3, 2, 1, 1, 1, 4, 2, 1, 4, 7, 3, 8, 4 | 18 | +12 |
| price discovery | 1, 2, 1, 3, 2, 2, 2, 5, 3, 7, 3, 8, 3 | 18 | +11 |
| greenwashing | 6, 4, 5, 4, 3, 4, 6, 8, 1, 5, 11, 8, 3 | 24 | +11 |
| asset pricing | 3, 4, 8, 7, 1, 6, 12, 7, 7, 15, 8, 5, 7 | 28 | +10 |

More: https://scholar4ai.com/trends.md



## Top research keywords (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Keyword | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [China](https://scholar4ai.com/keywords/china.md) | 27 | 3 |
| 2 | [Chinese listed companies](https://scholar4ai.com/keywords/chinese-listed-companies.md) | 26 | 1 |
| 3 | [corporate governance](https://scholar4ai.com/keywords/corporate-governance.md) | 23 | 1 |
| 4 | [emerging markets](https://scholar4ai.com/keywords/emerging-markets.md) | 18 | 3 |
| 5 | [financial constraints](https://scholar4ai.com/keywords/financial-constraints.md) | 15 | 0 |
| 6 | [digital transformation](https://scholar4ai.com/keywords/digital-transformation.md) | 13 | 2 |
| 6 | financial stability | 13 | 1 |
| 6 | stock returns | 13 | 0 |
| 9 | green finance | 12 | 1 |
| 10 | [artificial intelligence](https://scholar4ai.com/keywords/artificial-intelligence.md) | 11 | 0 |
| 11 | climate risk | 10 | 0 |
| 11 | cryptocurrency market | 10 | 0 |
| 11 | [information asymmetry](https://scholar4ai.com/keywords/information-asymmetry.md) | 10 | 0 |
| 11 | market microstructure | 10 | 1 |
| 15 | corporate disclosure | 9 | 0 |
| 15 | corporate innovation | 9 | 0 |
| 15 | digital economy | 9 | 0 |
| 15 | financial contagion | 9 | 0 |
| 15 | mergers and acquisitions | 9 | 0 |
| 20 | Chinese enterprises | 8 | 0 |

## Top universities and research institutions (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Institution | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | Central University of Finance and Economics | 11 | 1 |
| 1 | [Southwestern University of Finance and Economics](https://scholar4ai.com/institutions/southwestern-university-of-finance-and-economics.md) | 11 | 0 |
| 3 | University of International Business and Economics | 9 | 0 |
| 4 | Shanghai University of Finance and Economics | 7 | 0 |
| 4 | City University of Macau | 7 | 0 |
| 4 | [Xi'an Jiaotong University](https://scholar4ai.com/institutions/xi-an-jiaotong-university.md) | 7 | 0 |
| 7 | Xiamen University | 6 | 0 |
| 7 | Capital University of Economics and Business | 6 | 0 |
| 9 | Jinan University | 5 | 0 |
| 9 | Hunan University | 5 | 0 |
| 9 | Guangdong University Of Finances and Economics | 5 | 1 |
| 9 | Shandong University of Finance and Economics | 5 | 0 |
| 9 | [Renmin University of China](https://scholar4ai.com/institutions/renmin-university-of-china.md) | 5 | 0 |
| 14 | Shanghai University | 4 | 0 |
| 14 | Nanjing Normal University | 4 | 0 |
| 14 | Corvinus University of Budapest | 4 | 0 |
| 14 | University of South Africa | 4 | 0 |
| 14 | Beijing Technology and Business University | 4 | 0 |
| 14 | University of Macau | 4 | 0 |
| 14 | [Fudan University](https://scholar4ai.com/institutions/fudan-university.md) | 4 | 2 |

## Top institution web domains (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Web domain | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | cufe.edu.cn | 11 | 1 |
| 1 | [swufe.edu.cn](https://scholar4ai.com/domains/swufe.edu.cn.md) | 11 | 0 |
| 3 | uibe.edu.cn | 9 | 0 |
| 4 | cityu.edu.mo | 7 | 0 |
| 4 | sufe.edu.cn | 7 | 0 |
| 4 | [xjtu.edu.cn](https://scholar4ai.com/domains/xjtu.edu.cn.md) | 7 | 0 |
| 7 | cueb.edu.cn | 6 | 0 |
| 7 | xmu.edu.cn | 6 | 0 |
| 9 | gdufe.edu.cn | 5 | 1 |
| 9 | hnu.edu.cn | 5 | 0 |
| 9 | jnu.edu.cn | 5 | 0 |
| 9 | [ruc.edu.cn](https://scholar4ai.com/domains/ruc.edu.cn.md) | 5 | 0 |
| 9 | sdufe.edu.cn | 5 | 0 |
| 14 | btbu.edu.cn | 4 | 0 |
| 14 | [fudan.edu.cn](https://scholar4ai.com/domains/fudan.edu.cn.md) | 4 | 2 |
| 14 | njnu.edu.cn | 4 | 0 |
| 14 | nju.edu.cn | 4 | 0 |
| 14 | shu.edu.cn | 4 | 0 |
| 14 | um.edu.mo | 4 | 0 |
| 14 | uni-corvinus.hu | 4 | 0 |

## Top countries (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Country | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | China (CN) | 209 | 8 |
| 2 | United States (US) | 63 | 2 |
| 3 | United Kingdom (GB) | 20 | 0 |
| 4 | South Korea (KR) | 18 | 1 |
| 5 | Australia (AU) | 15 | 0 |
| 5 | Turkey (TR) | 15 | 3 |
| 7 | India (IN) | 14 | 3 |
| 7 | Italy (IT) | 14 | 0 |
| 9 | Germany (DE) | 13 | 1 |
| 10 | Macao (MO) | 11 | 0 |
| 11 | Japan (JP) | 10 | 0 |
| 12 | Spain (ES) | 9 | 1 |
| 13 | Chile (CL) | 8 | 1 |
| 13 | France (FR) | 8 | 2 |
| 15 | Belgium (BE) | 7 | 0 |
| 15 | Canada (CA) | 7 | 1 |
| 17 | Brazil (BR) | 6 | 1 |
| 17 | Malaysia (MY) | 6 | 1 |
| 17 | Pakistan (PK) | 6 | 0 |
| 17 | South Africa (ZA) | 6 | 0 |

## Research fields (top 9)

*Top 9 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Field | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | Economics, Econometrics and Finance | 255 | 16 |
| 2 | Business, Management and Accounting | 165 | 10 |
| 3 | Social Sciences | 23 | 1 |
| 4 | Decision Sciences | 17 | 1 |
| 5 | Computer Science | 13 | 0 |
| 6 | Environmental Science | 6 | 0 |
| 7 | Agricultural and Biological Sciences | 2 | 1 |
| 8 | Energy | 1 | 1 |
| 8 | Engineering | 1 | 0 |

## Papers per journal (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Journal | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [Finance research letters](https://scholar4ai.com/journals/finance-research-letters.md) | 127 | 3 |
| 2 | [International Review of Economics & Finance](https://scholar4ai.com/journals/international-review-of-economics-finance.md) | 97 | 6 |
| 3 | [International Review of Financial Analysis](https://scholar4ai.com/journals/international-review-of-financial-analysis.md) | 45 | 1 |
| 4 | [Research in International Business and Finance](https://scholar4ai.com/journals/research-in-international-business-and-finance.md) | 34 | 1 |
| 5 | [Pacific-Basin Finance Journal](https://scholar4ai.com/journals/pacific-basin-finance-journal.md) | 18 | 1 |
| 6 | [The North American Journal of Economics and Finance](https://scholar4ai.com/journals/the-north-american-journal-of-economics-and-finance.md) | 16 | 0 |
| 7 | [Emerging Markets Review](https://scholar4ai.com/journals/emerging-markets-review.md) | 11 | 1 |
| 7 | [The Quarterly Review of Economics and Finance](https://scholar4ai.com/journals/the-quarterly-review-of-economics-and-finance.md) | 11 | 0 |
| 9 | [Journal of Financial Economics](https://scholar4ai.com/journals/journal-of-financial-economics.md) | 10 | 2 |
| 9 | [Journal of Financial Stability](https://scholar4ai.com/journals/journal-of-financial-stability.md) | 10 | 0 |
| 11 | [Journal of International Financial Markets Institutions and Money](https://scholar4ai.com/journals/journal-of-international-financial-markets-institutions-and-money.md) | 9 | 3 |
| 12 | [Journal of Behavioral and Experimental Finance](https://scholar4ai.com/journals/journal-of-behavioral-and-experimental-finance.md) | 8 | 0 |
| 13 | [Asia-Pacific Financial Markets](https://scholar4ai.com/journals/asia-pacific-financial-markets.md) | 7 | 0 |
| 13 | [Digital Finance](https://scholar4ai.com/journals/digital-finance.md) | 7 | 1 |
| 13 | [Financial Innovation](https://scholar4ai.com/journals/financial-innovation.md) | 7 | 1 |
| 13 | [Journal of Banking & Finance](https://scholar4ai.com/journals/journal-of-banking-finance.md) | 7 | 0 |
| 13 | [Journal of Banking Regulation](https://scholar4ai.com/journals/journal-of-banking-regulation.md) | 7 | 2 |
| 13 | [Journal of Financial Services Marketing](https://scholar4ai.com/journals/journal-of-financial-services-marketing.md) | 7 | 1 |
| 19 | [Financial markets and portfolio management](https://scholar4ai.com/journals/financial-markets-and-portfolio-management.md) | 5 | 2 |
| 19 | [Journal of Economics and Finance](https://scholar4ai.com/journals/journal-of-economics-and-finance.md) | 5 | 3 |

## Most-used equations and models (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Equation or model | Formula | Papers |
|---:|---|---|---:|
| 1 | [Difference-in-differences](https://scholar4ai.com/equations/difference-in-differences.md) | $Y_{it} = \alpha_i + \lambda_t + \delta\,(\mathit{Treat}_i \times \mathit{Post}_t) + \varepsilon_{it}$ | 43 |
| 2 | [Instrumental variables (two-stage least squares)](https://scholar4ai.com/equations/instrumental-variables-two-stage-least-squares.md) | $\hat\beta_{\mathrm{2SLS}} = (X^\top P_Z X)^{-1} X^\top P_Z\, y,\quad P_Z = Z (Z^\top Z)^{-1} Z^\top$ | 35 |
| 2 | [Ordinary least squares (OLS)](https://scholar4ai.com/equations/ordinary-least-squares-ols.md) | $\hat\beta = (X^\top X)^{-1} X^\top y$ | 35 |
| 4 | [Mediation model (indirect effect)](https://scholar4ai.com/equations/mediation-model-indirect-effect.md) | $M = \alpha_0 + a X + e_1,\quad Y = \beta_0 + c' X + b M + e_2,\quad \text{indirect effect} = a\,b$ | 26 |
| 5 | [Two-way fixed-effects panel regression](https://scholar4ai.com/equations/two-way-fixed-effects-panel-regression.md) | $y_{it} = \alpha_i + \lambda_t + x_{it}^\top\beta + \varepsilon_{it}$ | 23 |
| 5 | [Vector autoregression (VAR)](https://scholar4ai.com/equations/vector-autoregression-var.md) | $y_t = c + \sum_{i=1}^{p} A_i\, y_{t-i} + \varepsilon_t$ | 23 |
| 7 | [GARCH](https://scholar4ai.com/equations/garch.md) | $\sigma_t^2 = \omega + \alpha\,\varepsilon_{t-1}^2 + \beta\,\sigma_{t-1}^2$ | 21 |
| 8 | [Propensity score matching](https://scholar4ai.com/equations/propensity-score-matching.md) | $e(x) = \Pr(D = 1 \mid X = x)$ | 20 |
| 9 | [Logistic regression (logit)](https://scholar4ai.com/equations/logistic-regression-logit.md) | $\Pr(y = 1 \mid x) = \frac{1}{1 + e^{-x^\top\beta}}$ | 15 |
| 9 | [Quantile regression](https://scholar4ai.com/equations/quantile-regression.md) | $\hat\beta_\tau = \operatorname*{arg\,min}_{\beta} \sum_{i} \rho_\tau(y_i - x_i^\top\beta),\quad \rho_\tau(u) = u\,(\tau - \mathbf{1}\{u < 0\})$ | 15 |

## Most-used data (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Data category | Most-mentioned datasets | Papers |
|---:|---|---|---:|
| 1 | [Stock market indices](https://scholar4ai.com/datasets/stock-market-indices.md) | S&P 500 (18), CSI 300 / Shanghai Composite (4), MSCI indices (2), Euro Stoxx / DAX (2) … | 25 |
| 2 | [China financial and firm databases](https://scholar4ai.com/datasets/china-financial-and-firm-databases.md) | CSMAR (14), CNRDS (5), Wind (4) | 15 |
| 3 | [Macroeconomic and development indicators](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md) | World Development Indicators (World Bank) (10), IMF data (IFS, WEO, DOTS) (4), Eurostat (3), Penn World Table (1) … | 14 |
| 4 | [Corporate filings and text](https://scholar4ai.com/datasets/corporate-filings-and-text.md) | Loughran-McDonald dictionary (9), Earnings call transcripts (6), 10-K filings (2) | 12 |
| 4 | [Options and volatility data](https://scholar4ai.com/datasets/options-and-volatility-data.md) | VIX (12), OVX (crude oil volatility index) (2) | 12 |
| 6 | [Financial data platforms](https://scholar4ai.com/datasets/financial-data-platforms.md) | Refinitiv Eikon / LSEG Workspace (3), Refinitiv Datastream (2), FactSet (2), WRDS (2) … | 11 |
| 7 | [China household and social surveys](https://scholar4ai.com/datasets/china-household-and-social-surveys.md) | China Household Finance Survey (CHFS) (8), China Family Panel Studies (CFPS) (3), China Health and Nutrition Survey (CHNS) (1) | 10 |
| 7 | [China statistical yearbooks and regional indices](https://scholar4ai.com/datasets/china-statistical-yearbooks-and-regional-indices.md) | Marketization index (Fan Gang) (7), China Statistical Yearbook (1), China City Statistical Yearbook (1), National Bureau of Statistics of China (1) … | 10 |
| 9 | [US stock market data](https://scholar4ai.com/datasets/us-stock-market-data.md) | CRSP (9), NYSE TAQ (1) | 9 |
| 10 | [Uncertainty and sentiment indices](https://scholar4ai.com/datasets/uncertainty-and-sentiment-indices.md) | Economic Policy Uncertainty index (3), Climate Policy Uncertainty index (3), Trade Policy Uncertainty index (3) | 7 |


## Latest finance papers

*Latest finance papers*

| Date | Title | Journal | Authors | Affiliations |
|---|---|---|---|---|
| 2026-10-24 | [Reflex without persistence: The dollar’s multi-day safe haven reversal in 2025](https://doi.org/10.1016/j.intfin.2026.102413) | [Journal of International Financial Markets Institutions and Money](https://scholar4ai.com/journals/journal-of-international-financial-markets-institutions-and-money.md) | Orhan Torul | Boğaziçi University |
| 2026-10-24 | [Editorial Board](https://doi.org/10.1016/s1042-4431(26)00135-6) | [Journal of International Financial Markets Institutions and Money](https://scholar4ai.com/journals/journal-of-international-financial-markets-institutions-and-money.md) |  |  |
| 2026-10-23 | [Volatility persistence across global equity markets: long-memory diagnostics versus horizon-specific VaR rankings](https://doi.org/10.1057/s41283-026-00247-2) | [Risk Management](https://scholar4ai.com/journals/risk-management.md) | Zhixing Lin, Shuoyan Lin | Jiangxi University of Software Professional Technology; Guangdong University Of Finances and Economics |
| 2026-10-23 | [Dynamic prediction of loss given default in Norwegian retail credit card debt: advanced machine learning versus statistical models](https://doi.org/10.1007/s42521-026-00230-2) | [Digital Finance](https://scholar4ai.com/journals/digital-finance.md) | Cathy Ho, Isabel A. C. Sandersen, Espen Selliken et al. | Norwegian University of Science and Technology |
| 2026-10-23 | [Central bank tone and financial markets: evidence from the European central bank](https://doi.org/10.1007/s12197-026-09792-8) | [Journal of Economics and Finance](https://scholar4ai.com/journals/journal-of-economics-and-finance.md) | Müge SAĞLAM BEZGİN | Karamanoğlu Mehmetbey University |
| 2026-10-23 | [From pixels to profits: trading arbitrage portfolios based on image representations](https://doi.org/10.1007/s11408-026-00507-x) | [Financial markets and portfolio management](https://scholar4ai.com/journals/financial-markets-and-portfolio-management.md) | Niklas Paluszkiewicz | Universität Ulm |
| 2026-10-23 | [Managerial slack, information environment and stock price crash risk: Evidence from China](https://doi.org/10.1016/j.iref.2026.106011) | [International Review of Economics & Finance](https://scholar4ai.com/journals/international-review-of-economics-finance.md) | Chuyue Wu, Yuyang Ling, Yikai Han | Central University of Finance and Economics |
| 2026-10-23 | [Dynamic and regime-dependent associations of business confidence and employment with new business creation in La Araucanía, Chile](https://doi.org/10.1007/s12197-026-09793-7) | [Journal of Economics and Finance](https://scholar4ai.com/journals/journal-of-economics-and-finance.md) | Humberto Salas Jara, Leonardo Rivas Solar, Francisco Javier Miranda González | Universidad Autónoma de Chile; Universidad de Valladolid; Universidad de Extremadura |
| 2026-10-23 | [AI herding in financial markets: An experiment with large language models](https://doi.org/10.1016/j.pacfin.2026.103415) | [Pacific-Basin Finance Journal](https://scholar4ai.com/journals/pacific-basin-finance-journal.md) | Leping Zhang, Guangyou Zhou | Fudan University |
| 2026-10-23 | [A fund by any other name: short-lived effects of name changes on European fund flows](https://doi.org/10.1007/s11408-026-00505-z) | [Financial markets and portfolio management](https://scholar4ai.com/journals/financial-markets-and-portfolio-management.md) | Víctor Barros, Bruno Pinhão, J M de Almeida et al. | University of Lisbon; Banco de Portugal; Iscte – Instituto Universitário de Lisboa |
| 2026-10-22 | [Do long-term yields help to estimate short-term risk premia?](https://doi.org/10.1016/j.jfineco.2026.104386) | [Journal of Financial Economics](https://scholar4ai.com/journals/journal-of-financial-economics.md) | Andrea Berardi, Roger Brown, Stephen M. Schaefer |  |
| 2026-10-22 | [Monetary policy regimes and the shape of Bank balance-sheet pass-through: Evidence from Türkiye](https://doi.org/10.1016/j.irfa.2026.105479) | [International Review of Financial Analysis](https://scholar4ai.com/journals/international-review-of-financial-analysis.md) | Hakan Bal | Beykent University |
| 2026-10-22 | [Exploring alternative methods for evaluating real earnings management: an overview, comparison, and assessment](https://doi.org/10.1007/s11156-026-01555-0) | [Review of Quantitative Finance and Accounting](https://scholar4ai.com/journals/review-of-quantitative-finance-and-accounting.md) | Wen‐Ju Liao, Cheng-Few Lee, Hao‐Chang Sung | Feng Chia University; Rutgers, The State University of New Jersey; National Chung Cheng University |
| 2026-10-22 | [Research on the spatiotemporal prediction of supply chain finance risk contagion based on a dynamic graph attention diffusion mechanism](https://doi.org/10.1186/s40854-026-00980-3) | [Financial Innovation](https://scholar4ai.com/journals/financial-innovation.md) | Yulong Zhang, Yulong Zhang, Yaqing Zhang et al. | Hengshui University; Columbia University |
| 2026-10-22 | [From compliance to competitiveness: how ESG creates lasting bank advantage](https://doi.org/10.1057/s41283-026-00246-3) | [Risk Management](https://scholar4ai.com/journals/risk-management.md) | Mohammed R. M. Salem | Universiti Putra Malaysia; Universiti Utara Malaysia |

## Questions this page answers

### What are the most common topics in recent finance research?

In the last 30 days, the most frequent OpenAlex keywords in papers from the tracked finance journals were “China” (27), “Chinese listed companies” (26), “corporate governance” (23), “emerging markets” (18) and “financial constraints” (15).

### Which universities and institutions publish the most finance research?

In the last 30 days, the institutions with the most papers in the tracked finance journals were Central University of Finance and Economics (11), Southwestern University of Finance and Economics (11), University of International Business and Economics (9), Shanghai University of Finance and Economics (7) and City University of Macau (7).

### Which countries publish the most finance research?

Counting papers with at least one author at an institution in the country, the leaders over the last 30 days were China (209), United States (63), United Kingdom (20), South Korea (18) and Australia (15).

### Which equations and models do recent finance research papers use most?

Counting papers that mention them by name in the title, abstract, keywords or full text, the most frequent in the tracked finance journals over the last 30 days were Difference-in-differences (43), Instrumental variables (two-stage least squares) (35), Ordinary least squares (OLS) (35), Mediation model (indirect effect) (26) and Two-way fixed-effects panel regression (23).

### What data do recent finance research papers use?

Counting papers that mention a dataset by name, the kinds of data used most over the last 30 days were Stock market indices (25), China financial and firm databases (15), Macroeconomic and development indicators (14), Corporate filings and text (12) and Options and volatility data (12); the most-mentioned datasets were S&P 500 (18), CSMAR (14), VIX (12), World Development Indicators (World Bank) (10) and CRSP (9).

### How current is this data?

It is rebuilt from OpenAlex 4 times a day (00:00, 06:00, 12:00, 18:00, UTC+09:00); the last update was 2026-10-26 11:39 UTC+09:00. OpenAlex usually indexes new papers within a few days of publication.


---

---
title: "Accounting research rankings and trends"
description: "In the last 30 days (2026-09-27 to 2026-10-26), 58 papers were published in the 16 tracked accounting journals (as indexed by OpenAlex). The most frequent keywords were “corporate governance” (10), “Chinese listed companies” (5) and “management control” (4)."
url: https://scholar4ai.com/categories/accounting
markdown_url: https://scholar4ai.com/categories/accounting.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T11:39:12+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# Accounting research rankings

Papers in the 16 tracked accounting journals, window 2026-09-27 – 2026-10-26 (30 days). Keywords, institutions, web domains and countries are ranked by paper count.

- Papers (30 days): 58
- Papers (last 7 days): 7
- Journals: 16
- Institutions: 126
- Countries: 27
- Web domains: 123

## Key findings

- In the last 30 days (2026-09-27 to 2026-10-26), 58 papers were published in the 16 tracked accounting journals (as indexed by OpenAlex).
- The most frequent keywords were “corporate governance” (10), “Chinese listed companies” (5) and “management control” (4).
- The institutions with the most papers were Bowling Green State University (2), Tianjin University (2) and Elon University (2).
- United States was involved in 29% of the papers (17), followed by China (15) and Australia (8).
- Among institution web domains, .edu appeared on 29% of the papers.
- The most-mentioned equations and models were Instrumental variables (two-stage least squares) (6), Difference-in-differences (5) and Ordinary least squares (OLS) (4).
- The most-mentioned kinds of data were Bank regulatory data (2), Company financial statements (2) and Financial data platforms (2); the most-mentioned datasets were Amadeus (Bureau van Dijk) (1), Audit Analytics (1) and Bloomberg (1).

## Papers per day

*Papers per publication date*

| Date | Papers |
|---|---:|
| 2026-09-27 | 1 |
| 2026-09-28 | 2 |
| 2026-09-29 | 1 |
| 2026-09-30 | 4 |
| 2026-10-01 | 19 |
| 2026-10-02 | 0 |
| 2026-10-03 | 2 |
| 2026-10-04 | 0 |
| 2026-10-05 | 1 |
| 2026-10-06 | 1 |
| 2026-10-07 | 2 |
| 2026-10-08 | 0 |
| 2026-10-09 | 1 |
| 2026-10-10 | 2 |
| 2026-10-11 | 0 |
| 2026-10-12 | 3 |
| 2026-10-13 | 3 |
| 2026-10-14 | 0 |
| 2026-10-15 | 2 |
| 2026-10-16 | 3 |
| 2026-10-17 | 2 |
| 2026-10-18 | 0 |
| 2026-10-19 | 2 |
| 2026-10-20 | 1 |
| 2026-10-21 | 1 |
| 2026-10-22 | 1 |
| 2026-10-23 | 3 |
| 2026-10-24 | 1 |
| 2026-10-25 | 0 |
| 2026-10-26 | 0 |

## Growing over the year (Jul 2026–Sep 2026 vs Oct 2025–Jun 2026)

*Papers in Jul 2026–Sep 2026 beyond what the keyword's share in Oct 2025–Jun 2026 predicts*

| Keyword | Papers per month, Oct 25 – Oct 26* | Papers Jul 2026–Sep 2026 | vs. earlier share |
|---|---|---:|---:|
| accounting education | 0, 0, 1, 1, 3, 2, 5, 4, 1, 6, 2, 2, 2 | 10 | +4 |
| moderating effect | 0, 0, 3, 1, 1, 1, 4, 1, 0, 3, 1, 4, 1 | 8 | +4 |
| information transparency | 1, 0, 0, 0, 1, 0, 0, 0, 1, 3, 1, 1, 0 | 5 | +4 |
| corporate disclosure | 1, 1, 1, 0, 0, 4, 0, 1, 2, 2, 3, 2, 1 | 7 | +4 |
| ESG performance | 1, 1, 1, 4, 3, 0, 1, 1, 1, 2, 2, 3, 1 | 7 | +3 |

More: https://scholar4ai.com/trends.md



## Top research keywords (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Keyword | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [corporate governance](https://scholar4ai.com/keywords/corporate-governance.md) | 10 | 2 |
| 2 | [Chinese listed companies](https://scholar4ai.com/keywords/chinese-listed-companies.md) | 5 | 0 |
| 3 | management control | 4 | 1 |
| 4 | audit fees | 3 | 0 |
| 4 | corporate sustainability | 3 | 0 |
| 4 | financial reporting quality | 3 | 1 |
| 4 | [information asymmetry](https://scholar4ai.com/keywords/information-asymmetry.md) | 3 | 1 |
| 4 | management accounting | 3 | 0 |
| 9 | ESG performance | 2 | 0 |
| 9 | IFRS | 2 | 1 |
| 9 | Indian listed companies | 2 | 1 |
| 9 | accounting education | 2 | 0 |
| 9 | accounting standards | 2 | 0 |
| 9 | analyst reports | 2 | 0 |
| 9 | asymmetric cost behavior | 2 | 0 |
| 9 | audit risk | 2 | 0 |
| 9 | earnings management | 2 | 1 |
| 9 | executive compensation | 2 | 0 |
| 9 | [financial constraints](https://scholar4ai.com/keywords/financial-constraints.md) | 2 | 1 |
| 9 | financial stability | 2 | 0 |

## Top universities and research institutions (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Institution | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | Bowling Green State University | 2 | 0 |
| 1 | [Tianjin University](https://scholar4ai.com/institutions/tianjin-university.md) | 2 | 0 |
| 1 | Elon University | 2 | 0 |
| 1 | University of St.Gallen | 2 | 0 |
| 1 | Manouba University | 2 | 1 |
| 6 | Vienna University of Economics and Business | 1 | 0 |
| 6 | Rutgers, The State University of New Jersey | 1 | 0 |
| 6 | University of Bergamo | 1 | 0 |
| 6 | University of East Anglia | 1 | 0 |
| 6 | Macau University of Science and Technology | 1 | 0 |
| 6 | Creighton University | 1 | 0 |
| 6 | Tulane University | 1 | 0 |
| 6 | University of Jordan | 1 | 0 |
| 6 | University of Hawaiʻi at Mānoa | 1 | 0 |
| 6 | Northern Border University | 1 | 1 |
| 6 | Weber State University | 1 | 0 |
| 6 | University of Verona | 1 | 0 |
| 6 | Universidad Complutense de Madrid | 1 | 0 |
| 6 | Johannes Kepler University of Linz | 1 | 0 |
| 6 | University of Nebraska at Omaha | 1 | 0 |

## Top institution web domains (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Web domain | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | bgsu.edu | 2 | 0 |
| 1 | elon.edu | 2 | 0 |
| 1 | rnu.tn | 2 | 1 |
| 1 | [tju.edu.cn](https://scholar4ai.com/domains/tju.edu.cn.md) | 2 | 0 |
| 1 | unisg.ch | 2 | 0 |
| 6 | adelphi.edu | 1 | 0 |
| 6 | alfredhealth.org.au | 1 | 0 |
| 6 | ankara.edu.tr | 1 | 0 |
| 6 | bentley.edu | 1 | 0 |
| 6 | [bit.edu.cn](https://scholar4ai.com/domains/bit.edu.cn.md) | 1 | 0 |
| 6 | bits-pilani.ac.in | 1 | 0 |
| 6 | calstate.edu | 1 | 0 |
| 6 | city.ac.uk | 1 | 0 |
| 6 | citystgeorges.ac.uk | 1 | 0 |
| 6 | collegeofidaho.edu | 1 | 1 |
| 6 | cqu.edu.cn | 1 | 0 |
| 6 | cqut.edu.cn | 1 | 0 |
| 6 | creighton.edu | 1 | 0 |
| 6 | csulb.edu | 1 | 0 |
| 6 | csus.edu | 1 | 0 |

## Top countries (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Country | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | United States (US) | 17 | 2 |
| 2 | China (CN) | 15 | 1 |
| 3 | Australia (AU) | 8 | 1 |
| 4 | Canada (CA) | 4 | 0 |
| 4 | United Kingdom (GB) | 4 | 0 |
| 4 | India (IN) | 4 | 1 |
| 7 | Austria (AT) | 2 | 0 |
| 7 | Switzerland (CH) | 2 | 0 |
| 7 | Germany (DE) | 2 | 0 |
| 7 | Italy (IT) | 2 | 0 |
| 7 | New Zealand (NZ) | 2 | 0 |
| 7 | Saudi Arabia (SA) | 2 | 1 |
| 7 | Tunisia (TN) | 2 | 1 |
| 7 | Vietnam (VN) | 2 | 0 |
| 15 | Brazil (BR) | 1 | 0 |
| 15 | Spain (ES) | 1 | 0 |
| 15 | France (FR) | 1 | 0 |
| 15 | Indonesia (ID) | 1 | 0 |
| 15 | Jordan (JO) | 1 | 0 |
| 15 | South Korea (KR) | 1 | 0 |

## Research fields (top 5)

*Top 5 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Field | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | Business, Management and Accounting | 48 | 7 |
| 2 | Economics, Econometrics and Finance | 5 | 0 |
| 3 | Social Sciences | 3 | 0 |
| 4 | Decision Sciences | 1 | 0 |
| 4 | Psychology | 1 | 0 |

## Papers per journal (top 16)

*Top 16 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Journal | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [The British Accounting Review](https://scholar4ai.com/journals/the-british-accounting-review.md) | 10 | 0 |
| 2 | [International Journal of Disclosure and Governance](https://scholar4ai.com/journals/international-journal-of-disclosure-and-governance.md) | 9 | 3 |
| 2 | [Journal of Contemporary Accounting & Economics](https://scholar4ai.com/journals/journal-of-contemporary-accounting-economics.md) | 9 | 0 |
| 4 | [Journal of Accounting and Economics](https://scholar4ai.com/journals/journal-of-accounting-and-economics.md) | 6 | 0 |
| 5 | [Journal of Accounting and Public Policy](https://scholar4ai.com/journals/journal-of-accounting-and-public-policy.md) | 5 | 1 |
| 5 | [Journal of Accounting Education](https://scholar4ai.com/journals/journal-of-accounting-education.md) | 5 | 2 |
| 7 | [Accounting Organizations and Society](https://scholar4ai.com/journals/accounting-organizations-and-society.md) | 3 | 0 |
| 7 | [Journal of Management Control](https://scholar4ai.com/journals/journal-of-management-control.md) | 3 | 1 |
| 9 | [International Journal of Accounting Information Systems](https://scholar4ai.com/journals/international-journal-of-accounting-information-systems.md) | 2 | 0 |
| 9 | [Journal of Management & Governance](https://scholar4ai.com/journals/journal-of-management-governance.md) | 2 | 0 |
| 11 | [Advances in Accounting](https://scholar4ai.com/journals/advances-in-accounting.md) | 1 | 0 |
| 11 | [Critical Perspectives on Accounting](https://scholar4ai.com/journals/critical-perspectives-on-accounting.md) | 1 | 0 |
| 11 | [Journal of International Accounting Auditing and Taxation](https://scholar4ai.com/journals/journal-of-international-accounting-auditing-and-taxation.md) | 1 | 0 |
| 11 | [Review of Accounting Studies](https://scholar4ai.com/journals/review-of-accounting-studies.md) | 1 | 0 |
| 15 | [China Journal of Accounting Research](https://scholar4ai.com/journals/china-journal-of-accounting-research.md) | 0 | 0 |
| 15 | [Management Accounting Research](https://scholar4ai.com/journals/management-accounting-research.md) | 0 | 0 |

## Most-used equations and models (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Equation or model | Formula | Papers |
|---:|---|---|---:|
| 1 | [Instrumental variables (two-stage least squares)](https://scholar4ai.com/equations/instrumental-variables-two-stage-least-squares.md) | $\hat\beta_{\mathrm{2SLS}} = (X^\top P_Z X)^{-1} X^\top P_Z\, y,\quad P_Z = Z (Z^\top Z)^{-1} Z^\top$ | 6 |
| 2 | [Difference-in-differences](https://scholar4ai.com/equations/difference-in-differences.md) | $Y_{it} = \alpha_i + \lambda_t + \delta\,(\mathit{Treat}_i \times \mathit{Post}_t) + \varepsilon_{it}$ | 5 |
| 3 | [Ordinary least squares (OLS)](https://scholar4ai.com/equations/ordinary-least-squares-ols.md) | $\hat\beta = (X^\top X)^{-1} X^\top y$ | 4 |
| 4 | [Structural equation modeling (SEM)](https://scholar4ai.com/equations/structural-equation-modeling-sem.md) | $\eta = B\eta + \Gamma\xi + \zeta,\quad y = \Lambda_y\,\eta + \varepsilon$ | 3 |
| 5 | [Mediation model (indirect effect)](https://scholar4ai.com/equations/mediation-model-indirect-effect.md) | $M = \alpha_0 + a X + e_1,\quad Y = \beta_0 + c' X + b M + e_2,\quad \text{indirect effect} = a\,b$ | 2 |
| 5 | [Probit model](https://scholar4ai.com/equations/probit-model.md) | $\Pr(y = 1 \mid x) = \Phi(x^\top\beta)$ | 2 |
| 5 | [Propensity score matching](https://scholar4ai.com/equations/propensity-score-matching.md) | $e(x) = \Pr(D = 1 \mid X = x)$ | 2 |
| 5 | [Quantile regression](https://scholar4ai.com/equations/quantile-regression.md) | $\hat\beta_\tau = \operatorname*{arg\,min}_{\beta} \sum_{i} \rho_\tau(y_i - x_i^\top\beta),\quad \rho_\tau(u) = u\,(\tau - \mathbf{1}\{u < 0\})$ | 2 |
| 9 | [ARIMA](https://scholar4ai.com/equations/arima.md) | $\phi(L)\,(1 - L)^{d}\, y_t = \theta(L)\,\varepsilon_t$ | 1 |
| 9 | [Cox proportional hazards model](https://scholar4ai.com/equations/cox-proportional-hazards-model.md) | $h(t \mid x) = h_0(t)\,\exp(x^\top\beta)$ | 1 |

## Most-used data (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Data category | Most-mentioned datasets | Papers |
|---:|---|---|---:|
| 1 | [Bank regulatory data](https://scholar4ai.com/datasets/bank-regulatory-data.md) | Call Reports (1), FR Y-9C (1), Credit registers (e.g. AnaCredit) (1) | 2 |
| 1 | [Company financial statements](https://scholar4ai.com/datasets/company-financial-statements.md) | Compustat (1), Amadeus (Bureau van Dijk) (1) | 2 |
| 1 | [Financial data platforms](https://scholar4ai.com/datasets/financial-data-platforms.md) | Refinitiv Datastream (1), Bloomberg (1) | 2 |
| 4 | [Asset pricing factor data](https://scholar4ai.com/datasets/asset-pricing-factor-data.md) | Hou-Xue-Zhang q-factors (1) | 1 |
| 4 | [Corporate filings and text](https://scholar4ai.com/datasets/corporate-filings-and-text.md) | Earnings call transcripts (1) | 1 |
| 4 | [Corporate governance and executive data](https://scholar4ai.com/datasets/corporate-governance-and-executive-data.md) | Audit Analytics (1) | 1 |
| 4 | [ESG and sustainability ratings](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md) | MSCI ESG / KLD (1) | 1 |
| 4 | [Macroeconomic and development indicators](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md) | World Development Indicators (World Bank) (1) | 1 |
| 4 | [Real estate and housing data](https://scholar4ai.com/datasets/real-estate-and-housing-data.md) | S&P CoreLogic Case-Shiller (1) | 1 |
| 4 | [Stock market indices](https://scholar4ai.com/datasets/stock-market-indices.md) | S&P 500 (1) | 1 |


## Latest accounting papers

*Latest accounting papers*

| Date | Title | Journal | Authors | Affiliations |
|---|---|---|---|---|
| 2026-10-24 | [Digital competency, data science literacy, and fraud detection skills among generation Z accountants: Evidence from Peru](https://doi.org/10.1016/j.jaccedu.2026.101057) | [Journal of Accounting Education](https://scholar4ai.com/journals/journal-of-accounting-education.md) | Jorge Miguel Chávez-Díaz, Elsa Esther Choy Zevallos, Lázaro Miguel Ángel Cotrina Reyes et al. |  |
| 2026-10-23 | [Accounting on the spectrum: a framework for inclusive accounting education](https://doi.org/10.1016/j.jaccedu.2026.101056) | [Journal of Accounting Education](https://scholar4ai.com/journals/journal-of-accounting-education.md) | Matthew J. Sargent, Andrew I. Ellestad, Bradley Gene Winton | University of Southern Mississippi; College of Idaho |
| 2026-10-23 | [From mandate to message: how CSR spending enhances financial disclosure readability](https://doi.org/10.1057/s41310-026-00434-3) | [International Journal of Disclosure and Governance](https://scholar4ai.com/journals/international-journal-of-disclosure-and-governance.md) | Somya Arora, Tulika Sharma | Flame University; Indian Institute of Management Tiruchirappalli |
| 2026-10-23 | [Bilingual framing of data privacy disclosure: evidence from Saudi listed companies](https://doi.org/10.1057/s41310-026-00433-4) | [International Journal of Disclosure and Governance](https://scholar4ai.com/journals/international-journal-of-disclosure-and-governance.md) | Ola Mohammed Alghasham | Northern Border University |
| 2026-10-22 | [The integration between Sustainability Management Systems (SMS) and Sustainability Management Accounting Controls (SMCS): a review of contemporary literature and implications for future research](https://doi.org/10.1007/s00187-026-00432-0) | [Journal of Management Control](https://scholar4ai.com/journals/journal-of-management-control.md) | Udani Chathurika Edirisinghe, Mohammad Alamgir Hossain, Manzurul Alam | Sabaragamuwa University of Sri Lanka; Murdoch University |
| 2026-10-21 | [Impact of international financial reporting standards (IFRS) on financial reporting quality: a bibliometric analysis](https://doi.org/10.1057/s41310-026-00437-0) | [International Journal of Disclosure and Governance](https://scholar4ai.com/journals/international-journal-of-disclosure-and-governance.md) | Mouna Baccouri, Nemer Badwan | Manouba University; Palestine Technical University - Kadoorie |
| 2026-10-20 | [The dark side of adopting more stringent fair disclosure in a relational economy: evidence from site visits in China](https://doi.org/10.1016/j.jaccpubpol.2026.107480) | [Journal of Accounting and Public Policy](https://scholar4ai.com/journals/journal-of-accounting-and-public-policy.md) | Jeff Zeyun Chen, Zengquan Li, Liangyu Zhao | Texas Christian University; Shanghai University of Finance and Economics; Wuhan University |
| 2026-10-19 | [Debt enforcement and cash dividend policy: Chinese evidence from the establishment of specialized bankruptcy courts](https://doi.org/10.1016/j.jcae.2026.100606) | [Journal of Contemporary Accounting & Economics](https://scholar4ai.com/journals/journal-of-contemporary-accounting-economics.md) | Yueying Yuan, Yue Li, Yuxiang Zhong | Huazhong University of Science and Technology; University of Science and Technology Beijing |
| 2026-10-19 | [Organizational centralization of accounting functions and forward-looking disclosure quality: evidence from financial shared service centers](https://doi.org/10.1016/j.jaccpubpol.2026.107483) | [Journal of Accounting and Public Policy](https://scholar4ai.com/journals/journal-of-accounting-and-public-policy.md) | Jinsong Liu, Qianwei Ying, Mengrui Han | Sichuan University; Science and Technology Department of Sichuan Province; Nanjing University of Finance and Economics |
| 2026-10-17 | [The implications of student personality types for excellent teaching in accounting higher education: A systematic literature review](https://doi.org/10.1016/j.jaccedu.2026.101055) | [Journal of Accounting Education](https://scholar4ai.com/journals/journal-of-accounting-education.md) | Permata Ayu Widyasari, Mariela Carvajal, Muhammad Tahir Suleman | University of Surabaya; University of Otago; King Fahd University of Petroleum and Minerals |
| 2026-10-17 | [Combining management control practices for organizational learning: A situational strength perspective](https://doi.org/10.1016/j.aos.2026.101662) | [Accounting Organizations and Society](https://scholar4ai.com/journals/accounting-organizations-and-society.md) | Flavia Schmid, Frank H. M. Verbeeten, Klaus Möller | University of St.Gallen; University of Applied Sciences St. Gallen; University of Applied Sciences Utrecht; … |
| 2026-10-16 | [Audit committee adoption after regulatory re-specification: operating-asset reporting exposure and board conversion capability](https://doi.org/10.1057/s41310-026-00435-2) | [International Journal of Disclosure and Governance](https://scholar4ai.com/journals/international-journal-of-disclosure-and-governance.md) | Nguyen Hoang Phi Nam | Ho Chi Minh City Open University |
| 2026-10-16 | [Critical accountants in the face of ecological crises: towards a more-than-human critical resilience](https://doi.org/10.1016/j.cpa.2026.102889) | [Critical Perspectives on Accounting](https://scholar4ai.com/journals/critical-perspectives-on-accounting.md) | Diane‐Laure Arjaliès | Western University; Ivey Business School |
| 2026-10-16 | [Population aging and corporate asymmetric cost behavior: The roles of labor shortage and automation](https://doi.org/10.1016/j.jcae.2026.100605) | [Journal of Contemporary Accounting & Economics](https://scholar4ai.com/journals/journal-of-contemporary-accounting-economics.md) | Xiaofeng Quan, Chenhao Guo, Qingbo Yuan et al. | Soochow University; Macau University of Science and Technology; Capital University of Economics and Business; … |
| 2026-10-15 | [Geographic proximity of investors and firms' ESG performance: Evidence on local retail and institutional investors](https://doi.org/10.1016/j.adiac.2026.100906) | [Advances in Accounting](https://scholar4ai.com/journals/advances-in-accounting.md) | Chengfeng Du, Huiqi Gan, Khondkar Karim | University of Massachusetts Lowell |

## Questions this page answers

### What are the most common topics in recent accounting research?

In the last 30 days, the most frequent OpenAlex keywords in papers from the tracked accounting journals were “corporate governance” (10), “Chinese listed companies” (5), “management control” (4), “audit fees” (3) and “corporate sustainability” (3).

### Which universities and institutions publish the most accounting research?

In the last 30 days, the institutions with the most papers in the tracked accounting journals were Bowling Green State University (2), Tianjin University (2), Elon University (2), University of St.Gallen (2) and Manouba University (2).

### Which countries publish the most accounting research?

Counting papers with at least one author at an institution in the country, the leaders over the last 30 days were United States (17), China (15), Australia (8), Canada (4) and United Kingdom (4).

### Which equations and models do recent accounting research papers use most?

Counting papers that mention them by name in the title, abstract, keywords or full text, the most frequent in the tracked accounting journals over the last 30 days were Instrumental variables (two-stage least squares) (6), Difference-in-differences (5), Ordinary least squares (OLS) (4), Structural equation modeling (SEM) (3) and Mediation model (indirect effect) (2).

### What data do recent accounting research papers use?

Counting papers that mention a dataset by name, the kinds of data used most over the last 30 days were Bank regulatory data (2), Company financial statements (2), Financial data platforms (2), Asset pricing factor data (1) and Corporate filings and text (1); the most-mentioned datasets were Amadeus (Bureau van Dijk) (1), Audit Analytics (1), Bloomberg (1), Call Reports (1) and Compustat (1).

### How current is this data?

It is rebuilt from OpenAlex 4 times a day (00:00, 06:00, 12:00, 18:00, UTC+09:00); the last update was 2026-10-26 11:39 UTC+09:00. OpenAlex usually indexes new papers within a few days of publication.


---

---
title: "Economics research: top topics, institutions and countries"
description: "In the last 30 days (2026-09-27 to 2026-10-26), 784 papers were published in the 83 tracked economics journals (as indexed by OpenAlex). The most frequent keywords were “China” (44), “monetary policy” (18) and “Chinese listed companies” (14)."
url: https://scholar4ai.com/categories/economics
markdown_url: https://scholar4ai.com/categories/economics.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T11:39:12+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# Economics research rankings

Papers in the 83 tracked economics journals, window 2026-09-27 – 2026-10-26 (30 days). Keywords, institutions, web domains and countries are ranked by paper count.

- Papers (30 days): 784
- Papers (last 7 days): 92
- Journals: 83
- Institutions: 982
- Countries: 78
- Web domains: 928

## Key findings

- In the last 30 days (2026-09-27 to 2026-10-26), 784 papers were published in the 83 tracked economics journals (as indexed by OpenAlex).
- The most frequent keywords were “China” (44), “monetary policy” (18) and “Chinese listed companies” (14).
- The institutions with the most papers were Centre National de la Recherche Scientifique (20), Peking University (12) and Centre for Economic Policy Research (12).
- China was involved in 23% of the papers (178), followed by United States (140) and United Kingdom (80).
- Among institution web domains, .edu.cn appeared on 23% of the papers.
- The most-mentioned equations and models were Difference-in-differences (60), Instrumental variables (two-stage least squares) (45) and Logistic regression (logit) (35).
- The most-mentioned kinds of data were Macroeconomic and development indicators (33), European and other national household panels (14) and China household and social surveys (13); the most-mentioned datasets were Eurostat (15), World Development Indicators (World Bank) (14) and China Family Panel Studies (CFPS) (6).

## Papers per day

*Papers per publication date*

| Date | Papers |
|---|---:|
| 2026-09-27 | 9 |
| 2026-09-28 | 32 |
| 2026-09-29 | 30 |
| 2026-09-30 | 37 |
| 2026-10-01 | 220 |
| 2026-10-02 | 14 |
| 2026-10-03 | 16 |
| 2026-10-04 | 5 |
| 2026-10-05 | 35 |
| 2026-10-06 | 22 |
| 2026-10-07 | 25 |
| 2026-10-08 | 20 |
| 2026-10-09 | 28 |
| 2026-10-10 | 28 |
| 2026-10-11 | 17 |
| 2026-10-12 | 26 |
| 2026-10-13 | 24 |
| 2026-10-14 | 21 |
| 2026-10-15 | 23 |
| 2026-10-16 | 13 |
| 2026-10-17 | 30 |
| 2026-10-18 | 5 |
| 2026-10-19 | 12 |
| 2026-10-20 | 7 |
| 2026-10-21 | 23 |
| 2026-10-22 | 21 |
| 2026-10-23 | 21 |
| 2026-10-24 | 20 |
| 2026-10-25 | 0 |
| 2026-10-26 | 0 |

## Growing over the year (Jul 2026–Sep 2026 vs Oct 2025–Jun 2026)

*Papers in Jul 2026–Sep 2026 beyond what the keyword's share in Oct 2025–Jun 2026 predicts*

| Keyword | Papers per month, Oct 25 – Oct 26* | Papers Jul 2026–Sep 2026 | vs. earlier share |
|---|---|---:|---:|
| [corporate governance](https://scholar4ai.com/keywords/corporate-governance.md) | 8, 9, 10, 7, 9, 7, 3, 7, 6, 12, 10, 13, 7 | 35 | +13 |
| total factor productivity | 6, 6, 7, 7, 9, 10, 7, 1, 9, 6, 12, 15, 6 | 33 | +12 |
| systemic risk | 5, 5, 4, 5, 6, 3, 2, 1, 4, 7, 6, 9, 3 | 22 | +10 |
| treatment effect heterogeneity | 1, 1, 3, 1, 1, 3, 1, 0, 2, 5, 5, 4, 0 | 14 | +10 |
| absorptive capacity | 2, 3, 2, 4, 2, 3, 2, 2, 2, 8, 2, 7, 4 | 17 | +10 |

More: https://scholar4ai.com/trends.md



## Top research keywords (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Keyword | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [China](https://scholar4ai.com/keywords/china.md) | 44 | 2 |
| 2 | [monetary policy](https://scholar4ai.com/keywords/monetary-policy.md) | 18 | 0 |
| 3 | [Chinese listed companies](https://scholar4ai.com/keywords/chinese-listed-companies.md) | 14 | 0 |
| 3 | [energy transition](https://scholar4ai.com/keywords/energy-transition.md) | 14 | 1 |
| 3 | regional disparities | 14 | 4 |
| 6 | climate policy | 13 | 1 |
| 7 | CO2 emissions | 12 | 0 |
| 7 | carbon pricing | 12 | 0 |
| 7 | [information asymmetry](https://scholar4ai.com/keywords/information-asymmetry.md) | 12 | 0 |
| 10 | developing countries | 11 | 1 |
| 10 | economic growth | 11 | 2 |
| 10 | environmental policy | 11 | 0 |
| 10 | human capital | 11 | 0 |
| 10 | income inequality | 11 | 2 |
| 15 | [COVID-19 pandemic](https://scholar4ai.com/keywords/covid-19-pandemic.md) | 10 | 3 |
| 15 | [emerging markets](https://scholar4ai.com/keywords/emerging-markets.md) | 10 | 0 |
| 15 | geopolitical risk | 10 | 0 |
| 18 | digitalization | 9 | 1 |
| 18 | [financial constraints](https://scholar4ai.com/keywords/financial-constraints.md) | 9 | 0 |
| 18 | foreign direct investment | 9 | 2 |

## Top universities and research institutions (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Institution | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [Centre National de la Recherche Scientifique](https://scholar4ai.com/institutions/centre-national-de-la-recherche-scientifique.md) | 20 | 3 |
| 2 | [Peking University](https://scholar4ai.com/institutions/peking-university.md) | 12 | 1 |
| 2 | Centre for Economic Policy Research | 12 | 2 |
| 4 | [Renmin University of China](https://scholar4ai.com/institutions/renmin-university-of-china.md) | 11 | 0 |
| 5 | University of Cambridge | 9 | 1 |
| 6 | Luxembourg Institute of Socio-Economic Research | 8 | 1 |
| 7 | National Bureau of Economic Research | 7 | 1 |
| 7 | Jinan University | 7 | 1 |
| 7 | Korea University | 7 | 2 |
| 10 | [Beijing Institute of Technology](https://scholar4ai.com/institutions/beijing-institute-of-technology.md) | 6 | 0 |
| 10 | World Bank | 6 | 0 |
| 10 | Central University of Finance and Economics | 6 | 0 |
| 10 | Shanghai University of Finance and Economics | 6 | 1 |
| 10 | [Southwestern University of Finance and Economics](https://scholar4ai.com/institutions/southwestern-university-of-finance-and-economics.md) | 6 | 1 |
| 15 | University of Pisa | 5 | 2 |
| 15 | Australian National University | 5 | 0 |
| 15 | Zhongnan University of Economics and Law | 5 | 0 |
| 15 | National University of Singapore | 5 | 0 |
| 15 | Chinese University of Hong Kong | 5 | 1 |
| 15 | Guangdong University Of Finances and Economics | 5 | 0 |

## Top institution web domains (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Web domain | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [cnrs.fr](https://scholar4ai.com/domains/cnrs.fr.md) | 21 | 4 |
| 2 | cepr.org | 12 | 2 |
| 2 | [pku.edu.cn](https://scholar4ai.com/domains/pku.edu.cn.md) | 12 | 1 |
| 4 | [ruc.edu.cn](https://scholar4ai.com/domains/ruc.edu.cn.md) | 11 | 0 |
| 5 | worldbank.org | 10 | 1 |
| 6 | cam.ac.uk | 9 | 1 |
| 7 | liser.lu | 8 | 1 |
| 8 | jnu.edu.cn | 7 | 1 |
| 8 | korea.ac.kr | 7 | 2 |
| 8 | nber.org | 7 | 1 |
| 11 | [bit.edu.cn](https://scholar4ai.com/domains/bit.edu.cn.md) | 6 | 0 |
| 11 | cufe.edu.cn | 6 | 0 |
| 11 | psl.eu | 6 | 0 |
| 11 | sufe.edu.cn | 6 | 1 |
| 11 | [swufe.edu.cn](https://scholar4ai.com/domains/swufe.edu.cn.md) | 6 | 1 |
| 16 | anu.edu.au | 5 | 0 |
| 16 | berkeley.edu | 5 | 0 |
| 16 | cornell.edu | 5 | 0 |
| 16 | cuhk.edu.hk | 5 | 1 |
| 16 | gdufe.edu.cn | 5 | 0 |

## Top countries (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Country | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | China (CN) | 178 | 16 |
| 2 | United States (US) | 140 | 15 |
| 3 | United Kingdom (GB) | 80 | 11 |
| 4 | Italy (IT) | 63 | 10 |
| 5 | Germany (DE) | 50 | 6 |
| 6 | Spain (ES) | 46 | 5 |
| 6 | France (FR) | 46 | 7 |
| 8 | Australia (AU) | 24 | 2 |
| 9 | South Korea (KR) | 22 | 4 |
| 10 | Canada (CA) | 21 | 1 |
| 11 | Japan (JP) | 20 | 2 |
| 12 | Switzerland (CH) | 18 | 2 |
| 13 | India (IN) | 17 | 2 |
| 14 | Netherlands (NL) | 16 | 4 |
| 14 | Turkey (TR) | 16 | 5 |
| 16 | Hong Kong (HK) | 15 | 2 |
| 17 | Denmark (DK) | 12 | 2 |
| 17 | Sweden (SE) | 12 | 2 |
| 19 | Ireland (IE) | 10 | 1 |
| 19 | Luxembourg (LU) | 10 | 1 |

## Research fields (top 15)

*Top 15 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Field | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | Economics, Econometrics and Finance | 325 | 31 |
| 2 | Social Sciences | 161 | 16 |
| 3 | Business, Management and Accounting | 88 | 9 |
| 4 | Decision Sciences | 57 | 8 |
| 5 | Engineering | 38 | 8 |
| 6 | Energy | 30 | 8 |
| 7 | Environmental Science | 28 | 2 |
| 8 | Health Professions | 14 | 2 |
| 9 | Agricultural and Biological Sciences | 9 | 0 |
| 10 | Mathematics | 7 | 3 |
| 10 | Medicine | 7 | 1 |
| 12 | Computer Science | 4 | 1 |
| 12 | Psychology | 4 | 1 |
| 14 | Physics and Astronomy | 2 | 0 |
| 15 | Arts and Humanities | 1 | 0 |

## Papers per journal (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Journal | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [Economic Analysis and Policy](https://scholar4ai.com/journals/economic-analysis-and-policy.md) | 71 | 1 |
| 2 | [Energy Policy](https://scholar4ai.com/journals/energy-policy.md) | 53 | 12 |
| 3 | [Economics Letters](https://scholar4ai.com/journals/economics-letters.md) | 49 | 0 |
| 4 | [Energy Economics](https://scholar4ai.com/journals/energy-economics.md) | 42 | 0 |
| 5 | [Economic Modelling](https://scholar4ai.com/journals/economic-modelling.md) | 41 | 1 |
| 6 | [Ecological Economics](https://scholar4ai.com/journals/ecological-economics.md) | 27 | 5 |
| 6 | [Journal of Economic Behavior & Organization](https://scholar4ai.com/journals/journal-of-economic-behavior-organization.md) | 27 | 3 |
| 6 | [World Development](https://scholar4ai.com/journals/world-development.md) | 27 | 6 |
| 9 | [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | 24 | 9 |
| 9 | [The Annals of Regional Science](https://scholar4ai.com/journals/the-annals-of-regional-science.md) | 24 | 3 |
| 11 | [Journal of Macroeconomics](https://scholar4ai.com/journals/journal-of-macroeconomics.md) | 14 | 0 |
| 12 | [Food Policy](https://scholar4ai.com/journals/food-policy.md) | 13 | 3 |
| 12 | [Journal of Asian Economics](https://scholar4ai.com/journals/journal-of-asian-economics.md) | 13 | 0 |
| 12 | [Journal of Mathematical Economics](https://scholar4ai.com/journals/journal-of-mathematical-economics.md) | 13 | 0 |
| 12 | [Research Policy](https://scholar4ai.com/journals/research-policy.md) | 13 | 4 |
| 12 | [Utilities Policy](https://scholar4ai.com/journals/utilities-policy.md) | 13 | 5 |
| 17 | [Empirical Economics](https://scholar4ai.com/journals/empirical-economics.md) | 12 | 4 |
| 17 | [Journal of Public Economics](https://scholar4ai.com/journals/journal-of-public-economics.md) | 12 | 3 |
| 19 | [Games and Economic Behavior](https://scholar4ai.com/journals/games-and-economic-behavior.md) | 11 | 0 |
| 20 | [Economic Systems](https://scholar4ai.com/journals/economic-systems.md) | 10 | 0 |

## Most-used equations and models (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Equation or model | Formula | Papers |
|---:|---|---|---:|
| 1 | [Difference-in-differences](https://scholar4ai.com/equations/difference-in-differences.md) | $Y_{it} = \alpha_i + \lambda_t + \delta\,(\mathit{Treat}_i \times \mathit{Post}_t) + \varepsilon_{it}$ | 60 |
| 2 | [Instrumental variables (two-stage least squares)](https://scholar4ai.com/equations/instrumental-variables-two-stage-least-squares.md) | $\hat\beta_{\mathrm{2SLS}} = (X^\top P_Z X)^{-1} X^\top P_Z\, y,\quad P_Z = Z (Z^\top Z)^{-1} Z^\top$ | 45 |
| 3 | [Logistic regression (logit)](https://scholar4ai.com/equations/logistic-regression-logit.md) | $\Pr(y = 1 \mid x) = \frac{1}{1 + e^{-x^\top\beta}}$ | 35 |
| 4 | [Ordinary least squares (OLS)](https://scholar4ai.com/equations/ordinary-least-squares-ols.md) | $\hat\beta = (X^\top X)^{-1} X^\top y$ | 34 |
| 5 | [Two-way fixed-effects panel regression](https://scholar4ai.com/equations/two-way-fixed-effects-panel-regression.md) | $y_{it} = \alpha_i + \lambda_t + x_{it}^\top\beta + \varepsilon_{it}$ | 21 |
| 6 | [Vector autoregression (VAR)](https://scholar4ai.com/equations/vector-autoregression-var.md) | $y_t = c + \sum_{i=1}^{p} A_i\, y_{t-i} + \varepsilon_t$ | 20 |
| 7 | [Propensity score matching](https://scholar4ai.com/equations/propensity-score-matching.md) | $e(x) = \Pr(D = 1 \mid X = x)$ | 18 |
| 7 | [Quantile regression](https://scholar4ai.com/equations/quantile-regression.md) | $\hat\beta_\tau = \operatorname*{arg\,min}_{\beta} \sum_{i} \rho_\tau(y_i - x_i^\top\beta),\quad \rho_\tau(u) = u\,(\tau - \mathbf{1}\{u < 0\})$ | 18 |
| 9 | [Cointegration and error correction (VECM)](https://scholar4ai.com/equations/cointegration-and-error-correction-vecm.md) | $\Delta y_t = \Pi\, y_{t-1} + \sum_{i=1}^{p-1}\Gamma_i\,\Delta y_{t-i} + \varepsilon_t$ | 14 |
| 9 | [Generalized method of moments (GMM)](https://scholar4ai.com/equations/generalized-method-of-moments-gmm.md) | $\hat\theta = \operatorname*{arg\,min}_{\theta}\ \bar g(\theta)^\top W\, \bar g(\theta),\quad \bar g(\theta) = \frac{1}{n}\sum_{i=1}^{n} g(w_i, \theta)$ | 14 |

## Most-used data (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Data category | Most-mentioned datasets | Papers |
|---:|---|---|---:|
| 1 | [Macroeconomic and development indicators](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md) | Eurostat (15), World Development Indicators (World Bank) (14), Penn World Table (6), OECD statistics (4) … | 33 |
| 2 | [European and other national household panels](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md) | Labour Force Surveys (5), SHARE (4), German Socio-Economic Panel (SOEP) (2), Understanding Society / BHPS (2) … | 14 |
| 3 | [China household and social surveys](https://scholar4ai.com/datasets/china-household-and-social-surveys.md) | China Family Panel Studies (CFPS) (6), CHARLS (3), China Household Finance Survey (CHFS) (2), Chinese General Social Survey (CGSS) (2) … | 13 |
| 4 | [US household and labour surveys](https://scholar4ai.com/datasets/us-household-and-labour-surveys.md) | Health and Retirement Study (4), Current Population Survey (3), PSID (1), Survey of Consumer Finances (1) … | 9 |
| 5 | [China statistical yearbooks and regional indices](https://scholar4ai.com/datasets/china-statistical-yearbooks-and-regional-indices.md) | Marketization index (Fan Gang) (3), China Statistical Yearbook (2), National Bureau of Statistics of China (2), China Energy Statistical Yearbook (1) | 7 |
| 5 | [Stock market indices](https://scholar4ai.com/datasets/stock-market-indices.md) | S&P 500 (3), MSCI indices (2), CSI 300 / Shanghai Composite (2), Euro Stoxx / DAX (2) | 7 |
| 5 | [Uncertainty and sentiment indices](https://scholar4ai.com/datasets/uncertainty-and-sentiment-indices.md) | Economic Policy Uncertainty index (3), Climate Policy Uncertainty index (3), Trade Policy Uncertainty index (2) | 7 |
| 5 | [US macroeconomic data](https://scholar4ai.com/datasets/us-macroeconomic-data.md) | Bureau of Economic Analysis (3), University of Michigan Surveys of Consumers (3), FRED (2), Bureau of Labor Statistics (1) | 7 |
| 9 | [Financial data platforms](https://scholar4ai.com/datasets/financial-data-platforms.md) | Refinitiv Datastream (1), WRDS (1), Investing.com (1) | 3 |
| 9 | [Options and volatility data](https://scholar4ai.com/datasets/options-and-volatility-data.md) | VIX (3) | 3 |


## Latest economics papers

*Latest economics papers*

| Date | Title | Journal | Authors | Affiliations |
|---|---|---|---|---|
| 2026-10-24 | [Forging holistic nuclear governance: The unifying role of China's atomic energy law in risk regulation and industry promotion](https://doi.org/10.1016/j.enpol.2026.115681) | [Energy Policy](https://scholar4ai.com/journals/energy-policy.md) | BangDa HU |  |
| 2026-10-24 | [Framed norms: the effect of choice and belief information on tax compliance](https://doi.org/10.1007/s10797-026-10009-1) | [International Tax and Public Finance](https://scholar4ai.com/journals/international-tax-and-public-finance.md) | Federico Atzori, Vittorio Pelligra | University of Cagliari |
| 2026-10-24 | [University shocks and research productivity inequality: Evidence from the COVID-19 pandemic](https://doi.org/10.1016/j.jebo.2026.107802) | [Journal of Economic Behavior & Organization](https://scholar4ai.com/journals/journal-of-economic-behavior-organization.md) | Yuxiang Jiang, R. Liu, Dominik M. Rösch et al. | Southwestern University of Finance and Economics; University at Buffalo, State University of New York |
| 2026-10-24 | [Recent Developments at the CMA: 2025–26](https://doi.org/10.1007/s11151-026-10066-0) | [Review of Industrial Organization](https://scholar4ai.com/journals/review-of-industrial-organization.md) | Catherine Colebrook, Helen Ferguson, Jenny Haydock et al. |  |
| 2026-10-24 | [Coal subsidies and long-term contract pricing: a perspective from factor marketization reforms](https://doi.org/10.1016/j.enpol.2026.115678) | [Energy Policy](https://scholar4ai.com/journals/energy-policy.md) | Xiaofei Wang, Nan Liu, Kuijian Qu et al. | Shandong Institute of Business and Technology; Hunan Normal University; Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education) |
| 2026-10-24 | [The intensive margin of food access and its association with health outcomes: evidence of heterogeneous effects across U.S. Communities](https://doi.org/10.1016/j.foodpol.2026.103212) | [Food Policy](https://scholar4ai.com/journals/food-policy.md) | Qingwei Qiao, Syed Badruddoza, Modhurima Dey Amin et al. | Washington State University; Texas Tech University; Economic Research Service |
| 2026-10-24 | [Geopolitical risk and energy poverty in African net energy importers: Localized effects, structural vulnerability, and conditional resilience](https://doi.org/10.1016/j.enpol.2026.115680) | [Energy Policy](https://scholar4ai.com/journals/energy-policy.md) | Amine Kendri, Hafid El Hassani |  |
| 2026-10-24 | [Fiscal information and stated willingness to pay taxes: evidence from a survey experiment](https://doi.org/10.1007/s10797-026-10007-3) | [International Tax and Public Finance](https://scholar4ai.com/journals/international-tax-and-public-finance.md) | José María Durán-Cabré, Alejandro Esteller Moré, Riccardo Secomandi | Barcelona School of Economics; Universitat de Barcelona; University of Ferrara |
| 2026-10-24 | [Oil import diversification and the macroeconomic transmission of oil supply shocks in the Eurozone](https://doi.org/10.1007/s00181-026-02986-3) | [Empirical Economics](https://scholar4ai.com/journals/empirical-economics.md) | Antonio J. Garzón, Mehmet Pinar | Universidad de Cádiz; Istanbul Bilgi University; Universidad de Sevilla |
| 2026-10-24 | [Evolutionary Game Analysis of Value Co-Creation in Green Innovation Chains Under Revenue-Sharing Contracts](https://doi.org/10.1007/s10614-026-11450-4) | [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | Rongjian Xie, Sifan Wang, Qianyao Yang et al. | Nanjing University of Information Science and Technology; Nanjing University of Science and Technology |
| 2026-10-24 | [Inequality matters: A systematic review of heterogeneity in integrated assessment models](https://doi.org/10.1016/j.ecolecon.2026.109280) | [Ecological Economics](https://scholar4ai.com/journals/ecological-economics.md) | Sara Pavesio, Jaime Nieto, Mercedes Molpeceres et al. | Universidad de Valladolid |
| 2026-10-24 | [A poorly targeted subsidy? The fiscal and social costs of EU VAT expenditures](https://doi.org/10.1007/s10797-026-10008-2) | [International Tax and Public Finance](https://scholar4ai.com/journals/international-tax-and-public-finance.md) | Michael Christl | Universidad Loyola Andalucía |
| 2026-10-24 | [Dynamic Evolution of Financialization: A Complex Network Analysis](https://doi.org/10.1007/s10614-026-11453-1) | [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | Jun Hu, Chengbin Chu, Jinyan Wu | Inner Mongolia University; Université Gustave Eiffel |
| 2026-10-24 | [From household peak to base: Understanding residential electricity consumption in the context of an energy transition](https://doi.org/10.1016/j.jup.2026.102357) | [Utilities Policy](https://scholar4ai.com/journals/utilities-policy.md) | Ewa Vick, Yuting Yang, Janie M. Chermak | University of New Mexico; University of Montana; Tianjin University; … |
| 2026-10-24 | [Do Airlines Adopt Sustainable Aviation Fuels Under Modal Competition?](https://doi.org/10.1007/s11151-026-10067-z) | [Review of Industrial Organization](https://scholar4ai.com/journals/review-of-industrial-organization.md) | Sai Bravo-Melgarejo, Estelle Malavolti | Commissariat à l'Énergie Atomique et aux Énergies Alternatives; Université Paris-Saclay; CEA Paris-Saclay; … |

## Questions this page answers

### What are the most common topics in recent economics research?

In the last 30 days, the most frequent OpenAlex keywords in papers from the tracked economics journals were “China” (44), “monetary policy” (18), “Chinese listed companies” (14), “energy transition” (14) and “regional disparities” (14).

### Which universities and institutions publish the most economics research?

In the last 30 days, the institutions with the most papers in the tracked economics journals were Centre National de la Recherche Scientifique (20), Peking University (12), Centre for Economic Policy Research (12), Renmin University of China (11) and University of Cambridge (9).

### Which countries publish the most economics research?

Counting papers with at least one author at an institution in the country, the leaders over the last 30 days were China (178), United States (140), United Kingdom (80), Italy (63) and Germany (50).

### Which equations and models do recent economics research papers use most?

Counting papers that mention them by name in the title, abstract, keywords or full text, the most frequent in the tracked economics journals over the last 30 days were Difference-in-differences (60), Instrumental variables (two-stage least squares) (45), Logistic regression (logit) (35), Ordinary least squares (OLS) (34) and Two-way fixed-effects panel regression (21).

### What data do recent economics research papers use?

Counting papers that mention a dataset by name, the kinds of data used most over the last 30 days were Macroeconomic and development indicators (33), European and other national household panels (14), China household and social surveys (13), US household and labour surveys (9) and China statistical yearbooks and regional indices (7); the most-mentioned datasets were Eurostat (15), World Development Indicators (World Bank) (14), China Family Panel Studies (CFPS) (6), Penn World Table (6) and Labour Force Surveys (5).

### How current is this data?

It is rebuilt from OpenAlex 4 times a day (00:00, 06:00, 12:00, 18:00, UTC+09:00); the last update was 2026-10-26 11:39 UTC+09:00. OpenAlex usually indexes new papers within a few days of publication.


---

---
title: "Management research rankings and trends"
description: "In the last 30 days (2026-09-27 to 2026-10-26), 274 papers were published in the 25 tracked management journals (as indexed by OpenAlex). The most frequent keywords were “Chinese listed companies” (10), “digital transformation” (10) and “dynamic capabilities” (10)."
url: https://scholar4ai.com/categories/management
markdown_url: https://scholar4ai.com/categories/management.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T11:39:12+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# Management research rankings

Papers in the 25 tracked management journals, window 2026-09-27 – 2026-10-26 (30 days). Keywords, institutions, web domains and countries are ranked by paper count.

- Papers (30 days): 274
- Papers (last 7 days): 44
- Journals: 25
- Institutions: 513
- Countries: 65
- Web domains: 506

## Key findings

- In the last 30 days (2026-09-27 to 2026-10-26), 274 papers were published in the 25 tracked management journals (as indexed by OpenAlex).
- The most frequent keywords were “Chinese listed companies” (10), “digital transformation” (10) and “dynamic capabilities” (10).
- The institutions with the most papers were BI Norwegian Business School (7), Lappeenranta-Lahti University of Technology (5) and University of Reading (5).
- China was involved in 29% of the papers (79), followed by United States (43) and United Kingdom (32).
- Among institution web domains, .edu.cn appeared on 28% of the papers.
- The most-mentioned equations and models were Structural equation modeling (SEM) (34), Mediation model (indirect effect) (20) and Ordinary least squares (OLS) (17).
- The most-mentioned kinds of data were Macroeconomic and development indicators (9), Company financial statements (6) and ESG and sustainability ratings (5); the most-mentioned datasets were World Development Indicators (World Bank) (5), S&P 500 (4) and Compustat (3).

## Papers per day

*Papers per publication date*

| Date | Papers |
|---|---:|
| 2026-09-27 | 5 |
| 2026-09-28 | 8 |
| 2026-09-29 | 10 |
| 2026-09-30 | 13 |
| 2026-10-01 | 37 |
| 2026-10-02 | 9 |
| 2026-10-03 | 7 |
| 2026-10-04 | 6 |
| 2026-10-05 | 11 |
| 2026-10-06 | 13 |
| 2026-10-07 | 8 |
| 2026-10-08 | 11 |
| 2026-10-09 | 9 |
| 2026-10-10 | 10 |
| 2026-10-11 | 2 |
| 2026-10-12 | 10 |
| 2026-10-13 | 8 |
| 2026-10-14 | 5 |
| 2026-10-15 | 13 |
| 2026-10-16 | 13 |
| 2026-10-17 | 13 |
| 2026-10-18 | 0 |
| 2026-10-19 | 9 |
| 2026-10-20 | 9 |
| 2026-10-21 | 13 |
| 2026-10-22 | 8 |
| 2026-10-23 | 11 |
| 2026-10-24 | 3 |
| 2026-10-25 | 0 |
| 2026-10-26 | 0 |

## Growing over the year (Jul 2026–Sep 2026 vs Oct 2025–Jun 2026)

*Papers in Jul 2026–Sep 2026 beyond what the keyword's share in Oct 2025–Jun 2026 predicts*

| Keyword | Papers per month, Oct 25 – Oct 26* | Papers Jul 2026–Sep 2026 | vs. earlier share |
|---|---|---:|---:|
| absorptive capacity | 2, 2, 2, 4, 4, 5, 2, 2, 3, 6, 8, 7, 0 | 21 | +12 |
| customer satisfaction | 4, 3, 2, 3, 2, 2, 1, 1, 1, 7, 4, 6, 0 | 17 | +11 |
| knowledge-based view | 2, 3, 3, 1, 3, 3, 3, 2, 2, 5, 3, 7, 0 | 15 | +8 |
| human-AI collaboration | 2, 3, 2, 1, 1, 1, 5, 1, 7, 5, 4, 6, 5 | 15 | +7 |
| customer engagement | 1, 2, 0, 3, 0, 3, 3, 8, 3, 6, 4, 3, 1 | 13 | +5 |

More: https://scholar4ai.com/trends.md



## Top research keywords (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Keyword | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [Chinese listed companies](https://scholar4ai.com/keywords/chinese-listed-companies.md) | 10 | 0 |
| 1 | [digital transformation](https://scholar4ai.com/keywords/digital-transformation.md) | 10 | 0 |
| 1 | dynamic capabilities | 10 | 3 |
| 4 | [artificial intelligence](https://scholar4ai.com/keywords/artificial-intelligence.md) | 9 | 0 |
| 4 | [corporate governance](https://scholar4ai.com/keywords/corporate-governance.md) | 9 | 1 |
| 6 | corporate social responsibility | 8 | 3 |
| 6 | [green innovation](https://scholar4ai.com/keywords/green-innovation.md) | 8 | 0 |
| 6 | small and medium-sized enterprises | 8 | 1 |
| 9 | digitalization | 7 | 0 |
| 9 | innovation | 7 | 2 |
| 9 | institutional theory | 7 | 1 |
| 9 | multinational corporations | 7 | 1 |
| 9 | technology adoption | 7 | 0 |
| 14 | [China](https://scholar4ai.com/keywords/china.md) | 6 | 0 |
| 14 | consumer behavior | 6 | 2 |
| 14 | firm performance | 6 | 0 |
| 14 | internationalization | 6 | 1 |
| 14 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 6 | 1 |
| 14 | moderating effect | 6 | 1 |
| 14 | resource-based view | 6 | 1 |

## Top universities and research institutions (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Institution | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | BI Norwegian Business School | 7 | 0 |
| 2 | Lappeenranta-Lahti University of Technology | 5 | 1 |
| 2 | University of Reading | 5 | 0 |
| 4 | Tongji University | 4 | 0 |
| 4 | [Sun Yat-sen University](https://scholar4ai.com/institutions/sun-yat-sen-university.md) | 4 | 1 |
| 4 | Copenhagen Business School | 4 | 0 |
| 4 | Paris School of Business | 4 | 0 |
| 4 | [Renmin University of China](https://scholar4ai.com/institutions/renmin-university-of-china.md) | 4 | 0 |
| 4 | University of Vaasa | 4 | 0 |
| 4 | [Xi'an Jiaotong University](https://scholar4ai.com/institutions/xi-an-jiaotong-university.md) | 4 | 1 |
| 4 | University of Science and Technology Beijing | 4 | 1 |
| 12 | NEOMA Business School | 3 | 1 |
| 12 | The University of Sydney | 3 | 1 |
| 12 | Baylor University | 3 | 0 |
| 12 | Zhongnan University of Economics and Law | 3 | 1 |
| 12 | Shanghai University of Finance and Economics | 3 | 0 |
| 12 | Xiamen University | 3 | 1 |
| 12 | Utrecht University | 3 | 0 |
| 12 | Yonsei University | 3 | 0 |
| 12 | University of Siegen | 3 | 1 |

## Top institution web domains (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Web domain | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | bi.no | 7 | 0 |
| 2 | lut.fi | 5 | 1 |
| 2 | reading.ac.uk | 5 | 0 |
| 4 | cbs.dk | 4 | 0 |
| 4 | psbedu.paris | 4 | 0 |
| 4 | [ruc.edu.cn](https://scholar4ai.com/domains/ruc.edu.cn.md) | 4 | 0 |
| 4 | [sysu.edu.cn](https://scholar4ai.com/domains/sysu.edu.cn.md) | 4 | 1 |
| 4 | [tongji.edu.cn](https://scholar4ai.com/domains/tongji.edu.cn.md) | 4 | 0 |
| 4 | ustb.edu.cn | 4 | 1 |
| 4 | uwasa.fi | 4 | 0 |
| 4 | [xjtu.edu.cn](https://scholar4ai.com/domains/xjtu.edu.cn.md) | 4 | 1 |
| 12 | baylor.edu | 3 | 0 |
| 12 | birmingham.ac.uk | 3 | 0 |
| 12 | edhec.edu | 3 | 1 |
| 12 | iitd.ac.in | 3 | 0 |
| 12 | knust.edu.gh | 3 | 2 |
| 12 | monash.edu | 3 | 0 |
| 12 | neoma-bs.com | 3 | 1 |
| 12 | rmit.edu.au | 3 | 0 |
| 12 | [scu.edu.cn](https://scholar4ai.com/domains/scu.edu.cn.md) | 3 | 0 |

## Top countries (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Country | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | China (CN) | 79 | 8 |
| 2 | United States (US) | 43 | 9 |
| 3 | United Kingdom (GB) | 32 | 4 |
| 4 | France (FR) | 21 | 4 |
| 5 | Germany (DE) | 20 | 2 |
| 6 | Italy (IT) | 18 | 1 |
| 7 | Australia (AU) | 15 | 4 |
| 8 | Finland (FI) | 11 | 1 |
| 8 | India (IN) | 11 | 3 |
| 8 | South Korea (KR) | 11 | 1 |
| 8 | Norway (NO) | 11 | 2 |
| 12 | Canada (CA) | 10 | 2 |
| 13 | Netherlands (NL) | 8 | 0 |
| 14 | Denmark (DK) | 7 | 0 |
| 14 | Spain (ES) | 7 | 2 |
| 16 | Hong Kong (HK) | 6 | 1 |
| 16 | Saudi Arabia (SA) | 6 | 2 |
| 16 | Sweden (SE) | 6 | 1 |
| 16 | South Africa (ZA) | 6 | 1 |
| 20 | United Arab Emirates (AE) | 5 | 1 |

## Research fields (top 13)

*Top 13 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Field | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | Business, Management and Accounting | 152 | 21 |
| 2 | Social Sciences | 37 | 3 |
| 3 | Decision Sciences | 23 | 6 |
| 4 | Economics, Econometrics and Finance | 19 | 4 |
| 5 | Computer Science | 16 | 4 |
| 6 | Environmental Science | 6 | 1 |
| 6 | Psychology | 6 | 0 |
| 8 | Engineering | 4 | 2 |
| 9 | Arts and Humanities | 2 | 0 |
| 9 | Health Professions | 2 | 1 |
| 11 | Agricultural and Biological Sciences | 1 | 0 |
| 11 | Biochemistry, Genetics and Molecular Biology | 1 | 1 |
| 11 | Physics and Astronomy | 1 | 0 |

## Papers per journal (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Journal | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [Journal of Business Research](https://scholar4ai.com/journals/journal-of-business-research.md) | 44 | 7 |
| 2 | [Technological Forecasting and Social Change](https://scholar4ai.com/journals/technological-forecasting-and-social-change.md) | 28 | 5 |
| 3 | [Future Business Journal](https://scholar4ai.com/journals/future-business-journal.md) | 22 | 4 |
| 3 | [Journal of Business Ethics](https://scholar4ai.com/journals/journal-of-business-ethics.md) | 22 | 2 |
| 5 | [Technovation](https://scholar4ai.com/journals/technovation.md) | 21 | 2 |
| 6 | [Journal of Innovation & Knowledge](https://scholar4ai.com/journals/journal-of-innovation-knowledge.md) | 14 | 0 |
| 7 | [Asia Pacific Journal of Management](https://scholar4ai.com/journals/asia-pacific-journal-of-management.md) | 12 | 1 |
| 8 | [The Journal of Technology Transfer](https://scholar4ai.com/journals/the-journal-of-technology-transfer.md) | 11 | 2 |
| 9 | [International Entrepreneurship and Management Journal](https://scholar4ai.com/journals/international-entrepreneurship-and-management-journal.md) | 10 | 3 |
| 10 | [Information Systems Frontiers](https://scholar4ai.com/journals/information-systems-frontiers.md) | 9 | 4 |
| 10 | [Journal of the Academy of Marketing Science](https://scholar4ai.com/journals/journal-of-the-academy-of-marketing-science.md) | 9 | 3 |
| 12 | [Industrial Marketing Management](https://scholar4ai.com/journals/industrial-marketing-management.md) | 8 | 1 |
| 12 | [International Journal of Information Management](https://scholar4ai.com/journals/international-journal-of-information-management.md) | 8 | 0 |
| 14 | [Journal of International Business Studies](https://scholar4ai.com/journals/journal-of-international-business-studies.md) | 7 | 1 |
| 14 | [Journal of International Management](https://scholar4ai.com/journals/journal-of-international-management.md) | 7 | 3 |
| 16 | [International Business Review](https://scholar4ai.com/journals/international-business-review.md) | 6 | 1 |
| 16 | [Journal of Business Venturing](https://scholar4ai.com/journals/journal-of-business-venturing.md) | 6 | 0 |
| 16 | [Review of Managerial Science](https://scholar4ai.com/journals/review-of-managerial-science.md) | 6 | 2 |
| 19 | [Journal of Business Economics](https://scholar4ai.com/journals/journal-of-business-economics.md) | 5 | 0 |
| 19 | [Long Range Planning](https://scholar4ai.com/journals/long-range-planning.md) | 5 | 0 |

## Most-used equations and models (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Equation or model | Formula | Papers |
|---:|---|---|---:|
| 1 | [Structural equation modeling (SEM)](https://scholar4ai.com/equations/structural-equation-modeling-sem.md) | $\eta = B\eta + \Gamma\xi + \zeta,\quad y = \Lambda_y\,\eta + \varepsilon$ | 34 |
| 2 | [Mediation model (indirect effect)](https://scholar4ai.com/equations/mediation-model-indirect-effect.md) | $M = \alpha_0 + a X + e_1,\quad Y = \beta_0 + c' X + b M + e_2,\quad \text{indirect effect} = a\,b$ | 20 |
| 3 | [Ordinary least squares (OLS)](https://scholar4ai.com/equations/ordinary-least-squares-ols.md) | $\hat\beta = (X^\top X)^{-1} X^\top y$ | 17 |
| 4 | [Instrumental variables (two-stage least squares)](https://scholar4ai.com/equations/instrumental-variables-two-stage-least-squares.md) | $\hat\beta_{\mathrm{2SLS}} = (X^\top P_Z X)^{-1} X^\top P_Z\, y,\quad P_Z = Z (Z^\top Z)^{-1} Z^\top$ | 13 |
| 4 | [Logistic regression (logit)](https://scholar4ai.com/equations/logistic-regression-logit.md) | $\Pr(y = 1 \mid x) = \frac{1}{1 + e^{-x^\top\beta}}$ | 13 |
| 6 | [Difference-in-differences](https://scholar4ai.com/equations/difference-in-differences.md) | $Y_{it} = \alpha_i + \lambda_t + \delta\,(\mathit{Treat}_i \times \mathit{Post}_t) + \varepsilon_{it}$ | 12 |
| 7 | [Two-way fixed-effects panel regression](https://scholar4ai.com/equations/two-way-fixed-effects-panel-regression.md) | $y_{it} = \alpha_i + \lambda_t + x_{it}^\top\beta + \varepsilon_{it}$ | 9 |
| 8 | [Propensity score matching](https://scholar4ai.com/equations/propensity-score-matching.md) | $e(x) = \Pr(D = 1 \mid X = x)$ | 7 |
| 9 | [Generalized method of moments (GMM)](https://scholar4ai.com/equations/generalized-method-of-moments-gmm.md) | $\hat\theta = \operatorname*{arg\,min}_{\theta}\ \bar g(\theta)^\top W\, \bar g(\theta),\quad \bar g(\theta) = \frac{1}{n}\sum_{i=1}^{n} g(w_i, \theta)$ | 6 |
| 9 | [Probit model](https://scholar4ai.com/equations/probit-model.md) | $\Pr(y = 1 \mid x) = \Phi(x^\top\beta)$ | 6 |

## Most-used data (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Data category | Most-mentioned datasets | Papers |
|---:|---|---|---:|
| 1 | [Macroeconomic and development indicators](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md) | World Development Indicators (World Bank) (5), Eurostat (3), IMF data (IFS, WEO, DOTS) (1), OECD statistics (1) | 9 |
| 2 | [Company financial statements](https://scholar4ai.com/datasets/company-financial-statements.md) | Compustat (3), Orbis (Bureau van Dijk) (2), S&P Capital IQ (1) | 6 |
| 3 | [ESG and sustainability ratings](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md) | MSCI ESG / KLD (2), CDP (Carbon Disclosure Project) (2), Refinitiv ESG (ASSET4) (1), Huazheng / Sino-Securities ESG (1) … | 5 |
| 3 | [Stock market indices](https://scholar4ai.com/datasets/stock-market-indices.md) | S&P 500 (4), MSCI indices (1), CSI 300 / Shanghai Composite (1), Dow Jones Industrial Average (1) | 5 |
| 5 | [China statistical yearbooks and regional indices](https://scholar4ai.com/datasets/china-statistical-yearbooks-and-regional-indices.md) | Marketization index (Fan Gang) (2), China City Statistical Yearbook (1) | 3 |
| 5 | [Corporate filings and text](https://scholar4ai.com/datasets/corporate-filings-and-text.md) | 10-K filings (1), Earnings call transcripts (1), XBRL filings (1) | 3 |
| 5 | [Corporate governance and executive data](https://scholar4ai.com/datasets/corporate-governance-and-executive-data.md) | BoardEx (2), ExecuComp (1) | 3 |
| 5 | [Mutual fund and hedge fund data](https://scholar4ai.com/datasets/mutual-fund-and-hedge-fund-data.md) | Lipper / TASS (3) | 3 |
| 5 | [US household and labour surveys](https://scholar4ai.com/datasets/us-household-and-labour-surveys.md) | Health and Retirement Study (1), IPUMS (1), Consumer Expenditure Survey (1) | 3 |
| 5 | [US macroeconomic data](https://scholar4ai.com/datasets/us-macroeconomic-data.md) | FRED (1), Bureau of Labor Statistics (1), University of Michigan Surveys of Consumers (1) | 3 |


## Latest management papers

*Latest management papers*

| Date | Title | Journal | Authors | Affiliations |
|---|---|---|---|---|
| 2026-10-24 | [From Big Data to Generative Artificial Intelligence: A Bibliometric Analysis of the Conceptual Structure and Thematic Evolution of Computational Social Science (2011–2025)](https://doi.org/10.1007/s10796-026-10830-3) | [Information Systems Frontiers](https://scholar4ai.com/journals/information-systems-frontiers.md) | Rukiye ÜNAL AYAZ, Hakan Özköse, Muhammet Berigel | Karadeniz Technical University |
| 2026-10-24 | [Too much of a good thing? Knowledge transfer and scientists’ job satisfaction](https://doi.org/10.1007/s10961-026-10400-4) | [The Journal of Technology Transfer](https://scholar4ai.com/journals/the-journal-of-technology-transfer.md) | Christian Schröder, Ann-Christin Grözinger, Arndt Werner et al. | University of Siegen; University of Göttingen; ZHAW Zurich University of Applied Sciences |
| 2026-10-24 | [AI vs. AI: An AI-Enabled Conversational Intervention to Inoculate Against AI-Generated Fake News](https://doi.org/10.1007/s10796-026-10827-y) | [Information Systems Frontiers](https://scholar4ai.com/journals/information-systems-frontiers.md) | Mohammad S. Najjar, Murtaza Nasir | Wichita State University |
| 2026-10-23 | [Managing the dual nature of customer engagement in product innovation: A sensegiving and sensemaking perspective](https://doi.org/10.1007/s11747-026-01206-9) | [Journal of the Academy of Marketing Science](https://scholar4ai.com/journals/journal-of-the-academy-of-marketing-science.md) | Zhenghao Michael Xia, Xiaodong Marcus Li, Kang Xie et al. | South China University of Technology; Sun Yat-sen University |
| 2026-10-23 | [Explainable medical artificial intelligence: The impact of agent and explanation types on health persuasion](https://doi.org/10.1016/j.jbusres.2026.116516) | [Journal of Business Research](https://scholar4ai.com/journals/journal-of-business-research.md) | You Li, Shaowei Chai | Wuhan University of Technology; Anhui University |
| 2026-10-23 | [What drives microfinance adoption among rural MSMES? a configurational FSQCA approach](https://doi.org/10.1186/s43093-026-01031-0) | [Future Business Journal](https://scholar4ai.com/journals/future-business-journal.md) | Jaratin Lily, Mori Kogid, Debbra Toria Nipo et al. | Universiti of Malaysia Sabah; Universiti Teknologi MARA |
| 2026-10-23 | [Matching for impact: a dyadic process model of mentor–venture alignment in entrepreneurial support organizations](https://doi.org/10.1007/s10961-026-10389-w) | [The Journal of Technology Transfer](https://scholar4ai.com/journals/the-journal-of-technology-transfer.md) | Ayna Yusubova, Marcus Dejardin | University of Agder; University of Namur; UCLouvain |
| 2026-10-23 | [Public support for regulatory research on the potential ecological effects of RF-EMF](https://doi.org/10.1016/j.techfore.2026.124938) | [Technological Forecasting and Social Change](https://scholar4ai.com/journals/technological-forecasting-and-social-change.md) | Laura Recuero Virto, Peter Bou Saba, Arno Thielens et al. | Centre Européen de Recherche en Economie Financière et Gestion des Entreprises; ICN Business School Nancy-Paris-Berlin; Université de Lorraine; … |
| 2026-10-23 | [Evaluating machine learning and econometric models for inflation forecasting: evidence from a Sub-Saharan African panel](https://doi.org/10.1186/s43093-026-01003-4) | [Future Business Journal](https://scholar4ai.com/journals/future-business-journal.md) | Baffour Osei, Abigail Boatemaa, Isaac Nyame et al. | University of Skills Training and Entrepreneurial Development; University of Bradford; Kwame Nkrumah University of Science and Technology; … |
| 2026-10-23 | [I deserve better: beauty work increases consumer preference for advanced products](https://doi.org/10.1016/j.jbusres.2026.116608) | [Journal of Business Research](https://scholar4ai.com/journals/journal-of-business-research.md) | Yawei Wang, Xianzheng Fei, Yanfen You | Wuhan Textile University; Zhongnan University of Economics and Law; University of Massachusetts Amherst |
| 2026-10-23 | [Extreme environmental distance and global sustainability engagement: Evidence from the United Nations Global Compact](https://doi.org/10.1016/j.jwb.2026.101789) | [Journal of World Business](https://scholar4ai.com/journals/journal-of-world-business.md) | Fujie Zhao, Joey Wenling Yang, Jing Yu et al. | University of Macau; The University of Western Australia; The University of Sydney; … |
| 2026-10-23 | [Why some countries lead and others lag: The political economy of promoting battery electric vehicles](https://doi.org/10.1016/j.techfore.2026.124907) | [Technological Forecasting and Social Change](https://scholar4ai.com/journals/technological-forecasting-and-social-change.md) | V. Anilan, Akshay Vij, Helen D. Barrie | The University of Adelaide |
| 2026-10-23 | [Women and workplace mental health: a systematic literature review and future research agenda](https://doi.org/10.1186/s43093-026-01029-8) | [Future Business Journal](https://scholar4ai.com/journals/future-business-journal.md) | Chitra Krishnan, Supriya Lamba Sahdev | Symbiosis International University; Alliance University |
| 2026-10-23 | [Foreign knowledge spillovers and new product development speed in boundary-spanning teams: A sufficiency–necessity perspective on digital interoperability and collaborative conflict resolution capabilities](https://doi.org/10.1016/j.indmarman.2026.10.006) | [Industrial Marketing Management](https://scholar4ai.com/journals/industrial-marketing-management.md) | Charles Baah, Yaw Agyabeng‐Mensah, Ebenezer Afum et al. | Charles Darwin University; Macquarie University; University of Bristol; … |
| 2026-10-22 | [Virtue re-coding: How institutional voids shape ethical practice in international business](https://doi.org/10.1016/j.ibusrev.2026.102669) | [International Business Review](https://scholar4ai.com/journals/international-business-review.md) | Bachar Moughayt | Université catholique de l'ouest |

## Questions this page answers

### What are the most common topics in recent management research?

In the last 30 days, the most frequent OpenAlex keywords in papers from the tracked management journals were “Chinese listed companies” (10), “digital transformation” (10), “dynamic capabilities” (10), “artificial intelligence” (9) and “corporate governance” (9).

### Which universities and institutions publish the most management research?

In the last 30 days, the institutions with the most papers in the tracked management journals were BI Norwegian Business School (7), Lappeenranta-Lahti University of Technology (5), University of Reading (5), Tongji University (4) and Sun Yat-sen University (4).

### Which countries publish the most management research?

Counting papers with at least one author at an institution in the country, the leaders over the last 30 days were China (79), United States (43), United Kingdom (32), France (21) and Germany (20).

### Which equations and models do recent management research papers use most?

Counting papers that mention them by name in the title, abstract, keywords or full text, the most frequent in the tracked management journals over the last 30 days were Structural equation modeling (SEM) (34), Mediation model (indirect effect) (20), Ordinary least squares (OLS) (17), Instrumental variables (two-stage least squares) (13) and Logistic regression (logit) (13).

### What data do recent management research papers use?

Counting papers that mention a dataset by name, the kinds of data used most over the last 30 days were Macroeconomic and development indicators (9), Company financial statements (6), ESG and sustainability ratings (5), Stock market indices (5) and China statistical yearbooks and regional indices (3); the most-mentioned datasets were World Development Indicators (World Bank) (5), S&P 500 (4), Compustat (3), Eurostat (3) and Lipper / TASS (3).

### How current is this data?

It is rebuilt from OpenAlex 4 times a day (00:00, 06:00, 12:00, 18:00, UTC+09:00); the last update was 2026-10-26 11:39 UTC+09:00. OpenAlex usually indexes new papers within a few days of publication.


---

---
title: "Operations Research rankings and trends"
description: "In the last 30 days (2026-09-27 to 2026-10-26), 240 papers were published in the 11 tracked operations research journals (as indexed by OpenAlex). The most frequent keywords were “mixed-integer linear programming” (12), “demand uncertainty” (8) and “supply chain coordination” (8)."
url: https://scholar4ai.com/categories/operations-research
markdown_url: https://scholar4ai.com/categories/operations-research.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T11:39:12+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# Operations Research rankings

Papers in the 11 tracked operations research journals, window 2026-09-27 – 2026-10-26 (30 days). Keywords, institutions, web domains and countries are ranked by paper count.

- Papers (30 days): 240
- Papers (last 7 days): 13
- Journals: 11
- Institutions: 363
- Countries: 51
- Web domains: 355

## Key findings

- In the last 30 days (2026-09-27 to 2026-10-26), 240 papers were published in the 11 tracked operations research journals (as indexed by OpenAlex).
- The most frequent keywords were “mixed-integer linear programming” (12), “demand uncertainty” (8) and “supply chain coordination” (8).
- The institutions with the most papers were Beihang University (9), Southwest Jiaotong University (8) and Hong Kong Polytechnic University (7).
- China was involved in 47% of the papers (112), followed by United States (29) and United Kingdom (15).
- Among institution web domains, .edu.cn appeared on 47% of the papers.
- The most-mentioned equations and models were Linear and integer programming (29), Logistic regression (logit) (8) and Data envelopment analysis (DEA) (4).
- The most-mentioned kinds of data were Stock market indices (5), Macroeconomic and development indicators (2) and China household and social surveys (1); the most-mentioned datasets were S&P 500 (4), Eurostat (2) and China Health and Nutrition Survey (CHNS) (1).

## Papers per day

*Papers per publication date*

| Date | Papers |
|---|---:|
| 2026-09-27 | 3 |
| 2026-09-28 | 7 |
| 2026-09-29 | 14 |
| 2026-09-30 | 12 |
| 2026-10-01 | 104 |
| 2026-10-02 | 3 |
| 2026-10-03 | 7 |
| 2026-10-04 | 0 |
| 2026-10-05 | 8 |
| 2026-10-06 | 3 |
| 2026-10-07 | 4 |
| 2026-10-08 | 8 |
| 2026-10-09 | 4 |
| 2026-10-10 | 3 |
| 2026-10-11 | 1 |
| 2026-10-12 | 1 |
| 2026-10-13 | 3 |
| 2026-10-14 | 3 |
| 2026-10-15 | 6 |
| 2026-10-16 | 22 |
| 2026-10-17 | 6 |
| 2026-10-18 | 2 |
| 2026-10-19 | 3 |
| 2026-10-20 | 9 |
| 2026-10-21 | 1 |
| 2026-10-22 | 1 |
| 2026-10-23 | 2 |
| 2026-10-24 | 0 |
| 2026-10-25 | 0 |
| 2026-10-26 | 0 |

## Growing over the year (Jul 2026–Sep 2026 vs Oct 2025–Jun 2026)

*Papers in Jul 2026–Sep 2026 beyond what the keyword's share in Oct 2025–Jun 2026 predicts*

| Keyword | Papers per month, Oct 25 – Oct 26* | Papers Jul 2026–Sep 2026 | vs. earlier share |
|---|---|---:|---:|
| price competition | 3, 1, 0, 0, 0, 1, 0, 0, 0, 3, 3, 5, 2 | 11 | +9 |
| drone delivery | 3, 2, 1, 0, 5, 1, 0, 3, 3, 5, 5, 5, 1 | 15 | +9 |
| supply chain coordination | 2, 2, 5, 6, 6, 4, 2, 4, 2, 6, 6, 7, 6 | 19 | +8 |
| local search | 4, 1, 2, 3, 3, 4, 2, 3, 0, 3, 6, 6, 1 | 15 | +8 |
| distributionally robust optimization | 9, 7, 2, 3, 3, 6, 5, 4, 5, 5, 12, 5, 6 | 22 | +7 |

More: https://scholar4ai.com/trends.md



## Top research keywords (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Keyword | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | mixed-integer linear programming | 12 | 1 |
| 2 | demand uncertainty | 8 | 0 |
| 2 | supply chain coordination | 8 | 0 |
| 2 | supply chain resilience | 8 | 0 |
| 5 | carbon emission reduction | 7 | 0 |
| 5 | last-mile delivery | 7 | 0 |
| 5 | two-stage stochastic programming | 7 | 0 |
| 8 | distributionally robust optimization | 6 | 0 |
| 8 | facility location | 6 | 0 |
| 10 | mixed-integer nonlinear programming | 5 | 0 |
| 10 | multi-objective optimization | 5 | 0 |
| 10 | portfolio optimization | 5 | 2 |
| 10 | robust optimization | 5 | 0 |
| 14 | Benders decomposition | 4 | 0 |
| 14 | adaptive large neighborhood search | 4 | 0 |
| 14 | game theory | 4 | 0 |
| 14 | inventory management | 4 | 0 |
| 14 | non-convex optimization | 4 | 1 |
| 14 | price competition | 4 | 0 |
| 14 | proximal policy optimization | 4 | 0 |

## Top universities and research institutions (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Institution | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | Beihang University | 9 | 0 |
| 2 | Southwest Jiaotong University | 8 | 1 |
| 3 | [Hong Kong Polytechnic University](https://scholar4ai.com/institutions/hong-kong-polytechnic-university.md) | 7 | 0 |
| 4 | [Beijing Jiaotong University](https://scholar4ai.com/institutions/beijing-jiaotong-university.md) | 6 | 0 |
| 4 | Dalian University of Technology | 6 | 0 |
| 4 | [Xi'an Jiaotong University](https://scholar4ai.com/institutions/xi-an-jiaotong-university.md) | 6 | 0 |
| 7 | University of Science and Technology of China | 5 | 0 |
| 7 | [Tianjin University](https://scholar4ai.com/institutions/tianjin-university.md) | 5 | 0 |
| 9 | Tongji University | 4 | 0 |
| 9 | Dongbei University of Finance and Economics | 4 | 0 |
| 9 | Hefei University of Technology | 4 | 0 |
| 9 | Northwestern Polytechnical University | 4 | 0 |
| 9 | Fuzhou University | 4 | 0 |
| 14 | Qingdao University | 3 | 0 |
| 14 | NEOMA Business School | 3 | 0 |
| 14 | [Centre National de la Recherche Scientifique](https://scholar4ai.com/institutions/centre-national-de-la-recherche-scientifique.md) | 3 | 0 |
| 14 | University of Shanghai for Science and Technology | 3 | 1 |
| 14 | Nanyang Technological University | 3 | 0 |
| 14 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | 3 | 0 |
| 14 | Wayne State University | 3 | 1 |

## Top institution web domains (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Web domain | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | buaa.edu.cn | 9 | 0 |
| 2 | swjtu.edu.cn | 8 | 1 |
| 3 | [polyu.edu.hk](https://scholar4ai.com/domains/polyu.edu.hk.md) | 7 | 0 |
| 4 | dlut.edu.cn | 6 | 0 |
| 4 | [njtu.edu.cn](https://scholar4ai.com/domains/njtu.edu.cn.md) | 6 | 0 |
| 4 | [xjtu.edu.cn](https://scholar4ai.com/domains/xjtu.edu.cn.md) | 6 | 0 |
| 7 | [tju.edu.cn](https://scholar4ai.com/domains/tju.edu.cn.md) | 5 | 0 |
| 7 | ustc.edu.cn | 5 | 0 |
| 9 | dufe.edu.cn | 4 | 0 |
| 9 | fzu.edu.cn | 4 | 0 |
| 9 | hfut.edu.cn | 4 | 0 |
| 9 | nwpu.edu.cn | 4 | 0 |
| 9 | [tongji.edu.cn](https://scholar4ai.com/domains/tongji.edu.cn.md) | 4 | 0 |
| 14 | [cnrs.fr](https://scholar4ai.com/domains/cnrs.fr.md) | 3 | 0 |
| 14 | cup.edu.cn | 3 | 0 |
| 14 | kuleuven.be | 3 | 0 |
| 14 | mit.edu | 3 | 0 |
| 14 | neoma-bs.com | 3 | 0 |
| 14 | ntu.edu.sg | 3 | 0 |
| 14 | qdu.edu.cn | 3 | 0 |

## Top countries (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Country | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | China (CN) | 112 | 5 |
| 2 | United States (US) | 29 | 2 |
| 3 | United Kingdom (GB) | 15 | 1 |
| 4 | France (FR) | 13 | 1 |
| 5 | Hong Kong (HK) | 12 | 0 |
| 6 | Australia (AU) | 11 | 1 |
| 6 | Turkey (TR) | 11 | 1 |
| 8 | Italy (IT) | 10 | 2 |
| 9 | Canada (CA) | 9 | 0 |
| 10 | India (IN) | 8 | 0 |
| 11 | Germany (DE) | 7 | 0 |
| 12 | Greece (GR) | 6 | 0 |
| 13 | Belgium (BE) | 5 | 0 |
| 13 | South Korea (KR) | 5 | 0 |
| 13 | Singapore (SG) | 5 | 0 |
| 13 | Vietnam (VN) | 5 | 0 |
| 17 | Iran (IR) | 4 | 1 |
| 17 | Norway (NO) | 4 | 0 |
| 19 | Chile (CL) | 3 | 0 |
| 19 | Spain (ES) | 3 | 0 |

## Research fields (top 13)

*Top 13 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Field | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | Business, Management and Accounting | 71 | 2 |
| 2 | Engineering | 67 | 2 |
| 3 | Computer Science | 33 | 5 |
| 4 | Decision Sciences | 29 | 3 |
| 5 | Economics, Econometrics and Finance | 11 | 1 |
| 6 | Social Sciences | 10 | 0 |
| 7 | Mathematics | 5 | 0 |
| 8 | Psychology | 3 | 0 |
| 9 | Health Professions | 2 | 0 |
| 9 | Medicine | 2 | 0 |
| 9 | Physics and Astronomy | 2 | 0 |
| 12 | Agricultural and Biological Sciences | 1 | 0 |
| 12 | Energy | 1 | 0 |

## Papers per journal (top 11)

*Top 11 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Journal | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [Transportation Research Part E Logistics and Transportation Review](https://scholar4ai.com/journals/transportation-research-part-e-logistics-and-transportation-review.md) | 52 | 0 |
| 2 | [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | 35 | 0 |
| 3 | [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | 29 | 5 |
| 3 | [Journal of Optimization Theory and Applications](https://scholar4ai.com/journals/journal-of-optimization-theory-and-applications.md) | 29 | 6 |
| 5 | [International Journal of Production Economics](https://scholar4ai.com/journals/international-journal-of-production-economics.md) | 25 | 1 |
| 6 | [Operational Research](https://scholar4ai.com/journals/operational-research.md) | 22 | 0 |
| 7 | [Computers & Operations Research](https://scholar4ai.com/journals/computers-operations-research.md) | 19 | 0 |
| 8 | [Omega](https://scholar4ai.com/journals/omega.md) | 12 | 1 |
| 9 | [Decision Support Systems](https://scholar4ai.com/journals/decision-support-systems.md) | 9 | 0 |
| 10 | [Mathematical Programming](https://scholar4ai.com/journals/mathematical-programming.md) | 5 | 0 |
| 11 | [OR Spectrum](https://scholar4ai.com/journals/or-spectrum.md) | 3 | 0 |

## Most-used equations and models (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Equation or model | Formula | Papers |
|---:|---|---|---:|
| 1 | [Linear and integer programming](https://scholar4ai.com/equations/linear-and-integer-programming.md) | $\min_{x}\ c^\top x\ \ \text{s.t.}\ \ A x \le b,\ x \ge 0,\ x_j \in \mathbb{Z}\ (j \in I)$ | 29 |
| 2 | [Logistic regression (logit)](https://scholar4ai.com/equations/logistic-regression-logit.md) | $\Pr(y = 1 \mid x) = \frac{1}{1 + e^{-x^\top\beta}}$ | 8 |
| 3 | [Data envelopment analysis (DEA)](https://scholar4ai.com/equations/data-envelopment-analysis-dea.md) | $\max_{u, v}\ \frac{\sum_r u_r\, y_{r0}}{\sum_i v_i\, x_{i0}}\ \ \text{s.t.}\ \ \frac{\sum_r u_r\, y_{rj}}{\sum_i v_i\, x_{ij}} \le 1\ \ \forall j$ | 4 |
| 3 | [Gradient descent](https://scholar4ai.com/equations/gradient-descent.md) | $\theta_{t+1} = \theta_t - \eta\, \nabla_\theta \mathcal{L}(\theta_t)$ | 4 |
| 3 | [Proximal policy optimization (PPO)](https://scholar4ai.com/equations/proximal-policy-optimization-ppo.md) | $L(\theta) = E_t\big[\min\big(r_t(\theta)\hat A_t,\ \mathrm{clip}(r_t(\theta), 1 - \epsilon, 1 + \epsilon)\,\hat A_t\big)\big]$ | 4 |
| 6 | [Nash equilibrium](https://scholar4ai.com/equations/nash-equilibrium.md) | $u_i(s_i^{*}, s_{-i}^{*}) \ge u_i(s_i, s_{-i}^{*})\quad \forall i,\ \forall s_i$ | 3 |
| 7 | [Expected shortfall (CVaR)](https://scholar4ai.com/equations/expected-shortfall-cvar.md) | $\mathrm{ES}_\alpha(L) = \frac{1}{1 - \alpha}\int_\alpha^{1} \mathrm{VaR}_u(L)\,du$ | 2 |
| 7 | [Gini coefficient](https://scholar4ai.com/equations/gini-coefficient.md) | $G = \frac{\sum_{i=1}^{n}\sum_{j=1}^{n} \lvert x_i - x_j \rvert}{2 n^2 \bar x}$ | 2 |
| 7 | [LASSO](https://scholar4ai.com/equations/lasso.md) | $\hat\beta = \operatorname*{arg\,min}_{\beta}\ \lVert y - X\beta \rVert_2^2 + \lambda \lVert \beta \rVert_1$ | 2 |
| 7 | [Random forest](https://scholar4ai.com/equations/random-forest.md) | $\hat f(x) = \frac{1}{B}\sum_{b=1}^{B} T_b(x)$ | 2 |

## Most-used data (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Data category | Most-mentioned datasets | Papers |
|---:|---|---|---:|
| 1 | [Stock market indices](https://scholar4ai.com/datasets/stock-market-indices.md) | S&P 500 (4), Euro Stoxx / DAX (1) | 5 |
| 2 | [Macroeconomic and development indicators](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md) | Eurostat (2) | 2 |
| 3 | [China household and social surveys](https://scholar4ai.com/datasets/china-household-and-social-surveys.md) | China Health and Nutrition Survey (CHNS) (1) | 1 |
| 3 | [Corporate filings and text](https://scholar4ai.com/datasets/corporate-filings-and-text.md) | Earnings call transcripts (1) | 1 |
| 3 | [ESG and sustainability ratings](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md) | Refinitiv ESG (ASSET4) (1) | 1 |
| 3 | [Financial data platforms](https://scholar4ai.com/datasets/financial-data-platforms.md) | Yahoo Finance (1) | 1 |
| 3 | [Optimization benchmark instances](https://scholar4ai.com/datasets/optimization-benchmark-instances.md) | PSPLIB (1) | 1 |
| 3 | [Ownership and insider trading](https://scholar4ai.com/datasets/ownership-and-insider-trading.md) | Thomson Reuters 13F holdings (1) | 1 |
| 3 | [Social media data](https://scholar4ai.com/datasets/social-media-data.md) | YouTube / TikTok (1) | 1 |
| 3 | [US stock market data](https://scholar4ai.com/datasets/us-stock-market-data.md) | CRSP (1) | 1 |


## Latest operations research papers

*Latest operations research papers*

| Date | Title | Journal | Authors | Affiliations |
|---|---|---|---|---|
| 2026-10-23 | [Sustainable development goals and waste management in the wheat supply chain under uncertain conditions: implications from an emerging economy](https://doi.org/10.1007/s10479-026-07452-y) | [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | Mahsa Rezaee Shirsavar, Misagh Rahbari, Mohammad Mohammadi | Kharazmi University; Islamic Azad University, Tehran; Islamic Azad University, West Tehran Branch |
| 2026-10-23 | [Learning Dominant States in Elementary Resource Constrained Shortest Path Problems](https://doi.org/10.1007/s10479-026-07457-7) | [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | Saverio Basso, Alessandro Minoli, Matteo Salani | Dalle Molle Institute for Artificial Intelligence Research |
| 2026-10-22 | [Cut the chit-chat: a new framework for the application of generative language models for portfolio construction](https://doi.org/10.1007/s10479-026-07418-0) | [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | Francesco A. Fabozzi, Ionuţ Florescu | Stevens Institute of Technology |
| 2026-10-21 | [A regional multi-criteria assessment of European education and training systems](https://doi.org/10.1016/j.omega.2026.103683) | [Omega](https://scholar4ai.com/journals/omega.md) | Fernando J. Osório, Luís Cândido Dias | Universidade do Porto; INESC TEC; University of Coimbra |
| 2026-10-20 | [The Shapley-solidarity Value Revisited](https://doi.org/10.1007/s10957-026-03129-5) | [Journal of Optimization Theory and Applications](https://scholar4ai.com/journals/journal-of-optimization-theory-and-applications.md) | Xun-Feng Hu | Guangxi University |
| 2026-10-20 | [Generalized Interval Variational Inequalities and Interval Optimization Problems on Hadamard Manifolds](https://doi.org/10.1007/s10957-026-03134-8) | [Journal of Optimization Theory and Applications](https://scholar4ai.com/journals/journal-of-optimization-theory-and-applications.md) | Shenglan Chen, Song Wang | Chongqing University of Posts and Telecommunications; Curtin University |
| 2026-10-20 | [Scaffolding the void: Digital platforms as intermediaries in supply chain traceability](https://doi.org/10.1016/j.ijpe.2026.110250) | [International Journal of Production Economics](https://scholar4ai.com/journals/international-journal-of-production-economics.md) | Quynh Do, Claudia Colicchia, Alessandro Creazza et al. | Lancaster University; Politecnico di Milano; University Carlo Cattaneo |
| 2026-10-20 | [Correction: Second-Order Characterizations of Tilt Stability in Composite Optimization](https://doi.org/10.1007/s10957-026-03128-6) | [Journal of Optimization Theory and Applications](https://scholar4ai.com/journals/journal-of-optimization-theory-and-applications.md) | B.Sh. Mordukhovich, Peipei Tang, Chengjing Wang | Wayne State University; Hangzhou City University; Southwest Jiaotong University |
| 2026-10-20 | [Admissible and Time-Optimal Control onto a Surface for Arbitrary Linear Systems](https://doi.org/10.1007/s10957-026-03133-9) | [Journal of Optimization Theory and Applications](https://scholar4ai.com/journals/journal-of-optimization-theory-and-applications.md) | V. I. Korobov, Oleh Vozniak | V. N. Karazin Kharkiv National University |
| 2026-10-20 | [$$\\sigma $$-Angle Boundedness and p-Cyclic Monotonicity for Affine Subspaces](https://doi.org/10.1007/s10957-026-03137-5) | [Journal of Optimization Theory and Applications](https://scholar4ai.com/journals/journal-of-optimization-theory-and-applications.md) | Jean-Pierre Crouzeix, Ernesto Oré, Eladio Ocaña | University of Clermont Auvergne; Clermont Université; National University of Engineering |
| 2026-10-20 | [The impact of AI investment on shipping outsourcing strategies for easing congestion under coastal piggybacking regulation](https://doi.org/10.1007/s10479-026-07441-1) | [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | Naimeng Liu, Cheng Zhang, Bin Liu | Shanghai University of Electric Power; Fudan University; University of Shanghai for Science and Technology |
| 2026-10-20 | [Geometric portfolio equilibria: constraint curvature and asset pricing](https://doi.org/10.1007/s10479-026-07466-6) | [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | Массимилиано Феррара | University of Reggio Calabria; Bocconi University; Okan University |
| 2026-10-20 | [Convergence Analysis of Stochastic Accelerated Gradient Methods for Generalized Smooth Optimizations](https://doi.org/10.1007/s10957-026-03102-2) | [Journal of Optimization Theory and Applications](https://scholar4ai.com/journals/journal-of-optimization-theory-and-applications.md) | Chenhao Yu, Yusu Hong, Junhong Lin | Zhejiang University |
| 2026-10-19 | [Optimizing product return timing in integrated viable circular manufacturing systems: a two-stage stochastic approach](https://doi.org/10.1007/s10479-026-07450-0) | [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | Sobhan Mostafayi Darmian, Fabio Sgarbossa | Norwegian University of Science and Technology |
| 2026-10-19 | [Advances in progressive decoupling methodology for problem decomposition in optimization](https://doi.org/10.1007/s10107-026-02430-5) | [Mathematical Programming](https://scholar4ai.com/journals/mathematical-programming.md) | Ralph Tyrrell Rockafellar | University of Washington |

## Questions this page answers

### What are the most common topics in recent operations research?

In the last 30 days, the most frequent OpenAlex keywords in papers from the tracked operations research journals were “mixed-integer linear programming” (12), “demand uncertainty” (8), “supply chain coordination” (8), “supply chain resilience” (8) and “carbon emission reduction” (7).

### Which universities and institutions publish the most operations research?

In the last 30 days, the institutions with the most papers in the tracked operations research journals were Beihang University (9), Southwest Jiaotong University (8), Hong Kong Polytechnic University (7), Beijing Jiaotong University (6) and Dalian University of Technology (6).

### Which countries publish the most operations research?

Counting papers with at least one author at an institution in the country, the leaders over the last 30 days were China (112), United States (29), United Kingdom (15), France (13) and Hong Kong (12).

### Which equations and models do recent operations research papers use most?

Counting papers that mention them by name in the title, abstract, keywords or full text, the most frequent in the tracked operations research journals over the last 30 days were Linear and integer programming (29), Logistic regression (logit) (8), Data envelopment analysis (DEA) (4), Gradient descent (4) and Proximal policy optimization (PPO) (4).

### What data do recent operations research papers use?

Counting papers that mention a dataset by name, the kinds of data used most over the last 30 days were Stock market indices (5), Macroeconomic and development indicators (2), China household and social surveys (1), Corporate filings and text (1) and ESG and sustainability ratings (1); the most-mentioned datasets were S&P 500 (4), Eurostat (2), China Health and Nutrition Survey (CHNS) (1), CRSP (1) and Earnings call transcripts (1).

### How current is this data?

It is rebuilt from OpenAlex 4 times a day (00:00, 06:00, 12:00, 18:00, UTC+09:00); the last update was 2026-10-26 11:39 UTC+09:00. OpenAlex usually indexes new papers within a few days of publication.


---

---
title: "AI research: top topics, institutions and countries"
description: "In the last 30 days (2026-09-27 to 2026-10-26), 1,124 papers were published in the 24 tracked AI journals (as indexed by OpenAlex). The most frequent keywords were “large language models” (70), “graph neural networks” (38) and “multimodal fusion” (29)."
url: https://scholar4ai.com/categories/ai
markdown_url: https://scholar4ai.com/categories/ai.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T11:39:12+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# AI research rankings

Papers in the 24 tracked AI journals, window 2026-09-27 – 2026-10-26 (30 days). Keywords, institutions, web domains and countries are ranked by paper count.

- Papers (30 days): 1,124
- Papers (last 7 days): 80
- Journals: 24
- Institutions: 1,176
- Countries: 74
- Web domains: 1,103

## Key findings

- In the last 30 days (2026-09-27 to 2026-10-26), 1,124 papers were published in the 24 tracked AI journals (as indexed by OpenAlex).
- The most frequent keywords were “large language models” (70), “graph neural networks” (38) and “multimodal fusion” (29).
- The institutions with the most papers were Chinese Academy of Sciences (30), Tsinghua University (25) and University of Electronic Science and Technology of China (22).
- China was involved in 63% of the papers (712), followed by United States (55) and India (50).
- Among institution web domains, .edu.cn appeared on 61% of the papers.
- The most-mentioned equations and models were Graph neural network (graph convolution) (73), Scaled dot-product attention (Transformer) (55) and Contrastive loss (InfoNCE) (39).
- The most-mentioned kinds of data were Image classification benchmarks (8), Object detection and segmentation benchmarks (3) and Social media data (3); the most-mentioned datasets were MNIST / Fashion-MNIST (5), CIFAR-10 / CIFAR-100 (4) and ImageNet (3).

## Papers per day

*Papers per publication date*

| Date | Papers |
|---|---:|
| 2026-09-27 | 15 |
| 2026-09-28 | 36 |
| 2026-09-29 | 42 |
| 2026-09-30 | 56 |
| 2026-10-01 | 366 |
| 2026-10-02 | 14 |
| 2026-10-03 | 35 |
| 2026-10-04 | 4 |
| 2026-10-05 | 21 |
| 2026-10-06 | 34 |
| 2026-10-07 | 40 |
| 2026-10-08 | 34 |
| 2026-10-09 | 44 |
| 2026-10-10 | 25 |
| 2026-10-11 | 13 |
| 2026-10-12 | 29 |
| 2026-10-13 | 36 |
| 2026-10-14 | 50 |
| 2026-10-15 | 41 |
| 2026-10-16 | 24 |
| 2026-10-17 | 46 |
| 2026-10-18 | 10 |
| 2026-10-19 | 29 |
| 2026-10-20 | 22 |
| 2026-10-21 | 21 |
| 2026-10-22 | 13 |
| 2026-10-23 | 20 |
| 2026-10-24 | 4 |
| 2026-10-25 | 0 |
| 2026-10-26 | 0 |

## Growing over the year (Jul 2026–Sep 2026 vs Oct 2025–Jun 2026)

*Papers in Jul 2026–Sep 2026 beyond what the keyword's share in Oct 2025–Jun 2026 predicts*

| Keyword | Papers per month, Oct 25 – Oct 26* | Papers Jul 2026–Sep 2026 | vs. earlier share |
|---|---|---:|---:|
| AI governance | 7, 5, 7, 4, 6, 10, 7, 13, 10, 10, 21, 10, 2 | 41 | +18 |
| mixture of experts | 7, 8, 8, 14, 6, 13, 13, 10, 13, 16, 19, 12, 10 | 47 | +17 |
| uncertainty estimation | 6, 2, 4, 9, 9, 11, 6, 9, 14, 13, 10, 16, 7 | 39 | +16 |
| foundation models | 3, 2, 5, 4, 6, 5, 8, 6, 10, 6, 9, 12, 4 | 27 | +11 |
| spatial transcriptomics | 1, 0, 2, 0, 1, 0, 1, 3, 2, 4, 3, 7, 1 | 14 | +11 |

More: https://scholar4ai.com/trends.md



## Top research keywords (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Keyword | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 70 | 4 |
| 2 | [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 38 | 1 |
| 3 | [multimodal fusion](https://scholar4ai.com/keywords/multimodal-fusion.md) | 29 | 0 |
| 4 | [contrastive learning](https://scholar4ai.com/keywords/contrastive-learning.md) | 27 | 0 |
| 5 | [explainable AI](https://scholar4ai.com/keywords/explainable-ai.md) | 24 | 2 |
| 6 | [knowledge distillation](https://scholar4ai.com/keywords/knowledge-distillation.md) | 23 | 1 |
| 7 | [federated learning](https://scholar4ai.com/keywords/federated-learning.md) | 21 | 4 |
| 8 | [class imbalance](https://scholar4ai.com/keywords/class-imbalance.md) | 19 | 1 |
| 8 | vision-language models | 19 | 0 |
| 10 | diffusion models | 18 | 1 |
| 10 | feature selection | 18 | 1 |
| 10 | transformer models | 18 | 2 |
| 13 | interpretability | 15 | 1 |
| 13 | reinforcement learning | 15 | 0 |
| 15 | attention mechanism | 14 | 2 |
| 15 | deep learning | 14 | 2 |
| 15 | domain adaptation | 14 | 1 |
| 15 | retrieval-augmented generation | 14 | 1 |
| 19 | convolutional neural networks | 13 | 1 |
| 20 | computational efficiency | 12 | 1 |

## Top universities and research institutions (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Institution | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 30 | 5 |
| 2 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 25 | 4 |
| 3 | [University of Electronic Science and Technology of China](https://scholar4ai.com/institutions/university-of-electronic-science-and-technology-of-china.md) | 22 | 1 |
| 3 | [National University of Defense Technology](https://scholar4ai.com/institutions/national-university-of-defense-technology.md) | 22 | 0 |
| 5 | [Beijing Institute of Technology](https://scholar4ai.com/institutions/beijing-institute-of-technology.md) | 21 | 1 |
| 5 | [Wuhan University](https://scholar4ai.com/institutions/wuhan-university.md) | 21 | 0 |
| 7 | [University of Chinese Academy of Sciences](https://scholar4ai.com/institutions/university-of-chinese-academy-of-sciences.md) | 20 | 3 |
| 8 | [Harbin Institute of Technology](https://scholar4ai.com/institutions/harbin-institute-of-technology.md) | 19 | 2 |
| 9 | [Beijing Jiaotong University](https://scholar4ai.com/institutions/beijing-jiaotong-university.md) | 17 | 1 |
| 10 | [Southeast University](https://scholar4ai.com/institutions/southeast-university.md) | 16 | 1 |
| 10 | Northeastern University | 16 | 2 |
| 12 | Xidian University | 15 | 0 |
| 13 | [Sun Yat-sen University](https://scholar4ai.com/institutions/sun-yat-sen-university.md) | 13 | 1 |
| 13 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | 13 | 3 |
| 13 | [Xi'an Jiaotong University](https://scholar4ai.com/institutions/xi-an-jiaotong-university.md) | 13 | 1 |
| 16 | Central South University | 12 | 0 |
| 16 | [Hong Kong Polytechnic University](https://scholar4ai.com/institutions/hong-kong-polytechnic-university.md) | 12 | 0 |
| 16 | Anhui University | 12 | 0 |
| 16 | Northwestern Polytechnical University | 12 | 0 |
| 16 | [Fudan University](https://scholar4ai.com/institutions/fudan-university.md) | 12 | 2 |

## Top institution web domains (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Web domain | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [cas.cn](https://scholar4ai.com/domains/cas.cn.md) | 37 | 5 |
| 2 | [tsinghua.edu.cn](https://scholar4ai.com/domains/tsinghua.edu.cn.md) | 26 | 4 |
| 3 | [nudt.edu.cn](https://scholar4ai.com/domains/nudt.edu.cn.md) | 22 | 0 |
| 3 | [uestc.edu.cn](https://scholar4ai.com/domains/uestc.edu.cn.md) | 22 | 1 |
| 3 | [whu.edu.cn](https://scholar4ai.com/domains/whu.edu.cn.md) | 22 | 0 |
| 6 | [bit.edu.cn](https://scholar4ai.com/domains/bit.edu.cn.md) | 21 | 1 |
| 7 | [ucas.ac.cn](https://scholar4ai.com/domains/ucas.ac.cn.md) | 20 | 3 |
| 8 | [hit.edu.cn](https://scholar4ai.com/domains/hit.edu.cn.md) | 19 | 2 |
| 9 | [njtu.edu.cn](https://scholar4ai.com/domains/njtu.edu.cn.md) | 17 | 1 |
| 9 | [seu.edu.cn](https://scholar4ai.com/domains/seu.edu.cn.md) | 17 | 1 |
| 11 | neu.edu.cn | 16 | 2 |
| 12 | xidian.edu.cn | 15 | 0 |
| 13 | [sjtu.edu.cn](https://scholar4ai.com/domains/sjtu.edu.cn.md) | 13 | 3 |
| 13 | [sysu.edu.cn](https://scholar4ai.com/domains/sysu.edu.cn.md) | 13 | 1 |
| 13 | [xjtu.edu.cn](https://scholar4ai.com/domains/xjtu.edu.cn.md) | 13 | 1 |
| 16 | ahu.edu.cn | 12 | 0 |
| 16 | csu.edu.cn | 12 | 0 |
| 16 | [fudan.edu.cn](https://scholar4ai.com/domains/fudan.edu.cn.md) | 12 | 2 |
| 16 | nwpu.edu.cn | 12 | 0 |
| 16 | [polyu.edu.hk](https://scholar4ai.com/domains/polyu.edu.hk.md) | 12 | 0 |

## Top countries (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Country | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | China (CN) | 712 | 49 |
| 2 | United States (US) | 55 | 7 |
| 3 | India (IN) | 50 | 2 |
| 4 | United Kingdom (GB) | 43 | 4 |
| 5 | Hong Kong (HK) | 34 | 3 |
| 6 | Australia (AU) | 33 | 3 |
| 7 | South Korea (KR) | 29 | 1 |
| 8 | Singapore (SG) | 24 | 3 |
| 9 | Turkey (TR) | 22 | 0 |
| 10 | United Arab Emirates (AE) | 21 | 4 |
| 11 | Italy (IT) | 19 | 0 |
| 12 | Canada (CA) | 15 | 2 |
| 12 | Iran (IR) | 15 | 0 |
| 14 | Spain (ES) | 14 | 0 |
| 14 | Malaysia (MY) | 14 | 0 |
| 14 | Saudi Arabia (SA) | 14 | 1 |
| 17 | Germany (DE) | 13 | 0 |
| 17 | Japan (JP) | 13 | 2 |
| 19 | Bangladesh (BD) | 11 | 1 |
| 19 | Egypt (EG) | 11 | 1 |

## Research fields (top 19)

*Top 19 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Field | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | Computer Science | 690 | 43 |
| 2 | Engineering | 184 | 12 |
| 3 | Social Sciences | 34 | 1 |
| 4 | Medicine | 31 | 3 |
| 5 | Decision Sciences | 27 | 6 |
| 6 | Physics and Astronomy | 23 | 4 |
| 7 | Neuroscience | 19 | 1 |
| 8 | Psychology | 18 | 1 |
| 9 | Biochemistry, Genetics and Molecular Biology | 17 | 1 |
| 10 | Business, Management and Accounting | 15 | 2 |
| 11 | Agricultural and Biological Sciences | 8 | 0 |
| 12 | Mathematics | 7 | 1 |
| 13 | Materials Science | 5 | 1 |
| 14 | Arts and Humanities | 3 | 0 |
| 14 | Environmental Science | 3 | 0 |
| 14 | Health Professions | 3 | 0 |
| 17 | Economics, Econometrics and Finance | 2 | 0 |
| 18 | Dentistry | 1 | 0 |
| 18 | Earth and Planetary Sciences | 1 | 0 |

## Papers per journal (top 20)

*Top 20 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Journal | Papers | Last 7 days |
|---:|---|---:|---:|
| 1 | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 249 | 14 |
| 2 | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 188 | 9 |
| 3 | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 110 | 6 |
| 4 | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 90 | 0 |
| 5 | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 89 | 6 |
| 6 | [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | 71 | 1 |
| 7 | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 64 | 8 |
| 8 | [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | 54 | 2 |
| 9 | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | 39 | 10 |
| 10 | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 28 | 9 |
| 10 | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | 28 | 4 |
| 12 | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 20 | 1 |
| 13 | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | 18 | 1 |
| 14 | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | 17 | 5 |
| 15 | [AI & Society](https://scholar4ai.com/journals/ai-society.md) | 11 | 0 |
| 15 | [Artificial Intelligence](https://scholar4ai.com/journals/artificial-intelligence.md) | 11 | 1 |
| 17 | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | 10 | 3 |
| 18 | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | 8 | 0 |
| 19 | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 5 | 0 |
| 20 | [IEEE Transactions on Evolutionary Computation](https://scholar4ai.com/journals/ieee-transactions-on-evolutionary-computation.md) | 4 | 0 |

## Most-used equations and models (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Equation or model | Formula | Papers |
|---:|---|---|---:|
| 1 | [Graph neural network (graph convolution)](https://scholar4ai.com/equations/graph-neural-network-graph-convolution.md) | $H^{(l+1)} = \sigma\big(\tilde D^{-1/2} \tilde A\, \tilde D^{-1/2} H^{(l)} W^{(l)}\big)$ | 73 |
| 2 | [Scaled dot-product attention (Transformer)](https://scholar4ai.com/equations/scaled-dot-product-attention-transformer.md) | $\mathrm{Attention}(Q, K, V) = \mathrm{softmax}\Big(\frac{Q K^\top}{\sqrt{d_k}}\Big) V$ | 55 |
| 3 | [Contrastive loss (InfoNCE)](https://scholar4ai.com/equations/contrastive-loss-infonce.md) | $\mathcal{L} = -\log \frac{\exp(\mathrm{sim}(z_i, z_j)/\tau)}{\sum_{k \ne i} \exp(\mathrm{sim}(z_i, z_k)/\tau)}$ | 39 |
| 4 | [Convolutional neural network (CNN)](https://scholar4ai.com/equations/convolutional-neural-network-cnn.md) | $(I * K)(i, j) = \sum_{m}\sum_{n} I(i + m,\, j + n)\, K(m, n)$ | 30 |
| 5 | [Logistic regression (logit)](https://scholar4ai.com/equations/logistic-regression-logit.md) | $\Pr(y = 1 \mid x) = \frac{1}{1 + e^{-x^\top\beta}}$ | 25 |
| 6 | [Long short-term memory (LSTM)](https://scholar4ai.com/equations/long-short-term-memory-lstm.md) | $c_t = f_t \odot c_{t-1} + i_t \odot \tilde c_t,\quad h_t = o_t \odot \tanh(c_t)$ | 15 |
| 7 | [Gradient boosting (XGBoost)](https://scholar4ai.com/equations/gradient-boosting-xgboost.md) | $\mathcal{L} = \sum_i l(y_i, \hat y_i) + \sum_k \Omega(f_k),\quad \Omega(f) = \gamma T + \tfrac{1}{2}\lambda \lVert w \rVert^2$ | 10 |
| 7 | [Shapley value (SHAP)](https://scholar4ai.com/equations/shapley-value-shap.md) | $\phi_i(v) = \sum_{S \subseteq N \setminus \{i\}} \frac{\lvert S \rvert!\,(n - \lvert S \rvert - 1)!}{n!}\big[v(S \cup \{i\}) - v(S)\big]$ | 10 |
| 9 | [Random forest](https://scholar4ai.com/equations/random-forest.md) | $\hat f(x) = \frac{1}{B}\sum_{b=1}^{B} T_b(x)$ | 9 |
| 9 | [Support vector machine (SVM)](https://scholar4ai.com/equations/support-vector-machine-svm.md) | $\min_{w, b, \xi}\ \tfrac{1}{2}\lVert w \rVert^2 + C \sum_i \xi_i\ \ \text{s.t.}\ \ y_i (w^\top x_i + b) \ge 1 - \xi_i,\ \xi_i \ge 0$ | 9 |

## Most-used data (top 10)

*Top 10 · 2026-09-27 – 2026-10-26 (30 days)*

| # | Data category | Most-mentioned datasets | Papers |
|---:|---|---|---:|
| 1 | [Image classification benchmarks](https://scholar4ai.com/datasets/image-classification-benchmarks.md) | MNIST / Fashion-MNIST (5), CIFAR-10 / CIFAR-100 (4), ImageNet (3) | 8 |
| 2 | [Object detection and segmentation benchmarks](https://scholar4ai.com/datasets/object-detection-and-segmentation-benchmarks.md) | KITTI (3), nuScenes / Waymo Open / Argoverse (1) | 3 |
| 2 | [Social media data](https://scholar4ai.com/datasets/social-media-data.md) | Twitter / X (2), Facebook (2), Reddit (1) | 3 |
| 4 | [China household and social surveys](https://scholar4ai.com/datasets/china-household-and-social-surveys.md) | China Household Finance Survey (CHFS) (1), China Labor-force Dynamics Survey (CLDS) (1) | 2 |
| 4 | [Credit scoring and lending data](https://scholar4ai.com/datasets/credit-scoring-and-lending-data.md) | German Credit (UCI Statlog) (2) | 2 |
| 4 | [Face, person and video benchmarks](https://scholar4ai.com/datasets/face-person-and-video-benchmarks.md) | Face recognition (LFW, CelebA, VGGFace2) (1), Action recognition (Kinetics, UCF101, HMDB51) (1) | 2 |
| 4 | [Medical and clinical datasets](https://scholar4ai.com/datasets/medical-and-clinical-datasets.md) | MIMIC (1), ISIC skin lesion archive (1) | 2 |
| 4 | [Mutual fund and hedge fund data](https://scholar4ai.com/datasets/mutual-fund-and-hedge-fund-data.md) | Lipper / TASS (2) | 2 |
| 4 | [Online reviews, e-commerce and recommender data](https://scholar4ai.com/datasets/online-reviews-e-commerce-and-recommender-data.md) | Amazon reviews (1), Taobao / Tmall / JD.com (1) | 2 |
| 4 | [Reinforcement learning environments](https://scholar4ai.com/datasets/reinforcement-learning-environments.md) | D4RL (1), SUMO traffic simulator (1) | 2 |


## Latest AI papers

*Latest AI papers*

| Date | Title | Journal | Authors | Affiliations |
|---|---|---|---|---|
| 2026-10-24 | [PF-Mamba-UNet: An Anatomical-Prior-Guided Medical Image Segmentation Network Based on Mamba and Spatial-Channel Collaborative Attention Fusion](https://doi.org/10.1007/s12559-026-10667-7) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Leyi Xiao, Kangshun Liao, Baoxian Zhou et al. | Hainan University |
| 2026-10-24 | [A multi-stage multi-level attention-based feature learning framework for multi-endpoint survival prediction in head and neck cancer](https://doi.org/10.1007/s10489-026-07392-x) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Xiaoying Pan, Xiaoying Pan, Xingzhao Pan et al. | Xi’an University of Posts and Telecommunications; University of California, Los Angeles |
| 2026-10-24 | [Instance-adaptive coevolutionary learning of constructive heuristics for multi-objective stochastic resource allocation with preferences](https://doi.org/10.1016/j.eswa.2026.134636) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Jingyu Zhang, Bin Xin, Bo Liu et al. | Beijing Institute of Technology; Chinese Academy of Sciences; Academy of Mathematics and Systems Science |
| 2026-10-24 | [A priority-aware dual-channel feature fusion method for urban rail service traffic classification](https://doi.org/10.1007/s10489-026-07560-z) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Xinpeng Liu, Junhui Zhao, Zhengyuan Wu et al. | Beijing Jiaotong University |
| 2026-10-23 | [Neuro-native paradigms of brain foundation models: evolution, taxonomy, and perspectives](https://doi.org/10.1007/s10462-026-11748-7) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | Zepeng Li, Molei Zhang, Tianyi Li et al. | Lanzhou University |
| 2026-10-23 | [Multimodal continual federated learning for human activity recognition: Trends and challenges](https://doi.org/10.1007/s10489-026-07562-x) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | André L. S. Conde, Rodolfo I. Meneguette, Robson E. De Grande et al. | Universidade Estadual Paulista (Unesp); Institute of Mathematics and Computer Science; Brock University |
| 2026-10-23 | [Cross3DReg: A Real-world Cross-source Point Cloud Registration Benchmark](https://doi.org/10.1007/s11263-026-03095-5) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Zongyi Xu, Z. Lang, Yilong Chen et al. | Chongqing University of Posts and Telecommunications; Alibaba Group (China); Shanghai Jiao Tong University; … |
| 2026-10-23 | [Collaborative enhancement of global and disentangled representations for sequential recommendation](https://doi.org/10.1007/s10115-026-02921-z) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | Li Liu, Yiliang Lai, Chen Lin | Xiamen University; Sanming University; Assumption University; … |
| 2026-10-23 | [On the robustness verification of binary neural networks](https://doi.org/10.1007/s10462-026-11661-z) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | Srećko Ðurašinović | Nanyang Technological University; CNRS@CREATE Ltd (Singapore) |
| 2026-10-23 | [A unified framework for large-foundation-model-based multi-phrasing synthetic VQA data generation and downstream VQA training](https://doi.org/10.1016/j.ipm.2026.105255) | [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | Zhiyue Liu, Jian Qin, Wenkai Zhou et al. | Guangxi University; Sun Yat-sen University |
| 2026-10-23 | [Towards minute-level pedestrian trajectory prediction in crowded traffic scenarios](https://doi.org/10.1016/j.patcog.2026.115198) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Yonghao Dong, Sanping Zhou, Gang Hua et al. | Xi'an Jiaotong University; Amazon (United States) |
| 2026-10-23 | [Retrieval-augmented generation in biomedicine: a survey of technologies, datasets, and clinical applications](https://doi.org/10.1007/s10462-026-11743-y) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | Jiawei He, Boya Zhang, Hossein Rouhizadeh et al. | University of Geneva; Hunan City University; Henan University; … |
| 2026-10-23 | [Designing sustainable urban intelligence: a green AI framework and survey](https://doi.org/10.1007/s10462-026-11730-3) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | Elda Cina, Zakwan AlArnaout, Ersin Elbasi Α et al. | American University of the Middle East |
| 2026-10-23 | [Synergizing Disentangled Representation and Split Federated Learning to Break Centralized Bottlenecks in Gaze Estimation](https://doi.org/10.1007/s11263-026-03084-8) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Yunlong Wang, Zhengquan Luo, Kunbo Zhang et al. | Nanyang Technological University; Chinese Academy of Sciences; Institute of Automation; … |
| 2026-10-23 | [Efficient equivariant framework for universal charge density prediction](https://doi.org/10.1038/s42256-026-01326-5) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Xiwen Li, Zaizhou Xin, Hongyu Yu et al. | Fudan University; State Key Laboratory of Surface Physics |

## Questions this page answers

### What are the most common topics in recent AI research?

In the last 30 days, the most frequent OpenAlex keywords in papers from the tracked AI journals were “large language models” (70), “graph neural networks” (38), “multimodal fusion” (29), “contrastive learning” (27) and “explainable AI” (24).

### Which universities and institutions publish the most AI research?

In the last 30 days, the institutions with the most papers in the tracked AI journals were Chinese Academy of Sciences (30), Tsinghua University (25), University of Electronic Science and Technology of China (22), National University of Defense Technology (22) and Beijing Institute of Technology (21).

### Which countries publish the most AI research?

Counting papers with at least one author at an institution in the country, the leaders over the last 30 days were China (712), United States (55), India (50), United Kingdom (43) and Hong Kong (34).

### Which equations and models do recent AI research papers use most?

Counting papers that mention them by name in the title, abstract, keywords or full text, the most frequent in the tracked AI journals over the last 30 days were Graph neural network (graph convolution) (73), Scaled dot-product attention (Transformer) (55), Contrastive loss (InfoNCE) (39), Convolutional neural network (CNN) (30) and Logistic regression (logit) (25).

### What data do recent AI research papers use?

Counting papers that mention a dataset by name, the kinds of data used most over the last 30 days were Image classification benchmarks (8), Object detection and segmentation benchmarks (3), Social media data (3), China household and social surveys (2) and Credit scoring and lending data (2); the most-mentioned datasets were MNIST / Fashion-MNIST (5), CIFAR-10 / CIFAR-100 (4), ImageNet (3), KITTI (3) and Facebook (2).

### How current is this data?

It is rebuilt from OpenAlex 4 times a day (00:00, 06:00, 12:00, 18:00, UTC+09:00); the last update was 2026-10-26 11:39 UTC+09:00. OpenAlex usually indexes new papers within a few days of publication.


---

---
title: "Research trends: fastest-growing topics, past 12 months"
description: "Papers per month in all tracked journals: 3,130 to 3,970 over the last 12 complete months. Fastest-growing keywords in all tracked journals (Jul 2026–Sep 2026 vs Oct 2025–Jun 2026): “absorptive capacity” (44 papers, +22 vs. its earlier share), “AI governance” (52 papers, +20 vs. its earlier share) and “uncertainty estimation” (41 papers, +18 vs. its earlier share)."
url: https://scholar4ai.com/trends
markdown_url: https://scholar4ai.com/trends.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T11:39:12+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# Research trends over the last 12 months

Monthly paper counts and keyword trends from the archive of every paper this site has collected, Oct 2025 to Oct 2026 (* marks the current month, which is still incomplete). Growing keywords are ranked by how many more papers they had in Jul 2026–Sep 2026 than their share of papers in Oct 2025–Jun 2026 predicts, among keywords whose share grew at least 1.5 times.

## Key findings

- Papers per month in all tracked journals: 3,130 to 3,970 over the last 12 complete months.
- Fastest-growing keywords in all tracked journals (Jul 2026–Sep 2026 vs Oct 2025–Jun 2026): “absorptive capacity” (44 papers, +22 vs. its earlier share), “AI governance” (52 papers, +20 vs. its earlier share) and “uncertainty estimation” (41 papers, +18 vs. its earlier share).
- Papers per month in the tracked finance journals: 522 to 741 over the last 12 complete months.
- Fastest-growing keywords in the tracked finance journals (Jul 2026–Sep 2026 vs Oct 2025–Jun 2026): “artificial intelligence” (31 papers, +14 vs. its earlier share), “corporate finance” (18 papers, +12 vs. its earlier share) and “price discovery” (18 papers, +11 vs. its earlier share).
- Papers per month in the tracked accounting journals: 49 to 92 over the last 12 complete months.
- Fastest-growing keywords in the tracked accounting journals (Jul 2026–Sep 2026 vs Oct 2025–Jun 2026): “accounting education” (10 papers, +4 vs. its earlier share), “moderating effect” (8 papers, +4 vs. its earlier share) and “information transparency” (5 papers, +4 vs. its earlier share).
- Papers per month in the tracked economics journals: 774 to 960 over the last 12 complete months.
- Fastest-growing keywords in the tracked economics journals (Jul 2026–Sep 2026 vs Oct 2025–Jun 2026): “corporate governance” (35 papers, +13 vs. its earlier share), “total factor productivity” (33 papers, +12 vs. its earlier share) and “systemic risk” (22 papers, +10 vs. its earlier share).
- Papers per month in the tracked management journals: 270 to 398 over the last 12 complete months.
- Fastest-growing keywords in the tracked management journals (Jul 2026–Sep 2026 vs Oct 2025–Jun 2026): “absorptive capacity” (21 papers, +12 vs. its earlier share), “customer satisfaction” (17 papers, +11 vs. its earlier share) and “knowledge-based view” (15 papers, +8 vs. its earlier share).
- Papers per month in the tracked operations research journals: 238 to 331 over the last 12 complete months.
- Fastest-growing keywords in the tracked operations research journals (Jul 2026–Sep 2026 vs Oct 2025–Jun 2026): “price competition” (11 papers, +9 vs. its earlier share), “drone delivery” (15 papers, +9 vs. its earlier share) and “supply chain coordination” (19 papers, +8 vs. its earlier share).
- Papers per month in the tracked AI journals: 1,123 to 1,580 over the last 12 complete months.
- Fastest-growing keywords in the tracked AI journals (Jul 2026–Sep 2026 vs Oct 2025–Jun 2026): “AI governance” (41 papers, +18 vs. its earlier share), “mixture of experts” (47 papers, +17 vs. its earlier share) and “uncertainty estimation” (39 papers, +16 vs. its earlier share).

## Papers per month

*Papers per month by publication date*

| Month | All journals | Finance | Accounting | Economics | Management | Operations Research | AI |
|---|---:|---:|---:|---:|---:|---:|---:|
| Oct 2025 | 3,130 | 619 | 49 | 801 | 270 | 268 | 1,123 |
| Nov 2025 | 3,454 | 741 | 82 | 901 | 354 | 238 | 1,138 |
| Dec 2025 | 3,474 | 702 | 56 | 806 | 287 | 274 | 1,349 |
| Jan 2026 | 3,539 | 567 | 82 | 885 | 389 | 295 | 1,321 |
| Feb 2026 | 3,425 | 522 | 82 | 912 | 359 | 266 | 1,284 |
| Mar 2026 | 3,970 | 691 | 68 | 938 | 398 | 295 | 1,580 |
| Apr 2026 | 3,681 | 645 | 81 | 819 | 344 | 288 | 1,504 |
| May 2026 | 3,652 | 600 | 78 | 844 | 343 | 331 | 1,456 |
| Jun 2026 | 3,720 | 605 | 86 | 960 | 316 | 309 | 1,444 |
| Jul 2026 | 3,574 | 625 | 92 | 910 | 366 | 281 | 1,300 |
| Aug 2026 | 3,366 | 546 | 57 | 774 | 304 | 283 | 1,402 |
| Sep 2026 | 3,597 | 668 | 67 | 931 | 346 | 287 | 1,298 |
| Oct 2026 * | 2,553 | 410 | 50 | 676 | 238 | 204 | 975 |

## All journals

### Most frequent keywords

*Most frequent keywords, Oct 2025 to Sep 2026*

| Keyword | Trend | 12 months | Oct 25 | Nov 25 | Dec 25 | Jan 26 | Feb 26 | Mar 26 | Apr 26 | May 26 | Jun 26 | Jul 26 | Aug 26 | Sep 26 | Oct 26* |
|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| [China](https://scholar4ai.com/keywords/china.md) |  | 1,599 | 132 | 139 | 157 | 130 | 131 | 147 | 149 | 120 | 131 | 132 | 113 | 118 | 65 |
| [large language models](https://scholar4ai.com/keywords/large-language-models.md) |  | 1,312 | 68 | 94 | 103 | 90 | 83 | 129 | 129 | 126 | 147 | 111 | 122 | 110 | 72 |
| [Chinese listed companies](https://scholar4ai.com/keywords/chinese-listed-companies.md) |  | 1,274 | 110 | 122 | 150 | 111 | 86 | 99 | 106 | 89 | 111 | 95 | 91 | 104 | 46 |
| [corporate governance](https://scholar4ai.com/keywords/corporate-governance.md) |  | 887 | 82 | 99 | 81 | 74 | 70 | 88 | 71 | 55 | 70 | 64 | 62 | 71 | 39 |
| [financial constraints](https://scholar4ai.com/keywords/financial-constraints.md) |  | 840 | 71 | 99 | 89 | 73 | 66 | 75 | 70 | 65 | 68 | 58 | 54 | 52 | 25 |
| state-owned enterprises |  | 666 | 63 | 73 | 71 | 56 | 34 | 68 | 61 | 45 | 47 | 53 | 48 | 47 | 18 |
| [emerging markets](https://scholar4ai.com/keywords/emerging-markets.md) |  | 611 | 36 | 46 | 50 | 48 | 59 | 57 | 53 | 55 | 62 | 52 | 31 | 62 | 30 |
| [COVID-19 pandemic](https://scholar4ai.com/keywords/covid-19-pandemic.md) |  | 610 | 56 | 55 | 59 | 45 | 65 | 58 | 49 | 48 | 48 | 50 | 49 | 28 | 24 |
| [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) |  | 551 | 49 | 32 | 47 | 44 | 33 | 50 | 59 | 47 | 51 | 54 | 44 | 41 | 32 |
| [information asymmetry](https://scholar4ai.com/keywords/information-asymmetry.md) |  | 534 | 31 | 61 | 32 | 51 | 33 | 43 | 38 | 54 | 57 | 48 | 39 | 47 | 25 |
| [contrastive learning](https://scholar4ai.com/keywords/contrastive-learning.md) |  | 528 | 31 | 38 | 48 | 38 | 45 | 56 | 58 | 38 | 46 | 40 | 49 | 41 | 21 |
| [digital transformation](https://scholar4ai.com/keywords/digital-transformation.md) |  | 519 | 52 | 49 | 48 | 42 | 42 | 52 | 45 | 42 | 36 | 39 | 31 | 41 | 27 |
| [artificial intelligence](https://scholar4ai.com/keywords/artificial-intelligence.md) |  | 499 | 34 | 32 | 56 | 42 | 35 | 45 | 43 | 29 | 36 | 50 | 48 | 49 | 26 |
| [multimodal fusion](https://scholar4ai.com/keywords/multimodal-fusion.md) |  | 481 | 32 | 36 | 34 | 46 | 46 | 44 | 41 | 44 | 46 | 44 | 41 | 27 | 24 |
| [class imbalance](https://scholar4ai.com/keywords/class-imbalance.md) |  | 453 | 36 | 29 | 36 | 36 | 34 | 43 | 44 | 45 | 40 | 29 | 47 | 34 | 19 |

### Fastest-growing keywords

*Keywords with the most papers beyond their earlier share, Jul 2026–Sep 2026 vs Oct 2025–Jun 2026*

| Keyword | Papers per month, Oct 25 – Oct 26* | Papers Oct 2025–Jun 2026 | Papers Jul 2026–Sep 2026 | Expected at earlier share | Extra papers | Share growth |
|---|---|---:|---:|---:|---:|---:|
| absorptive capacity | 5, 6, 9, 9, 8, 9, 8, 8, 5, 17, 12, 15, 8 | 67 | 44 | 22.0 | +22.0 | ×2.0 |
| AI governance | 12, 7, 9, 6, 7, 13, 12, 19, 11, 12, 25, 15, 5 | 96 | 52 | 31.6 | +20.4 | ×1.6 |
| uncertainty estimation | 6, 2, 4, 9, 9, 11, 6, 9, 14, 14, 10, 17, 7 | 70 | 41 | 23.0 | +18.0 | ×1.8 |
| mixture of experts | 7, 8, 8, 14, 6, 13, 13, 11, 14, 16, 19, 12, 10 | 94 | 47 | 30.9 | +16.1 | ×1.5 |
| human-AI collaboration | 4, 7, 6, 7, 3, 6, 12, 6, 11, 10, 14, 12, 8 | 62 | 36 | 20.4 | +15.6 | ×1.8 |
| financial contagion | 3, 4, 4, 9, 7, 7, 4, 5, 5, 10, 10, 11, 11 | 48 | 31 | 15.8 | +15.2 | ×2.0 |
| monetary policy transmission | 5, 7, 9, 6, 9, 12, 7, 9, 9, 16, 8, 15, 8 | 73 | 39 | 24.0 | +15.0 | ×1.6 |
| corporate finance | 4, 3, 2, 3, 2, 4, 2, 1, 4, 10, 4, 9, 5 | 25 | 23 | 8.2 | +14.8 | ×2.8 |
| innovation policy | 2, 5, 8, 6, 8, 4, 3, 6, 7, 10, 7, 13, 7 | 49 | 30 | 16.1 | +13.9 | ×1.9 |
| stablecoins | 1, 5, 1, 3, 1, 5, 1, 4, 4, 5, 5, 12, 3 | 25 | 22 | 8.2 | +13.8 | ×2.7 |

## Finance

### Most frequent keywords

*Most frequent keywords, Oct 2025 to Sep 2026*

| Keyword | Trend | 12 months | Oct 25 | Nov 25 | Dec 25 | Jan 26 | Feb 26 | Mar 26 | Apr 26 | May 26 | Jun 26 | Jul 26 | Aug 26 | Sep 26 | Oct 26* |
|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| [Chinese listed companies](https://scholar4ai.com/keywords/chinese-listed-companies.md) |  | 852 | 77 | 91 | 112 | 62 | 42 | 62 | 78 | 64 | 75 | 60 | 69 | 60 | 18 |
| [China](https://scholar4ai.com/keywords/china.md) |  | 679 | 53 | 78 | 77 | 37 | 50 | 64 | 68 | 54 | 52 | 61 | 46 | 39 | 23 |
| [financial constraints](https://scholar4ai.com/keywords/financial-constraints.md) |  | 563 | 44 | 68 | 65 | 45 | 31 | 56 | 54 | 44 | 49 | 40 | 38 | 29 | 12 |
| [corporate governance](https://scholar4ai.com/keywords/corporate-governance.md) |  | 517 | 55 | 60 | 52 | 39 | 31 | 62 | 49 | 29 | 40 | 32 | 34 | 34 | 17 |
| state-owned enterprises |  | 425 | 46 | 52 | 51 | 29 | 19 | 43 | 45 | 27 | 28 | 28 | 31 | 26 | 6 |
| [information asymmetry](https://scholar4ai.com/keywords/information-asymmetry.md) |  | 305 | 19 | 38 | 19 | 31 | 11 | 27 | 22 | 36 | 26 | 28 | 25 | 23 | 6 |
| [emerging markets](https://scholar4ai.com/keywords/emerging-markets.md) |  | 298 | 18 | 29 | 28 | 19 | 25 | 24 | 24 | 28 | 29 | 29 | 13 | 32 | 14 |
| [COVID-19 pandemic](https://scholar4ai.com/keywords/covid-19-pandemic.md) |  | 266 | 21 | 18 | 27 | 19 | 43 | 30 | 21 | 14 | 19 | 21 | 22 | 11 | 7 |
| regional heterogeneity |  | 226 | 32 | 45 | 32 | 13 | 9 | 24 | 12 | 11 | 22 | 12 | 8 | 6 | 2 |
| listed companies |  | 215 | 29 | 38 | 34 | 10 | 8 | 17 | 24 | 9 | 12 | 12 | 10 | 12 | 2 |
| moderating effect |  | 208 | 35 | 44 | 34 | 7 | 6 | 15 | 17 | 12 | 11 | 11 | 8 | 8 | 4 |
| [digital transformation](https://scholar4ai.com/keywords/digital-transformation.md) |  | 195 | 26 | 20 | 29 | 13 | 9 | 15 | 16 | 15 | 16 | 11 | 9 | 16 | 10 |
| green finance |  | 185 | 17 | 27 | 19 | 14 | 14 | 13 | 14 | 15 | 18 | 5 | 11 | 18 | 10 |
| [green innovation](https://scholar4ai.com/keywords/green-innovation.md) |  | 177 | 16 | 23 | 20 | 15 | 13 | 8 | 16 | 12 | 16 | 7 | 18 | 13 | 5 |
| systemic risk |  | 172 | 11 | 12 | 20 | 13 | 20 | 20 | 13 | 16 | 11 | 13 | 12 | 11 | 6 |

### Fastest-growing keywords

*Keywords with the most papers beyond their earlier share, Jul 2026–Sep 2026 vs Oct 2025–Jun 2026*

| Keyword | Papers per month, Oct 25 – Oct 26* | Papers Oct 2025–Jun 2026 | Papers Jul 2026–Sep 2026 | Expected at earlier share | Extra papers | Share growth |
|---|---|---:|---:|---:|---:|---:|
| [artificial intelligence](https://scholar4ai.com/keywords/artificial-intelligence.md) | 4, 5, 12, 4, 3, 8, 8, 4, 6, 10, 12, 9, 8 | 54 | 31 | 17.4 | +13.6 | ×1.8 |
| corporate finance | 3, 2, 1, 1, 1, 4, 2, 1, 4, 7, 3, 8, 4 | 19 | 18 | 6.1 | +11.9 | ×2.9 |
| price discovery | 1, 2, 1, 3, 2, 2, 2, 5, 3, 7, 3, 8, 3 | 21 | 18 | 6.8 | +11.2 | ×2.6 |
| greenwashing | 6, 4, 5, 4, 3, 4, 6, 8, 1, 5, 11, 8, 3 | 41 | 24 | 13.2 | +10.8 | ×1.8 |
| asset pricing | 3, 4, 8, 7, 1, 6, 12, 7, 7, 15, 8, 5, 7 | 55 | 28 | 17.8 | +10.2 | ×1.6 |
| financial contagion | 2, 3, 4, 4, 4, 5, 3, 4, 2, 9, 4, 7, 9 | 31 | 20 | 10.0 | +10.0 | ×2.0 |
| stablecoins | 0, 4, 1, 2, 1, 5, 1, 4, 4, 4, 5, 8, 2 | 22 | 17 | 7.1 | +9.9 | ×2.4 |
| financial intermediation | 1, 1, 1, 1, 0, 4, 0, 0, 2, 3, 3, 7, 1 | 10 | 13 | 3.2 | +9.8 | ×4.0 |
| stock price crash risk | 4, 6, 7, 5, 3, 8, 8, 8, 5, 11, 8, 8, 3 | 54 | 27 | 17.4 | +9.6 | ×1.6 |
| macroprudential policy | 5, 1, 3, 2, 8, 9, 5, 5, 6, 13, 3, 7, 7 | 44 | 23 | 14.2 | +8.8 | ×1.6 |

## Accounting

### Most frequent keywords

*Most frequent keywords, Oct 2025 to Sep 2026*

| Keyword | Trend | 12 months | Oct 25 | Nov 25 | Dec 25 | Jan 26 | Feb 26 | Mar 26 | Apr 26 | May 26 | Jun 26 | Jul 26 | Aug 26 | Sep 26 | Oct 26* |
|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| [corporate governance](https://scholar4ai.com/keywords/corporate-governance.md) |  | 157 | 12 | 18 | 9 | 18 | 18 | 10 | 11 | 13 | 20 | 10 | 9 | 9 | 8 |
| [Chinese listed companies](https://scholar4ai.com/keywords/chinese-listed-companies.md) |  | 49 | 4 | 4 | 7 | 8 | 3 | 3 | 4 | 0 | 3 | 3 | 5 | 5 | 4 |
| [information asymmetry](https://scholar4ai.com/keywords/information-asymmetry.md) |  | 38 | 0 | 3 | 1 | 5 | 4 | 3 | 1 | 2 | 11 | 1 | 5 | 2 | 3 |
| audit quality |  | 33 | 0 | 2 | 3 | 3 | 7 | 1 | 4 | 2 | 4 | 1 | 3 | 3 | 1 |
| state-owned enterprises |  | 31 | 1 | 2 | 3 | 7 | 1 | 1 | 4 | 4 | 2 | 2 | 4 | 0 | 0 |
| corporate social responsibility |  | 29 | 1 | 3 | 1 | 2 | 2 | 2 | 3 | 3 | 4 | 3 | 2 | 3 | 0 |
| accounting education |  | 27 | 0 | 0 | 1 | 1 | 3 | 2 | 5 | 4 | 1 | 6 | 2 | 2 | 2 |
| [emerging markets](https://scholar4ai.com/keywords/emerging-markets.md) |  | 27 | 0 | 3 | 2 | 1 | 4 | 1 | 3 | 2 | 4 | 1 | 4 | 2 | 0 |
| earnings management |  | 26 | 1 | 1 | 2 | 5 | 4 | 0 | 2 | 2 | 3 | 4 | 1 | 1 | 2 |
| [financial constraints](https://scholar4ai.com/keywords/financial-constraints.md) |  | 26 | 3 | 1 | 1 | 4 | 5 | 2 | 4 | 0 | 2 | 1 | 3 | 0 | 2 |
| [China](https://scholar4ai.com/keywords/china.md) |  | 25 | 2 | 2 | 2 | 4 | 2 | 2 | 4 | 3 | 0 | 2 | 2 | 0 | 0 |
| voluntary disclosure |  | 23 | 0 | 2 | 2 | 1 | 2 | 2 | 3 | 3 | 1 | 2 | 3 | 2 | 1 |
| financial reporting quality |  | 22 | 1 | 2 | 2 | 3 | 1 | 1 | 4 | 1 | 2 | 2 | 0 | 3 | 3 |
| firm performance |  | 22 | 1 | 2 | 1 | 2 | 0 | 5 | 3 | 0 | 4 | 2 | 0 | 2 | 0 |
| executive compensation |  | 21 | 1 | 0 | 1 | 2 | 3 | 2 | 4 | 1 | 3 | 1 | 1 | 2 | 2 |

### Fastest-growing keywords

*Keywords with the most papers beyond their earlier share, Jul 2026–Sep 2026 vs Oct 2025–Jun 2026*

| Keyword | Papers per month, Oct 25 – Oct 26* | Papers Oct 2025–Jun 2026 | Papers Jul 2026–Sep 2026 | Expected at earlier share | Extra papers | Share growth |
|---|---|---:|---:|---:|---:|---:|
| accounting education | 0, 0, 1, 1, 3, 2, 5, 4, 1, 6, 2, 2, 2 | 17 | 10 | 5.5 | +4.5 | ×1.8 |
| moderating effect | 0, 0, 3, 1, 1, 1, 4, 1, 0, 3, 1, 4, 1 | 11 | 8 | 3.6 | +4.4 | ×2.2 |
| information transparency | 1, 0, 0, 0, 1, 0, 0, 0, 1, 3, 1, 1, 0 | 3 | 5 | 1.0 | +4.0 | ×5.1 |
| corporate disclosure | 1, 1, 1, 0, 0, 4, 0, 1, 2, 2, 3, 2, 1 | 10 | 7 | 3.3 | +3.7 | ×2.1 |
| ESG performance | 1, 1, 1, 4, 3, 0, 1, 1, 1, 2, 2, 3, 1 | 13 | 7 | 4.2 | +2.8 | ×1.7 |
| financial reporting | 1, 1, 1, 2, 2, 0, 3, 2, 1, 3, 1, 3, 0 | 13 | 7 | 4.2 | +2.8 | ×1.7 |
| Chinese enterprises | 0, 0, 0, 3, 0, 1, 1, 1, 1, 3, 2, 0, 1 | 7 | 5 | 2.3 | +2.7 | ×2.2 |
| generative AI | 1, 2, 0, 0, 0, 1, 3, 0, 1, 2, 2, 1, 0 | 8 | 5 | 2.6 | +2.4 | ×1.9 |

## Economics

### Most frequent keywords

*Most frequent keywords, Oct 2025 to Sep 2026*

| Keyword | Trend | 12 months | Oct 25 | Nov 25 | Dec 25 | Jan 26 | Feb 26 | Mar 26 | Apr 26 | May 26 | Jun 26 | Jul 26 | Aug 26 | Sep 26 | Oct 26* |
|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| [China](https://scholar4ai.com/keywords/china.md) |  | 767 | 68 | 50 | 66 | 82 | 63 | 71 | 66 | 52 | 71 | 53 | 57 | 68 | 36 |
| [COVID-19 pandemic](https://scholar4ai.com/keywords/covid-19-pandemic.md) |  | 236 | 20 | 31 | 23 | 18 | 13 | 18 | 19 | 18 | 23 | 23 | 16 | 14 | 10 |
| [energy transition](https://scholar4ai.com/keywords/energy-transition.md) |  | 210 | 17 | 17 | 14 | 15 | 21 | 20 | 17 | 21 | 14 | 15 | 18 | 21 | 10 |
| [Chinese listed companies](https://scholar4ai.com/keywords/chinese-listed-companies.md) |  | 195 | 14 | 14 | 16 | 23 | 21 | 14 | 10 | 13 | 20 | 20 | 9 | 21 | 14 |
| income inequality |  | 195 | 18 | 14 | 18 | 16 | 9 | 20 | 11 | 16 | 24 | 19 | 13 | 17 | 7 |
| [financial constraints](https://scholar4ai.com/keywords/financial-constraints.md) |  | 187 | 16 | 19 | 18 | 21 | 21 | 12 | 10 | 16 | 14 | 14 | 8 | 18 | 9 |
| economic growth |  | 185 | 16 | 8 | 13 | 22 | 16 | 8 | 18 | 17 | 17 | 22 | 13 | 15 | 10 |
| environmental regulation |  | 167 | 16 | 16 | 9 | 19 | 14 | 16 | 15 | 10 | 17 | 13 | 7 | 15 | 6 |
| CO2 emissions |  | 163 | 9 | 9 | 9 | 14 | 13 | 29 | 17 | 14 | 17 | 14 | 8 | 10 | 12 |
| developing countries |  | 162 | 16 | 16 | 14 | 12 | 16 | 15 | 13 | 18 | 12 | 10 | 5 | 15 | 6 |
| state-owned enterprises |  | 159 | 13 | 11 | 17 | 14 | 13 | 18 | 7 | 10 | 12 | 17 | 9 | 18 | 9 |
| [green innovation](https://scholar4ai.com/keywords/green-innovation.md) |  | 148 | 14 | 8 | 15 | 16 | 12 | 15 | 13 | 14 | 4 | 14 | 12 | 11 | 7 |
| [monetary policy](https://scholar4ai.com/keywords/monetary-policy.md) |  | 146 | 19 | 9 | 11 | 12 | 17 | 11 | 6 | 16 | 14 | 10 | 8 | 13 | 15 |
| [emerging markets](https://scholar4ai.com/keywords/emerging-markets.md) |  | 145 | 11 | 7 | 14 | 10 | 17 | 18 | 10 | 13 | 13 | 11 | 5 | 16 | 9 |
| gender differences |  | 139 | 14 | 12 | 9 | 18 | 11 | 14 | 11 | 11 | 12 | 12 | 8 | 7 | 6 |

### Fastest-growing keywords

*Keywords with the most papers beyond their earlier share, Jul 2026–Sep 2026 vs Oct 2025–Jun 2026*

| Keyword | Papers per month, Oct 25 – Oct 26* | Papers Oct 2025–Jun 2026 | Papers Jul 2026–Sep 2026 | Expected at earlier share | Extra papers | Share growth |
|---|---|---:|---:|---:|---:|---:|
| [corporate governance](https://scholar4ai.com/keywords/corporate-governance.md) | 8, 9, 10, 7, 9, 7, 3, 7, 6, 12, 10, 13, 7 | 66 | 35 | 21.9 | +13.1 | ×1.6 |
| total factor productivity | 6, 6, 7, 7, 9, 10, 7, 1, 9, 6, 12, 15, 6 | 62 | 33 | 20.6 | +12.4 | ×1.6 |
| systemic risk | 5, 5, 4, 5, 6, 3, 2, 1, 4, 7, 6, 9, 3 | 35 | 22 | 11.6 | +10.4 | ×1.9 |
| treatment effect heterogeneity | 1, 1, 3, 1, 1, 3, 1, 0, 2, 5, 5, 4, 0 | 13 | 14 | 4.3 | +9.7 | ×3.2 |
| absorptive capacity | 2, 3, 2, 4, 2, 3, 2, 2, 2, 8, 2, 7, 4 | 22 | 17 | 7.3 | +9.7 | ×2.3 |
| [artificial intelligence](https://scholar4ai.com/keywords/artificial-intelligence.md) | 6, 2, 9, 5, 7, 8, 3, 5, 5, 8, 7, 11, 2 | 50 | 26 | 16.6 | +9.4 | ×1.6 |
| environmental degradation | 1, 2, 3, 1, 1, 1, 4, 1, 1, 3, 4, 6, 0 | 15 | 13 | 5.0 | +8.0 | ×2.6 |
| digital governance | 0, 2, 1, 0, 0, 0, 0, 1, 0, 4, 1, 4, 0 | 4 | 9 | 1.3 | +7.7 | ×6.8 |
| employment | 2, 1, 2, 2, 3, 2, 2, 6, 5, 5, 5, 6, 2 | 25 | 16 | 8.3 | +7.7 | ×1.9 |
| social welfare (welfare economics) | 6, 4, 5, 2, 1, 3, 5, 7, 4, 3, 10, 7, 1 | 37 | 20 | 12.3 | +7.7 | ×1.6 |

## Management

### Most frequent keywords

*Most frequent keywords, Oct 2025 to Sep 2026*

| Keyword | Trend | 12 months | Oct 25 | Nov 25 | Dec 25 | Jan 26 | Feb 26 | Mar 26 | Apr 26 | May 26 | Jun 26 | Jul 26 | Aug 26 | Sep 26 | Oct 26* |
|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| [digital transformation](https://scholar4ai.com/keywords/digital-transformation.md) |  | 202 | 14 | 22 | 14 | 18 | 22 | 23 | 15 | 17 | 11 | 16 | 14 | 16 | 10 |
| [Chinese listed companies](https://scholar4ai.com/keywords/chinese-listed-companies.md) |  | 159 | 13 | 11 | 11 | 16 | 20 | 19 | 13 | 11 | 10 | 12 | 7 | 16 | 9 |
| dynamic capabilities |  | 149 | 5 | 10 | 6 | 12 | 17 | 18 | 12 | 20 | 13 | 11 | 10 | 15 | 8 |
| [artificial intelligence](https://scholar4ai.com/keywords/artificial-intelligence.md) |  | 141 | 5 | 13 | 12 | 14 | 10 | 14 | 13 | 7 | 15 | 18 | 8 | 12 | 8 |
| [emerging markets](https://scholar4ai.com/keywords/emerging-markets.md) |  | 131 | 6 | 7 | 5 | 16 | 12 | 13 | 16 | 11 | 15 | 11 | 8 | 11 | 5 |
| small and medium-sized enterprises |  | 126 | 10 | 17 | 8 | 12 | 11 | 12 | 8 | 11 | 9 | 8 | 11 | 9 | 8 |
| moderating effect |  | 124 | 8 | 12 | 9 | 15 | 18 | 13 | 9 | 10 | 5 | 9 | 6 | 10 | 5 |
| multinational corporations |  | 124 | 2 | 12 | 6 | 10 | 13 | 11 | 17 | 12 | 10 | 11 | 10 | 10 | 5 |
| generative AI |  | 121 | 5 | 12 | 9 | 10 | 7 | 13 | 17 | 4 | 15 | 7 | 13 | 9 | 3 |
| corporate social responsibility |  | 116 | 8 | 11 | 10 | 8 | 13 | 10 | 11 | 9 | 13 | 6 | 9 | 8 | 8 |
| [China](https://scholar4ai.com/keywords/china.md) |  | 101 | 9 | 7 | 10 | 3 | 12 | 7 | 10 | 10 | 7 | 13 | 6 | 7 | 5 |
| [green innovation](https://scholar4ai.com/keywords/green-innovation.md) |  | 101 | 9 | 5 | 6 | 12 | 6 | 10 | 12 | 12 | 9 | 7 | 6 | 7 | 8 |
| [corporate governance](https://scholar4ai.com/keywords/corporate-governance.md) |  | 99 | 6 | 11 | 7 | 9 | 11 | 8 | 8 | 4 | 3 | 10 | 8 | 14 | 7 |
| firm performance |  | 99 | 4 | 11 | 9 | 10 | 15 | 5 | 4 | 7 | 11 | 9 | 5 | 9 | 4 |
| resource-based view |  | 96 | 5 | 1 | 10 | 13 | 6 | 9 | 12 | 7 | 6 | 9 | 10 | 8 | 4 |

### Fastest-growing keywords

*Keywords with the most papers beyond their earlier share, Jul 2026–Sep 2026 vs Oct 2025–Jun 2026*

| Keyword | Papers per month, Oct 25 – Oct 26* | Papers Oct 2025–Jun 2026 | Papers Jul 2026–Sep 2026 | Expected at earlier share | Extra papers | Share growth |
|---|---|---:|---:|---:|---:|---:|
| absorptive capacity | 2, 2, 2, 4, 4, 5, 2, 2, 3, 6, 8, 7, 0 | 26 | 21 | 8.6 | +12.4 | ×2.4 |
| customer satisfaction | 4, 3, 2, 3, 2, 2, 1, 1, 1, 7, 4, 6, 0 | 19 | 17 | 6.3 | +10.7 | ×2.7 |
| knowledge-based view | 2, 3, 3, 1, 3, 3, 3, 2, 2, 5, 3, 7, 0 | 22 | 15 | 7.3 | +7.7 | ×2.0 |
| human-AI collaboration | 2, 3, 2, 1, 1, 1, 5, 1, 7, 5, 4, 6, 5 | 23 | 15 | 7.6 | +7.4 | ×2.0 |
| customer engagement | 1, 2, 0, 3, 0, 3, 3, 8, 3, 6, 4, 3, 1 | 23 | 13 | 7.6 | +5.4 | ×1.7 |
| business strategy | 1, 0, 2, 0, 0, 0, 1, 1, 0, 1, 1, 5, 1 | 5 | 7 | 1.7 | +5.3 | ×4.2 |
| inclusive growth | 1, 1, 0, 0, 0, 0, 1, 0, 0, 3, 0, 3, 0 | 3 | 6 | 1.0 | +5.0 | ×6.0 |
| influencer marketing | 1, 1, 0, 3, 2, 1, 2, 2, 3, 4, 5, 1, 2 | 15 | 10 | 5.0 | +5.0 | ×2.0 |
| developing countries | 3, 4, 3, 3, 4, 3, 3, 0, 1, 5, 3, 5, 1 | 24 | 13 | 8.0 | +5.0 | ×1.6 |
| public procurement | 0, 0, 1, 0, 0, 1, 1, 0, 1, 3, 2, 1, 1 | 4 | 6 | 1.3 | +4.7 | ×4.5 |

## Operations Research

### Most frequent keywords

*Most frequent keywords, Oct 2025 to Sep 2026*

| Keyword | Trend | 12 months | Oct 25 | Nov 25 | Dec 25 | Jan 26 | Feb 26 | Mar 26 | Apr 26 | May 26 | Jun 26 | Jul 26 | Aug 26 | Sep 26 | Oct 26* |
|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| mixed-integer linear programming |  | 145 | 17 | 14 | 13 | 15 | 10 | 10 | 14 | 11 | 13 | 11 | 13 | 4 | 11 |
| demand uncertainty |  | 110 | 6 | 12 | 13 | 11 | 6 | 11 | 12 | 9 | 12 | 2 | 7 | 9 | 6 |
| multi-objective optimization |  | 96 | 9 | 7 | 10 | 9 | 6 | 3 | 10 | 9 | 5 | 7 | 15 | 6 | 5 |
| vehicle routing |  | 93 | 7 | 6 | 7 | 8 | 4 | 10 | 4 | 8 | 10 | 9 | 8 | 12 | 1 |
| mixed integer programming |  | 90 | 9 | 4 | 9 | 9 | 7 | 7 | 6 | 10 | 11 | 5 | 6 | 7 | 2 |
| supply chain resilience |  | 88 | 7 | 6 | 9 | 3 | 11 | 11 | 7 | 8 | 6 | 6 | 8 | 6 | 8 |
| robust optimization |  | 76 | 7 | 3 | 5 | 9 | 9 | 7 | 6 | 7 | 4 | 6 | 2 | 11 | 4 |
| distributionally robust optimization |  | 66 | 9 | 7 | 2 | 3 | 3 | 6 | 5 | 4 | 5 | 5 | 12 | 5 | 6 |
| makespan minimization |  | 66 | 4 | 2 | 3 | 7 | 7 | 6 | 9 | 6 | 11 | 3 | 4 | 4 | 0 |
| adaptive large neighborhood search |  | 62 | 5 | 4 | 8 | 8 | 8 | 4 | 2 | 5 | 7 | 5 | 3 | 3 | 4 |
| last-mile delivery |  | 62 | 4 | 6 | 5 | 3 | 3 | 4 | 5 | 6 | 6 | 8 | 7 | 5 | 5 |
| Benders decomposition |  | 60 | 5 | 2 | 4 | 5 | 6 | 4 | 7 | 7 | 6 | 6 | 2 | 6 | 3 |
| multi-criteria decision making |  | 57 | 5 | 4 | 1 | 4 | 7 | 5 | 4 | 9 | 11 | 2 | 3 | 2 | 0 |
| two-stage stochastic programming |  | 57 | 9 | 5 | 6 | 7 | 1 | 4 | 4 | 4 | 7 | 5 | 3 | 2 | 6 |
| combinatorial optimization |  | 55 | 3 | 3 | 3 | 6 | 6 | 4 | 6 | 3 | 3 | 3 | 10 | 5 | 2 |

### Fastest-growing keywords

*Keywords with the most papers beyond their earlier share, Jul 2026–Sep 2026 vs Oct 2025–Jun 2026*

| Keyword | Papers per month, Oct 25 – Oct 26* | Papers Oct 2025–Jun 2026 | Papers Jul 2026–Sep 2026 | Expected at earlier share | Extra papers | Share growth |
|---|---|---:|---:|---:|---:|---:|
| price competition | 3, 1, 0, 0, 0, 1, 0, 0, 0, 3, 3, 5, 2 | 5 | 11 | 1.7 | +9.3 | ×6.6 |
| drone delivery | 3, 2, 1, 0, 5, 1, 0, 3, 3, 5, 5, 5, 1 | 18 | 15 | 6.0 | +9.0 | ×2.5 |
| supply chain coordination | 2, 2, 5, 6, 6, 4, 2, 4, 2, 6, 6, 7, 6 | 33 | 19 | 11.0 | +8.0 | ×1.7 |
| local search | 4, 1, 2, 3, 3, 4, 2, 3, 0, 3, 6, 6, 1 | 22 | 15 | 7.3 | +7.7 | ×2.0 |
| distributionally robust optimization | 9, 7, 2, 3, 3, 6, 5, 4, 5, 5, 12, 5, 6 | 44 | 22 | 14.6 | +7.4 | ×1.5 |
| supplier selection | 0, 1, 1, 1, 2, 2, 1, 2, 3, 3, 4, 4, 3 | 13 | 11 | 4.3 | +6.7 | ×2.5 |
| Stackelberg game | 2, 3, 1, 9, 2, 3, 4, 2, 3, 9, 5, 2, 1 | 29 | 16 | 9.6 | +6.4 | ×1.7 |
| matheuristic | 3, 5, 1, 4, 2, 2, 3, 3, 2, 5, 4, 5, 1 | 25 | 14 | 8.3 | +5.7 | ×1.7 |
| live streaming e-commerce | 0, 1, 0, 1, 1, 1, 0, 1, 1, 5, 0, 2, 2 | 6 | 7 | 2.0 | +5.0 | ×3.5 |
| digital platforms | 2, 0, 2, 2, 1, 0, 1, 0, 1, 3, 1, 4, 2 | 9 | 8 | 3.0 | +5.0 | ×2.7 |

## AI

### Most frequent keywords

*Most frequent keywords, Oct 2025 to Sep 2026*

| Keyword | Trend | 12 months | Oct 25 | Nov 25 | Dec 25 | Jan 26 | Feb 26 | Mar 26 | Apr 26 | May 26 | Jun 26 | Jul 26 | Aug 26 | Sep 26 | Oct 26* |
|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| [large language models](https://scholar4ai.com/keywords/large-language-models.md) |  | 1,099 | 54 | 81 | 90 | 70 | 71 | 106 | 112 | 111 | 129 | 82 | 98 | 95 | 55 |
| [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) |  | 529 | 49 | 29 | 45 | 39 | 32 | 49 | 57 | 46 | 47 | 53 | 44 | 39 | 31 |
| [contrastive learning](https://scholar4ai.com/keywords/contrastive-learning.md) |  | 518 | 31 | 38 | 48 | 36 | 43 | 55 | 56 | 38 | 46 | 39 | 48 | 40 | 21 |
| [multimodal fusion](https://scholar4ai.com/keywords/multimodal-fusion.md) |  | 475 | 32 | 36 | 34 | 45 | 46 | 43 | 41 | 42 | 45 | 44 | 40 | 27 | 23 |
| [class imbalance](https://scholar4ai.com/keywords/class-imbalance.md) |  | 411 | 33 | 27 | 32 | 34 | 31 | 39 | 38 | 41 | 35 | 26 | 43 | 32 | 17 |
| diffusion models |  | 342 | 24 | 24 | 28 | 35 | 21 | 26 | 43 | 32 | 25 | 31 | 25 | 28 | 17 |
| attention mechanism |  | 337 | 29 | 35 | 30 | 33 | 26 | 30 | 26 | 29 | 28 | 27 | 20 | 24 | 13 |
| interpretability |  | 332 | 28 | 35 | 26 | 16 | 24 | 34 | 35 | 29 | 36 | 32 | 18 | 19 | 15 |
| [knowledge distillation](https://scholar4ai.com/keywords/knowledge-distillation.md) |  | 327 | 21 | 24 | 28 | 25 | 24 | 23 | 30 | 44 | 27 | 27 | 24 | 30 | 17 |
| [explainable AI](https://scholar4ai.com/keywords/explainable-ai.md) |  | 324 | 30 | 21 | 24 | 12 | 31 | 31 | 28 | 34 | 29 | 21 | 31 | 32 | 20 |
| Mamba |  | 283 | 13 | 12 | 24 | 28 | 22 | 14 | 31 | 32 | 28 | 25 | 30 | 24 | 9 |
| [federated learning](https://scholar4ai.com/keywords/federated-learning.md) |  | 282 | 17 | 28 | 22 | 35 | 26 | 21 | 23 | 18 | 23 | 21 | 27 | 21 | 21 |
| transformer models |  | 280 | 21 | 20 | 28 | 21 | 25 | 31 | 24 | 26 | 22 | 26 | 19 | 17 | 13 |
| vision-language models |  | 270 | 14 | 14 | 20 | 23 | 29 | 27 | 23 | 27 | 25 | 23 | 19 | 26 | 17 |
| computational efficiency |  | 269 | 15 | 18 | 26 | 25 | 21 | 27 | 29 | 30 | 27 | 16 | 19 | 16 | 10 |

### Fastest-growing keywords

*Keywords with the most papers beyond their earlier share, Jul 2026–Sep 2026 vs Oct 2025–Jun 2026*

| Keyword | Papers per month, Oct 25 – Oct 26* | Papers Oct 2025–Jun 2026 | Papers Jul 2026–Sep 2026 | Expected at earlier share | Extra papers | Share growth |
|---|---|---:|---:|---:|---:|---:|
| AI governance | 7, 5, 7, 4, 6, 10, 7, 13, 10, 10, 21, 10, 2 | 69 | 41 | 22.6 | +18.4 | ×1.8 |
| mixture of experts | 7, 8, 8, 14, 6, 13, 13, 10, 13, 16, 19, 12, 10 | 92 | 47 | 30.2 | +16.8 | ×1.6 |
| uncertainty estimation | 6, 2, 4, 9, 9, 11, 6, 9, 14, 13, 10, 16, 7 | 70 | 39 | 23.0 | +16.0 | ×1.7 |
| foundation models | 3, 2, 5, 4, 6, 5, 8, 6, 10, 6, 9, 12, 4 | 49 | 27 | 16.1 | +10.9 | ×1.7 |
| spatial transcriptomics | 1, 0, 2, 0, 1, 0, 1, 3, 2, 4, 3, 7, 1 | 10 | 14 | 3.3 | +10.7 | ×4.3 |
| cross-dataset generalization | 4, 1, 5, 5, 7, 3, 4, 4, 8, 5, 9, 10, 2 | 41 | 24 | 13.4 | +10.6 | ×1.8 |
| reproducibility | 1, 2, 3, 4, 4, 2, 0, 1, 4, 4, 5, 6, 4 | 21 | 15 | 6.9 | +8.1 | ×2.2 |
| aerial imagery | 0, 1, 2, 0, 0, 0, 0, 1, 0, 6, 2, 1, 1 | 4 | 9 | 1.3 | +7.7 | ×6.9 |
| LoRa | 1, 2, 3, 7, 0, 4, 7, 1, 4, 3, 5, 9, 1 | 29 | 17 | 9.5 | +7.5 | ×1.8 |
| inference latency | 0, 0, 2, 3, 0, 2, 3, 3, 1, 1, 6, 5, 4 | 14 | 12 | 4.6 | +7.4 | ×2.6 |

## Questions this page answers

### Which topics in research overall grew the most over the past year?

Comparing Jul 2026–Sep 2026 with Oct 2025–Jun 2026, the keywords with the most papers beyond their earlier share were “absorptive capacity” (44 papers, +22 vs. its earlier share), “AI governance” (52 papers, +20 vs. its earlier share), “uncertainty estimation” (41 papers, +18 vs. its earlier share), “mixture of experts” (47 papers, +16 vs. its earlier share) and “human-AI collaboration” (36 papers, +16 vs. its earlier share).

### Which topics in finance research grew the most over the past year?

Comparing Jul 2026–Sep 2026 with Oct 2025–Jun 2026, the keywords with the most papers beyond their earlier share were “artificial intelligence” (31 papers, +14 vs. its earlier share), “corporate finance” (18 papers, +12 vs. its earlier share), “price discovery” (18 papers, +11 vs. its earlier share), “greenwashing” (24 papers, +11 vs. its earlier share) and “asset pricing” (28 papers, +10 vs. its earlier share).

### Which topics in accounting research grew the most over the past year?

Comparing Jul 2026–Sep 2026 with Oct 2025–Jun 2026, the keywords with the most papers beyond their earlier share were “accounting education” (10 papers, +4 vs. its earlier share), “moderating effect” (8 papers, +4 vs. its earlier share), “information transparency” (5 papers, +4 vs. its earlier share), “corporate disclosure” (7 papers, +4 vs. its earlier share) and “ESG performance” (7 papers, +3 vs. its earlier share).

### Which topics in economics research grew the most over the past year?

Comparing Jul 2026–Sep 2026 with Oct 2025–Jun 2026, the keywords with the most papers beyond their earlier share were “corporate governance” (35 papers, +13 vs. its earlier share), “total factor productivity” (33 papers, +12 vs. its earlier share), “systemic risk” (22 papers, +10 vs. its earlier share), “treatment effect heterogeneity” (14 papers, +10 vs. its earlier share) and “absorptive capacity” (17 papers, +10 vs. its earlier share).

### Which topics in management research grew the most over the past year?

Comparing Jul 2026–Sep 2026 with Oct 2025–Jun 2026, the keywords with the most papers beyond their earlier share were “absorptive capacity” (21 papers, +12 vs. its earlier share), “customer satisfaction” (17 papers, +11 vs. its earlier share), “knowledge-based view” (15 papers, +8 vs. its earlier share), “human-AI collaboration” (15 papers, +7 vs. its earlier share) and “customer engagement” (13 papers, +5 vs. its earlier share).

### Which topics in operations research grew the most over the past year?

Comparing Jul 2026–Sep 2026 with Oct 2025–Jun 2026, the keywords with the most papers beyond their earlier share were “price competition” (11 papers, +9 vs. its earlier share), “drone delivery” (15 papers, +9 vs. its earlier share), “supply chain coordination” (19 papers, +8 vs. its earlier share), “local search” (15 papers, +8 vs. its earlier share) and “distributionally robust optimization” (22 papers, +7 vs. its earlier share).

### Which topics in AI research grew the most over the past year?

Comparing Jul 2026–Sep 2026 with Oct 2025–Jun 2026, the keywords with the most papers beyond their earlier share were “AI governance” (41 papers, +18 vs. its earlier share), “mixture of experts” (47 papers, +17 vs. its earlier share), “uncertainty estimation” (39 papers, +16 vs. its earlier share), “foundation models” (27 papers, +11 vs. its earlier share) and “spatial transcriptomics” (14 papers, +11 vs. its earlier share).


---

---
title: "Top research keywords: finance, economics, AI"
description: "Top research keywords in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days: large language models (89), China (79), Chinese listed companies (56), corporate governance (49) and graph neural networks (39)."
url: https://scholar4ai.com/rankings/keywords
markdown_url: https://scholar4ai.com/rankings/keywords.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T11:39:12+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# Top research keywords

A paper counts once for each OpenAlex keyword it has. Window: 2026-09-27 – 2026-10-26 (30 days). Showing the top 100 of 11,406; the full list is in /data/rankings.json.

## Key findings

- Top research keywords in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days: large language models (89), China (79), Chinese listed companies (56), corporate governance (49) and graph neural networks (39).
- 11,406 keywords appear in the window.

*Top research keywords · 2026-09-27 – 2026-10-26 (30 days)*

| # | Keyword | Papers | Finance | Accounting | Economics | Management | Operations Research | AI | Last 7 days |
|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 89 | 5 | 1 | 4 | 6 | 3 | 70 | 8 |
| 2 | [China](https://scholar4ai.com/keywords/china.md) | 79 | 27 | 0 | 44 | 6 | 1 | 1 | 5 |
| 3 | [Chinese listed companies](https://scholar4ai.com/keywords/chinese-listed-companies.md) | 56 | 26 | 5 | 14 | 10 | 1 | 0 | 1 |
| 4 | [corporate governance](https://scholar4ai.com/keywords/corporate-governance.md) | 49 | 23 | 10 | 7 | 9 | 0 | 0 | 4 |
| 5 | [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 39 | 0 | 0 | 0 | 1 | 0 | 38 | 1 |
| 6 | [artificial intelligence](https://scholar4ai.com/keywords/artificial-intelligence.md) | 36 | 11 | 1 | 6 | 9 | 1 | 8 | 0 |
| 7 | [emerging markets](https://scholar4ai.com/keywords/emerging-markets.md) | 35 | 18 | 0 | 10 | 5 | 1 | 1 | 4 |
| 8 | [information asymmetry](https://scholar4ai.com/keywords/information-asymmetry.md) | 31 | 10 | 3 | 12 | 2 | 3 | 1 | 1 |
| 9 | [digital transformation](https://scholar4ai.com/keywords/digital-transformation.md) | 30 | 13 | 1 | 2 | 10 | 3 | 1 | 2 |
| 9 | [multimodal fusion](https://scholar4ai.com/keywords/multimodal-fusion.md) | 30 | 0 | 0 | 0 | 1 | 0 | 29 | 0 |
| 11 | [financial constraints](https://scholar4ai.com/keywords/financial-constraints.md) | 28 | 15 | 2 | 9 | 1 | 1 | 0 | 1 |
| 12 | [contrastive learning](https://scholar4ai.com/keywords/contrastive-learning.md) | 27 | 0 | 0 | 0 | 0 | 0 | 27 | 0 |
| 13 | [explainable AI](https://scholar4ai.com/keywords/explainable-ai.md) | 26 | 0 | 0 | 0 | 2 | 0 | 24 | 3 |
| 13 | [monetary policy](https://scholar4ai.com/keywords/monetary-policy.md) | 26 | 8 | 0 | 18 | 0 | 0 | 0 | 1 |
| 15 | [COVID-19 pandemic](https://scholar4ai.com/keywords/covid-19-pandemic.md) | 24 | 7 | 0 | 10 | 4 | 1 | 2 | 4 |
| 15 | [green innovation](https://scholar4ai.com/keywords/green-innovation.md) | 24 | 6 | 1 | 7 | 8 | 2 | 0 | 0 |
| 17 | [energy transition](https://scholar4ai.com/keywords/energy-transition.md) | 23 | 5 | 0 | 14 | 3 | 0 | 1 | 1 |
| 17 | [federated learning](https://scholar4ai.com/keywords/federated-learning.md) | 23 | 0 | 0 | 0 | 1 | 1 | 21 | 4 |
| 17 | [knowledge distillation](https://scholar4ai.com/keywords/knowledge-distillation.md) | 23 | 0 | 0 | 0 | 0 | 0 | 23 | 1 |
| 20 | [class imbalance](https://scholar4ai.com/keywords/class-imbalance.md) | 22 | 2 | 0 | 0 | 1 | 0 | 19 | 1 |
| 21 | CO2 emissions | 21 | 6 | 0 | 12 | 1 | 2 | 0 | 0 |
| 21 | carbon pricing | 21 | 6 | 0 | 12 | 2 | 0 | 1 | 3 |
| 21 | digitalization | 21 | 5 | 0 | 9 | 7 | 0 | 0 | 1 |
| 24 | financial stability | 20 | 13 | 2 | 5 | 0 | 0 | 0 | 1 |
| 24 | innovation | 20 | 3 | 1 | 8 | 7 | 1 | 0 | 2 |
| 24 | transformer models | 20 | 0 | 0 | 1 | 0 | 1 | 18 | 2 |
| 27 | Chinese enterprises | 19 | 8 | 1 | 6 | 4 | 0 | 0 | 0 |
| 27 | digital economy | 19 | 9 | 0 | 7 | 3 | 0 | 0 | 0 |
| 27 | economic growth | 19 | 5 | 1 | 11 | 1 | 0 | 1 | 3 |
| 27 | state-owned enterprises | 19 | 7 | 0 | 9 | 2 | 1 | 0 | 0 |
| 27 | stock returns | 19 | 13 | 1 | 4 | 1 | 0 | 0 | 0 |
| 27 | vision-language models | 19 | 0 | 0 | 0 | 0 | 0 | 19 | 0 |
| 33 | climate policy | 18 | 3 | 0 | 13 | 2 | 0 | 0 | 1 |
| 33 | diffusion models | 18 | 0 | 0 | 0 | 0 | 0 | 18 | 1 |
| 33 | feature selection | 18 | 0 | 0 | 0 | 0 | 0 | 18 | 1 |
| 33 | reinforcement learning | 18 | 0 | 0 | 1 | 0 | 2 | 15 | 0 |
| 37 | deep learning | 17 | 0 | 0 | 1 | 0 | 2 | 14 | 2 |
| 37 | geopolitical risk | 17 | 7 | 0 | 10 | 0 | 0 | 0 | 0 |
| 37 | green finance | 17 | 12 | 0 | 3 | 1 | 0 | 1 | 1 |
| 37 | income inequality | 17 | 6 | 0 | 11 | 0 | 0 | 0 | 3 |
| 37 | interpretability | 17 | 0 | 0 | 0 | 1 | 1 | 15 | 1 |
| 37 | moderating effect | 17 | 6 | 2 | 2 | 6 | 1 | 0 | 1 |
| 43 | environmental policy | 16 | 4 | 0 | 11 | 1 | 0 | 0 | 1 |
| 43 | technology adoption | 16 | 3 | 0 | 4 | 7 | 2 | 0 | 1 |
| 45 | climate risk | 15 | 10 | 0 | 4 | 1 | 0 | 0 | 0 |
| 45 | corporate sustainability | 15 | 6 | 3 | 1 | 5 | 0 | 0 | 0 |
| 45 | emissions trading | 15 | 5 | 0 | 6 | 1 | 2 | 1 | 1 |
| 45 | environmental regulation | 15 | 3 | 0 | 8 | 3 | 1 | 0 | 2 |
| 45 | firm performance | 15 | 5 | 0 | 4 | 6 | 0 | 0 | 0 |
| 45 | institutional quality | 15 | 3 | 0 | 9 | 3 | 0 | 0 | 1 |
| 45 | regional disparities | 15 | 0 | 0 | 14 | 0 | 1 | 0 | 5 |
| 45 | small and medium-sized enterprises | 15 | 2 | 1 | 3 | 8 | 1 | 0 | 2 |
| 45 | time series forecasting | 15 | 2 | 0 | 0 | 0 | 1 | 12 | 1 |
| 45 | total factor productivity | 15 | 5 | 0 | 7 | 3 | 0 | 0 | 1 |
| 55 | attention mechanism | 14 | 0 | 0 | 0 | 0 | 0 | 14 | 2 |
| 55 | convolutional neural networks | 14 | 1 | 0 | 0 | 0 | 0 | 13 | 2 |
| 55 | corporate disclosure | 14 | 9 | 1 | 2 | 2 | 0 | 0 | 1 |
| 55 | corporate social responsibility | 14 | 4 | 1 | 1 | 8 | 0 | 0 | 3 |
| 55 | cross-national comparison | 14 | 3 | 0 | 8 | 3 | 0 | 0 | 2 |
| 55 | deep reinforcement learning | 14 | 0 | 0 | 2 | 1 | 3 | 8 | 0 |
| 55 | developing countries | 14 | 2 | 0 | 11 | 1 | 0 | 0 | 2 |
| 55 | domain adaptation | 14 | 0 | 0 | 0 | 0 | 0 | 14 | 1 |
| 55 | firm size | 14 | 3 | 1 | 7 | 3 | 0 | 0 | 0 |
| 55 | foreign direct investment | 14 | 4 | 0 | 9 | 1 | 0 | 0 | 3 |
| 55 | human capital | 14 | 2 | 0 | 11 | 1 | 0 | 0 | 0 |
| 55 | mixed-integer linear programming | 14 | 0 | 0 | 0 | 0 | 12 | 2 | 1 |
| 55 | retrieval-augmented generation | 14 | 0 | 0 | 0 | 0 | 0 | 14 | 1 |
| 68 | anomaly detection | 13 | 1 | 0 | 0 | 1 | 0 | 11 | 0 |
| 68 | climate change | 13 | 3 | 0 | 5 | 1 | 3 | 1 | 1 |
| 68 | computational efficiency | 13 | 0 | 0 | 1 | 0 | 0 | 12 | 1 |
| 68 | entrepreneurship | 13 | 2 | 1 | 5 | 5 | 0 | 0 | 2 |
| 68 | inflation | 13 | 4 | 0 | 7 | 2 | 0 | 0 | 3 |
| 68 | macroprudential policy | 13 | 7 | 1 | 5 | 0 | 0 | 0 | 0 |
| 68 | supply chain resilience | 13 | 2 | 0 | 3 | 0 | 8 | 0 | 1 |
| 68 | trust | 13 | 2 | 0 | 6 | 5 | 0 | 0 | 1 |
| 76 | corporate environmental performance | 12 | 7 | 0 | 3 | 2 | 0 | 0 | 0 |
| 76 | cryptocurrency market | 12 | 10 | 0 | 2 | 0 | 0 | 0 | 0 |
| 76 | financial contagion | 12 | 9 | 0 | 3 | 0 | 0 | 0 | 0 |
| 76 | global value chains | 12 | 3 | 0 | 7 | 2 | 0 | 0 | 1 |
| 76 | listed companies | 12 | 3 | 0 | 5 | 4 | 0 | 0 | 2 |
| 76 | multi-objective optimization | 12 | 0 | 0 | 1 | 0 | 5 | 6 | 1 |
| 76 | multi-view clustering | 12 | 0 | 0 | 0 | 0 | 0 | 12 | 0 |
| 76 | portfolio optimization | 12 | 4 | 0 | 1 | 0 | 5 | 2 | 2 |
| 76 | systemic risk | 12 | 7 | 0 | 4 | 0 | 1 | 0 | 0 |
| 85 | ESG disclosure | 11 | 5 | 1 | 2 | 2 | 1 | 0 | 0 |
| 85 | carbon emission reduction | 11 | 3 | 0 | 1 | 0 | 7 | 0 | 0 |
| 85 | climate change mitigation | 11 | 2 | 0 | 7 | 2 | 0 | 0 | 1 |
| 85 | corporate innovation | 11 | 9 | 0 | 2 | 0 | 0 | 0 | 0 |
| 85 | digital infrastructure | 11 | 4 | 0 | 7 | 0 | 0 | 0 | 2 |
| 85 | dynamic capabilities | 11 | 0 | 0 | 1 | 10 | 0 | 0 | 3 |
| 85 | energy efficiency | 11 | 2 | 0 | 5 | 0 | 3 | 1 | 2 |
| 85 | fintech | 11 | 8 | 0 | 2 | 0 | 0 | 1 | 0 |
| 85 | image classification | 11 | 0 | 0 | 0 | 0 | 0 | 11 | 2 |
| 85 | institutional theory | 11 | 2 | 0 | 1 | 7 | 1 | 0 | 2 |
| 85 | machine learning | 11 | 4 | 0 | 0 | 0 | 0 | 7 | 1 |
| 85 | market microstructure | 11 | 10 | 0 | 1 | 0 | 0 | 0 | 2 |
| 85 | metaheuristic optimization | 11 | 0 | 0 | 0 | 1 | 3 | 7 | 2 |
| 85 | mixture of experts | 11 | 0 | 0 | 0 | 0 | 0 | 11 | 1 |
| 85 | model interpretability | 11 | 1 | 0 | 0 | 0 | 0 | 10 | 1 |
| 85 | multi-agent reinforcement learning | 11 | 0 | 0 | 0 | 0 | 2 | 9 | 2 |


---

---
title: "Top universities and research institutions"
description: "Top universities and research institutions in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days: Chinese Academy of Sciences (35), Xi'an Jiaotong University (34), Tsinghua University (32), Beijing Institute of Technology (31) and Southwestern University of Finance and Economics (30)."
url: https://scholar4ai.com/rankings/institutions
markdown_url: https://scholar4ai.com/rankings/institutions.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T11:39:12+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# Top universities and research institutions

A paper counts once for each institution its authors are affiliated with. Window: 2026-09-27 – 2026-10-26 (30 days). Showing the top 100 of 2,594; the full list is in /data/rankings.json.

## Key findings

- Top universities and research institutions in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days: Chinese Academy of Sciences (35), Xi'an Jiaotong University (34), Tsinghua University (32), Beijing Institute of Technology (31) and Southwestern University of Finance and Economics (30).
- 2,594 institutions appear in the window.

*Top universities and research institutions · 2026-09-27 – 2026-10-26 (30 days)*

| # | Institution | Country | Web domain | Papers | Finance | Accounting | Economics | Management | Operations Research | AI | Last 7 days |
|---:|---|---|---|---:|---:|---:|---:|---:|---:|---:|---:|
| 1 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | China | cas.cn | 35 | 2 | 0 | 1 | 1 | 1 | 30 | 5 |
| 2 | [Xi'an Jiaotong University](https://scholar4ai.com/institutions/xi-an-jiaotong-university.md) | China | xjtu.edu.cn | 34 | 7 | 0 | 4 | 4 | 6 | 13 | 2 |
| 3 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | China | tsinghua.edu.cn | 32 | 0 | 0 | 5 | 2 | 0 | 25 | 4 |
| 4 | [Beijing Institute of Technology](https://scholar4ai.com/institutions/beijing-institute-of-technology.md) | China | bit.edu.cn | 31 | 1 | 1 | 6 | 0 | 2 | 21 | 1 |
| 5 | [Southwestern University of Finance and Economics](https://scholar4ai.com/institutions/southwestern-university-of-finance-and-economics.md) | China | swufe.edu.cn | 30 | 11 | 0 | 6 | 2 | 3 | 8 | 1 |
| 5 | [Wuhan University](https://scholar4ai.com/institutions/wuhan-university.md) | China | whu.edu.cn | 30 | 4 | 1 | 3 | 0 | 1 | 21 | 1 |
| 7 | [Centre National de la Recherche Scientifique](https://scholar4ai.com/institutions/centre-national-de-la-recherche-scientifique.md) | France | cnrs.fr | 29 | 1 | 0 | 20 | 0 | 3 | 5 | 5 |
| 8 | [Beijing Jiaotong University](https://scholar4ai.com/institutions/beijing-jiaotong-university.md) | China | njtu.edu.cn | 26 | 1 | 0 | 2 | 0 | 6 | 17 | 1 |
| 8 | [University of Chinese Academy of Sciences](https://scholar4ai.com/institutions/university-of-chinese-academy-of-sciences.md) | China | ucas.ac.cn | 26 | 2 | 0 | 1 | 2 | 1 | 20 | 3 |
| 10 | [University of Electronic Science and Technology of China](https://scholar4ai.com/institutions/university-of-electronic-science-and-technology-of-china.md) | China | uestc.edu.cn | 25 | 0 | 1 | 0 | 1 | 1 | 22 | 1 |
| 11 | [Hong Kong Polytechnic University](https://scholar4ai.com/institutions/hong-kong-polytechnic-university.md) | Hong Kong | polyu.edu.hk | 24 | 1 | 0 | 3 | 1 | 7 | 12 | 0 |
| 11 | [Peking University](https://scholar4ai.com/institutions/peking-university.md) | China | pku.edu.cn | 24 | 3 | 1 | 12 | 1 | 0 | 7 | 2 |
| 11 | [Renmin University of China](https://scholar4ai.com/institutions/renmin-university-of-china.md) | China | ruc.edu.cn | 24 | 5 | 0 | 11 | 4 | 0 | 4 | 0 |
| 14 | [Tianjin University](https://scholar4ai.com/institutions/tianjin-university.md) | China | tju.edu.cn | 23 | 2 | 2 | 3 | 2 | 5 | 9 | 2 |
| 14 | [National University of Defense Technology](https://scholar4ai.com/institutions/national-university-of-defense-technology.md) | China | nudt.edu.cn | 23 | 0 | 0 | 0 | 0 | 1 | 22 | 0 |
| 14 | [Sichuan University](https://scholar4ai.com/institutions/sichuan-university.md) | China | scu.edu.cn | 23 | 3 | 1 | 4 | 3 | 1 | 11 | 0 |
| 17 | [Sun Yat-sen University](https://scholar4ai.com/institutions/sun-yat-sen-university.md) | China | sysu.edu.cn | 22 | 2 | 0 | 3 | 4 | 0 | 13 | 2 |
| 17 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | China | sjtu.edu.cn | 22 | 3 | 0 | 3 | 0 | 3 | 13 | 3 |
| 17 | [Harbin Institute of Technology](https://scholar4ai.com/institutions/harbin-institute-of-technology.md) | China | hit.edu.cn | 22 | 0 | 0 | 1 | 1 | 1 | 19 | 2 |
| 17 | [Fudan University](https://scholar4ai.com/institutions/fudan-university.md) | China | fudan.edu.cn | 22 | 4 | 0 | 4 | 1 | 1 | 12 | 5 |
| 17 | [Zhejiang University](https://scholar4ai.com/institutions/zhejiang-university.md) | China | zju.edu.cn | 22 | 1 | 1 | 4 | 2 | 2 | 12 | 5 |
| 17 | [Southeast University](https://scholar4ai.com/institutions/southeast-university.md) | China | seu.edu.cn | 22 | 2 | 0 | 3 | 0 | 1 | 16 | 2 |
| 23 | Tongji University | China | tongji.edu.cn | 21 | 2 | 0 | 1 | 4 | 4 | 10 | 1 |
| 23 | Shanghai University of Finance and Economics | China | sufe.edu.cn | 21 | 7 | 1 | 6 | 3 | 2 | 2 | 2 |
| 25 | Huazhong University of Science and Technology | China | hust.edu.cn | 20 | 3 | 1 | 3 | 2 | 1 | 10 | 3 |
| 25 | Beihang University | China | buaa.edu.cn | 20 | 0 | 0 | 3 | 0 | 9 | 8 | 0 |
| 27 | Central University of Finance and Economics | China | cufe.edu.cn | 19 | 11 | 1 | 6 | 1 | 0 | 0 | 1 |
| 27 | Jinan University | China | jnu.edu.cn | 19 | 5 | 0 | 7 | 1 | 0 | 6 | 2 |
| 27 | Xiamen University | China | xmu.edu.cn | 19 | 6 | 1 | 1 | 3 | 2 | 6 | 3 |
| 27 | Nanjing University | China | nju.edu.cn | 19 | 4 | 0 | 2 | 2 | 0 | 11 | 0 |
| 31 | Northwestern Polytechnical University | China | nwpu.edu.cn | 18 | 0 | 0 | 0 | 2 | 4 | 12 | 0 |
| 31 | Dalian University of Technology | China | dlut.edu.cn | 18 | 1 | 0 | 1 | 0 | 6 | 10 | 2 |
| 31 | Southwest Jiaotong University | China | swjtu.edu.cn | 18 | 1 | 0 | 1 | 1 | 8 | 7 | 1 |
| 31 | Northeastern University | China | neu.edu.cn | 18 | 0 | 0 | 0 | 0 | 2 | 16 | 2 |
| 35 | Central South University | China | csu.edu.cn | 17 | 1 | 0 | 2 | 1 | 1 | 12 | 1 |
| 35 | Xidian University | China | xidian.edu.cn | 17 | 0 | 0 | 2 | 0 | 0 | 15 | 0 |
| 35 | National University of Singapore | Singapore | nus.edu.sg | 17 | 1 | 0 | 5 | 0 | 2 | 9 | 1 |
| 35 | Fuzhou University | China | fzu.edu.cn | 17 | 1 | 0 | 0 | 1 | 4 | 11 | 0 |
| 39 | University of International Business and Economics | China | uibe.edu.cn | 16 | 9 | 0 | 4 | 2 | 1 | 0 | 0 |
| 40 | Shanghai University | China | shu.edu.cn | 15 | 4 | 0 | 1 | 1 | 1 | 8 | 0 |
| 40 | Anhui University | China | ahu.edu.cn | 15 | 0 | 0 | 1 | 2 | 0 | 12 | 1 |
| 40 | Chongqing University | China | cqu.edu.cn | 15 | 3 | 1 | 2 | 1 | 1 | 7 | 0 |
| 40 | Hohai University | China | hhu.edu.cn | 15 | 2 | 0 | 1 | 1 | 0 | 11 | 0 |
| 40 | Hunan University | China | hnu.edu.cn | 15 | 5 | 0 | 0 | 1 | 0 | 9 | 0 |
| 40 | Nanjing University of Science and Technology | China | njust.edu.cn | 15 | 1 | 1 | 2 | 0 | 0 | 11 | 1 |
| 40 | City University of Macau | Macao | cityu.edu.mo | 15 | 7 | 0 | 4 | 1 | 1 | 2 | 0 |
| 40 | South China University of Technology | China | scut.edu.cn | 15 | 1 | 0 | 0 | 1 | 1 | 12 | 2 |
| 40 | University of Science and Technology Beijing | China | ustb.edu.cn | 15 | 0 | 1 | 0 | 4 | 3 | 7 | 1 |
| 49 | The University of Sydney | Australia | sydney.edu.au | 14 | 1 | 1 | 1 | 3 | 2 | 6 | 2 |
| 49 | Shandong University | China | sdu.edu.cn | 14 | 1 | 0 | 2 | 1 | 1 | 9 | 0 |
| 49 | Jilin University | China | jlu.edu.cn | 14 | 2 | 0 | 2 | 1 | 0 | 9 | 1 |
| 49 | Lanzhou University | China | lzu.edu.cn | 14 | 3 | 0 | 3 | 1 | 1 | 6 | 2 |
| 53 | University of Science and Technology of China | China | ustc.edu.cn | 13 | 0 | 0 | 1 | 0 | 5 | 7 | 1 |
| 53 | Chinese University of Hong Kong | Hong Kong | cuhk.edu.hk | 13 | 2 | 0 | 5 | 0 | 0 | 6 | 1 |
| 53 | Shenzhen University | China | szu.edu.cn | 13 | 2 | 0 | 0 | 0 | 2 | 9 | 2 |
| 53 | Nankai University | China | nankai.edu.cn | 13 | 2 | 0 | 5 | 2 | 0 | 4 | 1 |
| 53 | Centre for Economic Policy Research | United Kingdom | cepr.org | 13 | 1 | 0 | 12 | 0 | 0 | 0 | 2 |
| 53 | Shandong University of Finance and Economics | China | sdufe.edu.cn | 13 | 5 | 1 | 2 | 1 | 2 | 2 | 0 |
| 59 | Dongbei University of Finance and Economics | China | dufe.edu.cn | 12 | 2 | 0 | 3 | 1 | 4 | 2 | 3 |
| 59 | Guangdong University of Technology | China | gdut.edu.cn | 12 | 2 | 0 | 0 | 0 | 0 | 10 | 0 |
| 59 | Yonsei University | South Korea | yonsei.ac.kr | 12 | 0 | 1 | 4 | 3 | 1 | 3 | 0 |
| 59 | Korea University | South Korea | korea.ac.kr | 12 | 1 | 0 | 7 | 1 | 0 | 3 | 2 |
| 59 | University of Macau | Macao | um.edu.mo | 12 | 4 | 0 | 3 | 1 | 0 | 4 | 1 |
| 59 | Monash University | Australia | monash.edu | 12 | 2 | 0 | 2 | 3 | 0 | 5 | 0 |
| 59 | East China Normal University | China | ecnu.edu.cn | 12 | 2 | 0 | 2 | 2 | 1 | 5 | 0 |
| 59 | Capital University of Economics and Business | China | cueb.edu.cn | 12 | 6 | 1 | 3 | 0 | 0 | 2 | 0 |
| 67 | Chongqing University of Posts and Telecommunications | China | cqupt.edu.cn | 11 | 1 | 0 | 0 | 0 | 2 | 8 | 2 |
| 67 | Qingdao University | China | qdu.edu.cn | 11 | 3 | 0 | 2 | 1 | 3 | 2 | 1 |
| 67 | Beijing University of Posts and Telecommunications | China | bupt.edu.cn | 11 | 0 | 0 | 1 | 2 | 0 | 8 | 2 |
| 67 | Zhongnan University of Economics and Law | China | zuel.edu.cn | 11 | 3 | 0 | 5 | 3 | 0 | 0 | 1 |
| 67 | Hefei University of Technology | China | hfut.edu.cn | 11 | 1 | 0 | 0 | 0 | 4 | 6 | 0 |
| 67 | City University of Hong Kong | Hong Kong | cityu.edu.hk | 11 | 2 | 0 | 1 | 1 | 1 | 6 | 2 |
| 67 | Nanyang Technological University | Singapore | ntu.edu.sg | 11 | 0 | 0 | 1 | 0 | 3 | 7 | 2 |
| 67 | Hunan Normal University | China | hunnu.edu.cn | 11 | 3 | 0 | 3 | 2 | 0 | 3 | 2 |
| 67 | University of Cambridge | United Kingdom | cam.ac.uk | 11 | 1 | 0 | 9 | 0 | 0 | 1 | 1 |
| 67 | UNSW Sydney | Australia | unsw.edu.au | 11 | 1 | 1 | 5 | 1 | 1 | 2 | 0 |
| 67 | China University of Petroleum, East China | China | upc.edu.cn | 11 | 1 | 0 | 2 | 1 | 0 | 7 | 0 |
| 67 | Xi’an Jiaotong-Liverpool University | China | xjtlu.edu.cn | 11 | 2 | 1 | 1 | 3 | 1 | 3 | 0 |
| 67 | University of Hong Kong | Hong Kong | hku.hk | 11 | 0 | 0 | 2 | 1 | 2 | 6 | 1 |
| 80 | King Fahd University of Petroleum and Minerals | Saudi Arabia | kfupm.edu.sa | 10 | 2 | 1 | 0 | 1 | 1 | 5 | 0 |
| 80 | Wuhan University of Technology | China | whut.edu.cn | 10 | 3 | 0 | 0 | 1 | 1 | 5 | 2 |
| 80 | Guangdong University Of Finances and Economics | China | gdufe.edu.cn | 10 | 5 | 0 | 5 | 0 | 0 | 0 | 1 |
| 80 | Hainan University | China | hainanu.edu.cn | 10 | 2 | 0 | 1 | 0 | 0 | 7 | 1 |
| 80 | Beijing Normal University | China | bnu.edu.cn | 10 | 1 | 0 | 4 | 1 | 0 | 4 | 0 |
| 80 | China University of Geosciences | China | cug.edu.cn | 10 | 2 | 0 | 2 | 1 | 1 | 4 | 0 |
| 80 | Xi'an University of Technology | China | xaut.edu.cn | 10 | 4 | 1 | 1 | 0 | 1 | 3 | 1 |
| 80 | University College London | United Kingdom | ucl.ac.uk | 10 | 2 | 0 | 4 | 1 | 2 | 1 | 1 |
| 88 | National Bureau of Economic Research | United States | nber.org | 9 | 2 | 0 | 7 | 0 | 0 | 0 | 1 |
| 88 | BI Norwegian Business School | Norway | bi.no | 9 | 1 | 0 | 0 | 7 | 1 | 0 | 0 |
| 88 | Shandong Normal University | China | sdnu.edu.cn | 9 | 1 | 0 | 0 | 1 | 0 | 7 | 1 |
| 88 | Central China Normal University | China | ccnu.edu.cn | 9 | 1 | 0 | 0 | 0 | 1 | 7 | 0 |
| 88 | Ocean University of China | China | ouc.edu.cn | 9 | 2 | 1 | 0 | 0 | 1 | 5 | 1 |
| 88 | Zhejiang University of Finance and Economics | China | zufe.edu.cn | 9 | 3 | 0 | 1 | 3 | 1 | 1 | 1 |
| 88 | University of Bologna | Italy | unibo.it | 9 | 0 | 0 | 2 | 2 | 1 | 4 | 2 |
| 88 | Macquarie University | Australia | mq.edu.au | 9 | 3 | 0 | 1 | 2 | 1 | 2 | 1 |
| 88 | KU Leuven | Belgium | kuleuven.be | 9 | 3 | 0 | 2 | 1 | 3 | 0 | 0 |
| 97 | University of Pisa | Italy | unipi.it | 8 | 2 | 0 | 5 | 1 | 0 | 0 | 2 |
| 97 | Macau University of Science and Technology | Macao | must.edu.mo | 8 | 1 | 1 | 0 | 1 | 0 | 5 | 1 |
| 97 | Australian National University | Australia | anu.edu.au | 8 | 2 | 0 | 5 | 0 | 0 | 1 | 1 |
| 97 | Chongqing Normal University | China | cqnu.edu.cn | 8 | 0 | 0 | 0 | 0 | 1 | 7 | 0 |


---

---
title: "Top institution web domains: finance, economics, AI"
description: "Top institution web domains in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days: cas.cn (42), xjtu.edu.cn (34), tsinghua.edu.cn (33), bit.edu.cn (31) and cnrs.fr (31)."
url: https://scholar4ai.com/rankings/domains
markdown_url: https://scholar4ai.com/rankings/domains.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T11:39:12+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# Top institution web domains

Institutions are grouped by the web domain of their official site (OpenAlex / ROR data). A paper counts once for each domain among its authors' institutions. Window: 2026-09-27 – 2026-10-26 (30 days). Showing the top 100 of 2,425; the full list is in /data/rankings.json.

## Key findings

- Top institution web domains in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days: cas.cn (42), xjtu.edu.cn (34), tsinghua.edu.cn (33), bit.edu.cn (31) and cnrs.fr (31).
- 2,425 domains appear in the window.

*Top institution web domains · 2026-09-27 – 2026-10-26 (30 days)*

| # | Web domain | Suffix | Institutions | Papers | Finance | Accounting | Economics | Management | Operations Research | AI | Last 7 days |
|---:|---|---|---|---:|---:|---:|---:|---:|---:|---:|---:|
| 1 | [cas.cn](https://scholar4ai.com/domains/cas.cn.md) | cn | Chinese Academy of Sciences, Institute of Automation, Changchun Institute of Optics, Fine Mechanics and Physics … | 42 | 2 | 0 | 1 | 1 | 1 | 37 | 5 |
| 2 | [xjtu.edu.cn](https://scholar4ai.com/domains/xjtu.edu.cn.md) | edu.cn | Xi'an Jiaotong University | 34 | 7 | 0 | 4 | 4 | 6 | 13 | 2 |
| 3 | [tsinghua.edu.cn](https://scholar4ai.com/domains/tsinghua.edu.cn.md) | edu.cn | Tsinghua University, Tsinghua Shenzhen International Graduate School, Beijing National Research Center for Information Science and Technology | 33 | 0 | 0 | 5 | 2 | 0 | 26 | 4 |
| 4 | [bit.edu.cn](https://scholar4ai.com/domains/bit.edu.cn.md) | edu.cn | Beijing Institute of Technology | 31 | 1 | 1 | 6 | 0 | 2 | 21 | 1 |
| 4 | [cnrs.fr](https://scholar4ai.com/domains/cnrs.fr.md) | fr | Centre National de la Recherche Scientifique, Groupe d'Analyse et de Théorie Economique Lyon St Etienne, Centre Interuniversitaire de Recherche et d’Ingénierie des Matériaux … | 31 | 1 | 0 | 21 | 0 | 3 | 6 | 7 |
| 4 | [whu.edu.cn](https://scholar4ai.com/domains/whu.edu.cn.md) | edu.cn | Wuhan University, State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing | 31 | 4 | 1 | 3 | 0 | 1 | 22 | 1 |
| 7 | [swufe.edu.cn](https://scholar4ai.com/domains/swufe.edu.cn.md) | edu.cn | Southwestern University of Finance and Economics | 30 | 11 | 0 | 6 | 2 | 3 | 8 | 1 |
| 8 | [njtu.edu.cn](https://scholar4ai.com/domains/njtu.edu.cn.md) | edu.cn | Beijing Jiaotong University | 26 | 1 | 0 | 2 | 0 | 6 | 17 | 1 |
| 8 | [ucas.ac.cn](https://scholar4ai.com/domains/ucas.ac.cn.md) | ac.cn | University of Chinese Academy of Sciences | 26 | 2 | 0 | 1 | 2 | 1 | 20 | 3 |
| 10 | [uestc.edu.cn](https://scholar4ai.com/domains/uestc.edu.cn.md) | edu.cn | University of Electronic Science and Technology of China | 25 | 0 | 1 | 0 | 1 | 1 | 22 | 1 |
| 11 | [pku.edu.cn](https://scholar4ai.com/domains/pku.edu.cn.md) | edu.cn | Peking University | 24 | 3 | 1 | 12 | 1 | 0 | 7 | 2 |
| 11 | [polyu.edu.hk](https://scholar4ai.com/domains/polyu.edu.hk.md) | edu.hk | Hong Kong Polytechnic University | 24 | 1 | 0 | 3 | 1 | 7 | 12 | 0 |
| 11 | [ruc.edu.cn](https://scholar4ai.com/domains/ruc.edu.cn.md) | edu.cn | Renmin University of China | 24 | 5 | 0 | 11 | 4 | 0 | 4 | 0 |
| 14 | [nudt.edu.cn](https://scholar4ai.com/domains/nudt.edu.cn.md) | edu.cn | National University of Defense Technology | 23 | 0 | 0 | 0 | 0 | 1 | 22 | 0 |
| 14 | [scu.edu.cn](https://scholar4ai.com/domains/scu.edu.cn.md) | edu.cn | Sichuan University | 23 | 3 | 1 | 4 | 3 | 1 | 11 | 0 |
| 14 | [seu.edu.cn](https://scholar4ai.com/domains/seu.edu.cn.md) | edu.cn | Southeast University, Jiangsu Provincial Key Laboratory of Network and Information Security | 23 | 2 | 0 | 3 | 0 | 1 | 17 | 2 |
| 14 | [tju.edu.cn](https://scholar4ai.com/domains/tju.edu.cn.md) | edu.cn | Tianjin University | 23 | 2 | 2 | 3 | 2 | 5 | 9 | 2 |
| 14 | [tongji.edu.cn](https://scholar4ai.com/domains/tongji.edu.cn.md) | edu.cn | Tongji University, State Key Laboratory of Pollution Control and Resource Reuse | 23 | 2 | 0 | 3 | 4 | 4 | 10 | 1 |
| 19 | [fudan.edu.cn](https://scholar4ai.com/domains/fudan.edu.cn.md) | edu.cn | Fudan University, State Key Laboratory of Surface Physics, Shanghai Medical College of Fudan University | 22 | 4 | 0 | 4 | 1 | 1 | 12 | 5 |
| 19 | [hit.edu.cn](https://scholar4ai.com/domains/hit.edu.cn.md) | edu.cn | Harbin Institute of Technology | 22 | 0 | 0 | 1 | 1 | 1 | 19 | 2 |
| 19 | [sjtu.edu.cn](https://scholar4ai.com/domains/sjtu.edu.cn.md) | edu.cn | Shanghai Jiao Tong University | 22 | 3 | 0 | 3 | 0 | 3 | 13 | 3 |
| 19 | [sysu.edu.cn](https://scholar4ai.com/domains/sysu.edu.cn.md) | edu.cn | Sun Yat-sen University, Guangdong Province Key Laboratory of Information Security Technology | 22 | 2 | 0 | 3 | 4 | 0 | 13 | 2 |
| 19 | [zju.edu.cn](https://scholar4ai.com/domains/zju.edu.cn.md) | edu.cn | Zhejiang University, State Key Laboratory Fluid Power and Mechatronic Systems | 22 | 1 | 1 | 4 | 2 | 2 | 12 | 5 |
| 24 | sufe.edu.cn | edu.cn | Shanghai University of Finance and Economics | 21 | 7 | 1 | 6 | 3 | 2 | 2 | 2 |
| 25 | buaa.edu.cn | edu.cn | Beihang University | 20 | 0 | 0 | 3 | 0 | 9 | 8 | 0 |
| 25 | hust.edu.cn | edu.cn | Huazhong University of Science and Technology | 20 | 3 | 1 | 3 | 2 | 1 | 10 | 3 |
| 27 | cufe.edu.cn | edu.cn | Central University of Finance and Economics | 19 | 11 | 1 | 6 | 1 | 0 | 0 | 1 |
| 27 | jnu.edu.cn | edu.cn | Jinan University | 19 | 5 | 0 | 7 | 1 | 0 | 6 | 2 |
| 27 | nju.edu.cn | edu.cn | Nanjing University | 19 | 4 | 0 | 2 | 2 | 0 | 11 | 0 |
| 27 | xmu.edu.cn | edu.cn | Xiamen University | 19 | 6 | 1 | 1 | 3 | 2 | 6 | 3 |
| 31 | dlut.edu.cn | edu.cn | Dalian University of Technology | 18 | 1 | 0 | 1 | 0 | 6 | 10 | 2 |
| 31 | neu.edu.cn | edu.cn | Northeastern University | 18 | 0 | 0 | 0 | 0 | 2 | 16 | 2 |
| 31 | nwpu.edu.cn | edu.cn | Northwestern Polytechnical University | 18 | 0 | 0 | 0 | 2 | 4 | 12 | 0 |
| 31 | swjtu.edu.cn | edu.cn | Southwest Jiaotong University | 18 | 1 | 0 | 1 | 1 | 8 | 7 | 1 |
| 35 | csu.edu.cn | edu.cn | Central South University | 17 | 1 | 0 | 2 | 1 | 1 | 12 | 1 |
| 35 | fzu.edu.cn | edu.cn | Fuzhou University | 17 | 1 | 0 | 0 | 1 | 4 | 11 | 0 |
| 35 | nus.edu.sg | edu.sg | National University of Singapore, Energy Studies Institute, Institute of Data Science … | 17 | 1 | 0 | 5 | 0 | 2 | 9 | 1 |
| 35 | xidian.edu.cn | edu.cn | Xidian University | 17 | 0 | 0 | 2 | 0 | 0 | 15 | 0 |
| 39 | uibe.edu.cn | edu.cn | University of International Business and Economics | 16 | 9 | 0 | 4 | 2 | 1 | 0 | 0 |
| 40 | ahu.edu.cn | edu.cn | Anhui University | 15 | 0 | 0 | 1 | 2 | 0 | 12 | 1 |
| 40 | cityu.edu.mo | edu.mo | City University of Macau | 15 | 7 | 0 | 4 | 1 | 1 | 2 | 0 |
| 40 | cqu.edu.cn | edu.cn | Chongqing University | 15 | 3 | 1 | 2 | 1 | 1 | 7 | 0 |
| 40 | hhu.edu.cn | edu.cn | Hohai University | 15 | 2 | 0 | 1 | 1 | 0 | 11 | 0 |
| 40 | hnu.edu.cn | edu.cn | Hunan University | 15 | 5 | 0 | 0 | 1 | 0 | 9 | 0 |
| 40 | njust.edu.cn | edu.cn | Nanjing University of Science and Technology, National Key Laboratory of Transient Physics | 15 | 1 | 1 | 2 | 0 | 0 | 11 | 1 |
| 40 | scut.edu.cn | edu.cn | South China University of Technology | 15 | 1 | 0 | 0 | 1 | 1 | 12 | 2 |
| 40 | shu.edu.cn | edu.cn | Shanghai University | 15 | 4 | 0 | 1 | 1 | 1 | 8 | 0 |
| 40 | ustb.edu.cn | edu.cn | University of Science and Technology Beijing | 15 | 0 | 1 | 0 | 4 | 3 | 7 | 1 |
| 49 | jlu.edu.cn | edu.cn | Jilin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University | 14 | 2 | 0 | 2 | 1 | 0 | 9 | 1 |
| 49 | lzu.edu.cn | edu.cn | Lanzhou University | 14 | 3 | 0 | 3 | 1 | 1 | 6 | 2 |
| 49 | sdu.edu.cn | edu.cn | Shandong University | 14 | 1 | 0 | 2 | 1 | 1 | 9 | 0 |
| 49 | sydney.edu.au | edu.au | The University of Sydney | 14 | 1 | 1 | 1 | 3 | 2 | 6 | 2 |
| 53 | cepr.org | org | Centre for Economic Policy Research | 13 | 1 | 0 | 12 | 0 | 0 | 0 | 2 |
| 53 | cuhk.edu.hk | edu.hk | Chinese University of Hong Kong | 13 | 2 | 0 | 5 | 0 | 0 | 6 | 1 |
| 53 | nankai.edu.cn | edu.cn | Nankai University | 13 | 2 | 0 | 5 | 2 | 0 | 4 | 1 |
| 53 | sdufe.edu.cn | edu.cn | Shandong University of Finance and Economics | 13 | 5 | 1 | 2 | 1 | 2 | 2 | 0 |
| 53 | szu.edu.cn | edu.cn | Shenzhen University, Shenzhen University Health Science Center | 13 | 2 | 0 | 0 | 0 | 2 | 9 | 2 |
| 53 | ustc.edu.cn | edu.cn | University of Science and Technology of China | 13 | 0 | 0 | 1 | 0 | 5 | 7 | 1 |
| 59 | cueb.edu.cn | edu.cn | Capital University of Economics and Business | 12 | 6 | 1 | 3 | 0 | 0 | 2 | 0 |
| 59 | dufe.edu.cn | edu.cn | Dongbei University of Finance and Economics | 12 | 2 | 0 | 3 | 1 | 4 | 2 | 3 |
| 59 | ecnu.edu.cn | edu.cn | East China Normal University | 12 | 2 | 0 | 2 | 2 | 1 | 5 | 0 |
| 59 | gdut.edu.cn | edu.cn | Guangdong University of Technology | 12 | 2 | 0 | 0 | 0 | 0 | 10 | 0 |
| 59 | korea.ac.kr | ac.kr | Korea University | 12 | 1 | 0 | 7 | 1 | 0 | 3 | 2 |
| 59 | monash.edu | edu | Monash University | 12 | 2 | 0 | 2 | 3 | 0 | 5 | 0 |
| 59 | um.edu.mo | edu.mo | University of Macau | 12 | 4 | 0 | 3 | 1 | 0 | 4 | 1 |
| 59 | unsw.edu.au | edu.au | UNSW Sydney, Kirby Institute, UNSW Canberra | 12 | 1 | 1 | 5 | 1 | 2 | 2 | 0 |
| 59 | yonsei.ac.kr | ac.kr | Yonsei University, Yonsei University Mirae Campus | 12 | 0 | 1 | 4 | 3 | 1 | 3 | 0 |
| 68 | bupt.edu.cn | edu.cn | Beijing University of Posts and Telecommunications | 11 | 0 | 0 | 1 | 2 | 0 | 8 | 2 |
| 68 | cam.ac.uk | ac.uk | University of Cambridge | 11 | 1 | 0 | 9 | 0 | 0 | 1 | 1 |
| 68 | cityu.edu.hk | edu.hk | City University of Hong Kong | 11 | 2 | 0 | 1 | 1 | 1 | 6 | 2 |
| 68 | cqupt.edu.cn | edu.cn | Chongqing University of Posts and Telecommunications | 11 | 1 | 0 | 0 | 0 | 2 | 8 | 2 |
| 68 | hfut.edu.cn | edu.cn | Hefei University of Technology | 11 | 1 | 0 | 0 | 0 | 4 | 6 | 0 |
| 68 | hku.hk | hk | University of Hong Kong | 11 | 0 | 0 | 2 | 1 | 2 | 6 | 1 |
| 68 | hunnu.edu.cn | edu.cn | Hunan Normal University, Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education) | 11 | 3 | 0 | 3 | 2 | 0 | 3 | 2 |
| 68 | ntu.edu.sg | edu.sg | Nanyang Technological University | 11 | 0 | 0 | 1 | 0 | 3 | 7 | 2 |
| 68 | qdu.edu.cn | edu.cn | Qingdao University | 11 | 3 | 0 | 2 | 1 | 3 | 2 | 1 |
| 68 | rnu.tn | tn | University of Sousse, Manouba University, University of Kairouan … | 11 | 1 | 2 | 5 | 1 | 0 | 2 | 1 |
| 68 | upc.edu.cn | edu.cn | China University of Petroleum, East China | 11 | 1 | 0 | 2 | 1 | 0 | 7 | 0 |
| 68 | worldbank.org | org | World Bank, World Bank Group | 11 | 1 | 0 | 10 | 0 | 0 | 0 | 1 |
| 68 | xjtlu.edu.cn | edu.cn | Xi’an Jiaotong-Liverpool University | 11 | 2 | 1 | 1 | 3 | 1 | 3 | 0 |
| 68 | zuel.edu.cn | edu.cn | Zhongnan University of Economics and Law | 11 | 3 | 0 | 5 | 3 | 0 | 0 | 1 |
| 82 | bnu.edu.cn | edu.cn | Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning | 10 | 1 | 0 | 4 | 1 | 0 | 4 | 0 |
| 82 | cug.edu.cn | edu.cn | China University of Geosciences | 10 | 2 | 0 | 2 | 1 | 1 | 4 | 0 |
| 82 | gdufe.edu.cn | edu.cn | Guangdong University Of Finances and Economics | 10 | 5 | 0 | 5 | 0 | 0 | 0 | 1 |
| 82 | hainanu.edu.cn | edu.cn | Hainan University | 10 | 2 | 0 | 1 | 0 | 0 | 7 | 1 |
| 82 | kfupm.edu.sa | edu.sa | King Fahd University of Petroleum and Minerals | 10 | 2 | 1 | 0 | 1 | 1 | 5 | 0 |
| 82 | ucl.ac.uk | ac.uk | University College London | 10 | 2 | 0 | 4 | 1 | 2 | 1 | 1 |
| 82 | whut.edu.cn | edu.cn | Wuhan University of Technology | 10 | 3 | 0 | 0 | 1 | 1 | 5 | 2 |
| 82 | xaut.edu.cn | edu.cn | Xi'an University of Technology | 10 | 4 | 1 | 1 | 0 | 1 | 3 | 1 |
| 90 | bi.no | no | BI Norwegian Business School | 9 | 1 | 0 | 0 | 7 | 1 | 0 | 0 |
| 90 | ccnu.edu.cn | edu.cn | Central China Normal University | 9 | 1 | 0 | 0 | 0 | 1 | 7 | 0 |
| 90 | kuleuven.be | be | KU Leuven | 9 | 3 | 0 | 2 | 1 | 3 | 0 | 0 |
| 90 | mq.edu.au | edu.au | Macquarie University | 9 | 3 | 0 | 1 | 2 | 1 | 2 | 1 |
| 90 | nber.org | org | National Bureau of Economic Research | 9 | 2 | 0 | 7 | 0 | 0 | 0 | 1 |
| 90 | ouc.edu.cn | edu.cn | Ocean University of China | 9 | 2 | 1 | 0 | 0 | 1 | 5 | 1 |
| 90 | sdnu.edu.cn | edu.cn | Shandong Normal University | 9 | 1 | 0 | 0 | 1 | 0 | 7 | 1 |
| 90 | unibo.it | it | University of Bologna | 9 | 0 | 0 | 2 | 2 | 1 | 4 | 2 |
| 90 | zufe.edu.cn | edu.cn | Zhejiang University of Finance and Economics | 9 | 3 | 0 | 1 | 3 | 1 | 1 | 1 |
| 99 | anu.edu.au | edu.au | Australian National University | 8 | 2 | 0 | 5 | 0 | 0 | 1 | 1 |
| 99 | cbs.dk | dk | Copenhagen Business School | 8 | 2 | 0 | 2 | 4 | 0 | 0 | 1 |

## By domain suffix

*Papers by domain suffix*

| # | Suffix | Domains | Papers | Last 7 days |
|---:|---|---:|---:|---:|
| 1 | edu.cn | 469 | 1,276 | 83 |
| 2 | edu | 306 | 396 | 44 |
| 3 | ac.uk | 86 | 177 | 18 |
| 4 | it | 69 | 117 | 12 |
| 5 | com | 99 | 115 | 12 |
| 6 | org | 64 | 102 | 9 |
| 7 | edu.au | 32 | 99 | 10 |
| 8 | cn | 47 | 93 | 10 |
| 8 | de | 76 | 93 | 8 |
| 10 | ac.kr | 41 | 78 | 7 |
| 11 | ac.in | 75 | 77 | 8 |
| 12 | fr | 81 | 73 | 15 |
| 13 | edu.tr | 59 | 69 | 11 |
| 14 | ca | 39 | 66 | 6 |
| 15 | edu.hk | 8 | 64 | 5 |
| 16 | es | 34 | 54 | 7 |
| 17 | ac.cn | 18 | 47 | 3 |
| 18 | edu.sg | 6 | 43 | 4 |
| 19 | ac.jp | 35 | 42 | 5 |
| 20 | edu.mo | 5 | 36 | 2 |
| 20 | eu | 13 | 36 | 5 |
| 22 | nl | 19 | 32 | 3 |
| 23 | ch | 19 | 30 | 4 |
| 24 | edu.sa | 15 | 29 | 4 |
| 25 | edu.my | 22 | 28 | 2 |
| 25 | gov.cn | 19 | 28 | 1 |
| 27 | ac.ae | 14 | 27 | 4 |
| 27 | se | 23 | 27 | 4 |
| 29 | dk | 8 | 25 | 3 |
| 29 | edu.in | 20 | 25 | 6 |
| 29 | edu.pk | 29 | 25 | 2 |
| 32 | com.cn | 22 | 24 | 1 |
| 33 | ac.ir | 23 | 23 | 2 |
| 34 | be | 11 | 22 | 2 |
| 34 | edu.vn | 20 | 22 | 1 |
| 34 | no | 11 | 22 | 4 |
| 37 | ac.nz | 9 | 21 | 2 |
| 38 | fi | 11 | 20 | 3 |
| 38 | gr | 13 | 20 | 1 |
| 40 | edu.tw | 18 | 19 | 1 |
| 41 | cl | 18 | 18 | 2 |
| 42 | ac.za | 7 | 17 | 1 |
| 42 | br | 16 | 17 | 3 |
| 42 | pt | 17 | 17 | 3 |
| 45 | ie | 10 | 15 | 2 |
| 45 | org.cn | 13 | 15 | 1 |
| 47 | edu.eg | 15 | 14 | 2 |
| 48 | ac.at | 5 | 13 | 2 |
| 48 | hu | 4 | 13 | 2 |
| 50 | edu.az | 7 | 12 | 2 |
| 50 | tn | 4 | 12 | 1 |
| 52 | hk | 1 | 11 | 1 |
| 52 | net | 11 | 11 | 0 |
| 54 | gov | 5 | 10 | 1 |
| 54 | lu | 3 | 10 | 1 |
| 56 | ac.il | 7 | 9 | 0 |
| 56 | cat | 6 | 9 | 0 |
| 56 | edu.bd | 4 | 9 | 1 |
| 59 | pl | 6 | 8 | 1 |
| 60 | ac.ma | 4 | 7 | 0 |
| 60 | edu.jo | 7 | 7 | 0 |
| 60 | edu.kw | 4 | 7 | 1 |
| 60 | edu.lb | 6 | 7 | 1 |
| 60 | edu.pe | 4 | 7 | 2 |
| 60 | ro | 9 | 7 | 2 |
| 60 | ru | 6 | 7 | 0 |
| 67 | ac.bd | 6 | 6 | 1 |
| 67 | ac.th | 3 | 6 | 0 |
| 67 | at | 4 | 6 | 1 |
| 67 | cz | 5 | 6 | 0 |
| 67 | edu.ar | 3 | 6 | 0 |
| 67 | edu.ng | 12 | 6 | 0 |
| 67 | edu.om | 2 | 6 | 0 |
| 67 | edu.pl | 5 | 6 | 2 |
| 67 | paris | 1 | 6 | 0 |
| 67 | si | 4 | 6 | 0 |
| 77 | ac.cy | 3 | 5 | 0 |
| 77 | ac.id | 5 | 5 | 0 |
| 77 | dz | 4 | 5 | 1 |
| 77 | edu.co | 5 | 5 | 1 |
| 77 | in | 5 | 5 | 1 |
| 77 | mx | 4 | 5 | 1 |
| 77 | my | 3 | 5 | 0 |
| 77 | net.cn | 3 | 5 | 1 |
| 77 | sk | 5 | 5 | 0 |
| 86 | ac | 3 | 4 | 1 |
| 86 | cm | 4 | 4 | 0 |
| 86 | co.uk | 3 | 4 | 0 |
| 86 | edu.gh | 3 | 4 | 2 |
| 86 | edu.iq | 4 | 4 | 0 |
| 86 | gov.ar | 1 | 4 | 0 |
| 86 | hr | 1 | 4 | 0 |
| 86 | jp | 2 | 4 | 0 |
| 86 | kr | 2 | 4 | 0 |
| 86 | ma | 3 | 4 | 1 |
| 86 | org.au | 4 | 4 | 1 |
| 86 | university | 3 | 4 | 0 |
| 98 | edu.br | 3 | 3 | 1 |
| 98 | edu.dz | 2 | 3 | 0 |
| 98 | edu.mx | 3 | 3 | 0 |
| 98 | eus | 2 | 3 | 0 |
| 98 | gal | 3 | 3 | 0 |
| 98 | ir | 3 | 3 | 1 |
| 98 | uk | 2 | 3 | 0 |
| 98 | uz | 5 | 3 | 1 |
| 98 | vn | 2 | 3 | 0 |
| 107 | ac.lk | 2 | 2 | 1 |
| 107 | ac.tz | 3 | 2 | 0 |
| 107 | ae | 2 | 2 | 0 |
| 107 | berlin | 2 | 2 | 0 |
| 107 | bg | 2 | 2 | 1 |
| 107 | edu.bh | 2 | 2 | 0 |
| 107 | edu.ec | 2 | 2 | 0 |
| 107 | edu.et | 2 | 2 | 0 |
| 107 | edu.kz | 2 | 2 | 0 |
| 107 | edu.ly | 2 | 2 | 0 |
| 107 | edu.ph | 2 | 2 | 0 |
| 107 | edu.ps | 2 | 2 | 1 |
| 107 | edu.qa | 2 | 2 | 0 |
| 107 | edu.ye | 5 | 2 | 0 |
| 107 | ee | 2 | 2 | 0 |
| 107 | eg | 1 | 2 | 0 |
| 107 | global | 1 | 2 | 0 |
| 107 | int | 2 | 2 | 1 |
| 107 | kz | 1 | 2 | 0 |
| 107 | li | 2 | 2 | 0 |
| 107 | lt | 3 | 2 | 0 |
| 107 | md | 2 | 2 | 1 |
| 107 | or.kr | 1 | 2 | 0 |
| 107 | org.ua | 1 | 2 | 0 |
| 107 | ua | 2 | 2 | 1 |
| 132 | ac.cd | 1 | 1 | 0 |
| 132 | ac.ke | 1 | 1 | 0 |
| 132 | ac.mw | 1 | 1 | 0 |
| 132 | ac.rs | 1 | 1 | 0 |
| 132 | ac.vn | 1 | 1 | 0 |
| 132 | ai | 1 | 1 | 0 |
| 132 | am | 1 | 1 | 0 |
| 132 | au | 1 | 1 | 0 |
| 132 | az | 1 | 1 | 0 |
| 132 | co.kr | 1 | 1 | 0 |
| 132 | com.br | 1 | 1 | 0 |
| 132 | com.tr | 1 | 1 | 0 |
| 132 | edu.ge | 1 | 1 | 0 |
| 132 | edu.mk | 1 | 1 | 0 |
| 132 | edu.np | 1 | 1 | 0 |
| 132 | edu.sl | 1 | 1 | 0 |
| 132 | edu.sv | 2 | 1 | 0 |
| 132 | edu.ua | 1 | 1 | 0 |
| 132 | edu.uy | 2 | 1 | 0 |
| 132 | energy | 1 | 1 | 0 |
| 132 | go.jp | 1 | 1 | 0 |
| 132 | google | 1 | 1 | 1 |
| 132 | gouv.fr | 1 | 1 | 0 |
| 132 | gov.au | 1 | 1 | 1 |
| 132 | gov.br | 1 | 1 | 0 |
| 132 | gov.gh | 1 | 1 | 0 |
| 132 | gov.hu | 1 | 1 | 0 |
| 132 | gov.in | 1 | 1 | 0 |
| 132 | gov.pk | 1 | 1 | 0 |
| 132 | gov.tr | 1 | 1 | 0 |
| 132 | gov.ua | 1 | 1 | 0 |
| 132 | gov.uk | 1 | 1 | 0 |
| 132 | gov.vn | 1 | 1 | 0 |
| 132 | healthcare | 1 | 1 | 0 |
| 132 | krd | 1 | 1 | 0 |
| 132 | lk | 1 | 1 | 0 |
| 132 | org.br | 1 | 1 | 0 |
| 132 | org.gh | 1 | 1 | 0 |
| 132 | org.hk | 1 | 1 | 0 |
| 132 | res.in | 2 | 1 | 0 |
| 132 | sa | 1 | 1 | 0 |
| 132 | sg | 1 | 1 | 0 |
| 132 | sn | 1 | 1 | 0 |
| 132 | tg | 1 | 1 | 0 |
| 132 | us | 1 | 1 | 0 |


---

---
title: "Top countries: finance, economics, AI"
description: "Top countries in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days: China (1,305), United States (347), United Kingdom (194), Italy (126) and Australia (106)."
url: https://scholar4ai.com/rankings/countries
markdown_url: https://scholar4ai.com/rankings/countries.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T11:39:12+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# Top countries

A paper counts once for each country of its authors' institutions. Window: 2026-09-27 – 2026-10-26 (30 days). Showing the top 100 of 104; the full list is in /data/rankings.json.

## Key findings

- Top countries in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days: China (1,305), United States (347), United Kingdom (194), Italy (126) and Australia (106).
- 104 countries appear in the window.

*Top countries · 2026-09-27 – 2026-10-26 (30 days)*

| # | Country | Papers | Finance | Accounting | Economics | Management | Operations Research | AI | Last 7 days |
|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|
| 1 | China (CN) | 1,305 | 209 | 15 | 178 | 79 | 112 | 712 | 87 |
| 2 | United States (US) | 347 | 63 | 17 | 140 | 43 | 29 | 55 | 37 |
| 3 | United Kingdom (GB) | 194 | 20 | 4 | 80 | 32 | 15 | 43 | 20 |
| 4 | Italy (IT) | 126 | 14 | 2 | 63 | 18 | 10 | 19 | 13 |
| 5 | Australia (AU) | 106 | 15 | 8 | 24 | 15 | 11 | 33 | 11 |
| 6 | Germany (DE) | 105 | 13 | 2 | 50 | 20 | 7 | 13 | 9 |
| 7 | India (IN) | 104 | 14 | 4 | 17 | 11 | 8 | 50 | 11 |
| 8 | France (FR) | 98 | 8 | 1 | 46 | 21 | 13 | 9 | 16 |
| 9 | South Korea (KR) | 86 | 18 | 1 | 22 | 11 | 5 | 29 | 7 |
| 10 | Spain (ES) | 80 | 9 | 1 | 46 | 7 | 3 | 14 | 8 |
| 11 | Hong Kong (HK) | 72 | 5 | 0 | 15 | 6 | 12 | 34 | 6 |
| 12 | Turkey (TR) | 67 | 15 | 1 | 16 | 2 | 11 | 22 | 10 |
| 13 | Canada (CA) | 66 | 7 | 4 | 21 | 10 | 9 | 15 | 6 |
| 14 | Japan (JP) | 47 | 10 | 0 | 20 | 2 | 2 | 13 | 5 |
| 15 | Singapore (SG) | 43 | 3 | 0 | 9 | 2 | 5 | 24 | 4 |
| 16 | Netherlands (NL) | 38 | 5 | 1 | 16 | 8 | 3 | 5 | 4 |
| 17 | United Arab Emirates (AE) | 35 | 4 | 0 | 3 | 5 | 2 | 21 | 6 |
| 17 | Macao (MO) | 35 | 11 | 1 | 8 | 3 | 1 | 11 | 2 |
| 19 | Switzerland (CH) | 34 | 4 | 2 | 18 | 3 | 2 | 5 | 5 |
| 20 | Malaysia (MY) | 32 | 6 | 0 | 7 | 5 | 0 | 14 | 2 |
| 21 | Saudi Arabia (SA) | 31 | 5 | 2 | 2 | 6 | 2 | 14 | 4 |
| 22 | Belgium (BE) | 28 | 7 | 0 | 9 | 4 | 5 | 3 | 2 |
| 22 | Sweden (SE) | 28 | 1 | 0 | 12 | 6 | 1 | 8 | 4 |
| 24 | Norway (NO) | 26 | 2 | 0 | 8 | 11 | 4 | 1 | 6 |
| 25 | Denmark (DK) | 25 | 2 | 0 | 12 | 7 | 1 | 3 | 3 |
| 25 | Iran (IR) | 25 | 0 | 0 | 3 | 3 | 4 | 15 | 2 |
| 25 | Pakistan (PK) | 25 | 6 | 0 | 8 | 5 | 0 | 6 | 2 |
| 25 | Vietnam (VN) | 25 | 3 | 2 | 4 | 1 | 5 | 10 | 1 |
| 29 | Brazil (BR) | 21 | 6 | 1 | 6 | 0 | 2 | 6 | 4 |
| 29 | New Zealand (NZ) | 21 | 3 | 2 | 6 | 4 | 2 | 4 | 2 |
| 31 | Finland (FI) | 20 | 1 | 0 | 2 | 11 | 1 | 5 | 3 |
| 31 | Greece (GR) | 20 | 3 | 0 | 7 | 3 | 6 | 1 | 1 |
| 33 | Taiwan (TW) | 19 | 3 | 0 | 5 | 1 | 2 | 8 | 1 |
| 34 | Chile (CL) | 18 | 8 | 0 | 4 | 2 | 3 | 1 | 2 |
| 34 | Egypt (EG) | 18 | 1 | 0 | 3 | 2 | 1 | 11 | 2 |
| 36 | Austria (AT) | 17 | 3 | 2 | 5 | 4 | 1 | 2 | 2 |
| 36 | Portugal (PT) | 17 | 5 | 1 | 5 | 3 | 1 | 2 | 3 |
| 36 | South Africa (ZA) | 17 | 6 | 0 | 3 | 6 | 0 | 2 | 1 |
| 39 | Bangladesh (BD) | 16 | 2 | 0 | 1 | 1 | 1 | 11 | 2 |
| 40 | Ireland (IE) | 15 | 0 | 0 | 10 | 2 | 2 | 1 | 2 |
| 41 | Azerbaijan (AZ) | 14 | 3 | 0 | 6 | 0 | 0 | 5 | 2 |
| 42 | Hungary (HU) | 13 | 5 | 0 | 5 | 1 | 0 | 2 | 2 |
| 42 | Poland (PL) | 13 | 3 | 0 | 5 | 0 | 0 | 5 | 2 |
| 44 | Tunisia (TN) | 12 | 2 | 2 | 5 | 1 | 0 | 2 | 1 |
| 45 | Luxembourg (LU) | 10 | 0 | 0 | 10 | 0 | 0 | 0 | 1 |
| 45 | Morocco (MA) | 10 | 1 | 0 | 6 | 1 | 0 | 2 | 1 |
| 47 | Israel (IL) | 9 | 2 | 0 | 1 | 0 | 2 | 4 | 0 |
| 48 | Russia (RU) | 8 | 2 | 0 | 1 | 0 | 2 | 3 | 0 |
| 49 | Algeria (DZ) | 7 | 0 | 0 | 1 | 0 | 2 | 4 | 1 |
| 49 | Ghana (GH) | 7 | 0 | 0 | 3 | 4 | 0 | 0 | 2 |
| 49 | Jordan (JO) | 7 | 2 | 1 | 1 | 0 | 0 | 3 | 0 |
| 49 | Kuwait (KW) | 7 | 1 | 1 | 1 | 1 | 0 | 3 | 1 |
| 49 | Lebanon (LB) | 7 | 4 | 0 | 2 | 0 | 1 | 0 | 1 |
| 49 | Mexico (MX) | 7 | 0 | 0 | 3 | 3 | 0 | 1 | 2 |
| 49 | Peru (PE) | 7 | 0 | 0 | 6 | 0 | 1 | 0 | 2 |
| 49 | Romania (RO) | 7 | 2 | 0 | 1 | 2 | 2 | 0 | 2 |
| 49 | Thailand (TH) | 7 | 0 | 0 | 0 | 1 | 0 | 6 | 1 |
| 58 | Argentina (AR) | 6 | 0 | 0 | 4 | 0 | 1 | 1 | 0 |
| 58 | Cyprus (CY) | 6 | 2 | 0 | 1 | 1 | 2 | 0 | 0 |
| 58 | Czechia (CZ) | 6 | 3 | 0 | 2 | 0 | 1 | 0 | 0 |
| 58 | Iraq (IQ) | 6 | 1 | 0 | 0 | 0 | 0 | 5 | 0 |
| 58 | Nigeria (NG) | 6 | 0 | 0 | 2 | 2 | 0 | 2 | 0 |
| 58 | Oman (OM) | 6 | 2 | 0 | 0 | 1 | 1 | 2 | 0 |
| 58 | Slovenia (SI) | 6 | 0 | 0 | 1 | 1 | 0 | 4 | 0 |
| 65 | Colombia (CO) | 5 | 0 | 0 | 3 | 2 | 0 | 0 | 1 |
| 65 | Croatia (HR) | 5 | 1 | 0 | 1 | 0 | 1 | 2 | 0 |
| 65 | Indonesia (ID) | 5 | 2 | 1 | 1 | 1 | 0 | 0 | 0 |
| 65 | Kazakhstan (KZ) | 5 | 1 | 0 | 2 | 1 | 0 | 1 | 0 |
| 65 | Slovakia (SK) | 5 | 2 | 0 | 1 | 1 | 0 | 1 | 0 |
| 65 | Ukraine (UA) | 5 | 0 | 0 | 3 | 0 | 1 | 1 | 1 |
| 71 | Cameroon (CM) | 4 | 0 | 0 | 3 | 0 | 0 | 1 | 0 |
| 72 | Sri Lanka (LK) | 3 | 0 | 1 | 0 | 1 | 0 | 1 | 1 |
| 72 | Palestine (PS) | 3 | 0 | 1 | 0 | 0 | 0 | 2 | 1 |
| 72 | Uzbekistan (UZ) | 3 | 0 | 0 | 3 | 0 | 0 | 0 | 1 |
| 72 | Yemen (YE) | 3 | 0 | 0 | 0 | 2 | 0 | 1 | 0 |
| 76 | Bulgaria (BG) | 2 | 1 | 0 | 0 | 1 | 0 | 0 | 1 |
| 76 | Bahrain (BH) | 2 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| 76 | Ivory Coast (CI) | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 76 | Ecuador (EC) | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 76 | Estonia (EE) | 2 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| 76 | Ethiopia (ET) | 2 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| 76 | Liechtenstein (LI) | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 0 |
| 76 | Lithuania (LT) | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 0 |
| 76 | Libya (LY) | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 76 | Moldova (MD) | 2 | 0 | 0 | 0 | 0 | 0 | 2 | 1 |
| 76 | Philippines (PH) | 2 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
| 76 | Qatar (QA) | 2 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 76 | Tanzania (TZ) | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 0 |
| 89 | Armenia (AM) | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 89 | DR Congo (CD) | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 89 | Georgia (GE) | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 89 | Kenya (KE) | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 89 | North Korea (KP) | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 89 | Monaco (MC) | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 89 | Montenegro (ME) | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 89 | North Macedonia (MK) | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 89 | Malawi (MW) | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 89 | Nepal (NP) | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 89 | Serbia (RS) | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 89 | Sierra Leone (SL) | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |


---

---
title: "Research fields: finance, economics, AI"
description: "Research fields in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days: Computer Science (756), Economics, Econometrics and Finance (617), Business, Management and Accounting (539), Engineering (294) and Social Sciences (268)."
url: https://scholar4ai.com/rankings/fields
markdown_url: https://scholar4ai.com/rankings/fields.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T11:39:12+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# Research fields

A paper counts once, for the field of its primary OpenAlex topic. Window: 2026-09-27 – 2026-10-26 (30 days).

## Key findings

- Research fields in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days: Computer Science (756), Economics, Econometrics and Finance (617), Business, Management and Accounting (539), Engineering (294) and Social Sciences (268).
- 20 fields appear in the window.

*Research fields · 2026-09-27 – 2026-10-26 (30 days)*

| # | Field | Area | Papers | Last 7 days |
|---:|---|---|---:|---:|
| 1 | Computer Science | Physical Sciences | 756 | 53 |
| 2 | Economics, Econometrics and Finance | Social Sciences | 617 | 52 |
| 3 | Business, Management and Accounting | Social Sciences | 539 | 51 |
| 4 | Engineering | Physical Sciences | 294 | 24 |
| 5 | Social Sciences | Social Sciences | 268 | 21 |
| 6 | Decision Sciences | Social Sciences | 154 | 24 |
| 7 | Environmental Science | Physical Sciences | 43 | 3 |
| 8 | Medicine | Health Sciences | 40 | 4 |
| 9 | Energy | Physical Sciences | 32 | 9 |
| 9 | Psychology | Social Sciences | 32 | 2 |
| 11 | Physics and Astronomy | Physical Sciences | 28 | 4 |
| 12 | Agricultural and Biological Sciences | Life Sciences | 21 | 1 |
| 12 | Health Professions | Health Sciences | 21 | 3 |
| 14 | Mathematics | Physical Sciences | 19 | 4 |
| 14 | Neuroscience | Life Sciences | 19 | 1 |
| 16 | Biochemistry, Genetics and Molecular Biology | Life Sciences | 18 | 2 |
| 17 | Arts and Humanities | Social Sciences | 6 | 0 |
| 18 | Materials Science | Physical Sciences | 5 | 1 |
| 19 | Dentistry | Health Sciences | 1 | 0 |
| 19 | Earth and Planetary Sciences | Physical Sciences | 1 | 0 |


---

---
title: "Papers per journal: finance, economics, AI"
description: "Papers per journal in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days: Expert Systems with Applications (249), Pattern Recognition (188), Finance research letters (127), Knowledge-Based Systems (110) and International Review of Economics & Finance (97)."
url: https://scholar4ai.com/rankings/journals
markdown_url: https://scholar4ai.com/rankings/journals.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T11:39:12+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# Papers per journal

Papers published in each registered journal. Window: 2026-09-27 – 2026-10-26 (30 days). Showing the top 100 of 205; the full list is in /data/rankings.json.

## Key findings

- Papers per journal in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days: Expert Systems with Applications (249), Pattern Recognition (188), Finance research letters (127), Knowledge-Based Systems (110) and International Review of Economics & Finance (97).
- 205 journals appear in the window.

*Papers per journal · 2026-09-27 – 2026-10-26 (30 days)*

| # | Journal | Category | Publisher | ISSN | Papers | Last 7 days |
|---:|---|---|---|---|---:|---:|
| 1 | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | AI | Elsevier BV | 0957-4174 | 249 | 14 |
| 2 | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | AI | Elsevier BV | 0031-3203 | 188 | 9 |
| 3 | [Finance research letters](https://scholar4ai.com/journals/finance-research-letters.md) | Finance | Elsevier BV | 1544-6123 | 127 | 3 |
| 4 | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | AI | Elsevier BV | 0950-7051 | 110 | 6 |
| 5 | [International Review of Economics & Finance](https://scholar4ai.com/journals/international-review-of-economics-finance.md) | Finance | Elsevier BV | 1059-0560 | 97 | 6 |
| 6 | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | AI | Springer Science+Business Media | 0941-0643 | 90 | 0 |
| 7 | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | AI | Elsevier BV | 0893-6080 | 89 | 6 |
| 8 | [Economic Analysis and Policy](https://scholar4ai.com/journals/economic-analysis-and-policy.md) | Economics | Elsevier BV | 0313-5926 | 71 | 1 |
| 8 | [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | AI | Elsevier BV | 1566-2535 | 71 | 1 |
| 10 | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | AI | Springer Science+Business Media | 0924-669X | 64 | 8 |
| 11 | [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | AI | Elsevier BV | 0306-4573 | 54 | 2 |
| 12 | [Energy Policy](https://scholar4ai.com/journals/energy-policy.md) | Economics | Elsevier BV | 0301-4215 | 53 | 12 |
| 13 | [Transportation Research Part E Logistics and Transportation Review](https://scholar4ai.com/journals/transportation-research-part-e-logistics-and-transportation-review.md) | Operations Research | Elsevier BV | 1366-5545 | 52 | 0 |
| 14 | [Economics Letters](https://scholar4ai.com/journals/economics-letters.md) | Economics | Elsevier BV | 0165-1765 | 49 | 0 |
| 15 | [International Review of Financial Analysis](https://scholar4ai.com/journals/international-review-of-financial-analysis.md) | Finance | Elsevier BV | 1057-5219 | 45 | 1 |
| 16 | [Journal of Business Research](https://scholar4ai.com/journals/journal-of-business-research.md) | Management | Elsevier BV | 0148-2963 | 44 | 7 |
| 17 | [Energy Economics](https://scholar4ai.com/journals/energy-economics.md) | Economics | Elsevier BV | 0140-9883 | 42 | 0 |
| 18 | [Economic Modelling](https://scholar4ai.com/journals/economic-modelling.md) | Economics | Elsevier BV | 0264-9993 | 41 | 1 |
| 19 | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | AI | Springer Science+Business Media | 0920-5691 | 39 | 10 |
| 20 | [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | Operations Research | Elsevier BV | 0377-2217 | 35 | 0 |
| 21 | [Research in International Business and Finance](https://scholar4ai.com/journals/research-in-international-business-and-finance.md) | Finance | Elsevier BV | 0275-5319 | 34 | 1 |
| 22 | [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | Operations Research | Springer Science+Business Media | 0254-5330 | 29 | 5 |
| 22 | [Journal of Optimization Theory and Applications](https://scholar4ai.com/journals/journal-of-optimization-theory-and-applications.md) | Operations Research | Springer Science+Business Media | 0022-3239 | 29 | 6 |
| 24 | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | AI | Springer Science+Business Media | 0269-2821 | 28 | 9 |
| 24 | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | AI | Springer Science+Business Media | 0219-1377 | 28 | 4 |
| 24 | [Technological Forecasting and Social Change](https://scholar4ai.com/journals/technological-forecasting-and-social-change.md) | Management | Elsevier BV | 0040-1625 | 28 | 5 |
| 27 | [Ecological Economics](https://scholar4ai.com/journals/ecological-economics.md) | Economics | Elsevier BV | 0921-8009 | 27 | 5 |
| 27 | [Journal of Economic Behavior & Organization](https://scholar4ai.com/journals/journal-of-economic-behavior-organization.md) | Economics | Elsevier BV | 0167-2681 | 27 | 3 |
| 27 | [World Development](https://scholar4ai.com/journals/world-development.md) | Economics | Elsevier BV | 0305-750X | 27 | 6 |
| 30 | [International Journal of Production Economics](https://scholar4ai.com/journals/international-journal-of-production-economics.md) | Operations Research | Elsevier BV | 0925-5273 | 25 | 1 |
| 31 | [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | Economics | Springer Science+Business Media | 0927-7099 | 24 | 9 |
| 31 | [The Annals of Regional Science](https://scholar4ai.com/journals/the-annals-of-regional-science.md) | Economics | Springer Science+Business Media | 0570-1864 | 24 | 3 |
| 33 | [Future Business Journal](https://scholar4ai.com/journals/future-business-journal.md) | Management | Springer Science+Business Media | 2314-7210 | 22 | 4 |
| 33 | [Journal of Business Ethics](https://scholar4ai.com/journals/journal-of-business-ethics.md) | Management | Springer Science+Business Media | 0167-4544 | 22 | 2 |
| 33 | [Operational Research](https://scholar4ai.com/journals/operational-research.md) | Operations Research | Springer Science+Business Media | 1109-2858 | 22 | 0 |
| 36 | [Technovation](https://scholar4ai.com/journals/technovation.md) | Management | Elsevier BV | 0166-4972 | 21 | 2 |
| 37 | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | AI | Springer Science+Business Media | 0885-6125 | 20 | 1 |
| 38 | [Computers & Operations Research](https://scholar4ai.com/journals/computers-operations-research.md) | Operations Research | Elsevier BV | 0305-0548 | 19 | 0 |
| 39 | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | AI | Springer Science+Business Media | 2196-1115 | 18 | 1 |
| 39 | [Pacific-Basin Finance Journal](https://scholar4ai.com/journals/pacific-basin-finance-journal.md) | Finance | Elsevier BV | 0927-538X | 18 | 1 |
| 41 | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | AI | Nature Portfolio | 2522-5839 | 17 | 5 |
| 42 | [The North American Journal of Economics and Finance](https://scholar4ai.com/journals/the-north-american-journal-of-economics-and-finance.md) | Finance | Elsevier BV | 1062-9408 | 16 | 0 |
| 43 | [Journal of Innovation & Knowledge](https://scholar4ai.com/journals/journal-of-innovation-knowledge.md) | Management | Elsevier BV | 2444-569X | 14 | 0 |
| 43 | [Journal of Macroeconomics](https://scholar4ai.com/journals/journal-of-macroeconomics.md) | Economics | Elsevier BV | 0164-0704 | 14 | 0 |
| 45 | [Food Policy](https://scholar4ai.com/journals/food-policy.md) | Economics | Elsevier BV | 0306-9192 | 13 | 3 |
| 45 | [Journal of Asian Economics](https://scholar4ai.com/journals/journal-of-asian-economics.md) | Economics | Elsevier BV | 1049-0078 | 13 | 0 |
| 45 | [Journal of Mathematical Economics](https://scholar4ai.com/journals/journal-of-mathematical-economics.md) | Economics | Elsevier BV | 0304-4068 | 13 | 0 |
| 45 | [Research Policy](https://scholar4ai.com/journals/research-policy.md) | Economics | Elsevier BV | 0048-7333 | 13 | 4 |
| 45 | [Utilities Policy](https://scholar4ai.com/journals/utilities-policy.md) | Economics | Elsevier BV | 0957-1787 | 13 | 5 |
| 50 | [Asia Pacific Journal of Management](https://scholar4ai.com/journals/asia-pacific-journal-of-management.md) | Management | Springer Science+Business Media | 0217-4561 | 12 | 1 |
| 50 | [Empirical Economics](https://scholar4ai.com/journals/empirical-economics.md) | Economics | Springer Science+Business Media | 0377-7332 | 12 | 4 |
| 50 | [Journal of Public Economics](https://scholar4ai.com/journals/journal-of-public-economics.md) | Economics | Elsevier BV | 0047-2727 | 12 | 3 |
| 50 | [Omega](https://scholar4ai.com/journals/omega.md) | Operations Research | Elsevier BV | 0305-0483 | 12 | 1 |
| 54 | [AI & Society](https://scholar4ai.com/journals/ai-society.md) | AI | Springer Nature | 0951-5666 | 11 | 0 |
| 54 | [Artificial Intelligence](https://scholar4ai.com/journals/artificial-intelligence.md) | AI | Elsevier BV | 0004-3702 | 11 | 1 |
| 54 | [Emerging Markets Review](https://scholar4ai.com/journals/emerging-markets-review.md) | Finance | Elsevier BV | 1566-0141 | 11 | 1 |
| 54 | [Games and Economic Behavior](https://scholar4ai.com/journals/games-and-economic-behavior.md) | Economics | Elsevier BV | 0899-8256 | 11 | 0 |
| 54 | [The Journal of Technology Transfer](https://scholar4ai.com/journals/the-journal-of-technology-transfer.md) | Management | Springer Science+Business Media | 0892-9912 | 11 | 2 |
| 54 | [The Quarterly Review of Economics and Finance](https://scholar4ai.com/journals/the-quarterly-review-of-economics-and-finance.md) | Finance | Elsevier BV | 1062-9769 | 11 | 0 |
| 60 | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | AI | Springer Science+Business Media | 1866-9956 | 10 | 3 |
| 60 | [Economic Systems](https://scholar4ai.com/journals/economic-systems.md) | Economics | Elsevier BV | 0939-3625 | 10 | 0 |
| 60 | [International Entrepreneurship and Management Journal](https://scholar4ai.com/journals/international-entrepreneurship-and-management-journal.md) | Management | Springer Science+Business Media | 1554-7191 | 10 | 3 |
| 60 | [Journal of Economic Dynamics and Control](https://scholar4ai.com/journals/journal-of-economic-dynamics-and-control.md) | Economics | Elsevier BV | 0165-1889 | 10 | 0 |
| 60 | [Journal of Financial Economics](https://scholar4ai.com/journals/journal-of-financial-economics.md) | Finance | Elsevier BV | 0304-405X | 10 | 2 |
| 60 | [Journal of Financial Stability](https://scholar4ai.com/journals/journal-of-financial-stability.md) | Finance | Elsevier BV | 1572-3089 | 10 | 0 |
| 60 | [Journal of Monetary Economics](https://scholar4ai.com/journals/journal-of-monetary-economics.md) | Economics | Elsevier BV | 0304-3932 | 10 | 0 |
| 60 | [The British Accounting Review](https://scholar4ai.com/journals/the-british-accounting-review.md) | Accounting | Elsevier BV | 0890-8389 | 10 | 0 |
| 68 | [Decision Support Systems](https://scholar4ai.com/journals/decision-support-systems.md) | Operations Research | Elsevier BV | 0167-9236 | 9 | 0 |
| 68 | [European Economic Review](https://scholar4ai.com/journals/european-economic-review.md) | Economics | Elsevier BV | 0014-2921 | 9 | 0 |
| 68 | [Information Systems Frontiers](https://scholar4ai.com/journals/information-systems-frontiers.md) | Management | Springer Science+Business Media | 1387-3326 | 9 | 4 |
| 68 | [International Economics and Economic Policy](https://scholar4ai.com/journals/international-economics-and-economic-policy.md) | Economics | Springer Science+Business Media | 1612-4804 | 9 | 0 |
| 68 | [International Journal of Disclosure and Governance](https://scholar4ai.com/journals/international-journal-of-disclosure-and-governance.md) | Accounting | Palgrave Macmillan | 1741-3591 | 9 | 3 |
| 68 | [Journal of Behavioral and Experimental Economics](https://scholar4ai.com/journals/journal-of-behavioral-and-experimental-economics.md) | Economics | Elsevier BV | 2214-8043 | 9 | 0 |
| 68 | [Journal of Contemporary Accounting & Economics](https://scholar4ai.com/journals/journal-of-contemporary-accounting-economics.md) | Accounting | Elsevier BV | 1815-5669 | 9 | 0 |
| 68 | [Journal of Development Economics](https://scholar4ai.com/journals/journal-of-development-economics.md) | Economics | Elsevier BV | 0304-3878 | 9 | 0 |
| 68 | [Journal of International Financial Markets Institutions and Money](https://scholar4ai.com/journals/journal-of-international-financial-markets-institutions-and-money.md) | Finance | Elsevier BV | 1042-4431 | 9 | 3 |
| 68 | [Journal of the Academy of Marketing Science](https://scholar4ai.com/journals/journal-of-the-academy-of-marketing-science.md) | Management | Springer Science+Business Media | 0092-0703 | 9 | 3 |
| 78 | [China Economic Review](https://scholar4ai.com/journals/china-economic-review.md) | Economics | Elsevier BV | 1043-951X | 8 | 0 |
| 78 | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | AI | Springer Science+Business Media | 1384-5810 | 8 | 0 |
| 78 | [Economics & Human Biology](https://scholar4ai.com/journals/economics-human-biology.md) | Economics | Elsevier BV | 1570-677X | 8 | 0 |
| 78 | [Industrial Marketing Management](https://scholar4ai.com/journals/industrial-marketing-management.md) | Management | Elsevier BV | 0019-8501 | 8 | 1 |
| 78 | [International Journal of Information Management](https://scholar4ai.com/journals/international-journal-of-information-management.md) | Management | Elsevier BV | 0268-4012 | 8 | 0 |
| 78 | [Journal of Behavioral and Experimental Finance](https://scholar4ai.com/journals/journal-of-behavioral-and-experimental-finance.md) | Finance | Elsevier BV | 2214-6350 | 8 | 0 |
| 78 | [Journal of Economic Theory](https://scholar4ai.com/journals/journal-of-economic-theory.md) | Economics | Elsevier BV | 0022-0531 | 8 | 0 |
| 78 | [Journal of Policy Modeling](https://scholar4ai.com/journals/journal-of-policy-modeling.md) | Economics | Elsevier BV | 0161-8938 | 8 | 0 |
| 78 | [Journal of Population Economics](https://scholar4ai.com/journals/journal-of-population-economics.md) | Economics | Springer Science+Business Media | 0933-1433 | 8 | 1 |
| 78 | [Small Business Economics](https://scholar4ai.com/journals/small-business-economics.md) | Economics | Springer Science+Business Media | 0921-898X | 8 | 1 |
| 78 | [Structural Change and Economic Dynamics](https://scholar4ai.com/journals/structural-change-and-economic-dynamics.md) | Economics | Elsevier BV | 0954-349X | 8 | 0 |
| 89 | [Asia-Pacific Financial Markets](https://scholar4ai.com/journals/asia-pacific-financial-markets.md) | Finance | Springer Science+Business Media | 1387-2834 | 7 | 0 |
| 89 | [Digital Finance](https://scholar4ai.com/journals/digital-finance.md) | Finance | Springer Science+Business Media | 2524-6984 | 7 | 1 |
| 89 | [Economic Theory](https://scholar4ai.com/journals/economic-theory.md) | Economics | Springer Science+Business Media | 0938-2259 | 7 | 0 |
| 89 | [Financial Innovation](https://scholar4ai.com/journals/financial-innovation.md) | Finance | Springer Nature | 2199-4730 | 7 | 1 |
| 89 | [Journal of Banking & Finance](https://scholar4ai.com/journals/journal-of-banking-finance.md) | Finance | Elsevier BV | 0378-4266 | 7 | 0 |
| 89 | [Journal of Banking Regulation](https://scholar4ai.com/journals/journal-of-banking-regulation.md) | Finance | Palgrave Macmillan | 1745-6452 | 7 | 2 |
| 89 | [Journal of Financial Services Marketing](https://scholar4ai.com/journals/journal-of-financial-services-marketing.md) | Finance | Palgrave Macmillan | 1363-0539 | 7 | 1 |
| 89 | [Journal of International Business Studies](https://scholar4ai.com/journals/journal-of-international-business-studies.md) | Management | Palgrave Macmillan | 0047-2506 | 7 | 1 |
| 89 | [Journal of International Management](https://scholar4ai.com/journals/journal-of-international-management.md) | Management | Elsevier BV | 1075-4253 | 7 | 3 |
| 89 | [Labour Economics](https://scholar4ai.com/journals/labour-economics.md) | Economics | Elsevier BV | 0927-5371 | 7 | 0 |
| 89 | [Regional Science and Urban Economics](https://scholar4ai.com/journals/regional-science-and-urban-economics.md) | Economics | Elsevier BV | 0166-0462 | 7 | 0 |
| 89 | [Review of Economics of the Household](https://scholar4ai.com/journals/review-of-economics-of-the-household.md) | Economics | Springer Science+Business Media | 1569-5239 | 7 | 2 |


---

---
title: "Most-used equations and models: finance, economics, AI"
description: "The most-mentioned equations and models in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days were Difference-in-differences (121), Instrumental variables (two-stage least squares) (101), Logistic regression (logit) (97), Ordinary least squares (OLS) (93) and Graph neural network (graph convolution) (75)."
url: https://scholar4ai.com/rankings/equations
markdown_url: https://scholar4ai.com/rankings/equations.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T11:39:12+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# Most-used equations and models

A paper counts once for each named equation or model it mentions, in its title, abstract, keywords or full text; a paper that mentions several counts for each of them. Each row gives the formula and the name. Window: 2026-09-27 – 2026-10-26 (30 days).

## Key findings

- The most-mentioned equations and models in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days were Difference-in-differences (121), Instrumental variables (two-stage least squares) (101), Logistic regression (logit) (97), Ordinary least squares (OLS) (93) and Graph neural network (graph convolution) (75).
- Most-mentioned in each journal category: Finance: Difference-in-differences (43); Accounting: Instrumental variables (two-stage least squares) (6); Economics: Difference-in-differences (60); Management: Structural equation modeling (SEM) (34); Operations Research: Linear and integer programming (29); AI: Graph neural network (graph convolution) (73).
- 894 of the 2,971 papers (30%) mention at least one of the 112 watched equations and models.

*Most-used equations and models · 2026-09-27 – 2026-10-26 (30 days)*

| # | Equation or model | Formula | Area | Papers | By journal category | Last 7 days |
|---:|---|---|---|---:|---|---:|
| 1 | [Difference-in-differences](https://scholar4ai.com/equations/difference-in-differences.md) | $Y_{it} = \alpha_i + \lambda_t + \delta\,(\mathit{Treat}_i \times \mathit{Post}_t) + \varepsilon_{it}$ | Causal inference | 121 | Economics 60 · Finance 43 · Management 12 · Accounting 5 · AI 1 | 5 |
| 2 | [Instrumental variables (two-stage least squares)](https://scholar4ai.com/equations/instrumental-variables-two-stage-least-squares.md) | $\hat\beta_{\mathrm{2SLS}} = (X^\top P_Z X)^{-1} X^\top P_Z\, y,\quad P_Z = Z (Z^\top Z)^{-1} Z^\top$ | Causal inference | 101 | Economics 45 · Finance 35 · Management 13 · Accounting 6 · Operations Research 1 · AI 1 | 5 |
| 3 | [Logistic regression (logit)](https://scholar4ai.com/equations/logistic-regression-logit.md) | $\Pr(y = 1 \mid x) = \frac{1}{1 + e^{-x^\top\beta}}$ | Regression models | 97 | Economics 35 · AI 25 · Finance 15 · Management 13 · Operations Research 8 · Accounting 1 | 8 |
| 4 | [Ordinary least squares (OLS)](https://scholar4ai.com/equations/ordinary-least-squares-ols.md) | $\hat\beta = (X^\top X)^{-1} X^\top y$ | Regression models | 93 | Finance 35 · Economics 34 · Management 17 · Accounting 4 · AI 2 · Operations Research 1 | 7 |
| 5 | [Graph neural network (graph convolution)](https://scholar4ai.com/equations/graph-neural-network-graph-convolution.md) | $H^{(l+1)} = \sigma\big(\tilde D^{-1/2} \tilde A\, \tilde D^{-1/2} H^{(l)} W^{(l)}\big)$ | Machine learning | 75 | AI 73 · Finance 1 · Management 1 | 6 |
| 6 | [Mediation model (indirect effect)](https://scholar4ai.com/equations/mediation-model-indirect-effect.md) | $M = \alpha_0 + a X + e_1,\quad Y = \beta_0 + c' X + b M + e_2,\quad \text{indirect effect} = a\,b$ | Causal inference | 60 | Finance 26 · Management 20 · Economics 11 · Accounting 2 · Operations Research 1 | 5 |
| 7 | [Scaled dot-product attention (Transformer)](https://scholar4ai.com/equations/scaled-dot-product-attention-transformer.md) | $\mathrm{Attention}(Q, K, V) = \mathrm{softmax}\Big(\frac{Q K^\top}{\sqrt{d_k}}\Big) V$ | Machine learning | 56 | AI 55 · Operations Research 1 | 6 |
| 8 | [Two-way fixed-effects panel regression](https://scholar4ai.com/equations/two-way-fixed-effects-panel-regression.md) | $y_{it} = \alpha_i + \lambda_t + x_{it}^\top\beta + \varepsilon_{it}$ | Regression models | 55 | Finance 23 · Economics 21 · Management 9 · Operations Research 1 · AI 1 | 4 |
| 9 | [Structural equation modeling (SEM)](https://scholar4ai.com/equations/structural-equation-modeling-sem.md) | $\eta = B\eta + \Gamma\xi + \zeta,\quad y = \Lambda_y\,\eta + \varepsilon$ | Regression models | 50 | Management 34 · Finance 8 · Accounting 3 · AI 3 · Economics 1 · Operations Research 1 | 4 |
| 10 | [Vector autoregression (VAR)](https://scholar4ai.com/equations/vector-autoregression-var.md) | $y_t = c + \sum_{i=1}^{p} A_i\, y_{t-i} + \varepsilon_t$ | Time series | 49 | Finance 23 · Economics 20 · Management 3 · Operations Research 2 · AI 1 | 0 |
| 11 | [Propensity score matching](https://scholar4ai.com/equations/propensity-score-matching.md) | $e(x) = \Pr(D = 1 \mid X = x)$ | Causal inference | 48 | Finance 20 · Economics 18 · Management 7 · Accounting 2 · AI 1 | 1 |
| 12 | [Quantile regression](https://scholar4ai.com/equations/quantile-regression.md) | $\hat\beta_\tau = \operatorname*{arg\,min}_{\beta} \sum_{i} \rho_\tau(y_i - x_i^\top\beta),\quad \rho_\tau(u) = u\,(\tau - \mathbf{1}\{u < 0\})$ | Regression models | 43 | Economics 18 · Finance 15 · Management 4 · AI 3 · Accounting 2 · Operations Research 1 | 3 |
| 13 | [Contrastive loss (InfoNCE)](https://scholar4ai.com/equations/contrastive-loss-infonce.md) | $\mathcal{L} = -\log \frac{\exp(\mathrm{sim}(z_i, z_j)/\tau)}{\sum_{k \ne i} \exp(\mathrm{sim}(z_i, z_k)/\tau)}$ | Machine learning | 39 | AI 39 | 0 |
| 14 | [Generalized method of moments (GMM)](https://scholar4ai.com/equations/generalized-method-of-moments-gmm.md) | $\hat\theta = \operatorname*{arg\,min}_{\theta}\ \bar g(\theta)^\top W\, \bar g(\theta),\quad \bar g(\theta) = \frac{1}{n}\sum_{i=1}^{n} g(w_i, \theta)$ | Regression models | 35 | Economics 14 · Finance 13 · Management 6 · Accounting 1 · Operations Research 1 | 0 |
| 15 | [GARCH](https://scholar4ai.com/equations/garch.md) | $\sigma_t^2 = \omega + \alpha\,\varepsilon_{t-1}^2 + \beta\,\sigma_{t-1}^2$ | Time series | 33 | Finance 21 · Economics 10 · Management 1 · AI 1 | 2 |
| 15 | [Probit model](https://scholar4ai.com/equations/probit-model.md) | $\Pr(y = 1 \mid x) = \Phi(x^\top\beta)$ | Regression models | 33 | Economics 14 · Finance 11 · Management 6 · Accounting 2 | 2 |
| 17 | [Linear and integer programming](https://scholar4ai.com/equations/linear-and-integer-programming.md) | $\min_{x}\ c^\top x\ \ \text{s.t.}\ \ A x \le b,\ x \ge 0,\ x_j \in \mathbb{Z}\ (j \in I)$ | Efficiency and operations | 32 | Operations Research 29 · AI 3 | 2 |
| 18 | [Convolutional neural network (CNN)](https://scholar4ai.com/equations/convolutional-neural-network-cnn.md) | $(I * K)(i, j) = \sum_{m}\sum_{n} I(i + m,\, j + n)\, K(m, n)$ | Machine learning | 31 | AI 30 · Finance 1 | 4 |
| 19 | [Cointegration and error correction (VECM)](https://scholar4ai.com/equations/cointegration-and-error-correction-vecm.md) | $\Delta y_t = \Pi\, y_{t-1} + \sum_{i=1}^{p-1}\Gamma_i\,\Delta y_{t-i} + \varepsilon_t$ | Time series | 24 | Economics 14 · Finance 7 · Management 3 | 3 |
| 19 | [LASSO](https://scholar4ai.com/equations/lasso.md) | $\hat\beta = \operatorname*{arg\,min}_{\beta}\ \lVert y - X\beta \rVert_2^2 + \lambda \lVert \beta \rVert_1$ | Regression models | 24 | Economics 8 · Finance 7 · AI 6 · Operations Research 2 · Management 1 | 4 |
| 19 | [Long short-term memory (LSTM)](https://scholar4ai.com/equations/long-short-term-memory-lstm.md) | $c_t = f_t \odot c_{t-1} + i_t \odot \tilde c_t,\quad h_t = o_t \odot \tanh(c_t)$ | Machine learning | 24 | AI 15 · Economics 4 · Management 3 · Finance 1 · Operations Research 1 | 4 |
| 22 | [Gradient boosting (XGBoost)](https://scholar4ai.com/equations/gradient-boosting-xgboost.md) | $\mathcal{L} = \sum_i l(y_i, \hat y_i) + \sum_k \Omega(f_k),\quad \Omega(f) = \gamma T + \tfrac{1}{2}\lambda \lVert w \rVert^2$ | Machine learning | 22 | AI 10 · Finance 4 · Economics 4 · Management 4 | 4 |
| 22 | [Shapley value (SHAP)](https://scholar4ai.com/equations/shapley-value-shap.md) | $\phi_i(v) = \sum_{S \subseteq N \setminus \{i\}} \frac{\lvert S \rvert!\,(n - \lvert S \rvert - 1)!}{n!}\big[v(S \cup \{i\}) - v(S)\big]$ | Game theory and decision analysis | 22 | AI 10 · Economics 6 · Finance 2 · Management 2 · Operations Research 2 | 1 |
| 24 | [Total factor productivity (Solow residual)](https://scholar4ai.com/equations/total-factor-productivity-solow-residual.md) | $\ln A_t = \ln Y_t - \alpha \ln K_t - (1 - \alpha) \ln L_t$ | Growth, trade and environment | 21 | Economics 9 · Finance 8 · Management 4 | 1 |
| 25 | [Random forest](https://scholar4ai.com/equations/random-forest.md) | $\hat f(x) = \frac{1}{B}\sum_{b=1}^{B} T_b(x)$ | Machine learning | 20 | AI 9 · Finance 4 · Economics 3 · Management 2 · Operations Research 2 | 4 |
| 26 | [ARIMA](https://scholar4ai.com/equations/arima.md) | $\phi(L)\,(1 - L)^{d}\, y_t = \theta(L)\,\varepsilon_t$ | Time series | 19 | AI 7 · Economics 6 · Finance 4 · Accounting 1 · Management 1 | 2 |
| 26 | [Autoregressive distributed lag (ARDL)](https://scholar4ai.com/equations/autoregressive-distributed-lag-ardl.md) | $y_t = \alpha + \sum_{i=1}^{p}\phi_i\, y_{t-i} + \sum_{j=0}^{q}\beta_j^\top x_{t-j} + \varepsilon_t$ | Time series | 19 | Economics 10 · Finance 6 · Management 3 | 1 |
| 28 | [Granger causality](https://scholar4ai.com/equations/granger-causality.md) | $y_t = \sum_{i=1}^{p} a_i\, y_{t-i} + \sum_{j=1}^{p} b_j\, x_{t-j} + \varepsilon_t,\quad H_0: b_1 = \dots = b_p = 0$ | Time series | 16 | Finance 8 · Economics 4 · Management 2 · AI 2 | 1 |
| 28 | [Regression discontinuity design](https://scholar4ai.com/equations/regression-discontinuity-design.md) | $\tau = \lim_{x \downarrow c} E[Y \mid X = x] - \lim_{x \uparrow c} E[Y \mid X = x]$ | Causal inference | 16 | Economics 14 · Accounting 1 · AI 1 | 0 |
| 30 | [Gini coefficient](https://scholar4ai.com/equations/gini-coefficient.md) | $G = \frac{\sum_{i=1}^{n}\sum_{j=1}^{n} \lvert x_i - x_j \rvert}{2 n^2 \bar x}$ | Labour, inequality and competition | 15 | Economics 7 · Finance 3 · Management 2 · Operations Research 2 · AI 1 | 1 |
| 31 | [Markov-switching model](https://scholar4ai.com/equations/markov-switching-model.md) | $y_t = \mu_{s_t} + \varepsilon_t,\quad \Pr(s_t = j \mid s_{t-1} = i) = p_{ij}$ | Time series | 13 | Finance 6 · Economics 4 · Management 1 · Operations Research 1 · AI 1 | 1 |
| 31 | [Spatial autoregressive models (spatial Durbin)](https://scholar4ai.com/equations/spatial-autoregressive-models-spatial-durbin.md) | $y = \rho\, W y + X\beta + W X\theta + \varepsilon$ | Regression models | 13 | Economics 8 · Finance 5 | 1 |
| 33 | [Support vector machine (SVM)](https://scholar4ai.com/equations/support-vector-machine-svm.md) | $\min_{w, b, \xi}\ \tfrac{1}{2}\lVert w \rVert^2 + C \sum_i \xi_i\ \ \text{s.t.}\ \ y_i (w^\top x_i + b) \ge 1 - \xi_i,\ \xi_i \ge 0$ | Machine learning | 12 | AI 9 · Economics 1 · Management 1 · Operations Research 1 | 1 |
| 34 | [Event study (abnormal returns)](https://scholar4ai.com/equations/event-study-abnormal-returns.md) | $AR_{it} = R_{it} - (\hat\alpha_i + \hat\beta_i R_{mt}),\quad CAR_i = \sum_{t=t_1}^{t_2} AR_{it}$ | Causal inference | 11 | Finance 8 · Accounting 1 · Management 1 · Operations Research 1 | 1 |
| 34 | [Herfindahl-Hirschman index](https://scholar4ai.com/equations/herfindahl-hirschman-index.md) | $\mathit{HHI} = \sum_{i=1}^{N} s_i^2$ | Labour, inequality and competition | 11 | Finance 8 · Management 2 · Operations Research 1 | 0 |
| 36 | [Value at risk (VaR)](https://scholar4ai.com/equations/value-at-risk-var.md) | $\mathrm{VaR}_\alpha(L) = \inf\{\ell \in \mathbb{R} : \Pr(L > \ell) \le 1 - \alpha\}$ | Asset pricing and risk | 10 | Finance 4 · Economics 3 · AI 2 · Operations Research 1 | 1 |
| 37 | [Expected shortfall (CVaR)](https://scholar4ai.com/equations/expected-shortfall-cvar.md) | $\mathrm{ES}_\alpha(L) = \frac{1}{1 - \alpha}\int_\alpha^{1} \mathrm{VaR}_u(L)\,du$ | Asset pricing and risk | 9 | Finance 3 · Economics 2 · Operations Research 2 · AI 2 | 0 |
| 37 | [Nash equilibrium](https://scholar4ai.com/equations/nash-equilibrium.md) | $u_i(s_i^{*}, s_{-i}^{*}) \ge u_i(s_i, s_{-i}^{*})\quad \forall i,\ \forall s_i$ | Game theory and decision analysis | 9 | Economics 4 · Operations Research 3 · Finance 1 · Management 1 | 0 |
| 37 | [Synthetic control method](https://scholar4ai.com/equations/synthetic-control-method.md) | $\hat Y^{N}_{1t} = \sum_{j=2}^{J+1} w_j^{*}\, Y_{jt},\quad w_j^{*} \ge 0,\ \sum_j w_j^{*} = 1$ | Causal inference | 9 | Economics 5 · Finance 2 · Accounting 1 · Management 1 | 0 |
| 40 | [Data envelopment analysis (DEA)](https://scholar4ai.com/equations/data-envelopment-analysis-dea.md) | $\max_{u, v}\ \frac{\sum_r u_r\, y_{r0}}{\sum_i v_i\, x_{i0}}\ \ \text{s.t.}\ \ \frac{\sum_r u_r\, y_{rj}}{\sum_i v_i\, x_{ij}} \le 1\ \ \forall j$ | Efficiency and operations | 8 | Operations Research 4 · AI 2 · Finance 1 · Economics 1 | 0 |
| 40 | [Gradient descent](https://scholar4ai.com/equations/gradient-descent.md) | $\theta_{t+1} = \theta_t - \eta\, \nabla_\theta \mathcal{L}(\theta_t)$ | Machine learning | 8 | Operations Research 4 · AI 4 | 2 |
| 40 | [Heckman selection model](https://scholar4ai.com/equations/heckman-selection-model.md) | $E[y_i \mid x_i, s_i = 1] = x_i^\top\beta + \rho\,\sigma_\varepsilon\,\lambda(z_i^\top\gamma),\quad \lambda(\cdot) = \frac{\phi(\cdot)}{\Phi(\cdot)}$ | Causal inference | 8 | Economics 3 · Management 3 · Finance 2 | 1 |
| 40 | [Principal component analysis (PCA)](https://scholar4ai.com/equations/principal-component-analysis-pca.md) | $\max_{\lVert w \rVert = 1}\ w^\top \Sigma\, w$ | Machine learning | 8 | Finance 4 · Management 2 · AI 2 | 1 |
| 40 | [Q-learning](https://scholar4ai.com/equations/q-learning.md) | $Q(s, a) \leftarrow Q(s, a) + \alpha\big[r + \gamma \max_{a'} Q(s', a') - Q(s, a)\big]$ | Machine learning | 8 | AI 7 · Economics 1 | 1 |
| 40 | [Sharpe ratio](https://scholar4ai.com/equations/sharpe-ratio.md) | $S_p = \frac{E[R_p] - R_f}{\sigma_p}$ | Asset pricing and risk | 8 | Finance 4 · Economics 2 · AI 2 | 2 |
| 46 | [Kalman filter](https://scholar4ai.com/equations/kalman-filter.md) | $\hat x_{t \mid t} = \hat x_{t \mid t-1} + K_t\,(y_t - H\,\hat x_{t \mid t-1})$ | Time series | 7 | AI 4 · Finance 2 · Economics 1 | 0 |
| 47 | [Denoising diffusion model](https://scholar4ai.com/equations/denoising-diffusion-model.md) | $\mathcal{L} = E_{t, x_0, \epsilon}\Big[\left\lVert \epsilon - \epsilon_\theta\big(\sqrt{\bar\alpha_t}\, x_0 + \sqrt{1 - \bar\alpha_t}\, \epsilon,\ t\big) \right\rVert^2\Big]$ | Machine learning | 6 | AI 6 | 0 |
| 47 | [Low-rank adaptation (LoRA)](https://scholar4ai.com/equations/low-rank-adaptation-lora.md) | $W = W_0 + B A,\quad B \in \mathbb{R}^{d \times r},\ A \in \mathbb{R}^{r \times k},\ r \ll \min(d, k)$ | Machine learning | 6 | AI 6 | 0 |
| 47 | [Proximal policy optimization (PPO)](https://scholar4ai.com/equations/proximal-policy-optimization-ppo.md) | $L(\theta) = E_t\big[\min\big(r_t(\theta)\hat A_t,\ \mathrm{clip}(r_t(\theta), 1 - \epsilon, 1 + \epsilon)\,\hat A_t\big)\big]$ | Machine learning | 6 | Operations Research 4 · AI 2 | 0 |
| 47 | [Variational autoencoder (ELBO)](https://scholar4ai.com/equations/variational-autoencoder-elbo.md) | $\log p_\theta(x) \ge E_{q_\phi(z \mid x)}\big[\log p_\theta(x \mid z)\big] - D_{\mathrm{KL}}\big(q_\phi(z \mid x) \parallel p(z)\big)$ | Machine learning | 6 | AI 5 · Finance 1 | 1 |
| 51 | [Gated recurrent unit (GRU)](https://scholar4ai.com/equations/gated-recurrent-unit-gru.md) | $h_t = (1 - z_t) \odot h_{t-1} + z_t \odot \tilde h_t$ | Machine learning | 5 | AI 5 | 0 |
| 51 | [Generative adversarial network (GAN)](https://scholar4ai.com/equations/generative-adversarial-network-gan.md) | $\min_G \max_D\ E_x\big[\log D(x)\big] + E_z\big[\log\big(1 - D(G(z))\big)\big]$ | Machine learning | 5 | AI 4 · Finance 1 | 0 |
| 51 | [k-means clustering](https://scholar4ai.com/equations/k-means-clustering.md) | $\min_{C_1, \dots, C_k} \sum_{j=1}^{k} \sum_{x \in C_j} \lVert x - \mu_j \rVert^2$ | Machine learning | 5 | AI 4 · Economics 1 | 1 |
| 54 | [Kullback-Leibler divergence](https://scholar4ai.com/equations/kullback-leibler-divergence.md) | $D_{\mathrm{KL}}(P \parallel Q) = \sum_x P(x) \log \frac{P(x)}{Q(x)}$ | Machine learning | 4 | AI 4 | 0 |
| 55 | [Analytic hierarchy process (AHP)](https://scholar4ai.com/equations/analytic-hierarchy-process-ahp.md) | $A\, w = \lambda_{\max}\, w,\quad CR = \frac{(\lambda_{\max} - n)/(n - 1)}{RI}$ | Game theory and decision analysis | 3 | AI 3 | 0 |
| 55 | [Cox proportional hazards model](https://scholar4ai.com/equations/cox-proportional-hazards-model.md) | $h(t \mid x) = h_0(t)\,\exp(x^\top\beta)$ | Regression models | 3 | Finance 1 · Accounting 1 · Management 1 | 0 |
| 55 | [Gravity model of trade](https://scholar4ai.com/equations/gravity-model-of-trade.md) | $X_{ij} = G\,\frac{Y_i^{\beta_1}\, Y_j^{\beta_2}}{D_{ij}^{\beta_3}}$ | Growth, trade and environment | 3 | Economics 3 | 0 |
| 55 | [Mean-variance portfolio (Markowitz)](https://scholar4ai.com/equations/mean-variance-portfolio-markowitz.md) | $\min_{w}\ w^\top \Sigma\, w\ \ \text{s.t.}\ \ w^\top \mu = \mu_p,\ \ w^\top \mathbf{1} = 1$ | Asset pricing and risk | 3 | Finance 1 · Operations Research 1 · AI 1 | 0 |
| 55 | [New Keynesian Phillips curve](https://scholar4ai.com/equations/new-keynesian-phillips-curve.md) | $\pi_t = \beta\,E_t[\pi_{t+1}] + \kappa\, x_t$ | Macroeconomics | 3 | Economics 2 · Finance 1 | 0 |
| 55 | [TOPSIS](https://scholar4ai.com/equations/topsis.md) | $C_i = \frac{d_i^{-}}{d_i^{+} + d_i^{-}}$ | Game theory and decision analysis | 3 | AI 2 · Finance 1 | 1 |
| 61 | [Amihud illiquidity ratio](https://scholar4ai.com/equations/amihud-illiquidity-ratio.md) | $\mathit{ILLIQ}_i = \frac{1}{D_i}\sum_{d=1}^{D_i}\frac{\lvert R_{id} \rvert}{\mathit{DVOL}_{id}}$ | Asset pricing and risk | 2 | Finance 2 | 0 |
| 61 | [Bellman equation](https://scholar4ai.com/equations/bellman-equation.md) | $V(s) = \max_{a}\left\{ r(s, a) + \beta \sum_{s'} P(s' \mid s, a)\, V(s') \right\}$ | Game theory and decision analysis | 2 | Finance 1 · Economics 1 | 0 |
| 61 | [Capital asset pricing model (CAPM)](https://scholar4ai.com/equations/capital-asset-pricing-model-capm.md) | $E[R_i] = R_f + \beta_i\,(E[R_m] - R_f)$ | Asset pricing and risk | 2 | Finance 2 | 0 |
| 61 | [Carhart four-factor model](https://scholar4ai.com/equations/carhart-four-factor-model.md) | $R_{it} - R_{ft} = \alpha_i + b_i\,\mathit{MKT}_t + s_i\,\mathit{SMB}_t + h_i\,\mathit{HML}_t + m_i\,\mathit{UMD}_t + \varepsilon_{it}$ | Asset pricing and risk | 2 | Finance 1 · Economics 1 | 0 |
| 61 | [Dechow-Dichev accruals quality](https://scholar4ai.com/equations/dechow-dichev-accruals-quality.md) | $\Delta WC_{it} = \beta_0 + \beta_1\, CFO_{i,t-1} + \beta_2\, CFO_{it} + \beta_3\, CFO_{i,t+1} + \varepsilon_{it}$ | Accounting | 2 | Finance 1 · Accounting 1 | 1 |
| 61 | [Fama-French five-factor model](https://scholar4ai.com/equations/fama-french-five-factor-model.md) | $R_{it} - R_{ft} = \alpha_i + b_i\,\mathit{MKT}_t + s_i\,\mathit{SMB}_t + h_i\,\mathit{HML}_t + r_i\,\mathit{RMW}_t + c_i\,\mathit{CMA}_t + \varepsilon_{it}$ | Asset pricing and risk | 2 | Finance 2 | 0 |
| 61 | [Hodrick-Prescott filter](https://scholar4ai.com/equations/hodrick-prescott-filter.md) | $\min_{\{\tau_t\}} \sum_{t=1}^{T}(y_t - \tau_t)^2 + \lambda \sum_{t=2}^{T-1}\big[(\tau_{t+1} - \tau_t) - (\tau_t - \tau_{t-1})\big]^2$ | Time series | 2 | Finance 1 · Economics 1 | 0 |
| 61 | [Newsvendor model](https://scholar4ai.com/equations/newsvendor-model.md) | $Q^{*} = F^{-1}\Big(\frac{c_u}{c_u + c_o}\Big)$ | Efficiency and operations | 2 | Economics 1 · Operations Research 1 | 0 |
| 61 | [Softmax function](https://scholar4ai.com/equations/softmax-function.md) | $\mathrm{softmax}(z)_i = \frac{e^{z_i}}{\sum_{j=1}^{K} e^{z_j}}$ | Machine learning | 2 | AI 2 | 0 |
| 61 | [Solow growth model](https://scholar4ai.com/equations/solow-growth-model.md) | $\dot k = s\, f(k) - (n + \delta)\, k$ | Growth, trade and environment | 2 | Economics 2 | 1 |
| 61 | [Stackelberg game](https://scholar4ai.com/equations/stackelberg-game.md) | $\max_{x_L}\ \pi_L\big(x_L, x_F^{*}(x_L)\big),\quad x_F^{*}(x_L) = \operatorname*{arg\,max}_{x_F}\ \pi_F(x_L, x_F)$ | Game theory and decision analysis | 2 | Management 1 · Operations Research 1 | 0 |
| 61 | [Stochastic frontier analysis](https://scholar4ai.com/equations/stochastic-frontier-analysis.md) | $\ln y_i = f(x_i; \beta) + v_i - u_i,\quad u_i \ge 0$ | Efficiency and operations | 2 | Economics 2 | 0 |
| 61 | [Taylor rule](https://scholar4ai.com/equations/taylor-rule.md) | $i_t = r^{*} + \pi_t + 0.5\,(\pi_t - \pi^{*}) + 0.5\,(y_t - \bar y_t)$ | Macroeconomics | 2 | Economics 2 | 0 |
| 61 | [Tobin's q](https://scholar4ai.com/equations/tobin-s-q.md) | $q = \frac{\text{market value of assets}}{\text{replacement cost of assets}}$ | Asset pricing and risk | 2 | Finance 2 | 0 |
| 75 | [Adam optimizer](https://scholar4ai.com/equations/adam-optimizer.md) | $\theta_t = \theta_{t-1} - \alpha\,\frac{\hat m_t}{\sqrt{\hat v_t} + \epsilon}$ | Machine learning | 1 | Operations Research 1 | 0 |
| 75 | [Bayes' theorem](https://scholar4ai.com/equations/bayes-theorem.md) | $P(A \mid B) = \frac{P(B \mid A)\, P(A)}{P(B)}$ | Game theory and decision analysis | 1 | Finance 1 | 0 |
| 75 | [Best-worst method](https://scholar4ai.com/equations/best-worst-method.md) | $\min\ \xi\ \ \text{s.t.}\ \ \lvert w_B - a_{Bj}\, w_j \rvert \le \xi,\ \ \lvert w_j - a_{jW}\, w_W \rvert \le \xi,\ \ \sum_j w_j = 1$ | Game theory and decision analysis | 1 | AI 1 | 0 |
| 75 | [Coupling coordination degree](https://scholar4ai.com/equations/coupling-coordination-degree.md) | $C = \frac{2\sqrt{U_1 U_2}}{U_1 + U_2},\quad T = \alpha U_1 + \beta U_2,\quad D = \sqrt{C\, T}$ | Game theory and decision analysis | 1 | Finance 1 | 0 |
| 75 | [CoVaR (systemic risk)](https://scholar4ai.com/equations/covar-systemic-risk.md) | $\Pr\big(X^{\mathrm{sys}} \le \mathrm{CoVaR}_q^{\mathrm{sys} \mid i} \mid X^{i} = \mathrm{VaR}_q^{i}\big) = q$ | Asset pricing and risk | 1 | Finance 1 | 0 |
| 75 | [Cross-entropy loss](https://scholar4ai.com/equations/cross-entropy-loss.md) | $\mathcal{L} = -\sum_{c=1}^{C} y_c \log \hat y_c$ | Machine learning | 1 | AI 1 | 0 |
| 75 | [DEMATEL](https://scholar4ai.com/equations/dematel.md) | $T = X\,(I - X)^{-1}$ | Game theory and decision analysis | 1 | AI 1 | 0 |
| 75 | [Economic order quantity (EOQ)](https://scholar4ai.com/equations/economic-order-quantity-eoq.md) | $Q^{*} = \sqrt{\frac{2 D S}{H}}$ | Efficiency and operations | 1 | Operations Research 1 | 0 |
| 75 | [Entropy weight method](https://scholar4ai.com/equations/entropy-weight-method.md) | $e_j = -\frac{1}{\ln n}\sum_{i=1}^{n} p_{ij}\ln p_{ij},\quad w_j = \frac{1 - e_j}{\sum_k (1 - e_k)}$ | Game theory and decision analysis | 1 | AI 1 | 1 |
| 75 | [Environmental Kuznets curve](https://scholar4ai.com/equations/environmental-kuznets-curve.md) | $\ln E_{it} = \beta_0 + \beta_1 \ln Y_{it} + \beta_2 (\ln Y_{it})^2 + \varepsilon_{it}$ | Growth, trade and environment | 1 | Economics 1 | 1 |
| 75 | [Lerner index](https://scholar4ai.com/equations/lerner-index.md) | $L = \frac{P - MC}{P}$ | Labour, inequality and competition | 1 | Finance 1 | 0 |
| 75 | [Merton model (distance to default)](https://scholar4ai.com/equations/merton-model-distance-to-default.md) | $DD = \frac{\ln(V/D) + (\mu - \sigma_V^2/2)\,T}{\sigma_V\sqrt{T}}$ | Asset pricing and risk | 1 | Economics 1 | 0 |
| 75 | [Residual income valuation (Ohlson model)](https://scholar4ai.com/equations/residual-income-valuation-ohlson-model.md) | $P_t = B_t + \sum_{\tau=1}^{\infty}\frac{E_t[NI_{t+\tau} - r\,B_{t+\tau-1}]}{(1 + r)^{\tau}}$ | Accounting | 1 | Operations Research 1 | 0 |
| 75 | [Shannon entropy](https://scholar4ai.com/equations/shannon-entropy.md) | $H(X) = -\sum_x p(x) \log p(x)$ | Machine learning | 1 | AI 1 | 0 |

Also watched, with no paper in this window: Altman Z-score, Bass diffusion model, Basu asymmetric timeliness, Beneish M-score, Black-Scholes formula, CES production function, Cobb-Douglas production function, Consumption Euler equation, Covered interest parity, Dividend discount (Gordon growth) model, Fama-French three-factor model, Fisher equation, Heston stochastic volatility model, Jensen's alpha, Kaya identity, Little's law, Mincer earnings equation, Modified Jones model (discretionary accruals), Nash bargaining solution, Nelson-Siegel yield curve, Okun's law, ReLU activation, STIRPAT model, Uncovered interest parity.

## How the equations are found

The site watches a fixed list of 112 named equations and models in 11 areas, each with the names papers use for it (full names, abbreviations and common spellings). A paper counts when one of them appears in its title, abstract or OpenAlex keywords, or, for papers whose full text OpenAlex has indexed, anywhere in the full text (found with OpenAlex full-text search).

In this window OpenAlex has the abstract of 773 of the 2,971 papers (26%) and searchable full text for 545 (18%); titles and keywords are always there.

The full-text lookups go a group of names at a time, the stalest first, as far as the day's OpenAlex request budget allows: 30 of the 69 groups have had a lookup so far (between 2026-10-23 and 2026-10-26) and the rest are queued, so for now their counts rest on titles, abstracts and keywords.

The counts are therefore lower bounds that lean toward papers with open full text, and a mention is not proof that a paper uses the equation or dataset. Abstracts are only read to spot the names; they are neither stored nor republished.

## Questions this page answers

### Which equations and models are used most in finance, accounting, economics, management, operations research and AI research?

Counting papers that mention them by name, the most frequent in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days were Difference-in-differences (121), Instrumental variables (two-stage least squares) (101), Logistic regression (logit) (97), Ordinary least squares (OLS) (93) and Graph neural network (graph convolution) (75).

### Which equations and models are used most in finance research?

Counting papers that mention them by name, the most frequent in the tracked finance journals over the last 30 days were Difference-in-differences (43), Instrumental variables (two-stage least squares) (35), Ordinary least squares (OLS) (35), Mediation model (indirect effect) (26) and Two-way fixed-effects panel regression (23).

### Which equations and models are used most in accounting research?

Counting papers that mention them by name, the most frequent in the tracked accounting journals over the last 30 days were Instrumental variables (two-stage least squares) (6), Difference-in-differences (5), Ordinary least squares (OLS) (4), Structural equation modeling (SEM) (3) and Mediation model (indirect effect) (2).

### Which equations and models are used most in economics research?

Counting papers that mention them by name, the most frequent in the tracked economics journals over the last 30 days were Difference-in-differences (60), Instrumental variables (two-stage least squares) (45), Logistic regression (logit) (35), Ordinary least squares (OLS) (34) and Two-way fixed-effects panel regression (21).

### Which equations and models are used most in management research?

Counting papers that mention them by name, the most frequent in the tracked management journals over the last 30 days were Structural equation modeling (SEM) (34), Mediation model (indirect effect) (20), Ordinary least squares (OLS) (17), Instrumental variables (two-stage least squares) (13) and Logistic regression (logit) (13).

### Which equations and models are used most in operations research?

Counting papers that mention them by name, the most frequent in the tracked operations research journals over the last 30 days were Linear and integer programming (29), Logistic regression (logit) (8), Data envelopment analysis (DEA) (4), Gradient descent (4) and Proximal policy optimization (PPO) (4).

### Which equations and models are used most in AI research?

Counting papers that mention them by name, the most frequent in the tracked AI journals over the last 30 days were Graph neural network (graph convolution) (73), Scaled dot-product attention (Transformer) (55), Contrastive loss (InfoNCE) (39), Convolutional neural network (CNN) (30) and Logistic regression (logit) (25).


---

---
title: "Most-used data: finance, economics, AI"
description: "The kinds of data mentioned most in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days were Macroeconomic and development indicators (59), Stock market indices (45), China household and social surveys (26), China statistical yearbooks and regional indices (21) and Financial data platforms (20)."
url: https://scholar4ai.com/rankings/datasets
markdown_url: https://scholar4ai.com/rankings/datasets.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T11:39:12+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# Most-used data

Datasets are grouped into data categories. A paper counts once for each dataset it mentions by name, in its title, abstract, keywords or full text, and once for each category with at least one of them. Window: 2026-09-27 – 2026-10-26 (30 days).

## Key findings

- The kinds of data mentioned most in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days were Macroeconomic and development indicators (59), Stock market indices (45), China household and social surveys (26), China statistical yearbooks and regional indices (21) and Financial data platforms (20).
- The most-mentioned single datasets were S&P 500 (31), World Development Indicators (World Bank) (30), Eurostat (23), CSMAR (17) and VIX (16).
- Most-mentioned datasets in each journal category: Finance: S&P 500 (18) and CSMAR (14); Accounting: Amadeus (Bureau van Dijk) (1) and Audit Analytics (1); Economics: Eurostat (15) and World Development Indicators (World Bank) (14); Management: World Development Indicators (World Bank) (5) and S&P 500 (4); Operations Research: S&P 500 (4) and Eurostat (2); AI: MNIST / Fashion-MNIST (5) and CIFAR-10 / CIFAR-100 (4).
- 304 of the 2,971 papers (10%) mention at least one of the 371 watched datasets.

## By data category

*Data categories · 2026-09-27 – 2026-10-26 (30 days)*

| # | Data category | Most-mentioned datasets | Papers | By journal category | What it covers | Last 7 days |
|---:|---|---|---:|---|---|---:|
| 1 | [Macroeconomic and development indicators](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md) | World Development Indicators (World Bank) (30), Eurostat (23), Penn World Table (7), IMF data (IFS, WEO, DOTS) (7) … | 59 | Economics 33 · Finance 14 · Management 9 · Operations Research 2 · Accounting 1 | Cross-country series on output, prices, trade, population and development. | 7 |
| 2 | [Stock market indices](https://scholar4ai.com/datasets/stock-market-indices.md) | S&P 500 (31), CSI 300 / Shanghai Composite (8), MSCI indices (5), Euro Stoxx / DAX (5) … | 45 | Finance 25 · Economics 7 · Management 5 · Operations Research 5 · AI 2 · Accounting 1 | Benchmark equity indices used as market returns, samples or index-membership designs. | 3 |
| 3 | [China household and social surveys](https://scholar4ai.com/datasets/china-household-and-social-surveys.md) | China Household Finance Survey (CHFS) (11), China Family Panel Studies (CFPS) (9), China Health and Nutrition Survey (CHNS) (4), CHARLS (3) … | 26 | Economics 13 · Finance 10 · AI 2 · Operations Research 1 | Chinese household, ageing, health and social surveys. | 4 |
| 4 | [China statistical yearbooks and regional indices](https://scholar4ai.com/datasets/china-statistical-yearbooks-and-regional-indices.md) | Marketization index (Fan Gang) (13), China Statistical Yearbook (3), National Bureau of Statistics of China (3), China City Statistical Yearbook (2) … | 21 | Finance 10 · Economics 7 · Management 3 · AI 1 | Province- and city-level statistics from Chinese statistical yearbooks, and regional indices built from them. | 0 |
| 5 | [Financial data platforms](https://scholar4ai.com/datasets/financial-data-platforms.md) | Refinitiv Datastream (5), WRDS (4), Refinitiv Eikon / LSEG Workspace (3), Yahoo Finance (3) … | 20 | Finance 11 · Economics 3 · Accounting 2 · Management 2 · Operations Research 1 · AI 1 | Commercial terminals and data services with prices, fundamentals and economic series. | 1 |
| 6 | [China financial and firm databases](https://scholar4ai.com/datasets/china-financial-and-firm-databases.md) | CSMAR (17), CNRDS (6), Wind (5) | 19 | Finance 15 · Management 2 · Economics 1 · AI 1 | Financial statements, trading, ownership and governance data of Chinese listed companies. | 1 |
| 6 | [Corporate filings and text](https://scholar4ai.com/datasets/corporate-filings-and-text.md) | Earnings call transcripts (9), Loughran-McDonald dictionary (9), 10-K filings (4), XBRL filings (2) … | 19 | Finance 12 · Management 3 · Accounting 1 · Economics 1 · Operations Research 1 · AI 1 | Annual reports and other regulatory filings, earnings calls, and the word lists used to read them. | 1 |
| 8 | [European and other national household panels](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md) | Labour Force Surveys (5), SHARE (4), German Socio-Economic Panel (SOEP) (3), Understanding Society / BHPS (2) … | 17 | Economics 14 · Finance 3 | Household panels and surveys of Europe, Australia, Japan and Korea. | 4 |
| 9 | [Options and volatility data](https://scholar4ai.com/datasets/options-and-volatility-data.md) | VIX (16), OVX (crude oil volatility index) (2) | 16 | Finance 12 · Economics 3 · AI 1 | Option prices and implied volatilities, and volatility indices such as the VIX. | 0 |
| 10 | [Company financial statements](https://scholar4ai.com/datasets/company-financial-statements.md) | Compustat (10), Orbis (Bureau van Dijk) (3), Amadeus (Bureau van Dijk) (1), S&P Capital IQ (1) | 15 | Finance 6 · Management 6 · Accounting 2 · Economics 1 | Balance sheets, income statements and other accounting data of listed and private companies. | 1 |
| 10 | [Uncertainty and sentiment indices](https://scholar4ai.com/datasets/uncertainty-and-sentiment-indices.md) | Climate Policy Uncertainty index (7), Economic Policy Uncertainty index (6), Trade Policy Uncertainty index (5) | 15 | Finance 7 · Economics 7 · Management 1 | Text- and survey-based indices of policy uncertainty, geopolitical risk, investor sentiment and financial stress. | 1 |
| 12 | [US macroeconomic data](https://scholar4ai.com/datasets/us-macroeconomic-data.md) | University of Michigan Surveys of Consumers (5), FRED (4), Bureau of Economic Analysis (4), Bureau of Labor Statistics (2) … | 14 | Economics 7 · Finance 4 · Management 3 | US national accounts, prices, labour statistics and expectation surveys. | 2 |
| 13 | [ESG and sustainability ratings](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md) | MSCI ESG / KLD (3), CDP (Carbon Disclosure Project) (3), Huazheng / Sino-Securities ESG (3), Refinitiv ESG (ASSET4) (2) … | 13 | Finance 5 · Management 5 · Accounting 1 · Economics 1 · Operations Research 1 | Environmental, social and governance scores, controversies and carbon data of companies. | 0 |
| 13 | [US household and labour surveys](https://scholar4ai.com/datasets/us-household-and-labour-surveys.md) | Health and Retirement Study (5), Current Population Survey (3), IPUMS (2), Consumer Expenditure Survey (2) … | 13 | Economics 9 · Management 3 · AI 1 | US panel and cross-section surveys of households and workers: income, wealth, spending, health and work. | 1 |
| 13 | [US stock market data](https://scholar4ai.com/datasets/us-stock-market-data.md) | CRSP (12), NYSE TAQ (2) | 13 | Finance 9 · Economics 2 · Management 1 · Operations Research 1 | Prices, returns, trading volume, quotes and shares outstanding of US-listed stocks. | 4 |
| 16 | [Social media data](https://scholar4ai.com/datasets/social-media-data.md) | Twitter / X (5), Facebook (3), StockTwits (2), Reddit (1) … | 10 | Finance 5 · AI 3 · Economics 1 · Operations Research 1 | Posts and activity from social networks and investor forums. | 0 |
| 17 | [Mutual fund and hedge fund data](https://scholar4ai.com/datasets/mutual-fund-and-hedge-fund-data.md) | Lipper / TASS (7), Morningstar (2) | 9 | Finance 3 · Management 3 · AI 2 · Economics 1 | Returns, holdings, flows and fees of mutual funds, ETFs and hedge funds. | 0 |
| 18 | [Image classification benchmarks](https://scholar4ai.com/datasets/image-classification-benchmarks.md) | MNIST / Fashion-MNIST (5), CIFAR-10 / CIFAR-100 (4), ImageNet (3) | 8 | AI 8 | Labelled image collections used to train and compare image classifiers. | 1 |
| 19 | [Credit scoring and lending data](https://scholar4ai.com/datasets/credit-scoring-and-lending-data.md) | LendingClub (3), German Credit (UCI Statlog) (2), Taiwan credit card default (UCI) (1), Credit bureaus (Equifax, Experian, TransUnion) (1) | 7 | Finance 3 · AI 2 · Economics 1 · Management 1 | Loan-level lending data and the public datasets used to train credit-scoring models. | 0 |
| 20 | [Asset pricing factor data](https://scholar4ai.com/datasets/asset-pricing-factor-data.md) | Kenneth French Data Library (4), Hou-Xue-Zhang q-factors (2) | 6 | Finance 5 · Accounting 1 | Published factor returns and anomaly portfolios used as benchmarks. | 0 |
| 20 | [Corporate governance and executive data](https://scholar4ai.com/datasets/corporate-governance-and-executive-data.md) | BoardEx (3), ExecuComp (2), Audit Analytics (1) | 6 | Management 3 · Finance 2 · Accounting 1 | Boards, directors, executive pay, shareholder voting, audits and governance ratings. | 0 |
| 22 | [Bank regulatory data](https://scholar4ai.com/datasets/bank-regulatory-data.md) | Credit registers (e.g. AnaCredit) (3), Call Reports (1), FR Y-9C (1), BankFocus / BankScope (1) | 5 | Finance 2 · Accounting 2 · Economics 1 | Supervisory filings and balance sheets of banks, deposits by branch and credit registers. | 0 |
| 22 | [Bond, loan and credit data](https://scholar4ai.com/datasets/bond-loan-and-credit-data.md) | TRACE (3), DealScan (1), Markit CDS (1) | 5 | Finance 2 · Economics 1 · Management 1 · AI 1 | Corporate bond trades and terms, syndicated loans, credit default swaps and credit ratings. | 0 |
| 22 | [Commodity prices](https://scholar4ai.com/datasets/commodity-prices.md) | WTI crude oil (3), Brent crude oil (2), London Metal Exchange / LBMA (1) | 5 | Finance 2 · Economics 2 · AI 1 | Benchmark prices of crude oil, natural gas, metals and agricultural commodities. | 1 |
| 25 | [Search, news and web data](https://scholar4ai.com/datasets/search-news-and-web-data.md) | GDELT (2), Google Trends (1), Wikipedia (1) | 4 | Finance 2 · Management 1 · AI 1 | Search volumes, news archives and web content used to measure attention, news and sentiment. | 0 |
| 26 | [Analyst forecasts](https://scholar4ai.com/datasets/analyst-forecasts.md) | I/B/E/S (2), Zacks (2) | 3 | Finance 2 · Economics 1 | Sell-side analysts' earnings forecasts, recommendations and price targets. | 0 |
| 26 | [Cryptocurrency data](https://scholar4ai.com/datasets/cryptocurrency-data.md) | CoinGecko (2), CoinMarketCap (1) | 3 | Finance 3 | Prices, trading volumes and on-chain records of cryptocurrencies. | 0 |
| 26 | [Governance and institutional indicators](https://scholar4ai.com/datasets/governance-and-institutional-indicators.md) | Worldwide Governance Indicators (1), Corruption Perceptions Index (1), World Bank Doing Business (1) | 3 | Management 2 · Economics 1 | Country indices of governance, democracy, corruption, economic freedom, political risk and capital openness. | 0 |
| 26 | [M&A, IPO and private capital data](https://scholar4ai.com/datasets/m-a-ipo-and-private-capital-data.md) | Crunchbase (3) | 3 | Management 2 · Finance 1 | Deal-level data on mergers and acquisitions, equity and bond issues, venture capital and private equity. | 0 |
| 26 | [Object detection and segmentation benchmarks](https://scholar4ai.com/datasets/object-detection-and-segmentation-benchmarks.md) | KITTI (3), nuScenes / Waymo Open / Argoverse (1) | 3 | AI 3 | Images labelled with object boxes or pixel masks, including driving scenes. | 0 |
| 31 | [Developing-country household surveys](https://scholar4ai.com/datasets/developing-country-household-surveys.md) | Demographic and Health Surveys (DHS) (1), Living Standards Measurement Study (LSMS) (1) | 2 | Economics 2 | Household, health and living-standards surveys of low- and middle-income countries. | 1 |
| 31 | [Education data](https://scholar4ai.com/datasets/education-data.md) | TIMSS / PIRLS (2) | 2 | Finance 1 · Economics 1 | Student assessments and school and college records. | 0 |
| 31 | [Face, person and video benchmarks](https://scholar4ai.com/datasets/face-person-and-video-benchmarks.md) | Face recognition (LFW, CelebA, VGGFace2) (1), Action recognition (Kinetics, UCF101, HMDB51) (1) | 2 | AI 2 | Faces, people, poses, facial expressions and human actions in images and video. | 1 |
| 31 | [Firm surveys and census data](https://scholar4ai.com/datasets/firm-surveys-and-census-data.md) | World Bank Enterprise Surveys (2) | 2 | Finance 1 · Economics 1 | Surveys and administrative records of firms and plants, including private firms. | 0 |
| 31 | [Medical and clinical datasets](https://scholar4ai.com/datasets/medical-and-clinical-datasets.md) | MIMIC (1), ISIC skin lesion archive (1) | 2 | AI 2 | Clinical records, medical images and physiological signals used in medical machine learning. | 0 |
| 31 | [Online reviews, e-commerce and recommender data](https://scholar4ai.com/datasets/online-reviews-e-commerce-and-recommender-data.md) | Amazon reviews (1), Taobao / Tmall / JD.com (1) | 2 | AI 2 | Product and service reviews, ratings, purchases and user-item interactions. | 0 |
| 31 | [Optimization benchmark instances](https://scholar4ai.com/datasets/optimization-benchmark-instances.md) | PSPLIB (1), CEC benchmark functions (1) | 2 | Operations Research 1 · AI 1 | Standard problem instances for routing, scheduling and integer programming, and test functions for metaheuristics. | 0 |
| 31 | [Ownership and insider trading](https://scholar4ai.com/datasets/ownership-and-insider-trading.md) | Thomson Reuters 13F holdings (2) | 2 | Finance 1 · Operations Research 1 | Holdings of institutional investors (13F filings) and trades by corporate insiders. | 0 |
| 31 | [Reinforcement learning environments](https://scholar4ai.com/datasets/reinforcement-learning-environments.md) | D4RL (1), SUMO traffic simulator (1) | 2 | AI 2 | Simulators and benchmark suites for training and comparing reinforcement learning agents. | 1 |
| 31 | [Satellite and geospatial data](https://scholar4ai.com/datasets/satellite-and-geospatial-data.md) | Night-time lights (VIIRS, DMSP) (1), OpenStreetMap (1) | 2 | Economics 2 | Night-time lights, land cover, satellite imagery and maps. | 1 |
| 31 | [Text and NLP benchmarks](https://scholar4ai.com/datasets/text-and-nlp-benchmarks.md) | Natural language inference (SNLI, MultiNLI) (1), Retrieval and QA (MS MARCO, Natural Questions, HotpotQA) (1) | 2 | AI 2 | Corpora and benchmarks for language understanding, question answering and text classification. | 1 |
| 42 | [Health data](https://scholar4ai.com/datasets/health-data.md) | Oxford COVID-19 Government Response Tracker (1) | 1 | Finance 1 | Population health statistics, disease burden, health surveys and pandemic trackers. | 0 |
| 42 | [Industrial fault and prognostics benchmarks](https://scholar4ai.com/datasets/industrial-fault-and-prognostics-benchmarks.md) | Tennessee Eastman process (1) | 1 | AI 1 | Bearing, engine, battery and process data used to compare fault-diagnosis and remaining-life models. | 0 |
| 42 | [Job postings and occupation data](https://scholar4ai.com/datasets/job-postings-and-occupation-data.md) | LinkedIn (1) | 1 | Finance 1 | Online job ads, occupational task descriptions, worker profiles and employer reviews. | 0 |
| 42 | [Real estate and housing data](https://scholar4ai.com/datasets/real-estate-and-housing-data.md) | S&P CoreLogic Case-Shiller (1) | 1 | Accounting 1 | House prices and transactions, mortgages and rents. | 0 |
| 42 | [Remote sensing benchmarks](https://scholar4ai.com/datasets/remote-sensing-benchmarks.md) | DOTA / xView (1) | 1 | AI 1 | Hyperspectral and aerial image collections used to compare classification and detection methods. | 0 |
| 42 | [Speech and audio benchmarks](https://scholar4ai.com/datasets/speech-and-audio-benchmarks.md) | Speech emotion (IEMOCAP, RAVDESS) (1) | 1 | AI 1 | Speech, speaker and sound collections. | 0 |
| 42 | [Time-series and traffic benchmarks](https://scholar4ai.com/datasets/time-series-and-traffic-benchmarks.md) | Traffic (PeMS, METR-LA) (1) | 1 | AI 1 | Collections used to compare forecasting and time-series classification methods. | 0 |
| 42 | [Weather, climate and disaster data](https://scholar4ai.com/datasets/weather-climate-and-disaster-data.md) | CMIP climate projections (1) | 1 | Economics 1 | Weather observations, climate reanalyses and projections, and disaster records. | 0 |

## By dataset

*Single datasets · 2026-09-27 – 2026-10-26 (30 days)*

| # | Dataset | Data category | Papers | By journal category | Last 7 days |
|---:|---|---|---:|---|---:|
| 1 | [S&P 500](https://scholar4ai.com/datasets/stock-market-indices.md#s-p-500) | [Stock market indices](https://scholar4ai.com/datasets/stock-market-indices.md) | 31 | Finance 18 · Management 4 · Operations Research 4 · Economics 3 · Accounting 1 · AI 1 | 3 |
| 2 | [World Development Indicators (World Bank)](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md#world-development-indicators-world-bank) | [Macroeconomic and development indicators](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md) | 30 | Economics 14 · Finance 10 · Management 5 · Accounting 1 | 3 |
| 3 | [Eurostat](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md#eurostat) | [Macroeconomic and development indicators](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md) | 23 | Economics 15 · Finance 3 · Management 3 · Operations Research 2 | 3 |
| 4 | [CSMAR](https://scholar4ai.com/datasets/china-financial-and-firm-databases.md#csmar) | [China financial and firm databases](https://scholar4ai.com/datasets/china-financial-and-firm-databases.md) | 17 | Finance 14 · Management 2 · Economics 1 | 0 |
| 5 | [VIX](https://scholar4ai.com/datasets/options-and-volatility-data.md#vix) | [Options and volatility data](https://scholar4ai.com/datasets/options-and-volatility-data.md) | 16 | Finance 12 · Economics 3 · AI 1 | 0 |
| 6 | [Marketization index (Fan Gang)](https://scholar4ai.com/datasets/china-statistical-yearbooks-and-regional-indices.md#marketization-index-fan-gang) | [China statistical yearbooks and regional indices](https://scholar4ai.com/datasets/china-statistical-yearbooks-and-regional-indices.md) | 13 | Finance 7 · Economics 3 · Management 2 · AI 1 | 0 |
| 7 | [CRSP](https://scholar4ai.com/datasets/us-stock-market-data.md#crsp) | [US stock market data](https://scholar4ai.com/datasets/us-stock-market-data.md) | 12 | Finance 9 · Economics 1 · Management 1 · Operations Research 1 | 3 |
| 8 | [China Household Finance Survey (CHFS)](https://scholar4ai.com/datasets/china-household-and-social-surveys.md#china-household-finance-survey-chfs) | [China household and social surveys](https://scholar4ai.com/datasets/china-household-and-social-surveys.md) | 11 | Finance 8 · Economics 2 · AI 1 | 2 |
| 9 | [Compustat](https://scholar4ai.com/datasets/company-financial-statements.md#compustat) | [Company financial statements](https://scholar4ai.com/datasets/company-financial-statements.md) | 10 | Finance 6 · Management 3 · Accounting 1 | 0 |
| 10 | [China Family Panel Studies (CFPS)](https://scholar4ai.com/datasets/china-household-and-social-surveys.md#china-family-panel-studies-cfps) | [China household and social surveys](https://scholar4ai.com/datasets/china-household-and-social-surveys.md) | 9 | Economics 6 · Finance 3 | 1 |
| 10 | [Earnings call transcripts](https://scholar4ai.com/datasets/corporate-filings-and-text.md#earnings-call-transcripts) | [Corporate filings and text](https://scholar4ai.com/datasets/corporate-filings-and-text.md) | 9 | Finance 6 · Accounting 1 · Management 1 · Operations Research 1 | 0 |
| 10 | [Loughran-McDonald dictionary](https://scholar4ai.com/datasets/corporate-filings-and-text.md#loughran-mcdonald-dictionary) | [Corporate filings and text](https://scholar4ai.com/datasets/corporate-filings-and-text.md) | 9 | Finance 9 | 0 |
| 13 | [CSI 300 / Shanghai Composite](https://scholar4ai.com/datasets/stock-market-indices.md#csi-300-shanghai-composite) | [Stock market indices](https://scholar4ai.com/datasets/stock-market-indices.md) | 8 | Finance 4 · Economics 2 · Management 1 · AI 1 | 0 |
| 14 | [Climate Policy Uncertainty index](https://scholar4ai.com/datasets/uncertainty-and-sentiment-indices.md#climate-policy-uncertainty-index) | [Uncertainty and sentiment indices](https://scholar4ai.com/datasets/uncertainty-and-sentiment-indices.md) | 7 | Finance 3 · Economics 3 · Management 1 | 0 |
| 14 | [IMF data (IFS, WEO, DOTS)](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md#imf-data-ifs-weo-dots) | [Macroeconomic and development indicators](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md) | 7 | Finance 4 · Economics 2 · Management 1 | 1 |
| 14 | [Lipper / TASS](https://scholar4ai.com/datasets/mutual-fund-and-hedge-fund-data.md#lipper-tass) | [Mutual fund and hedge fund data](https://scholar4ai.com/datasets/mutual-fund-and-hedge-fund-data.md) | 7 | Management 3 · AI 2 · Finance 1 · Economics 1 | 0 |
| 14 | [Penn World Table](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md#penn-world-table) | [Macroeconomic and development indicators](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md) | 7 | Economics 6 · Finance 1 | 0 |
| 18 | [CNRDS](https://scholar4ai.com/datasets/china-financial-and-firm-databases.md#cnrds) | [China financial and firm databases](https://scholar4ai.com/datasets/china-financial-and-firm-databases.md) | 6 | Finance 5 · Management 1 | 0 |
| 18 | [Economic Policy Uncertainty index](https://scholar4ai.com/datasets/uncertainty-and-sentiment-indices.md#economic-policy-uncertainty-index) | [Uncertainty and sentiment indices](https://scholar4ai.com/datasets/uncertainty-and-sentiment-indices.md) | 6 | Finance 3 · Economics 3 | 1 |
| 20 | [Euro Stoxx / DAX](https://scholar4ai.com/datasets/stock-market-indices.md#euro-stoxx-dax) | [Stock market indices](https://scholar4ai.com/datasets/stock-market-indices.md) | 5 | Finance 2 · Economics 2 · Operations Research 1 | 0 |
| 20 | [Health and Retirement Study](https://scholar4ai.com/datasets/us-household-and-labour-surveys.md#health-and-retirement-study) | [US household and labour surveys](https://scholar4ai.com/datasets/us-household-and-labour-surveys.md) | 5 | Economics 4 · Management 1 | 1 |
| 20 | [Labour Force Surveys](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md#labour-force-surveys) | [European and other national household panels](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md) | 5 | Economics 5 | 1 |
| 20 | [MNIST / Fashion-MNIST](https://scholar4ai.com/datasets/image-classification-benchmarks.md#mnist-fashion-mnist) | [Image classification benchmarks](https://scholar4ai.com/datasets/image-classification-benchmarks.md) | 5 | AI 5 | 0 |
| 20 | [MSCI indices](https://scholar4ai.com/datasets/stock-market-indices.md#msci-indices) | [Stock market indices](https://scholar4ai.com/datasets/stock-market-indices.md) | 5 | Finance 2 · Economics 2 · Management 1 | 0 |
| 20 | [OECD statistics](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md#oecd-statistics) | [Macroeconomic and development indicators](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md) | 5 | Economics 4 · Management 1 | 0 |
| 20 | [Refinitiv Datastream](https://scholar4ai.com/datasets/financial-data-platforms.md#refinitiv-datastream) | [Financial data platforms](https://scholar4ai.com/datasets/financial-data-platforms.md) | 5 | Finance 2 · Accounting 1 · Economics 1 · Management 1 | 0 |
| 20 | [Trade Policy Uncertainty index](https://scholar4ai.com/datasets/uncertainty-and-sentiment-indices.md#trade-policy-uncertainty-index) | [Uncertainty and sentiment indices](https://scholar4ai.com/datasets/uncertainty-and-sentiment-indices.md) | 5 | Finance 3 · Economics 2 | 0 |
| 20 | [Twitter / X](https://scholar4ai.com/datasets/social-media-data.md#twitter-x) | [Social media data](https://scholar4ai.com/datasets/social-media-data.md) | 5 | Finance 2 · AI 2 · Economics 1 | 0 |
| 20 | [University of Michigan Surveys of Consumers](https://scholar4ai.com/datasets/us-macroeconomic-data.md#university-of-michigan-surveys-of-consumers) | [US macroeconomic data](https://scholar4ai.com/datasets/us-macroeconomic-data.md) | 5 | Economics 3 · Finance 1 · Management 1 | 0 |
| 20 | [Wind](https://scholar4ai.com/datasets/china-financial-and-firm-databases.md#wind) | [China financial and firm databases](https://scholar4ai.com/datasets/china-financial-and-firm-databases.md) | 5 | Finance 4 · AI 1 | 1 |
| 31 | [10-K filings](https://scholar4ai.com/datasets/corporate-filings-and-text.md#10-k-filings) | [Corporate filings and text](https://scholar4ai.com/datasets/corporate-filings-and-text.md) | 4 | Finance 2 · Management 1 · AI 1 | 0 |
| 31 | [Bureau of Economic Analysis](https://scholar4ai.com/datasets/us-macroeconomic-data.md#bureau-of-economic-analysis) | [US macroeconomic data](https://scholar4ai.com/datasets/us-macroeconomic-data.md) | 4 | Economics 3 · Finance 1 | 0 |
| 31 | [China Health and Nutrition Survey (CHNS)](https://scholar4ai.com/datasets/china-household-and-social-surveys.md#china-health-and-nutrition-survey-chns) | [China household and social surveys](https://scholar4ai.com/datasets/china-household-and-social-surveys.md) | 4 | Economics 2 · Finance 1 · Operations Research 1 | 1 |
| 31 | [CIFAR-10 / CIFAR-100](https://scholar4ai.com/datasets/image-classification-benchmarks.md#cifar-10-cifar-100) | [Image classification benchmarks](https://scholar4ai.com/datasets/image-classification-benchmarks.md) | 4 | AI 4 | 0 |
| 31 | [FRED](https://scholar4ai.com/datasets/us-macroeconomic-data.md#fred) | [US macroeconomic data](https://scholar4ai.com/datasets/us-macroeconomic-data.md) | 4 | Economics 2 · Finance 1 · Management 1 | 2 |
| 31 | [Kenneth French Data Library](https://scholar4ai.com/datasets/asset-pricing-factor-data.md#kenneth-french-data-library) | [Asset pricing factor data](https://scholar4ai.com/datasets/asset-pricing-factor-data.md) | 4 | Finance 4 | 0 |
| 31 | [SHARE](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md#share) | [European and other national household panels](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md) | 4 | Economics 4 | 1 |
| 31 | [WRDS](https://scholar4ai.com/datasets/financial-data-platforms.md#wrds) | [Financial data platforms](https://scholar4ai.com/datasets/financial-data-platforms.md) | 4 | Finance 2 · Economics 1 · AI 1 | 1 |
| 39 | [BoardEx](https://scholar4ai.com/datasets/corporate-governance-and-executive-data.md#boardex) | [Corporate governance and executive data](https://scholar4ai.com/datasets/corporate-governance-and-executive-data.md) | 3 | Management 2 · Finance 1 | 0 |
| 39 | [CDP (Carbon Disclosure Project)](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md#cdp-carbon-disclosure-project) | [ESG and sustainability ratings](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md) | 3 | Management 2 · Finance 1 | 0 |
| 39 | [CHARLS](https://scholar4ai.com/datasets/china-household-and-social-surveys.md#charls) | [China household and social surveys](https://scholar4ai.com/datasets/china-household-and-social-surveys.md) | 3 | Economics 3 | 0 |
| 39 | [China Statistical Yearbook](https://scholar4ai.com/datasets/china-statistical-yearbooks-and-regional-indices.md#china-statistical-yearbook) | [China statistical yearbooks and regional indices](https://scholar4ai.com/datasets/china-statistical-yearbooks-and-regional-indices.md) | 3 | Economics 2 · Finance 1 | 0 |
| 39 | [Credit registers (e.g. AnaCredit)](https://scholar4ai.com/datasets/bank-regulatory-data.md#credit-registers-e-g-anacredit) | [Bank regulatory data](https://scholar4ai.com/datasets/bank-regulatory-data.md) | 3 | Finance 1 · Accounting 1 · Economics 1 | 0 |
| 39 | [Crunchbase](https://scholar4ai.com/datasets/m-a-ipo-and-private-capital-data.md#crunchbase) | [M&A, IPO and private capital data](https://scholar4ai.com/datasets/m-a-ipo-and-private-capital-data.md) | 3 | Management 2 · Finance 1 | 0 |
| 39 | [Current Population Survey](https://scholar4ai.com/datasets/us-household-and-labour-surveys.md#current-population-survey) | [US household and labour surveys](https://scholar4ai.com/datasets/us-household-and-labour-surveys.md) | 3 | Economics 3 | 0 |
| 39 | [Dow Jones Industrial Average](https://scholar4ai.com/datasets/stock-market-indices.md#dow-jones-industrial-average) | [Stock market indices](https://scholar4ai.com/datasets/stock-market-indices.md) | 3 | Finance 1 · Management 1 · AI 1 | 0 |
| 39 | [Facebook](https://scholar4ai.com/datasets/social-media-data.md#facebook) | [Social media data](https://scholar4ai.com/datasets/social-media-data.md) | 3 | AI 2 · Finance 1 | 0 |
| 39 | [German Socio-Economic Panel (SOEP)](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md#german-socio-economic-panel-soep) | [European and other national household panels](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md) | 3 | Economics 2 · Finance 1 | 0 |
| 39 | [Huazheng / Sino-Securities ESG](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md#huazheng-sino-securities-esg) | [ESG and sustainability ratings](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md) | 3 | Finance 2 · Management 1 | 0 |
| 39 | [ImageNet](https://scholar4ai.com/datasets/image-classification-benchmarks.md#imagenet) | [Image classification benchmarks](https://scholar4ai.com/datasets/image-classification-benchmarks.md) | 3 | AI 3 | 1 |
| 39 | [Investing.com](https://scholar4ai.com/datasets/financial-data-platforms.md#investing-com) | [Financial data platforms](https://scholar4ai.com/datasets/financial-data-platforms.md) | 3 | Finance 1 · Economics 1 · Management 1 | 0 |
| 39 | [KITTI](https://scholar4ai.com/datasets/object-detection-and-segmentation-benchmarks.md#kitti) | [Object detection and segmentation benchmarks](https://scholar4ai.com/datasets/object-detection-and-segmentation-benchmarks.md) | 3 | AI 3 | 0 |
| 39 | [LendingClub](https://scholar4ai.com/datasets/credit-scoring-and-lending-data.md#lendingclub) | [Credit scoring and lending data](https://scholar4ai.com/datasets/credit-scoring-and-lending-data.md) | 3 | Finance 2 · Management 1 | 0 |
| 39 | [MSCI ESG / KLD](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md#msci-esg-kld) | [ESG and sustainability ratings](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md) | 3 | Management 2 · Accounting 1 | 0 |
| 39 | [National Bureau of Statistics of China](https://scholar4ai.com/datasets/china-statistical-yearbooks-and-regional-indices.md#national-bureau-of-statistics-of-china) | [China statistical yearbooks and regional indices](https://scholar4ai.com/datasets/china-statistical-yearbooks-and-regional-indices.md) | 3 | Economics 2 · Finance 1 | 0 |
| 39 | [Orbis (Bureau van Dijk)](https://scholar4ai.com/datasets/company-financial-statements.md#orbis-bureau-van-dijk) | [Company financial statements](https://scholar4ai.com/datasets/company-financial-statements.md) | 3 | Management 2 · Economics 1 | 1 |
| 39 | [Refinitiv Eikon / LSEG Workspace](https://scholar4ai.com/datasets/financial-data-platforms.md#refinitiv-eikon-lseg-workspace) | [Financial data platforms](https://scholar4ai.com/datasets/financial-data-platforms.md) | 3 | Finance 3 | 0 |
| 39 | [TRACE](https://scholar4ai.com/datasets/bond-loan-and-credit-data.md#trace) | [Bond, loan and credit data](https://scholar4ai.com/datasets/bond-loan-and-credit-data.md) | 3 | Finance 1 · Management 1 · AI 1 | 0 |
| 39 | [WTI crude oil](https://scholar4ai.com/datasets/commodity-prices.md#wti-crude-oil) | [Commodity prices](https://scholar4ai.com/datasets/commodity-prices.md) | 3 | Finance 1 · Economics 1 · AI 1 | 1 |
| 39 | [Yahoo Finance](https://scholar4ai.com/datasets/financial-data-platforms.md#yahoo-finance) | [Financial data platforms](https://scholar4ai.com/datasets/financial-data-platforms.md) | 3 | Finance 2 · Operations Research 1 | 0 |
| 61 | [Bloomberg](https://scholar4ai.com/datasets/financial-data-platforms.md#bloomberg) | [Financial data platforms](https://scholar4ai.com/datasets/financial-data-platforms.md) | 2 | Finance 1 · Accounting 1 | 0 |
| 61 | [Bloomberg ESG](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md#bloomberg-esg) | [ESG and sustainability ratings](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md) | 2 | Finance 2 | 0 |
| 61 | [Brent crude oil](https://scholar4ai.com/datasets/commodity-prices.md#brent-crude-oil) | [Commodity prices](https://scholar4ai.com/datasets/commodity-prices.md) | 2 | Finance 1 · AI 1 | 0 |
| 61 | [Bureau of Labor Statistics](https://scholar4ai.com/datasets/us-macroeconomic-data.md#bureau-of-labor-statistics) | [US macroeconomic data](https://scholar4ai.com/datasets/us-macroeconomic-data.md) | 2 | Economics 1 · Management 1 | 0 |
| 61 | [China City Statistical Yearbook](https://scholar4ai.com/datasets/china-statistical-yearbooks-and-regional-indices.md#china-city-statistical-yearbook) | [China statistical yearbooks and regional indices](https://scholar4ai.com/datasets/china-statistical-yearbooks-and-regional-indices.md) | 2 | Finance 1 · Management 1 | 0 |
| 61 | [Chinese General Social Survey (CGSS)](https://scholar4ai.com/datasets/china-household-and-social-surveys.md#chinese-general-social-survey-cgss) | [China household and social surveys](https://scholar4ai.com/datasets/china-household-and-social-surveys.md) | 2 | Economics 2 | 1 |
| 61 | [CoinGecko](https://scholar4ai.com/datasets/cryptocurrency-data.md#coingecko) | [Cryptocurrency data](https://scholar4ai.com/datasets/cryptocurrency-data.md) | 2 | Finance 2 | 0 |
| 61 | [Consumer Expenditure Survey](https://scholar4ai.com/datasets/us-household-and-labour-surveys.md#consumer-expenditure-survey) | [US household and labour surveys](https://scholar4ai.com/datasets/us-household-and-labour-surveys.md) | 2 | Economics 1 · Management 1 | 0 |
| 61 | [EU-SILC](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md#eu-silc) | [European and other national household panels](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md) | 2 | Economics 2 | 1 |
| 61 | [ExecuComp](https://scholar4ai.com/datasets/corporate-governance-and-executive-data.md#execucomp) | [Corporate governance and executive data](https://scholar4ai.com/datasets/corporate-governance-and-executive-data.md) | 2 | Finance 1 · Management 1 | 0 |
| 61 | [FactSet](https://scholar4ai.com/datasets/financial-data-platforms.md#factset) | [Financial data platforms](https://scholar4ai.com/datasets/financial-data-platforms.md) | 2 | Finance 2 | 0 |
| 61 | [GDELT](https://scholar4ai.com/datasets/search-news-and-web-data.md#gdelt) | [Search, news and web data](https://scholar4ai.com/datasets/search-news-and-web-data.md) | 2 | Finance 1 · AI 1 | 0 |
| 61 | [German Credit (UCI Statlog)](https://scholar4ai.com/datasets/credit-scoring-and-lending-data.md#german-credit-uci-statlog) | [Credit scoring and lending data](https://scholar4ai.com/datasets/credit-scoring-and-lending-data.md) | 2 | AI 2 | 0 |
| 61 | [Global Financial Development Database](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md#global-financial-development-database) | [Macroeconomic and development indicators](https://scholar4ai.com/datasets/macroeconomic-and-development-indicators.md) | 2 | Finance 1 · Economics 1 | 1 |
| 61 | [Hexun CSR ratings](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md#hexun-csr-ratings) | [ESG and sustainability ratings](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md) | 2 | Finance 1 · Management 1 | 0 |
| 61 | [Hou-Xue-Zhang q-factors](https://scholar4ai.com/datasets/asset-pricing-factor-data.md#hou-xue-zhang-q-factors) | [Asset pricing factor data](https://scholar4ai.com/datasets/asset-pricing-factor-data.md) | 2 | Finance 1 · Accounting 1 | 0 |
| 61 | [Household Finance and Consumption Survey](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md#household-finance-and-consumption-survey) | [European and other national household panels](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md) | 2 | Finance 2 | 0 |
| 61 | [I/B/E/S](https://scholar4ai.com/datasets/analyst-forecasts.md#i-b-e-s) | [Analyst forecasts](https://scholar4ai.com/datasets/analyst-forecasts.md) | 2 | Finance 2 | 0 |
| 61 | [IPUMS](https://scholar4ai.com/datasets/us-household-and-labour-surveys.md#ipums) | [US household and labour surveys](https://scholar4ai.com/datasets/us-household-and-labour-surveys.md) | 2 | Economics 1 · Management 1 | 0 |
| 61 | [Morningstar](https://scholar4ai.com/datasets/mutual-fund-and-hedge-fund-data.md#morningstar) | [Mutual fund and hedge fund data](https://scholar4ai.com/datasets/mutual-fund-and-hedge-fund-data.md) | 2 | Finance 2 | 0 |
| 61 | [NYSE TAQ](https://scholar4ai.com/datasets/us-stock-market-data.md#nyse-taq) | [US stock market data](https://scholar4ai.com/datasets/us-stock-market-data.md) | 2 | Finance 1 · Economics 1 | 1 |
| 61 | [OVX (crude oil volatility index)](https://scholar4ai.com/datasets/options-and-volatility-data.md#ovx-crude-oil-volatility-index) | [Options and volatility data](https://scholar4ai.com/datasets/options-and-volatility-data.md) | 2 | Finance 2 | 0 |
| 61 | [Refinitiv ESG (ASSET4)](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md#refinitiv-esg-asset4) | [ESG and sustainability ratings](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md) | 2 | Management 1 · Operations Research 1 | 0 |
| 61 | [StockTwits](https://scholar4ai.com/datasets/social-media-data.md#stocktwits) | [Social media data](https://scholar4ai.com/datasets/social-media-data.md) | 2 | Finance 2 | 0 |
| 61 | [SynTao Green Finance](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md#syntao-green-finance) | [ESG and sustainability ratings](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md) | 2 | Finance 1 · Economics 1 | 0 |
| 61 | [Thomson Reuters 13F holdings](https://scholar4ai.com/datasets/ownership-and-insider-trading.md#thomson-reuters-13f-holdings) | [Ownership and insider trading](https://scholar4ai.com/datasets/ownership-and-insider-trading.md) | 2 | Finance 1 · Operations Research 1 | 0 |
| 61 | [TIMSS / PIRLS](https://scholar4ai.com/datasets/education-data.md#timss-pirls) | [Education data](https://scholar4ai.com/datasets/education-data.md) | 2 | Finance 1 · Economics 1 | 0 |
| 61 | [Understanding Society / BHPS](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md#understanding-society-bhps) | [European and other national household panels](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md) | 2 | Economics 2 | 0 |
| 61 | [World Bank Enterprise Surveys](https://scholar4ai.com/datasets/firm-surveys-and-census-data.md#world-bank-enterprise-surveys) | [Firm surveys and census data](https://scholar4ai.com/datasets/firm-surveys-and-census-data.md) | 2 | Finance 1 · Economics 1 | 0 |
| 61 | [XBRL filings](https://scholar4ai.com/datasets/corporate-filings-and-text.md#xbrl-filings) | [Corporate filings and text](https://scholar4ai.com/datasets/corporate-filings-and-text.md) | 2 | Economics 1 · Management 1 | 1 |
| 61 | [Zacks](https://scholar4ai.com/datasets/analyst-forecasts.md#zacks) | [Analyst forecasts](https://scholar4ai.com/datasets/analyst-forecasts.md) | 2 | Finance 1 · Economics 1 | 0 |
| 92 | [Action recognition (Kinetics, UCF101, HMDB51)](https://scholar4ai.com/datasets/face-person-and-video-benchmarks.md#action-recognition-kinetics-ucf101-hmdb51) | [Face, person and video benchmarks](https://scholar4ai.com/datasets/face-person-and-video-benchmarks.md) | 1 | AI 1 | 0 |
| 92 | [Amadeus (Bureau van Dijk)](https://scholar4ai.com/datasets/company-financial-statements.md#amadeus-bureau-van-dijk) | [Company financial statements](https://scholar4ai.com/datasets/company-financial-statements.md) | 1 | Accounting 1 | 0 |
| 92 | [Amazon reviews](https://scholar4ai.com/datasets/online-reviews-e-commerce-and-recommender-data.md#amazon-reviews) | [Online reviews, e-commerce and recommender data](https://scholar4ai.com/datasets/online-reviews-e-commerce-and-recommender-data.md) | 1 | AI 1 | 0 |
| 92 | [Audit Analytics](https://scholar4ai.com/datasets/corporate-governance-and-executive-data.md#audit-analytics) | [Corporate governance and executive data](https://scholar4ai.com/datasets/corporate-governance-and-executive-data.md) | 1 | Accounting 1 | 0 |
| 92 | [BankFocus / BankScope](https://scholar4ai.com/datasets/bank-regulatory-data.md#bankfocus-bankscope) | [Bank regulatory data](https://scholar4ai.com/datasets/bank-regulatory-data.md) | 1 | Finance 1 | 0 |
| 92 | [Call Reports](https://scholar4ai.com/datasets/bank-regulatory-data.md#call-reports) | [Bank regulatory data](https://scholar4ai.com/datasets/bank-regulatory-data.md) | 1 | Accounting 1 | 0 |
| 92 | [CEC benchmark functions](https://scholar4ai.com/datasets/optimization-benchmark-instances.md#cec-benchmark-functions) | [Optimization benchmark instances](https://scholar4ai.com/datasets/optimization-benchmark-instances.md) | 1 | AI 1 | 0 |
| 92 | [China Energy Statistical Yearbook](https://scholar4ai.com/datasets/china-statistical-yearbooks-and-regional-indices.md#china-energy-statistical-yearbook) | [China statistical yearbooks and regional indices](https://scholar4ai.com/datasets/china-statistical-yearbooks-and-regional-indices.md) | 1 | Economics 1 | 0 |
| 92 | [China Labor-force Dynamics Survey (CLDS)](https://scholar4ai.com/datasets/china-household-and-social-surveys.md#china-labor-force-dynamics-survey-clds) | [China household and social surveys](https://scholar4ai.com/datasets/china-household-and-social-surveys.md) | 1 | AI 1 | 0 |
| 92 | [CMIP climate projections](https://scholar4ai.com/datasets/weather-climate-and-disaster-data.md#cmip-climate-projections) | [Weather, climate and disaster data](https://scholar4ai.com/datasets/weather-climate-and-disaster-data.md) | 1 | Economics 1 | 0 |
| 92 | [CoinMarketCap](https://scholar4ai.com/datasets/cryptocurrency-data.md#coinmarketcap) | [Cryptocurrency data](https://scholar4ai.com/datasets/cryptocurrency-data.md) | 1 | Finance 1 | 0 |
| 92 | [Corruption Perceptions Index](https://scholar4ai.com/datasets/governance-and-institutional-indicators.md#corruption-perceptions-index) | [Governance and institutional indicators](https://scholar4ai.com/datasets/governance-and-institutional-indicators.md) | 1 | Management 1 | 0 |
| 92 | [Credit bureaus (Equifax, Experian, TransUnion)](https://scholar4ai.com/datasets/credit-scoring-and-lending-data.md#credit-bureaus-equifax-experian-transunion) | [Credit scoring and lending data](https://scholar4ai.com/datasets/credit-scoring-and-lending-data.md) | 1 | Economics 1 | 0 |
| 92 | [D4RL](https://scholar4ai.com/datasets/reinforcement-learning-environments.md#d4rl) | [Reinforcement learning environments](https://scholar4ai.com/datasets/reinforcement-learning-environments.md) | 1 | AI 1 | 0 |
| 92 | [DealScan](https://scholar4ai.com/datasets/bond-loan-and-credit-data.md#dealscan) | [Bond, loan and credit data](https://scholar4ai.com/datasets/bond-loan-and-credit-data.md) | 1 | Finance 1 | 0 |
| 92 | [Demographic and Health Surveys (DHS)](https://scholar4ai.com/datasets/developing-country-household-surveys.md#demographic-and-health-surveys-dhs) | [Developing-country household surveys](https://scholar4ai.com/datasets/developing-country-household-surveys.md) | 1 | Economics 1 | 1 |
| 92 | [DOTA / xView](https://scholar4ai.com/datasets/remote-sensing-benchmarks.md#dota-xview) | [Remote sensing benchmarks](https://scholar4ai.com/datasets/remote-sensing-benchmarks.md) | 1 | AI 1 | 0 |
| 92 | [Face recognition (LFW, CelebA, VGGFace2)](https://scholar4ai.com/datasets/face-person-and-video-benchmarks.md#face-recognition-lfw-celeba-vggface2) | [Face, person and video benchmarks](https://scholar4ai.com/datasets/face-person-and-video-benchmarks.md) | 1 | AI 1 | 1 |
| 92 | [FR Y-9C](https://scholar4ai.com/datasets/bank-regulatory-data.md#fr-y-9c) | [Bank regulatory data](https://scholar4ai.com/datasets/bank-regulatory-data.md) | 1 | Accounting 1 | 0 |
| 92 | [Google Trends](https://scholar4ai.com/datasets/search-news-and-web-data.md#google-trends) | [Search, news and web data](https://scholar4ai.com/datasets/search-news-and-web-data.md) | 1 | Management 1 | 0 |
| 92 | [HILDA](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md#hilda) | [European and other national household panels](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md) | 1 | Economics 1 | 0 |
| 92 | [ISIC skin lesion archive](https://scholar4ai.com/datasets/medical-and-clinical-datasets.md#isic-skin-lesion-archive) | [Medical and clinical datasets](https://scholar4ai.com/datasets/medical-and-clinical-datasets.md) | 1 | AI 1 | 0 |
| 92 | [Japan and Korea household panels](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md#japan-and-korea-household-panels) | [European and other national household panels](https://scholar4ai.com/datasets/european-and-other-national-household-panels.md) | 1 | Economics 1 | 1 |
| 92 | [LinkedIn](https://scholar4ai.com/datasets/job-postings-and-occupation-data.md#linkedin) | [Job postings and occupation data](https://scholar4ai.com/datasets/job-postings-and-occupation-data.md) | 1 | Finance 1 | 0 |
| 92 | [Living Standards Measurement Study (LSMS)](https://scholar4ai.com/datasets/developing-country-household-surveys.md#living-standards-measurement-study-lsms) | [Developing-country household surveys](https://scholar4ai.com/datasets/developing-country-household-surveys.md) | 1 | Economics 1 | 0 |
| 92 | [London Metal Exchange / LBMA](https://scholar4ai.com/datasets/commodity-prices.md#london-metal-exchange-lbma) | [Commodity prices](https://scholar4ai.com/datasets/commodity-prices.md) | 1 | Economics 1 | 0 |
| 92 | [Markit CDS](https://scholar4ai.com/datasets/bond-loan-and-credit-data.md#markit-cds) | [Bond, loan and credit data](https://scholar4ai.com/datasets/bond-loan-and-credit-data.md) | 1 | Economics 1 | 0 |
| 92 | [MIMIC](https://scholar4ai.com/datasets/medical-and-clinical-datasets.md#mimic) | [Medical and clinical datasets](https://scholar4ai.com/datasets/medical-and-clinical-datasets.md) | 1 | AI 1 | 0 |
| 92 | [Natural language inference (SNLI, MultiNLI)](https://scholar4ai.com/datasets/text-and-nlp-benchmarks.md#natural-language-inference-snli-multinli) | [Text and NLP benchmarks](https://scholar4ai.com/datasets/text-and-nlp-benchmarks.md) | 1 | AI 1 | 1 |
| 92 | [Night-time lights (VIIRS, DMSP)](https://scholar4ai.com/datasets/satellite-and-geospatial-data.md#night-time-lights-viirs-dmsp) | [Satellite and geospatial data](https://scholar4ai.com/datasets/satellite-and-geospatial-data.md) | 1 | Economics 1 | 0 |
| 92 | [Nikkei 225 / TOPIX](https://scholar4ai.com/datasets/stock-market-indices.md#nikkei-225-topix) | [Stock market indices](https://scholar4ai.com/datasets/stock-market-indices.md) | 1 | Finance 1 | 0 |
| 92 | [nuScenes / Waymo Open / Argoverse](https://scholar4ai.com/datasets/object-detection-and-segmentation-benchmarks.md#nuscenes-waymo-open-argoverse) | [Object detection and segmentation benchmarks](https://scholar4ai.com/datasets/object-detection-and-segmentation-benchmarks.md) | 1 | AI 1 | 0 |
| 92 | [OpenStreetMap](https://scholar4ai.com/datasets/satellite-and-geospatial-data.md#openstreetmap) | [Satellite and geospatial data](https://scholar4ai.com/datasets/satellite-and-geospatial-data.md) | 1 | Economics 1 | 1 |
| 92 | [Oxford COVID-19 Government Response Tracker](https://scholar4ai.com/datasets/health-data.md#oxford-covid-19-government-response-tracker) | [Health data](https://scholar4ai.com/datasets/health-data.md) | 1 | Finance 1 | 0 |
| 92 | [Peking University Digital Financial Inclusion Index](https://scholar4ai.com/datasets/china-statistical-yearbooks-and-regional-indices.md#peking-university-digital-financial-inclusion-index) | [China statistical yearbooks and regional indices](https://scholar4ai.com/datasets/china-statistical-yearbooks-and-regional-indices.md) | 1 | Finance 1 | 0 |
| 92 | [PSID](https://scholar4ai.com/datasets/us-household-and-labour-surveys.md#psid) | [US household and labour surveys](https://scholar4ai.com/datasets/us-household-and-labour-surveys.md) | 1 | Economics 1 | 0 |
| 92 | [PSPLIB](https://scholar4ai.com/datasets/optimization-benchmark-instances.md#psplib) | [Optimization benchmark instances](https://scholar4ai.com/datasets/optimization-benchmark-instances.md) | 1 | Operations Research 1 | 0 |
| 92 | [Reddit](https://scholar4ai.com/datasets/social-media-data.md#reddit) | [Social media data](https://scholar4ai.com/datasets/social-media-data.md) | 1 | AI 1 | 0 |
| 92 | [RepRisk](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md#reprisk) | [ESG and sustainability ratings](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md) | 1 | Finance 1 | 0 |
| 92 | [Retrieval and QA (MS MARCO, Natural Questions, HotpotQA)](https://scholar4ai.com/datasets/text-and-nlp-benchmarks.md#retrieval-and-qa-ms-marco-natural-questions-hotpotqa) | [Text and NLP benchmarks](https://scholar4ai.com/datasets/text-and-nlp-benchmarks.md) | 1 | AI 1 | 0 |
| 92 | [S&P Capital IQ](https://scholar4ai.com/datasets/company-financial-statements.md#s-p-capital-iq) | [Company financial statements](https://scholar4ai.com/datasets/company-financial-statements.md) | 1 | Management 1 | 0 |
| 92 | [S&P CoreLogic Case-Shiller](https://scholar4ai.com/datasets/real-estate-and-housing-data.md#s-p-corelogic-case-shiller) | [Real estate and housing data](https://scholar4ai.com/datasets/real-estate-and-housing-data.md) | 1 | Accounting 1 | 0 |
| 92 | [SEC EDGAR](https://scholar4ai.com/datasets/corporate-filings-and-text.md#sec-edgar) | [Corporate filings and text](https://scholar4ai.com/datasets/corporate-filings-and-text.md) | 1 | Economics 1 | 1 |
| 92 | [SIPP](https://scholar4ai.com/datasets/us-household-and-labour-surveys.md#sipp) | [US household and labour surveys](https://scholar4ai.com/datasets/us-household-and-labour-surveys.md) | 1 | AI 1 | 0 |
| 92 | [Speech emotion (IEMOCAP, RAVDESS)](https://scholar4ai.com/datasets/speech-and-audio-benchmarks.md#speech-emotion-iemocap-ravdess) | [Speech and audio benchmarks](https://scholar4ai.com/datasets/speech-and-audio-benchmarks.md) | 1 | AI 1 | 0 |
| 92 | [SUMO traffic simulator](https://scholar4ai.com/datasets/reinforcement-learning-environments.md#sumo-traffic-simulator) | [Reinforcement learning environments](https://scholar4ai.com/datasets/reinforcement-learning-environments.md) | 1 | AI 1 | 1 |
| 92 | [Survey of Consumer Finances](https://scholar4ai.com/datasets/us-household-and-labour-surveys.md#survey-of-consumer-finances) | [US household and labour surveys](https://scholar4ai.com/datasets/us-household-and-labour-surveys.md) | 1 | Economics 1 | 0 |
| 92 | [Survey of Professional Forecasters](https://scholar4ai.com/datasets/us-macroeconomic-data.md#survey-of-professional-forecasters) | [US macroeconomic data](https://scholar4ai.com/datasets/us-macroeconomic-data.md) | 1 | Finance 1 | 0 |
| 92 | [Taiwan credit card default (UCI)](https://scholar4ai.com/datasets/credit-scoring-and-lending-data.md#taiwan-credit-card-default-uci) | [Credit scoring and lending data](https://scholar4ai.com/datasets/credit-scoring-and-lending-data.md) | 1 | Finance 1 | 0 |
| 92 | [Taobao / Tmall / JD.com](https://scholar4ai.com/datasets/online-reviews-e-commerce-and-recommender-data.md#taobao-tmall-jd-com) | [Online reviews, e-commerce and recommender data](https://scholar4ai.com/datasets/online-reviews-e-commerce-and-recommender-data.md) | 1 | AI 1 | 0 |
| 92 | [Tennessee Eastman process](https://scholar4ai.com/datasets/industrial-fault-and-prognostics-benchmarks.md#tennessee-eastman-process) | [Industrial fault and prognostics benchmarks](https://scholar4ai.com/datasets/industrial-fault-and-prognostics-benchmarks.md) | 1 | AI 1 | 0 |
| 92 | [Traffic (PeMS, METR-LA)](https://scholar4ai.com/datasets/time-series-and-traffic-benchmarks.md#traffic-pems-metr-la) | [Time-series and traffic benchmarks](https://scholar4ai.com/datasets/time-series-and-traffic-benchmarks.md) | 1 | AI 1 | 0 |
| 92 | [Trucost](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md#trucost) | [ESG and sustainability ratings](https://scholar4ai.com/datasets/esg-and-sustainability-ratings.md) | 1 | Finance 1 | 0 |
| 92 | [Wikipedia](https://scholar4ai.com/datasets/search-news-and-web-data.md#wikipedia) | [Search, news and web data](https://scholar4ai.com/datasets/search-news-and-web-data.md) | 1 | Finance 1 | 0 |
| 92 | [World Bank Doing Business](https://scholar4ai.com/datasets/governance-and-institutional-indicators.md#world-bank-doing-business) | [Governance and institutional indicators](https://scholar4ai.com/datasets/governance-and-institutional-indicators.md) | 1 | Management 1 | 0 |
| 92 | [Worldwide Governance Indicators](https://scholar4ai.com/datasets/governance-and-institutional-indicators.md#worldwide-governance-indicators) | [Governance and institutional indicators](https://scholar4ai.com/datasets/governance-and-institutional-indicators.md) | 1 | Economics 1 | 0 |
| 92 | [YouTube / TikTok](https://scholar4ai.com/datasets/social-media-data.md#youtube-tiktok) | [Social media data](https://scholar4ai.com/datasets/social-media-data.md) | 1 | Operations Research 1 | 0 |

Data categories with no paper in this window: Cross-country social and attitude surveys, Cybersecurity and anomaly detection benchmarks, Energy and emissions data, Financial NLP datasets, Graph learning benchmarks, International trade and input-output data, LLM evaluation benchmarks, Patents and innovation data, Public machine learning repositories.

Watched datasets with no paper in this window (223): AAII sentiment survey, ADE20K, ADNI, Afrobarometer / Latinobarometro, AG News / 20 Newsgroups, Airbnb, American Community Survey, American Time Use Survey, AQR factors, Atari (Arcade Learning Environment), AudioSet / ESC-50, Australian Credit (UCI), Baidu Index, Baker-Wurgler investor sentiment, Battery ageing data (NASA, CALCE), BIG-bench, BraTS, Caltech-101 / 256, CEADs (China emission accounts), CEPII BACI, Chest X-ray (CheXpert, ChestX-ray14), China Customs data, China Environmental Statistical Yearbook, China housing platforms (Lianjia, Anjuke, Fang.com), China patent office (CNIPA / SIPO), Chinese Household Income Project (CHIP), Chinese Industrial Enterprises Database, Chinese investor forums (Guba, Xueqiu), Chinn-Ito financial openness index, CIC-IDS2017 / CSE-CIC-IDS2018, Cityscapes, CLHLS, CMIE Prowess, Common Voice, CoNLL, Cora / CiteSeer / PubMed (Planetoid), CoreLogic, Credit card fraud data (Kaggle, PaySim, IEEE-CIS), CRSP Mutual Fund Database, CRU climate data, CryptoCompare, CVRPLIB, CWRU bearing data, DeepMind Control Suite, Domain adaptation benchmarks (Office-31, Office-Home, VisDA, DomainNet), Early Childhood Longitudinal Study, ECB SAFE survey, Economic freedom indices, EDGAR emissions database, EM-DAT disaster database, English Longitudinal Study of Ageing, ENTSO-E / Nord Pool electricity data, Eora, ERA5 reanalysis, Etherscan, ETT (electricity transformer temperature), EU ETS transaction log, Eurobarometer, European Patent Office / WIPO, European Social Survey, eVestment, Facial expression (FER2013, AffectNet, RAF-DB), Factiva, Fannie Mae / Freddie Mac loan data, FDIC Summary of Deposits, FEMA disaster data, FHFA house price index, FinanceBench, Financial PhraseBank, Financial stress indices, Fine-grained benchmarks (CUB-200, Stanford Cars, Oxford Flowers), FinQA / ConvFinQA / TAT-QA, FiQA, Freedom House, FTSE indices, Gallup World Poll, Geopolitical Risk index, Give Me Some Credit, Glassdoor, Glassnode, Global Burden of Disease, Global Carbon Project, Global Factor Data (JKP), Global Findex, Global Trade Alert, GLUE / SuperGLUE, Google Earth Engine, Google Patents, GSM8K / MATH, GTAP, Hedge Fund Research (HFR), Henry Hub natural gas, HMDA mortgage data, Home Credit, Houston 2013 / 2018, Hugging Face Hub, HumanEval / MBPP, iFinD, IMDb, Indeed, India Annual Survey of Industries, India Human Development Survey, India National Sample Survey, Indian Pines, Indonesia Family Life Survey, Industrial control anomalies (SWaT, WADI), Insider trading filings (Form 4), International Country Risk Guide (ICRG), International Energy Agency (IEA), International Social Survey Programme, IoT intrusion data (Bot-IoT, TON_IoT, CICIoT2023), IPEDS, ISS (Institutional Shareholder Services), Johns Hopkins COVID-19 data, Kaggle, Kaiko, Kauffman Firm Survey, KEEL, Landsat, LexisNexis, LibriSpeech, LIDC-IDRI, Lightcast (Burning Glass), LISS panel, Location and social recommendation data (Gowalla, Foursquare, Yelp, Epinions), M3 / M4 / M5 competitions, Maddison Project, Mergent FISD, MIPLIB, MMLU, MODIS, Molecule benchmarks (QM9, ZINC, MUTAG), MovieLens, MS COCO, MT-Bench / Chatbot Arena, MuJoCo, Multi-objective test problems (ZDT, DTLZ, WFG), Multiple Indicator Cluster Surveys, NAEP, NASA C-MAPSS turbofan data, NBER patent data, Netflix Prize, NLSY, NOAA, NOAA Storm Events Database, NSL-KDD / KDD Cup 99, NYC taxi trips, O*NET, OECD TiVA / ICIO, Open Graph Benchmark, Open Images, Open Source Asset Pricing, OpenAI Gym / Gymnasium, OpenML, OptionMetrics, OR-Library, Our World in Data, Paderborn bearing data, PASCAL VOC, PATSTAT, Pavia University / Pavia Centre, Person re-identification (Market-1501, DukeMTMC, MSMT17), PhysioNet / MIT-BIH, PISA, PitchBook, Polity, Pre-training corpora (Common Crawl, C4, The Pile), Preqin, PRONOSTIA / PHM 2012 bearings, Prosper, RavenPack, RESSET, Revelio Labs, Russell 1000 / 2000 / 3000, S&P Global Market Intelligence (SNL), S&P GSCI / Bloomberg Commodity Index, Salinas, Scene classification (NWPU-RESISC45, UC Merced, AID), SDC Platinum, Seeking Alpha, SEER cancer registry, Sentinel / Copernicus, Skeleton and pose (NTU RGB+D, Human3.6M), Solomon VRPTW instances, SQuAD, Stanford Education Data Archive, Stanford Sentiment Treebank, StarCraft, Statistical Review of World Energy, Sustainalytics, SVHN, Taillard instances, TCGA, TIMIT, TripAdvisor, TruthfulQA / HellaSwag / ARC, TSPLIB, UCI Machine Learning Repository, UCR / UEA archives, UK Biobank, UN Comtrade, UNSW-NB15, US Census business data (LBD, CBP, QCEW), US Energy Information Administration (EIA), US EPA emissions inventories (GHGRP, TRI), US health surveys (NHANES, BRFSS, MEPS), USPTO / PatentsView, V-Dem, VentureXpert / VentureSource, VoxCeleb, Weibo, WHO Global Health Observatory, WITS / TRAINS tariff data, World Bank Pink Sheet, World Input-Output Database (WIOD), World Uncertainty Index, World Values Survey, Worldscope, XJTU-SY bearings, Yelp, Young Lives, Zephyr (Bureau van Dijk), Zillow (ZTRAX, ZHVI).

## How the datasets are found

The site watches a fixed list of 371 datasets in 58 data categories, each with the names papers use for it (full names, abbreviations and common spellings). A paper counts when one of them appears in its title, abstract or OpenAlex keywords, or, for papers whose full text OpenAlex has indexed, anywhere in the full text (found with OpenAlex full-text search).

In this window OpenAlex has the abstract of 773 of the 2,971 papers (26%) and searchable full text for 545 (18%); titles and keywords are always there.

The full-text lookups go a group of names at a time, the stalest first, as far as the day's OpenAlex request budget allows: 30 of the 69 groups have had a lookup so far (between 2026-10-23 and 2026-10-26) and the rest are queued, so for now their counts rest on titles, abstracts and keywords.

The counts are therefore lower bounds that lean toward papers with open full text, and a mention is not proof that a paper uses the equation or dataset. Abstracts are only read to spot the names; they are neither stored nor republished.

## Questions this page answers

### What data is used most in finance, accounting, economics, management, operations research and AI research?

Counting papers that mention a dataset by name, the kinds of data used most in the tracked finance, accounting, economics, management, operations research and AI journals over the last 30 days were Macroeconomic and development indicators (59), Stock market indices (45), China household and social surveys (26), China statistical yearbooks and regional indices (21) and Financial data platforms (20); the most-mentioned datasets were S&P 500 (31), World Development Indicators (World Bank) (30), Eurostat (23), CSMAR (17) and VIX (16).

### What data is used most in finance research?

Counting papers that mention a dataset by name, the kinds of data used most in the tracked finance journals over the last 30 days were Stock market indices (25), China financial and firm databases (15), Macroeconomic and development indicators (14), Corporate filings and text (12) and Options and volatility data (12); the most-mentioned datasets were S&P 500 (18), CSMAR (14), VIX (12), World Development Indicators (World Bank) (10) and CRSP (9).

### What data is used most in accounting research?

Counting papers that mention a dataset by name, the kinds of data used most in the tracked accounting journals over the last 30 days were Bank regulatory data (2), Company financial statements (2), Financial data platforms (2), Asset pricing factor data (1) and Corporate filings and text (1); the most-mentioned datasets were Amadeus (Bureau van Dijk) (1), Audit Analytics (1), Bloomberg (1), Call Reports (1) and Compustat (1).

### What data is used most in economics research?

Counting papers that mention a dataset by name, the kinds of data used most in the tracked economics journals over the last 30 days were Macroeconomic and development indicators (33), European and other national household panels (14), China household and social surveys (13), US household and labour surveys (9) and China statistical yearbooks and regional indices (7); the most-mentioned datasets were Eurostat (15), World Development Indicators (World Bank) (14), China Family Panel Studies (CFPS) (6), Penn World Table (6) and Labour Force Surveys (5).

### What data is used most in management research?

Counting papers that mention a dataset by name, the kinds of data used most in the tracked management journals over the last 30 days were Macroeconomic and development indicators (9), Company financial statements (6), ESG and sustainability ratings (5), Stock market indices (5) and China statistical yearbooks and regional indices (3); the most-mentioned datasets were World Development Indicators (World Bank) (5), S&P 500 (4), Compustat (3), Eurostat (3) and Lipper / TASS (3).

### What data is used most in operations research?

Counting papers that mention a dataset by name, the kinds of data used most in the tracked operations research journals over the last 30 days were Stock market indices (5), Macroeconomic and development indicators (2), China household and social surveys (1), Corporate filings and text (1) and ESG and sustainability ratings (1); the most-mentioned datasets were S&P 500 (4), Eurostat (2), China Health and Nutrition Survey (CHNS) (1), CRSP (1) and Earnings call transcripts (1).

### What data is used most in AI research?

Counting papers that mention a dataset by name, the kinds of data used most in the tracked AI journals over the last 30 days were Image classification benchmarks (8), Object detection and segmentation benchmarks (3), Social media data (3), China household and social surveys (2) and Credit scoring and lending data (2); the most-mentioned datasets were MNIST / Fashion-MNIST (5), CIFAR-10 / CIFAR-100 (4), ImageNet (3), KITTI (3) and Facebook (2).


---

---
title: "About Scholar4AI: data, method, journals and license"
description: "How the rankings are made, where the data comes from, which journals are tracked, and what is not collected."
url: https://scholar4ai.com/about
markdown_url: https://scholar4ai.com/about.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T11:39:12+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# About

## What this is

Journal Research Rankings: live rankings of research keywords, universities, institution web domains, countries and fields across recent papers in leading Elsevier, Springer Nature and IEEE journals in finance, accounting, economics, management, operations research and AI, refreshed four times a day from OpenAlex. The rankings cover papers published in the registered journals during the last 30 days, overall and per journal category, and are rebuilt every day at 00:00, 06:00, 12:00, 18:00 (UTC+09:00).

## Data source

All bibliographic metadata comes from OpenAlex (https://openalex.org), which publishes it under the CC0 license. Titles link to the publisher's page through the DOI. New papers appear here once OpenAlex has indexed them, usually within a few days of publication.

## License

The rankings, text and data on this site are licensed under CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/): free to share and adapt for non-commercial purposes with attribution; commercial use needs permission. The underlying bibliographic records come from OpenAlex (CC0). When you reuse it, please cite the page URL and OpenAlex.

## How things are counted

A paper counts once for each OpenAlex keyword it has. A paper counts once for each institution its authors are affiliated with. Institutions are grouped by the web domain of their official site (OpenAlex / ROR data). A paper counts once for each domain among its authors' institutions. A paper counts once for each country of its authors' institutions. A paper counts once, for the field of its primary OpenAlex topic. Papers published in each registered journal. A paper counts once for each named equation or model it mentions, in its title, abstract, keywords or full text; a paper that mentions several counts for each of them. Datasets are grouped into data categories. A paper counts once for each dataset it mentions by name, in its title, abstract, keywords or full text, and once for each category with at least one of them. Web domains come from the official site URL of each institution in OpenAlex; the suffix is the last label, or the last two when it is an academic second-level domain such as ac.jp or edu.cn. Rank changes compare today's ranking with the one saved about a week earlier.

## Equations and datasets

The site watches a fixed list of 112 named equations and models in 11 areas and 371 datasets in 58 data categories, each with the names papers use for it (full names, abbreviations and common spellings). A paper counts when one of them appears in its title, abstract or OpenAlex keywords, or, for papers whose full text OpenAlex has indexed, anywhere in the full text (found with OpenAlex full-text search). In this window OpenAlex has the abstract of 773 of the 2,971 papers (26%) and searchable full text for 545 (18%); titles and keywords are always there. The full-text lookups go a group of names at a time, the stalest first, as far as the day's OpenAlex request budget allows: 30 of the 69 groups have had a lookup so far (between 2026-10-23 and 2026-10-26) and the rest are queued, so for now their counts rest on titles, abstracts and keywords. The counts are therefore lower bounds that lean toward papers with open full text, and a mention is not proof that a paper uses the equation or dataset. Abstracts are only read to spot the names; they are neither stored nor republished.

## Archive

Every paper the site has seen is kept in its archive database, including those that have left the ranking window: 45,135 papers collected since 2026-10-23, with daily rankings archived for 5 days since 2026-10-22.

## What is not collected

Only bibliographic metadata is kept: titles, author names, institutions, keywords and topics. Abstracts are read only to spot the names of equations and datasets, then dropped; they are never stored or republished. Contact details such as e-mail addresses are never collected, and free-text affiliation strings are not requested.

## For AI agents and AI search

Every page is available as Markdown: append .md to its URL or send Accept: text/markdown. /llms.txt indexes the site, /llms-full.txt holds the overview, the category dashboards, all rankings and this page in one file, and /data/rankings.json and /data/papers.json contain the full snapshot. Pages carry schema.org data (Dataset, ItemList, FAQPage). Crawlers, AI search engines and AI assistants are welcome; please cite the page URL and OpenAlex.

## Registered journals

*Registered journals*

| Journal | Category | Publisher | ISSN | OpenAlex |
|---|---|---|---|---|
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | AI | Elsevier BV | 0957-4174 | [S13144211](https://openalex.org/S13144211) |
| [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | AI | Elsevier BV | 0031-3203 | [S414566](https://openalex.org/S414566) |
| [Finance research letters](https://scholar4ai.com/journals/finance-research-letters.md) | Finance | Elsevier BV | 1544-6123 | [S88577320](https://openalex.org/S88577320) |
| [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | AI | Elsevier BV | 0950-7051 | [S10169007](https://openalex.org/S10169007) |
| [International Review of Economics & Finance](https://scholar4ai.com/journals/international-review-of-economics-finance.md) | Finance | Elsevier BV | 1059-0560 | [S168273071](https://openalex.org/S168273071) |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | AI | Springer Science+Business Media | 0941-0643 | [S147897268](https://openalex.org/S147897268) |
| [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | AI | Elsevier BV | 0893-6080 | [S123019304](https://openalex.org/S123019304) |
| [Economic Analysis and Policy](https://scholar4ai.com/journals/economic-analysis-and-policy.md) | Economics | Elsevier BV | 0313-5926 | [S2898574612](https://openalex.org/S2898574612) |
| [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | AI | Elsevier BV | 1566-2535 | [S7560371](https://openalex.org/S7560371) |
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | AI | Springer Science+Business Media | 0924-669X | [S74726891](https://openalex.org/S74726891) |
| [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | AI | Elsevier BV | 0306-4573 | [S174847851](https://openalex.org/S174847851) |
| [Energy Policy](https://scholar4ai.com/journals/energy-policy.md) | Economics | Elsevier BV | 0301-4215 | [S175056054](https://openalex.org/S175056054) |
| [Transportation Research Part E Logistics and Transportation Review](https://scholar4ai.com/journals/transportation-research-part-e-logistics-and-transportation-review.md) | Operations Research | Elsevier BV | 1366-5545 | [S173966628](https://openalex.org/S173966628) |
| [Economics Letters](https://scholar4ai.com/journals/economics-letters.md) | Economics | Elsevier BV | 0165-1765 | [S36178057](https://openalex.org/S36178057) |
| [International Review of Financial Analysis](https://scholar4ai.com/journals/international-review-of-financial-analysis.md) | Finance | Elsevier BV | 1057-5219 | [S145572626](https://openalex.org/S145572626) |
| [Journal of Business Research](https://scholar4ai.com/journals/journal-of-business-research.md) | Management | Elsevier BV | 0148-2963 | [S93284759](https://openalex.org/S93284759) |
| [Energy Economics](https://scholar4ai.com/journals/energy-economics.md) | Economics | Elsevier BV | 0140-9883 | [S94499970](https://openalex.org/S94499970) |
| [Economic Modelling](https://scholar4ai.com/journals/economic-modelling.md) | Economics | Elsevier BV | 0264-9993 | [S141184754](https://openalex.org/S141184754) |
| [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | AI | Springer Science+Business Media | 0920-5691 | [S25538012](https://openalex.org/S25538012) |
| [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | Operations Research | Elsevier BV | 0377-2217 | [S103321696](https://openalex.org/S103321696) |
| [Research in International Business and Finance](https://scholar4ai.com/journals/research-in-international-business-and-finance.md) | Finance | Elsevier BV | 0275-5319 | [S8133038](https://openalex.org/S8133038) |
| [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | Operations Research | Springer Science+Business Media | 0254-5330 | [S57667410](https://openalex.org/S57667410) |
| [Journal of Optimization Theory and Applications](https://scholar4ai.com/journals/journal-of-optimization-theory-and-applications.md) | Operations Research | Springer Science+Business Media | 0022-3239 | [S52430896](https://openalex.org/S52430896) |
| [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | AI | Springer Science+Business Media | 0269-2821 | [S122814990](https://openalex.org/S122814990) |
| [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | AI | Springer Science+Business Media | 0219-1377 | [S81770430](https://openalex.org/S81770430) |
| [Technological Forecasting and Social Change](https://scholar4ai.com/journals/technological-forecasting-and-social-change.md) | Management | Elsevier BV | 0040-1625 | [S39307421](https://openalex.org/S39307421) |
| [Ecological Economics](https://scholar4ai.com/journals/ecological-economics.md) | Economics | Elsevier BV | 0921-8009 | [S136211407](https://openalex.org/S136211407) |
| [Journal of Economic Behavior & Organization](https://scholar4ai.com/journals/journal-of-economic-behavior-organization.md) | Economics | Elsevier BV | 0167-2681 | [S62201805](https://openalex.org/S62201805) |
| [World Development](https://scholar4ai.com/journals/world-development.md) | Economics | Elsevier BV | 0305-750X | [S85457386](https://openalex.org/S85457386) |
| [International Journal of Production Economics](https://scholar4ai.com/journals/international-journal-of-production-economics.md) | Operations Research | Elsevier BV | 0925-5273 | [S184816971](https://openalex.org/S184816971) |
| [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | Economics | Springer Science+Business Media | 0927-7099 | [S98984247](https://openalex.org/S98984247) |
| [The Annals of Regional Science](https://scholar4ai.com/journals/the-annals-of-regional-science.md) | Economics | Springer Science+Business Media | 0570-1864 | [S203746597](https://openalex.org/S203746597) |
| [Future Business Journal](https://scholar4ai.com/journals/future-business-journal.md) | Management | Springer Science+Business Media | 2314-7210 | [S2898392467](https://openalex.org/S2898392467) |
| [Journal of Business Ethics](https://scholar4ai.com/journals/journal-of-business-ethics.md) | Management | Springer Science+Business Media | 0167-4544 | [S76633192](https://openalex.org/S76633192) |
| [Operational Research](https://scholar4ai.com/journals/operational-research.md) | Operations Research | Springer Science+Business Media | 1109-2858 | [S140563256](https://openalex.org/S140563256) |
| [Technovation](https://scholar4ai.com/journals/technovation.md) | Management | Elsevier BV | 0166-4972 | [S70698675](https://openalex.org/S70698675) |
| [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | AI | Springer Science+Business Media | 0885-6125 | [S62148650](https://openalex.org/S62148650) |
| [Computers & Operations Research](https://scholar4ai.com/journals/computers-operations-research.md) | Operations Research | Elsevier BV | 0305-0548 | [S173256270](https://openalex.org/S173256270) |
| [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | AI | Springer Science+Business Media | 2196-1115 | [S2737955091](https://openalex.org/S2737955091) |
| [Pacific-Basin Finance Journal](https://scholar4ai.com/journals/pacific-basin-finance-journal.md) | Finance | Elsevier BV | 0927-538X | [S29049940](https://openalex.org/S29049940) |
| [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | AI | Nature Portfolio | 2522-5839 | [S2912241403](https://openalex.org/S2912241403) |
| [The North American Journal of Economics and Finance](https://scholar4ai.com/journals/the-north-american-journal-of-economics-and-finance.md) | Finance | Elsevier BV | 1062-9408 | [S155526628](https://openalex.org/S155526628) |
| [Journal of Innovation & Knowledge](https://scholar4ai.com/journals/journal-of-innovation-knowledge.md) | Management | Elsevier BV | 2444-569X | [S2898307581](https://openalex.org/S2898307581) |
| [Journal of Macroeconomics](https://scholar4ai.com/journals/journal-of-macroeconomics.md) | Economics | Elsevier BV | 0164-0704 | [S176795851](https://openalex.org/S176795851) |
| [Food Policy](https://scholar4ai.com/journals/food-policy.md) | Economics | Elsevier BV | 0306-9192 | [S63571384](https://openalex.org/S63571384) |
| [Journal of Asian Economics](https://scholar4ai.com/journals/journal-of-asian-economics.md) | Economics | Elsevier BV | 1049-0078 | [S69458508](https://openalex.org/S69458508) |
| [Journal of Mathematical Economics](https://scholar4ai.com/journals/journal-of-mathematical-economics.md) | Economics | Elsevier BV | 0304-4068 | [S202934171](https://openalex.org/S202934171) |
| [Research Policy](https://scholar4ai.com/journals/research-policy.md) | Economics | Elsevier BV | 0048-7333 | [S9731383](https://openalex.org/S9731383) |
| [Utilities Policy](https://scholar4ai.com/journals/utilities-policy.md) | Economics | Elsevier BV | 0957-1787 | [S114076160](https://openalex.org/S114076160) |
| [Asia Pacific Journal of Management](https://scholar4ai.com/journals/asia-pacific-journal-of-management.md) | Management | Springer Science+Business Media | 0217-4561 | [S107036887](https://openalex.org/S107036887) |
| [Empirical Economics](https://scholar4ai.com/journals/empirical-economics.md) | Economics | Springer Science+Business Media | 0377-7332 | [S178258306](https://openalex.org/S178258306) |
| [Journal of Public Economics](https://scholar4ai.com/journals/journal-of-public-economics.md) | Economics | Elsevier BV | 0047-2727 | [S199447588](https://openalex.org/S199447588) |
| [Omega](https://scholar4ai.com/journals/omega.md) | Operations Research | Elsevier BV | 0305-0483 | [S4210190151](https://openalex.org/S4210190151) |
| [AI & Society](https://scholar4ai.com/journals/ai-society.md) | AI | Springer Nature | 0951-5666 | [S4210190517](https://openalex.org/S4210190517) |
| [Artificial Intelligence](https://scholar4ai.com/journals/artificial-intelligence.md) | AI | Elsevier BV | 0004-3702 | [S196139623](https://openalex.org/S196139623) |
| [Emerging Markets Review](https://scholar4ai.com/journals/emerging-markets-review.md) | Finance | Elsevier BV | 1566-0141 | [S197112600](https://openalex.org/S197112600) |
| [Games and Economic Behavior](https://scholar4ai.com/journals/games-and-economic-behavior.md) | Economics | Elsevier BV | 0899-8256 | [S94044085](https://openalex.org/S94044085) |
| [The Journal of Technology Transfer](https://scholar4ai.com/journals/the-journal-of-technology-transfer.md) | Management | Springer Science+Business Media | 0892-9912 | [S185905316](https://openalex.org/S185905316) |
| [The Quarterly Review of Economics and Finance](https://scholar4ai.com/journals/the-quarterly-review-of-economics-and-finance.md) | Finance | Elsevier BV | 1062-9769 | [S125273398](https://openalex.org/S125273398) |
| [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | AI | Springer Science+Business Media | 1866-9956 | [S133078663](https://openalex.org/S133078663) |
| [Economic Systems](https://scholar4ai.com/journals/economic-systems.md) | Economics | Elsevier BV | 0939-3625 | [S206007305](https://openalex.org/S206007305) |
| [International Entrepreneurship and Management Journal](https://scholar4ai.com/journals/international-entrepreneurship-and-management-journal.md) | Management | Springer Science+Business Media | 1554-7191 | [S36547454](https://openalex.org/S36547454) |
| [Journal of Economic Dynamics and Control](https://scholar4ai.com/journals/journal-of-economic-dynamics-and-control.md) | Economics | Elsevier BV | 0165-1889 | [S44585919](https://openalex.org/S44585919) |
| [Journal of Financial Economics](https://scholar4ai.com/journals/journal-of-financial-economics.md) | Finance | Elsevier BV | 0304-405X | [S149240962](https://openalex.org/S149240962) |
| [Journal of Financial Stability](https://scholar4ai.com/journals/journal-of-financial-stability.md) | Finance | Elsevier BV | 1572-3089 | [S121634618](https://openalex.org/S121634618) |
| [Journal of Monetary Economics](https://scholar4ai.com/journals/journal-of-monetary-economics.md) | Economics | Elsevier BV | 0304-3932 | [S6711363](https://openalex.org/S6711363) |
| [The British Accounting Review](https://scholar4ai.com/journals/the-british-accounting-review.md) | Accounting | Elsevier BV | 0890-8389 | [S197396629](https://openalex.org/S197396629) |
| [Decision Support Systems](https://scholar4ai.com/journals/decision-support-systems.md) | Operations Research | Elsevier BV | 0167-9236 | [S11479521](https://openalex.org/S11479521) |
| [European Economic Review](https://scholar4ai.com/journals/european-economic-review.md) | Economics | Elsevier BV | 0014-2921 | [S69338747](https://openalex.org/S69338747) |
| [Information Systems Frontiers](https://scholar4ai.com/journals/information-systems-frontiers.md) | Management | Springer Science+Business Media | 1387-3326 | [S181659395](https://openalex.org/S181659395) |
| [International Economics and Economic Policy](https://scholar4ai.com/journals/international-economics-and-economic-policy.md) | Economics | Springer Science+Business Media | 1612-4804 | [S5486590](https://openalex.org/S5486590) |
| [International Journal of Disclosure and Governance](https://scholar4ai.com/journals/international-journal-of-disclosure-and-governance.md) | Accounting | Palgrave Macmillan | 1741-3591 | [S71351135](https://openalex.org/S71351135) |
| [Journal of Behavioral and Experimental Economics](https://scholar4ai.com/journals/journal-of-behavioral-and-experimental-economics.md) | Economics | Elsevier BV | 2214-8043 | [S4210233753](https://openalex.org/S4210233753) |
| [Journal of Contemporary Accounting & Economics](https://scholar4ai.com/journals/journal-of-contemporary-accounting-economics.md) | Accounting | Elsevier BV | 1815-5669 | [S142725411](https://openalex.org/S142725411) |
| [Journal of Development Economics](https://scholar4ai.com/journals/journal-of-development-economics.md) | Economics | Elsevier BV | 0304-3878 | [S101209419](https://openalex.org/S101209419) |
| [Journal of International Financial Markets Institutions and Money](https://scholar4ai.com/journals/journal-of-international-financial-markets-institutions-and-money.md) | Finance | Elsevier BV | 1042-4431 | [S82743730](https://openalex.org/S82743730) |
| [Journal of the Academy of Marketing Science](https://scholar4ai.com/journals/journal-of-the-academy-of-marketing-science.md) | Management | Springer Science+Business Media | 0092-0703 | [S92522684](https://openalex.org/S92522684) |
| [China Economic Review](https://scholar4ai.com/journals/china-economic-review.md) | Economics | Elsevier BV | 1043-951X | [S19645890](https://openalex.org/S19645890) |
| [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | AI | Springer Science+Business Media | 1384-5810 | [S121920818](https://openalex.org/S121920818) |
| [Economics & Human Biology](https://scholar4ai.com/journals/economics-human-biology.md) | Economics | Elsevier BV | 1570-677X | [S19927788](https://openalex.org/S19927788) |
| [Industrial Marketing Management](https://scholar4ai.com/journals/industrial-marketing-management.md) | Management | Elsevier BV | 0019-8501 | [S31748134](https://openalex.org/S31748134) |
| [International Journal of Information Management](https://scholar4ai.com/journals/international-journal-of-information-management.md) | Management | Elsevier BV | 0268-4012 | [S189354248](https://openalex.org/S189354248) |
| [Journal of Behavioral and Experimental Finance](https://scholar4ai.com/journals/journal-of-behavioral-and-experimental-finance.md) | Finance | Elsevier BV | 2214-6350 | [S2764372386](https://openalex.org/S2764372386) |
| [Journal of Economic Theory](https://scholar4ai.com/journals/journal-of-economic-theory.md) | Economics | Elsevier BV | 0022-0531 | [S149131268](https://openalex.org/S149131268) |
| [Journal of Policy Modeling](https://scholar4ai.com/journals/journal-of-policy-modeling.md) | Economics | Elsevier BV | 0161-8938 | [S81022070](https://openalex.org/S81022070) |
| [Journal of Population Economics](https://scholar4ai.com/journals/journal-of-population-economics.md) | Economics | Springer Science+Business Media | 0933-1433 | [S18284184](https://openalex.org/S18284184) |
| [Small Business Economics](https://scholar4ai.com/journals/small-business-economics.md) | Economics | Springer Science+Business Media | 0921-898X | [S41869786](https://openalex.org/S41869786) |
| [Structural Change and Economic Dynamics](https://scholar4ai.com/journals/structural-change-and-economic-dynamics.md) | Economics | Elsevier BV | 0954-349X | [S67638930](https://openalex.org/S67638930) |
| [Asia-Pacific Financial Markets](https://scholar4ai.com/journals/asia-pacific-financial-markets.md) | Finance | Springer Science+Business Media | 1387-2834 | [S116955675](https://openalex.org/S116955675) |
| [Digital Finance](https://scholar4ai.com/journals/digital-finance.md) | Finance | Springer Science+Business Media | 2524-6984 | [S4210211077](https://openalex.org/S4210211077) |
| [Economic Theory](https://scholar4ai.com/journals/economic-theory.md) | Economics | Springer Science+Business Media | 0938-2259 | [S579031](https://openalex.org/S579031) |
| [Financial Innovation](https://scholar4ai.com/journals/financial-innovation.md) | Finance | Springer Nature | 2199-4730 | [S3034727917](https://openalex.org/S3034727917) |
| [Journal of Banking & Finance](https://scholar4ai.com/journals/journal-of-banking-finance.md) | Finance | Elsevier BV | 0378-4266 | [S2876017](https://openalex.org/S2876017) |
| [Journal of Banking Regulation](https://scholar4ai.com/journals/journal-of-banking-regulation.md) | Finance | Palgrave Macmillan | 1745-6452 | [S185277049](https://openalex.org/S185277049) |
| [Journal of Financial Services Marketing](https://scholar4ai.com/journals/journal-of-financial-services-marketing.md) | Finance | Palgrave Macmillan | 1363-0539 | [S126354654](https://openalex.org/S126354654) |
| [Journal of International Business Studies](https://scholar4ai.com/journals/journal-of-international-business-studies.md) | Management | Palgrave Macmillan | 0047-2506 | [S38024979](https://openalex.org/S38024979) |
| [Journal of International Management](https://scholar4ai.com/journals/journal-of-international-management.md) | Management | Elsevier BV | 1075-4253 | [S960608072](https://openalex.org/S960608072) |
| [Labour Economics](https://scholar4ai.com/journals/labour-economics.md) | Economics | Elsevier BV | 0927-5371 | [S163774179](https://openalex.org/S163774179) |
| [Regional Science and Urban Economics](https://scholar4ai.com/journals/regional-science-and-urban-economics.md) | Economics | Elsevier BV | 0166-0462 | [S107631327](https://openalex.org/S107631327) |
| [Review of Economics of the Household](https://scholar4ai.com/journals/review-of-economics-of-the-household.md) | Economics | Springer Science+Business Media | 1569-5239 | [S120253869](https://openalex.org/S120253869) |
| [International Business Review](https://scholar4ai.com/journals/international-business-review.md) | Management | Elsevier BV | 0969-5931 | [S23343376](https://openalex.org/S23343376) |
| [International Journal of Forecasting](https://scholar4ai.com/journals/international-journal-of-forecasting.md) | Economics | Elsevier BV | 0169-2070 | [S39841227](https://openalex.org/S39841227) |
| [International Tax and Public Finance](https://scholar4ai.com/journals/international-tax-and-public-finance.md) | Economics | Springer Science+Business Media | 0927-5940 | [S42192170](https://openalex.org/S42192170) |
| [Journal of Accounting and Economics](https://scholar4ai.com/journals/journal-of-accounting-and-economics.md) | Accounting | Elsevier BV | 0165-4101 | [S62142384](https://openalex.org/S62142384) |
| [Journal of Business Venturing](https://scholar4ai.com/journals/journal-of-business-venturing.md) | Management | Elsevier BV | 0883-9026 | [S66201313](https://openalex.org/S66201313) |
| [Journal of International Economics](https://scholar4ai.com/journals/journal-of-international-economics.md) | Economics | Elsevier BV | 0022-1996 | [S198098467](https://openalex.org/S198098467) |
| [Review of Managerial Science](https://scholar4ai.com/journals/review-of-managerial-science.md) | Management | Springer Science+Business Media | 1863-6683 | [S203071086](https://openalex.org/S203071086) |
| [The Journal of Economic Asymmetries](https://scholar4ai.com/journals/the-journal-of-economic-asymmetries.md) | Economics | Elsevier BV | 1703-4949 | [S2764587561](https://openalex.org/S2764587561) |
| [Environmental and Resource Economics](https://scholar4ai.com/journals/environmental-and-resource-economics.md) | Economics | Springer Science+Business Media | 0924-6460 | [S86125002](https://openalex.org/S86125002) |
| [Eurasian economic review :](https://scholar4ai.com/journals/eurasian-economic-review.md) | Economics | Springer Nature | 1309-422X | [S4210169982](https://openalex.org/S4210169982) |
| [Financial markets and portfolio management](https://scholar4ai.com/journals/financial-markets-and-portfolio-management.md) | Finance | Springer Science+Business Media | 1934-4554 | [S4210231290](https://openalex.org/S4210231290) |
| [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | AI | Institute of Electrical and Electronics Engineers | 2162-237X | [S4210175523](https://openalex.org/S4210175523) |
| [International Journal of Industrial Organization](https://scholar4ai.com/journals/international-journal-of-industrial-organization.md) | Economics | Elsevier BV | 0167-7187 | [S183232449](https://openalex.org/S183232449) |
| [Journal of Accounting and Public Policy](https://scholar4ai.com/journals/journal-of-accounting-and-public-policy.md) | Accounting | Elsevier BV | 0278-4254 | [S86762183](https://openalex.org/S86762183) |
| [Journal of Accounting Education](https://scholar4ai.com/journals/journal-of-accounting-education.md) | Accounting | Elsevier BV | 0748-5751 | [S6243863](https://openalex.org/S6243863) |
| [Journal of Business Economics](https://scholar4ai.com/journals/journal-of-business-economics.md) | Management | Springer Science+Business Media | 0044-2372 | [S2913204857](https://openalex.org/S2913204857) |
| [Journal of Econometrics](https://scholar4ai.com/journals/journal-of-econometrics.md) | Economics | Elsevier BV | 0304-4076 | [S127742747](https://openalex.org/S127742747) |
| [Journal of Economics and Finance](https://scholar4ai.com/journals/journal-of-economics-and-finance.md) | Finance | Springer Science+Business Media | 1055-0925 | [S171491724](https://openalex.org/S171491724) |
| [Journal of Health Economics](https://scholar4ai.com/journals/journal-of-health-economics.md) | Economics | Elsevier BV | 0167-6296 | [S166621295](https://openalex.org/S166621295) |
| [Journal of Industry Competition and Trade](https://scholar4ai.com/journals/journal-of-industry-competition-and-trade.md) | Economics | Springer Science+Business Media | 1566-1679 | [S160194987](https://openalex.org/S160194987) |
| [Journal of International Money and Finance](https://scholar4ai.com/journals/journal-of-international-money-and-finance.md) | Finance | Elsevier BV | 0261-5606 | [S10747938](https://openalex.org/S10747938) |
| [Long Range Planning](https://scholar4ai.com/journals/long-range-planning.md) | Management | Elsevier BV | 0024-6301 | [S9435936](https://openalex.org/S9435936) |
| [Mathematical Programming](https://scholar4ai.com/journals/mathematical-programming.md) | Operations Research | Springer Science+Business Media | 0025-5610 | [S193920097](https://openalex.org/S193920097) |
| [Public Choice](https://scholar4ai.com/journals/public-choice.md) | Economics | Springer Science+Business Media | 0048-5829 | [S7424199](https://openalex.org/S7424199) |
| [Review of World Economics](https://scholar4ai.com/journals/review-of-world-economics.md) | Economics | Springer Science+Business Media | 1610-2878 | [S78091837](https://openalex.org/S78091837) |
| [Electronic Markets](https://scholar4ai.com/journals/electronic-markets.md) | Management | Springer Science+Business Media | 1019-6781 | [S137519996](https://openalex.org/S137519996) |
| [Explorations in Economic History](https://scholar4ai.com/journals/explorations-in-economic-history.md) | Economics | Elsevier BV | 0014-4983 | [S145471836](https://openalex.org/S145471836) |
| [IEEE Transactions on Evolutionary Computation](https://scholar4ai.com/journals/ieee-transactions-on-evolutionary-computation.md) | AI | Institute of Electrical and Electronics Engineers | 1089-778X | [S93787993](https://openalex.org/S93787993) |
| [IEEE Transactions on Fuzzy Systems](https://scholar4ai.com/journals/ieee-transactions-on-fuzzy-systems.md) | AI | Institute of Electrical and Electronics Engineers | 1063-6706 | [S134177497](https://openalex.org/S134177497) |
| [Journal of commodity markets](https://scholar4ai.com/journals/journal-of-commodity-markets.md) | Finance | Elsevier BV | 2405-8513 | [S2898531903](https://openalex.org/S2898531903) |
| [Journal of Comparative Economics](https://scholar4ai.com/journals/journal-of-comparative-economics.md) | Economics | Elsevier BV | 0147-5967 | [S138645024](https://openalex.org/S138645024) |
| [Journal of Corporate Finance](https://scholar4ai.com/journals/journal-of-corporate-finance.md) | Finance | Elsevier BV | 0929-1199 | [S152282257](https://openalex.org/S152282257) |
| [Journal of Environmental Economics and Management](https://scholar4ai.com/journals/journal-of-environmental-economics-and-management.md) | Economics | Elsevier BV | 0095-0696 | [S200872515](https://openalex.org/S200872515) |
| [Journal of Risk and Uncertainty](https://scholar4ai.com/journals/journal-of-risk-and-uncertainty.md) | Economics | Springer Science+Business Media | 0895-5646 | [S106686155](https://openalex.org/S106686155) |
| [Journal of World Business](https://scholar4ai.com/journals/journal-of-world-business.md) | Management | Elsevier BV | 1090-9516 | [S143995394](https://openalex.org/S143995394) |
| [Portuguese Economic Journal](https://scholar4ai.com/journals/portuguese-economic-journal.md) | Economics | Springer Science+Business Media | 1617-982X | [S112983894](https://openalex.org/S112983894) |
| [Resource and Energy Economics](https://scholar4ai.com/journals/resource-and-energy-economics.md) | Economics | Elsevier BV | 0928-7655 | [S165058066](https://openalex.org/S165058066) |
| [Social Choice and Welfare](https://scholar4ai.com/journals/social-choice-and-welfare.md) | Economics | Springer Science+Business Media | 0176-1714 | [S107325288](https://openalex.org/S107325288) |
| [The Geneva Papers on Risk and Insurance Issues and Practice](https://scholar4ai.com/journals/the-geneva-papers-on-risk-and-insurance-issues-and-practice.md) | Finance | Palgrave Macmillan | 1018-5895 | [S151491554](https://openalex.org/S151491554) |
| [Theory and Decision](https://scholar4ai.com/journals/theory-and-decision.md) | Economics | Springer Science+Business Media | 0040-5833 | [S134979503](https://openalex.org/S134979503) |
| [Accounting Organizations and Society](https://scholar4ai.com/journals/accounting-organizations-and-society.md) | Accounting | Elsevier BV | 0361-3682 | [S198892436](https://openalex.org/S198892436) |
| [Experimental Economics](https://scholar4ai.com/journals/experimental-economics.md) | Economics | Springer Science+Business Media | 1386-4157 | [S181493553](https://openalex.org/S181493553) |
| [IEEE Transactions on Artificial Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-artificial-intelligence.md) | AI | Institute of Electrical and Electronics Engineers | 2691-4581 | [S4210169448](https://openalex.org/S4210169448) |
| [Insurance Mathematics and Economics](https://scholar4ai.com/journals/insurance-mathematics-and-economics.md) | Finance | Elsevier BV | 0167-6687 | [S198325475](https://openalex.org/S198325475) |
| [Journal of Economics and Business](https://scholar4ai.com/journals/journal-of-economics-and-business.md) | Finance | Elsevier BV | 0148-6195 | [S145099083](https://openalex.org/S145099083) |
| [Journal of Empirical Finance](https://scholar4ai.com/journals/journal-of-empirical-finance.md) | Finance | Elsevier BV | 0927-5398 | [S145875555](https://openalex.org/S145875555) |
| [Journal of Management Control](https://scholar4ai.com/journals/journal-of-management-control.md) | Accounting | Springer Science+Business Media | 2191-4761 | [S4210176106](https://openalex.org/S4210176106) |
| [Journal of Productivity Analysis](https://scholar4ai.com/journals/journal-of-productivity-analysis.md) | Economics | Springer Science+Business Media | 0895-562X | [S34280410](https://openalex.org/S34280410) |
| [OR Spectrum](https://scholar4ai.com/journals/or-spectrum.md) | Operations Research | Springer Science+Business Media | 0171-6468 | [S2502551423](https://openalex.org/S2502551423) |
| [Research in Economics](https://scholar4ai.com/journals/research-in-economics.md) | Economics | Elsevier BV | 1090-9443 | [S156260212](https://openalex.org/S156260212) |
| [Review of Industrial Organization](https://scholar4ai.com/journals/review-of-industrial-organization.md) | Economics | Springer Science+Business Media | 0889-938X | [S3416249](https://openalex.org/S3416249) |
| [Risk Management](https://scholar4ai.com/journals/risk-management.md) | Finance | Palgrave Macmillan | 1460-3799 | [S4210191937](https://openalex.org/S4210191937) |
| [Economics of Education Review](https://scholar4ai.com/journals/economics-of-education-review.md) | Economics | Elsevier BV | 0272-7757 | [S4888523](https://openalex.org/S4888523) |
| [Empirica](https://scholar4ai.com/journals/empirica.md) | Economics | Springer Science+Business Media | 0340-8744 | [S133770707](https://openalex.org/S133770707) |
| [IEEE Transactions on Knowledge and Data Engineering](https://scholar4ai.com/journals/ieee-transactions-on-knowledge-and-data-engineering.md) | AI | IEEE Computer Society | 1041-4347 | [S30698027](https://openalex.org/S30698027) |
| [International Journal of Accounting Information Systems](https://scholar4ai.com/journals/international-journal-of-accounting-information-systems.md) | Accounting | Elsevier BV | 1467-0895 | [S38901890](https://openalex.org/S38901890) |
| [Journal of Cultural Economics](https://scholar4ai.com/journals/journal-of-cultural-economics.md) | Economics | Springer Science+Business Media | 0885-2545 | [S31225034](https://openalex.org/S31225034) |
| [Journal of Economic Growth](https://scholar4ai.com/journals/journal-of-economic-growth.md) | Economics | Springer Science+Business Media | 1381-4338 | [S181171746](https://openalex.org/S181171746) |
| [Journal of Economic Psychology](https://scholar4ai.com/journals/journal-of-economic-psychology.md) | Economics | Elsevier BV | 0167-4870 | [S71968408](https://openalex.org/S71968408) |
| [Journal of Evolutionary Economics](https://scholar4ai.com/journals/journal-of-evolutionary-economics.md) | Economics | Springer Science+Business Media | 0936-9937 | [S121026525](https://openalex.org/S121026525) |
| [Journal of Family Business Strategy](https://scholar4ai.com/journals/journal-of-family-business-strategy.md) | Management | Elsevier BV | 1877-8585 | [S118119388](https://openalex.org/S118119388) |
| [Journal of Financial Intermediation](https://scholar4ai.com/journals/journal-of-financial-intermediation.md) | Finance | Elsevier BV | 1042-9573 | [S5984737](https://openalex.org/S5984737) |
| [Journal of Management & Governance](https://scholar4ai.com/journals/journal-of-management-governance.md) | Accounting | Springer Science+Business Media | 1385-3457 | [S6013065](https://openalex.org/S6013065) |
| [Journal of Regulatory Economics](https://scholar4ai.com/journals/journal-of-regulatory-economics.md) | Economics | Springer Science+Business Media | 0922-680X | [S114801022](https://openalex.org/S114801022) |
| [Journal of Revenue and Pricing Management](https://scholar4ai.com/journals/journal-of-revenue-and-pricing-management.md) | Finance | Palgrave Macmillan | 1476-6930 | [S58438227](https://openalex.org/S58438227) |
| [Journal of Urban Economics](https://scholar4ai.com/journals/journal-of-urban-economics.md) | Economics | Elsevier BV | 0094-1190 | [S147692640](https://openalex.org/S147692640) |
| [Management International Review](https://scholar4ai.com/journals/management-international-review.md) | Management | Springer Science+Business Media | 0938-8249 | [S128478731](https://openalex.org/S128478731) |
| [Review of Economic Dynamics](https://scholar4ai.com/journals/review-of-economic-dynamics.md) | Economics | Elsevier BV | 1094-2025 | [S163499366](https://openalex.org/S163499366) |
| [Review of Quantitative Finance and Accounting](https://scholar4ai.com/journals/review-of-quantitative-finance-and-accounting.md) | Finance | Springer Science+Business Media | 0924-865X | [S62110574](https://openalex.org/S62110574) |
| [The Journal of Economic Inequality](https://scholar4ai.com/journals/the-journal-of-economic-inequality.md) | Economics | Springer Science+Business Media | 1569-1721 | [S90873577](https://openalex.org/S90873577) |
| [The Leadership Quarterly](https://scholar4ai.com/journals/the-leadership-quarterly.md) | Management | Elsevier BV | 1048-9843 | [S152304618](https://openalex.org/S152304618) |
| [Advances in Accounting](https://scholar4ai.com/journals/advances-in-accounting.md) | Accounting | Elsevier BV | 0882-6110 | [S27001385](https://openalex.org/S27001385) |
| [Annals of Finance](https://scholar4ai.com/journals/annals-of-finance.md) | Finance | Springer Science+Business Media | 1614-2446 | [S146522396](https://openalex.org/S146522396) |
| [Borsa Istanbul Review](https://scholar4ai.com/journals/borsa-istanbul-review.md) | Finance | Elsevier BV | 2214-8450 | [S2764882396](https://openalex.org/S2764882396) |
| [Business Economics](https://scholar4ai.com/journals/business-economics.md) | Economics | Palgrave Macmillan | 0007-666X | [S162611689](https://openalex.org/S162611689) |
| [Central Bank Review](https://scholar4ai.com/journals/central-bank-review.md) | Finance | Elsevier BV | 1303-0701 | [S4210184829](https://openalex.org/S4210184829) |
| [Comparative Economic Studies](https://scholar4ai.com/journals/comparative-economic-studies.md) | Economics | Palgrave Macmillan | 0888-7233 | [S120949314](https://openalex.org/S120949314) |
| [Critical Perspectives on Accounting](https://scholar4ai.com/journals/critical-perspectives-on-accounting.md) | Accounting | Elsevier BV | 1045-2354 | [S66510378](https://openalex.org/S66510378) |
| [Eastern Economic Journal](https://scholar4ai.com/journals/eastern-economic-journal.md) | Economics | Palgrave Macmillan | 0094-5056 | [S7345157](https://openalex.org/S7345157) |
| [Global Finance Journal](https://scholar4ai.com/journals/global-finance-journal.md) | Finance | Elsevier BV | 1044-0283 | [S150702451](https://openalex.org/S150702451) |
| [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | AI | IEEE Computer Society | 0162-8828 | [S199944782](https://openalex.org/S199944782) |
| [IMF Economic Review](https://scholar4ai.com/journals/imf-economic-review.md) | Economics | Palgrave Macmillan | 2041-4161 | [S21260181](https://openalex.org/S21260181) |
| [International Review of Law and Economics](https://scholar4ai.com/journals/international-review-of-law-and-economics.md) | Economics | Elsevier BV | 0144-8188 | [S121409115](https://openalex.org/S121409115) |
| [Journal of Economic Interaction and Coordination](https://scholar4ai.com/journals/journal-of-economic-interaction-and-coordination.md) | Economics | Springer Science+Business Media | 1860-711X | [S146534985](https://openalex.org/S146534985) |
| [Journal of Financial Markets](https://scholar4ai.com/journals/journal-of-financial-markets.md) | Finance | Elsevier BV | 1386-4181 | [S127646213](https://openalex.org/S127646213) |
| [Journal of Housing Economics](https://scholar4ai.com/journals/journal-of-housing-economics.md) | Economics | Elsevier BV | 1051-1377 | [S57862637](https://openalex.org/S57862637) |
| [Journal of International Accounting Auditing and Taxation](https://scholar4ai.com/journals/journal-of-international-accounting-auditing-and-taxation.md) | Accounting | Elsevier BV | 1061-9518 | [S132432621](https://openalex.org/S132432621) |
| [Journal of Multinational Financial Management](https://scholar4ai.com/journals/journal-of-multinational-financial-management.md) | Finance | Elsevier BV | 1042-444X | [S92422469](https://openalex.org/S92422469) |
| [Review of Accounting Studies](https://scholar4ai.com/journals/review-of-accounting-studies.md) | Accounting | Springer Science+Business Media | 1380-6653 | [S11853582](https://openalex.org/S11853582) |
| [Review of Derivatives Research](https://scholar4ai.com/journals/review-of-derivatives-research.md) | Finance | Springer Science+Business Media | 1380-6645 | [S100711025](https://openalex.org/S100711025) |
| [The Journal of Real Estate Finance and Economics](https://scholar4ai.com/journals/the-journal-of-real-estate-finance-and-economics.md) | Finance | Springer Science+Business Media | 0895-5638 | [S202178085](https://openalex.org/S202178085) |
| [China Journal of Accounting Research](https://scholar4ai.com/journals/china-journal-of-accounting-research.md) | Accounting | Elsevier BV | 1755-3091 | [S2764806082](https://openalex.org/S2764806082) |
| [Finance and Stochastics](https://scholar4ai.com/journals/finance-and-stochastics.md) | Finance | Springer Science+Business Media | 0949-2984 | [S172526255](https://openalex.org/S172526255) |
| [Japan and the World Economy](https://scholar4ai.com/journals/japan-and-the-world-economy.md) | Economics | Elsevier BV | 0922-1425 | [S70935693](https://openalex.org/S70935693) |
| [Japanese Economic Review](https://scholar4ai.com/journals/japanese-economic-review.md) | Economics | Springer Science+Business Media | 1352-4739 | [S41930762](https://openalex.org/S41930762) |
| [Journal of Asset Management](https://scholar4ai.com/journals/journal-of-asset-management.md) | Finance | Palgrave Macmillan | 1470-8272 | [S206916388](https://openalex.org/S206916388) |
| [Journal of Banking and Financial Technology](https://scholar4ai.com/journals/journal-of-banking-and-financial-technology.md) | Finance | Springer Science+Business Media | 2524-7956 | [S4210177615](https://openalex.org/S4210177615) |
| [Journal of Derivatives & Hedge Funds](https://scholar4ai.com/journals/journal-of-derivatives-hedge-funds.md) | Finance | Palgrave Macmillan | 1753-9641 | [S2764871172](https://openalex.org/S2764871172) |
| [Journal of Economics](https://scholar4ai.com/journals/journal-of-economics.md) | Economics | Springer Science+Business Media | 0931-8658 | [S107033909](https://openalex.org/S107033909) |
| [Journal of Financial Services Research](https://scholar4ai.com/journals/journal-of-financial-services-research.md) | Finance | Springer Science+Business Media | 0920-8550 | [S79175213](https://openalex.org/S79175213) |
| [Journal of Labor Research](https://scholar4ai.com/journals/journal-of-labor-research.md) | Economics | Springer Science+Business Media | 0195-3613 | [S18354546](https://openalex.org/S18354546) |
| [Journal of the Japanese and International Economies](https://scholar4ai.com/journals/journal-of-the-japanese-and-international-economies.md) | Economics | Elsevier BV | 0889-1583 | [S160393188](https://openalex.org/S160393188) |
| [Management Accounting Research](https://scholar4ai.com/journals/management-accounting-research.md) | Accounting | Elsevier BV | 1044-5005 | [S114140714](https://openalex.org/S114140714) |
| [Mathematics and Financial Economics](https://scholar4ai.com/journals/mathematics-and-financial-economics.md) | Finance | Springer Science+Business Media | 1862-9679 | [S124412575](https://openalex.org/S124412575) |
| [The Geneva Risk and Insurance Review](https://scholar4ai.com/journals/the-geneva-risk-and-insurance-review.md) | Finance | Palgrave Macmillan | 1554-964X | [S194824457](https://openalex.org/S194824457) |


---

---
title: "Zoning reform and the homes that get built (research idea)"
description: "Housing supply, land use regulation and upzoning appear on 48 recent papers in the economics journals. Cities from Auckland to Minneapolis and states from California to Oregon have loosened zoning in the last decade. How many extra homes did the reforms produce, where, and did rents respond?"
url: https://scholar4ai.com/ideas/zoning-reform-and-housing-supply
markdown_url: https://scholar4ai.com/ideas/zoning-reform-and-housing-supply.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "housing", "cities"]
keywords: ["housing supply", "land use regulation", "upzoning"]
---

# Zoning reform and the homes that get built

Housing supply, land use regulation and upzoning appear on 48 recent papers in the economics journals. Cities from Auckland to Minneapolis and states from California to Oregon have loosened zoning in the last decade. How many extra homes did the reforms produce, where, and did rents respond?

## What prompted this

Since October 2025, 48 papers carry a zoning, land-use-regulation, housing-supply, building-regulation or upzoning keyword (37 in economics journals): "housing supply" on 19, "land use regulation" on 12, "upzoning" on 7, "building regulations" on 7. Housing prices appear on 123 papers, so the demand side is far better covered than the supply rules.

## Questions

- After upzoning, how much of the new building is on the rezoned parcels, and how long before permits turn into completed homes?
- Do rents fall in the rezoned neighbourhoods, in the city as a whole, or neither within five years?
- Which reforms worked: allowing more units per lot, cutting parking requirements, speeding approvals, or taxing undeveloped land?

## Data that could help

Parcel-level zoning maps before and after reforms, building permits and completions, rents from listing data, and the dates of reforms.

## Next step

Compare completions on upzoned and non-upzoned parcels in Auckland and Minneapolis in the years after their reforms.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| housing supply | 0 | – | 0, 1, 3, 0, 2, 3, 2, 2, 1, 3, 1, 1, 0 | 19 |
| land use regulation | 0 | – | 1, 0, 2, 1, 1, 3, 1, 0, 1, 1, 1, 0, 0 | 12 |
| upzoning | 0 | – | 0, 1, 2, 0, 0, 3, 1, 0, 0, 0, 0, 0, 0 | 7 |


---

---
title: "Zombie firms after the end of cheap money (research idea)"
description: "Capital reallocation is a fast-rising keyword in the economics journals. After a decade of near-zero rates, interest rates rose almost everywhere, and Japan ended negative rates in 2024. Did higher rates finally clear out zombie firms and move capital to productive ones?"
url: https://scholar4ai.com/ideas/zombie-firms-after-cheap-money
markdown_url: https://scholar4ai.com/ideas/zombie-firms-after-cheap-money.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "finance", "banking"]
keywords: ["zombie firms", "capital reallocation", "capital misallocation"]
---

# Zombie firms after the end of cheap money

Capital reallocation is a fast-rising keyword in the economics journals. After a decade of near-zero rates, interest rates rose almost everywhere, and Japan ended negative rates in 2024. Did higher rates finally clear out zombie firms and move capital to productive ones?

## What prompted this

In economics, "capital reallocation" had 6 papers in July–September 2026 against about 1 expected (see the [economics dashboard](/categories/economics)). Since October 2025, "zombie firms" appears on 21 papers, "capital reallocation" on 20 and "resource misallocation" on 43. Of the 24 papers with a zombie keyword, 8 also carry an interest-rate, monetary or credit keyword.

## Questions

- Did the share of zombie firms (firms that cannot cover interest from profits for years) fall after rates rose, and did it fall through exit, restructuring or recovery?
- Did banks keep lending to them, especially banks with weak capital, as Japanese banks did in the 1990s?
- Did capital and workers move to more productive firms, and did aggregate productivity respond?

## Data that could help

Firm accounts (Orbis, Compustat Global and national data such as Japanese listed-firm data), bank-firm loan links, and policy-rate changes by country.

## Next step

Compute the zombie share by country and year for 2015–2025 with a standard interest-coverage definition, and plot it against policy rates.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| zombie firms | 1 | #2550 | 3, 1, 2, 1, 2, 1, 3, 0, 3, 1, 2, 1, 1 | 20 |
| capital reallocation | 3 | #687 | 2, 0, 2, 0, 3, 0, 0, 2, 1, 4, 3, 3, 1 | 20 |
| capital misallocation | 1 | #2550 | 1, 3, 2, 0, 5, 1, 0, 2, 1, 1, 0, 3, 1 | 19 |


---

---
title: "Zero-day options and intraday volatility (research idea)"
description: "The VIX and market liquidity are rising keywords in the finance journals, while options that expire on the day they trade have become a large share of US index options volume. Yet none of the 192 recent option and VIX papers carries a zero-day or 0DTE keyword. Do same-day options make intraday markets calmer or more fragile?"
url: https://scholar4ai.com/ideas/zero-day-options-and-intraday-volatility
markdown_url: https://scholar4ai.com/ideas/zero-day-options-and-intraday-volatility.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "market microstructure"]
keywords: ["VIX", "intraday volatility", "retail investors"]
---

# Zero-day options and intraday volatility

The VIX and market liquidity are rising keywords in the finance journals, while options that expire on the day they trade have become a large share of US index options volume. Yet none of the 192 recent option and VIX papers carries a zero-day or 0DTE keyword. Do same-day options make intraday markets calmer or more fragile?

## What prompted this

In the finance journals, "VIX" had 13 papers in July–September 2026 against about 8 expected from its earlier share, and "market liquidity" had 13 against 7 (see the [finance dashboard](/categories/finance)). Since October 2025, 192 papers carry a VIX, option-pricing, options-market or implied-volatility keyword (137 in finance journals). Only 4 of them carry a retail or intraday keyword, and none carries a zero-day or 0DTE keyword.

## Questions

- Does dealers' hedging of same-day options dampen or amplify intraday index moves, depending on whether dealers are long or short gamma?
- Do retail investors lose money on these options on average, and more than on older short-dated options?
- Has the VIX, which is built from 30-day options, become less informative as trading moves to same-day contracts?

## Data that could help

Intraday trades and quotes of index options, estimates of dealer positioning, intraday index returns, and measures of retail order flow.

## Next step

Relate estimated dealer gamma in same-day index options to intraday S&P 500 volatility on each trading day of 2023–2025.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| VIX | 3 | #687 | 4, 4, 5, 2, 6, 3, 7, 3, 4, 7, 1, 7, 3 | 53 |
| intraday volatility | 0 | – | 2, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0 | 6 |
| retail investors | 6 | #263 | 7, 8, 8, 13, 10, 11, 9, 8, 11, 14, 10, 6, 5 | 115 |


---

---
title: "Graduates without jobs in China (research idea)"
description: "Youth employment and unemployment appear on 25 recent papers, mostly in economics, while China's youth unemployment rate reached record highs and the statistic itself was revised. What are graduates doing instead, how do families absorb the cost, and does the mismatch come from degrees or from the jobs?"
url: https://scholar4ai.com/ideas/youth-unemployment-in-china
markdown_url: https://scholar4ai.com/ideas/youth-unemployment-in-china.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "labor", "china"]
keywords: ["youth unemployment", "youth employment"]
---

# Graduates without jobs in China

Youth employment and unemployment appear on 25 recent papers, mostly in economics, while China's youth unemployment rate reached record highs and the statistic itself was revised. What are graduates doing instead, how do families absorb the cost, and does the mismatch come from degrees or from the jobs?

## What prompted this

Since October 2025, 25 papers carry a youth-unemployment, youth-employment, graduate-unemployment or college-graduate keyword (18 in economics journals): "youth employment" on 10, "youth unemployment" on 7, "recent college graduates" on 2. Six were published in July–September 2026, in line with earlier quarters, despite the size of the problem.

## Questions

- Where did the graduates of 2022–2025 go: further study, civil service exams, gig work, or back to their parents' homes, and for how long?
- Do fields of study with the largest enrolment growth have the worst outcomes, and did the private tutoring crackdown and property slump remove the jobs those graduates expected?
- Do families cut spending and saving to support unemployed graduates, and does that show up in consumption data?

## Data that could help

Graduate employment surveys, household panels with young adults' status, civil service exam applicant counts, and job posting data by education requirement.

## Next step

Track the employment status of 2022–2025 graduates in the China Family Panel Studies waves and compare fields of study.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| youth unemployment | 0 | – | 0, 1, 2, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0 | 7 |
| youth employment | 1 | #2550 | 0, 1, 1, 0, 0, 2, 0, 1, 1, 1, 2, 0, 1 | 9 |


---

---
title: "Women entrepreneurs and the finance they do not get"
description: "Women entrepreneurs appear on 32 recent papers in the management and economics journals, and 14 of 43 such papers carry a funding, finance or credit keyword. Women-led startups still receive a small share of venture capital, and women-owned small firms pay more for loans in several studies. Is the gap demand, supply, or the way applications are read?"
url: https://scholar4ai.com/ideas/women-entrepreneurs-and-finance
markdown_url: https://scholar4ai.com/ideas/women-entrepreneurs-and-finance.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "entrepreneurship", "finance"]
keywords: ["women entrepreneurs", "access to finance"]
---

# Women entrepreneurs and the finance they do not get

Women entrepreneurs appear on 32 recent papers in the management and economics journals, and 14 of 43 such papers carry a funding, finance or credit keyword. Women-led startups still receive a small share of venture capital, and women-owned small firms pay more for loans in several studies. Is the gap demand, supply, or the way applications are read?

## What prompted this

Since October 2025, 43 papers carry a women-entrepreneur or women-owned-business keyword (23 in management journals, 18 in economics): "women entrepreneurs" on 32, "women's entrepreneurship" on 7, "women-owned businesses" on 5. Fourteen of them also carry a funding, finance, venture, loan, credit, investor or crowdfunding keyword; "access to finance" appears on 65 papers in the archive, mostly about firms in general.

## Questions

- In loan and venture applications with the same business plan and financials, do women founders receive worse terms, and does the gap close when the evaluator is a model rather than a person?
- Do women ask for less, and if so, is that a response to expected rejection?
- Do funds and lenders that set targets for women-led firms change their deal flow, or only their reporting?

## Data that could help

Loan application data with decisions, venture deal data with founder gender (PitchBook, Crunchbase), and audit studies with matched applications.

## Next step

Run a correspondence study sending matched loan applications that differ only in the founder's gender to online lenders.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| women entrepreneurs | 2 | #1148 | 3, 1, 0, 4, 4, 3, 2, 3, 4, 2, 2, 3, 2 | 31 |
| access to finance | 4 | #458 | 7, 7, 6, 3, 7, 9, 1, 4, 4, 6, 0, 7, 4 | 61 |


---

---
title: "Evacuating from wildfires (research idea)"
description: "Forest fires, wildfire risk and smoke appear on 48 recent papers, split among economics, AI and operations research, and evacuation planning on 4. Fires now reach cities, and evacuations have failed with deadly results. Do evacuation models fit how people actually leave, and where should the roads, shelters and warnings be?"
url: https://scholar4ai.com/ideas/wildfire-evacuation-planning
markdown_url: https://scholar4ai.com/ideas/wildfire-evacuation-planning.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "climate"]
keywords: ["wildfire risk", "evacuation planning", "forest fires"]
---

# Evacuating from wildfires

Forest fires, wildfire risk and smoke appear on 48 recent papers, split among economics, AI and operations research, and evacuation planning on 4. Fires now reach cities, and evacuations have failed with deadly results. Do evacuation models fit how people actually leave, and where should the roads, shelters and warnings be?

## What prompted this

Since October 2025, 48 papers carry a wildfire, forest-fire, bushfire or evacuation keyword (19 in economics journals, 15 in AI, 10 in operations research): "forest fires" on 13, "wildfire smoke" on 7, "wildfire risk" on 6, "wildfire mitigation" on 4, "evacuation planning" on 4, "wildfire suppression" on 3. The economics papers measure damages, the AI papers detect fires, and the few evacuation models are rarely validated against real evacuations.

## Questions

- In recent evacuations, when did people leave relative to the orders, which routes did they take, and where did congestion form, compared with what the models assume?
- Can mobile phone traces calibrate departure-time distributions by neighbourhood and warning type?
- Which investments (road capacity, staged warnings, shelters in place) reduce expected deaths most per dollar in the highest-risk towns?

## Data that could help

Mobile phone mobility data during evacuations, evacuation order timings, road network data and fire spread records.

## Next step

Reconstruct the departure times and routes of one recent evacuation from mobility data and compare them with the output of a standard evacuation model.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| wildfire risk | 0 | – | 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 2, 0 | 6 |
| evacuation planning | 0 | – | 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0 | 4 |
| forest fires | 0 | – | 1, 2, 0, 1, 1, 2, 1, 1, 2, 1, 0, 1, 0 | 13 |


---

---
title: "Who writes the annual report now? (research idea)"
description: "Annual reports, 10-K filings, narrative disclosure and readability appear on 99 recent papers in the finance and accounting journals, but only 9 of them carry a generative-AI, language-model or AI-generated keyword. If language models now draft the management discussion, do the text measures researchers rely on still mean anything?"
url: https://scholar4ai.com/ideas/who-writes-the-annual-report-now
markdown_url: https://scholar4ai.com/ideas/who-writes-the-annual-report-now.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "finance", "ai"]
keywords: ["narrative disclosure", "readability", "10-K filings"]
---

# Who writes the annual report now?

Annual reports, 10-K filings, narrative disclosure and readability appear on 99 recent papers in the finance and accounting journals, but only 9 of them carry a generative-AI, language-model or AI-generated keyword. If language models now draft the management discussion, do the text measures researchers rely on still mean anything?

## What prompted this

Since October 2025, 99 papers carry an annual-report, 10-K, MD&A, narrative-disclosure, readability or textual-analysis keyword (60 in finance journals, 19 in accounting): "10-K filings" on 27, "narrative disclosure" on 13, "readability" on 10. Only 9 of the 99 also carry a generative-AI, ChatGPT, language-model or AI-generated keyword.

## Questions

- Can AI-written passages in filings be detected, and what share of recent filings contains them?
- Did readability, tone and boilerplate measures shift after 2023 in a way that reflects the drafting tool rather than the firm?
- Do investors react differently to AI-drafted and human-drafted disclosures, once they can tell them apart?

## Data that could help

The text of 10-K filings from SEC EDGAR for 2018–2025, AI-text detectors, and market reactions to filings.

## Next step

Run an AI-text detector on the MD&A sections of all 10-K filings for 2021–2025 and track the share flagged by year and by firm size.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| narrative disclosure | 2 | #1148 | 1, 2, 2, 1, 1, 1, 2, 0, 0, 0, 1, 1, 2 | 12 |
| readability | 1 | #2550 | 0, 1, 2, 0, 1, 0, 0, 1, 2, 2, 0, 1, 0 | 10 |
| 10-K filings | 0 | – | 3, 2, 1, 1, 0, 3, 4, 3, 4, 4, 2, 0, 0 | 27 |


---

---
title: "Who pays for carbon pricing? (research idea)"
description: "Income redistribution, welfare losses and social welfare are rising keywords in the economics journals, and carbon taxes and emissions trading are large topics. Yet only 34 of 422 recent carbon-pricing papers carry a distributional or household keyword. As carbon prices reach heating and transport fuels, who bears the cost, and does returning the revenue make it fair?"
url: https://scholar4ai.com/ideas/who-pays-for-carbon-pricing
markdown_url: https://scholar4ai.com/ideas/who-pays-for-carbon-pricing.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "energy", "households"]
keywords: ["carbon tax", "distributional effects", "revenue recycling"]
---

# Who pays for carbon pricing?

Income redistribution, welfare losses and social welfare are rising keywords in the economics journals, and carbon taxes and emissions trading are large topics. Yet only 34 of 422 recent carbon-pricing papers carry a distributional or household keyword. As carbon prices reach heating and transport fuels, who bears the cost, and does returning the revenue make it fair?

## What prompted this

In economics, "income redistribution" (7 papers in July–September 2026 against about 2 expected), "welfare loss" (10 against 5) and "social welfare" (20 against 12) are growing (see the [economics dashboard](/categories/economics)). Since October 2025, 422 papers carry a carbon-tax, carbon-pricing or emissions-trading keyword (260 in economics journals, 80 in finance, 46 in operations research). Only 34 of them carry a distribution, household, inequality, regressivity or redistribution keyword, and "revenue recycling" appears on 12.

## Questions

- How regressive are carbon prices on heating and motor fuel, across income groups and between cities and the countryside?
- Which ways of returning the revenue (equal dividends, lower income taxes, targeted transfers) leave most households better off?
- Does visible compensation raise public support for higher carbon prices?

## Data that could help

Household expenditure surveys, energy use by fuel, carbon price schedules (including the EU's new emissions trading for buildings and road transport), and survey experiments on public support.

## Next step

Compute the cost of a given carbon price for each income decile from a national household expenditure survey, with and without an equal per-person dividend.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| carbon tax | 5 | #342 | 11, 8, 9, 8, 9, 9, 4, 8, 7, 9, 9, 9, 4 | 100 |
| distributional effects | 2 | #1148 | 3, 3, 4, 6, 4, 1, 4, 5, 11, 7, 7, 1, 2 | 56 |
| revenue recycling | 0 | – | 3, 1, 1, 1, 0, 1, 0, 0, 2, 2, 0, 1, 0 | 12 |


---

---
title: "Do whistleblower rewards find fraud sooner?"
description: "Whistleblowing appears on 16 recent papers across the tracked journals, mostly on whether employees speak up. Reward programmes at the SEC and now the EU and other regulators pay tipsters a share of penalties. Do rewards bring forward the discovery of fraud, and do they change what happens inside firms?"
url: https://scholar4ai.com/ideas/whistleblower-rewards
markdown_url: https://scholar4ai.com/ideas/whistleblower-rewards.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "governance", "regulation"]
keywords: ["whistleblowing", "corporate fraud"]
---

# Do whistleblower rewards find fraud sooner?

Whistleblowing appears on 16 recent papers across the tracked journals, mostly on whether employees speak up. Reward programmes at the SEC and now the EU and other regulators pay tipsters a share of penalties. Do rewards bring forward the discovery of fraud, and do they change what happens inside firms?

## What prompted this

Since October 2025, 16 papers carry a whistleblowing keyword (7 in accounting journals, 4 in management): "whistleblowing" on 10, "whistleblower protection" on 4, "whistleblower rewards" on 1. "Corporate fraud" appears on 15 papers. Fraud detection as an AI topic is far larger, but the human channel through which most frauds are found is barely studied.

## Questions

- After a reward programme starts, are frauds discovered earlier in their life and with smaller investor losses?
- Do firms respond with better internal reporting channels, or with stricter confidentiality agreements?
- Do the rewards attract tips about the largest frauds, or many small ones?

## Data that could help

Enforcement actions with their discovery dates and tip sources, whistleblower award announcements, and restatements.

## Next step

Compare the time between fraud start and discovery for enforcement cases before and after the SEC's reward programme, using disclosed case timelines.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| whistleblowing | 0 | – | 2, 2, 1, 0, 0, 1, 1, 2, 0, 0, 1, 0, 0 | 10 |
| corporate fraud | 2 | #1148 | 1, 0, 1, 1, 2, 3, 1, 0, 0, 0, 2, 2, 2 | 13 |


---

---
title: "Where Chinese firms list now (research idea)"
description: "Initial public offerings appear on 48 recent papers, but only 7 of 91 IPO papers carry a listing-venue, cross-listing, Hong Kong, STAR Market or delisting keyword. Chinese firms have moved from New York to Hong Kong and Shanghai's STAR Market, and some have delisted from the US. Does the venue change who invests, how firms are valued and how they behave?"
url: https://scholar4ai.com/ideas/where-chinese-firms-list-now
markdown_url: https://scholar4ai.com/ideas/where-chinese-firms-list-now.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "china"]
keywords: ["initial public offerings", "STAR Market"]
---

# Where Chinese firms list now

Initial public offerings appear on 48 recent papers, but only 7 of 91 IPO papers carry a listing-venue, cross-listing, Hong Kong, STAR Market or delisting keyword. Chinese firms have moved from New York to Hong Kong and Shanghai's STAR Market, and some have delisted from the US. Does the venue change who invests, how firms are valued and how they behave?

## What prompted this

Since October 2025, 91 papers carry an IPO keyword (51 in finance journals), with "initial public offerings" on 48 and "IPO underpricing" on 19. Only 7 of the 91 also carry a listing-venue, cross-listing, Hong Kong, STAR Market, delisting or overseas-listing keyword; "STAR Market" appears on 8 papers in all.

## Questions

- Are firms that list in Hong Kong rather than the US valued lower, and does the difference reflect investor base, disclosure or politics?
- What happened to the firms that delisted from US exchanges: did they relist elsewhere, go private or shrink?
- Does the STAR Market's registration-based system produce IPO pricing closer to that of Hong Kong or of the old approval system?

## Data that could help

IPO and listing records across exchanges, delisting announcements, valuation multiples and ownership data (CSMAR, Wind, Bloomberg).

## Next step

Build a list of Chinese companies that listed abroad or delisted since 2019 and compare their valuation multiples with matched firms on domestic exchanges.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| initial public offerings | 3 | #687 | 8, 1, 4, 5, 3, 4, 2, 3, 4, 4, 2, 6, 3 | 46 |
| STAR Market | 0 | – | 2, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 1, 0 | 8 |


---

---
title: "When one customer dominates (research idea)"
description: "Supply chain concentration and supply chain relationships are among the fastest-growing keywords across the tracked journals. Finance papers treat a dominant customer as risk; management papers treat it as dependence, through resource dependence theory. The two rarely meet. Does depending on one big customer make suppliers more innovative or more fragile?"
url: https://scholar4ai.com/ideas/when-one-customer-dominates
markdown_url: https://scholar4ai.com/ideas/when-one-customer-dominates.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "finance", "supply chain"]
keywords: ["customer concentration", "supply chain concentration", "resource dependence theory"]
---

# When one customer dominates

Supply chain concentration and supply chain relationships are among the fastest-growing keywords across the tracked journals. Finance papers treat a dominant customer as risk; management papers treat it as dependence, through resource dependence theory. The two rarely meet. Does depending on one big customer make suppliers more innovative or more fragile?

## What prompted this

Across all journals, "supply chain concentration" had 18 papers in July–September 2026 against about 7 expected and "supply chain relationships" 17 against 5; in management, "resource dependence theory" grew too (10 against 5); see the [trends page](/trends). Since October 2025, 75 papers carry a customer-concentration or supply-chain-concentration keyword (43 in finance journals, 15 in economics, 10 in management), and only 3 of them carry the resource-dependence keyword.

## Questions

- Do suppliers with a dominant customer invest more in relationship-specific innovation, and does the customer share the gains?
- What happens to them when the customer leaves, is acquired or moves production abroad?
- Which responses reduce the risk: long-term contracts, cross-shareholdings or diversification?

## Data that could help

Customer disclosures in filings (Compustat segment data; the top-five customer disclosures of Chinese listed firms in CSMAR), patents and supply-chain links.

## Next step

Use the top-five customer disclosures of Chinese listed firms to find suppliers that lost a major customer, and compare their patenting and sales before and after.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| customer concentration | 0 | – | 3, 3, 8, 3, 1, 1, 7, 5, 1, 1, 1, 1, 0 | 35 |
| supply chain concentration | 2 | #1148 | 3, 2, 3, 1, 0, 5, 2, 3, 3, 5, 8, 5, 2 | 40 |
| resource dependence theory | 4 | #458 | 2, 2, 4, 5, 2, 4, 2, 7, 4, 9, 1, 7, 3 | 49 |


---

---
title: "What China's green patent boom actually invents"
description: "Green innovation is one of the largest topics in the finance and economics journals, much of it counting the green patents of Chinese firms. Only 10 of 641 recent papers on it carry a patent-quality, citation or novelty keyword. Is the boom in green patents a boom in green technology?"
url: https://scholar4ai.com/ideas/what-chinas-green-patents-invent
markdown_url: https://scholar4ai.com/ideas/what-chinas-green-patents-invent.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "innovation", "esg"]
keywords: ["green innovation", "green patents", "corporate green innovation"]
---

# What China's green patent boom actually invents

Green innovation is one of the largest topics in the finance and economics journals, much of it counting the green patents of Chinese firms. Only 10 of 641 recent papers on it carry a patent-quality, citation or novelty keyword. Is the boom in green patents a boom in green technology?

## What prompted this

Since October 2025, 641 papers carry a green-innovation or green-patent keyword (271 in finance journals, 215 in economics, 134 in management). Only 10 of them carry a patent-quality, citation, breakthrough, novelty or strategic-patenting keyword. "Green patents" alone appears on 49 papers.

## Questions

- How many green patents of Chinese listed firms are utility models rather than inventions, and how many are granted, cited or renewed?
- Do subsidies and policies that reward patent counts raise counts more than quality?
- Do the headline findings of the green-innovation literature hold when only granted, cited inventions count?

## Data that could help

Patent records with type, grant status, citations and renewals (CNIPA, PATSTAT), green technology classes (the Y02 tags), and firm data (CSMAR).

## Next step

For one year of green patents filed by Chinese listed firms, report the share of invention patents, the grant rate and citations, and compare them with green patents filed elsewhere.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| [green innovation](https://scholar4ai.com/keywords/green-innovation.md) | 24 | #15 | 40, 38, 42, 43, 31, 35, 42, 39, 31, 29, 36, 32, 23 | 438 |
| green patents | 2 | #1148 | 8, 3, 0, 6, 1, 4, 3, 8, 4, 4, 4, 2, 2 | 47 |
| corporate green innovation | 6 | #263 | 2, 10, 10, 5, 9, 5, 7, 9, 11, 4, 6, 5, 5 | 83 |


---

---
title: "The return of the wealth tax (research idea)"
description: "Wealth and inheritance taxes appear on 19 recent papers in the economics journals, with 8 in the last quarter against about 4 per quarter before. Several countries are debating or reintroducing taxes on large fortunes. What did the earlier wealth taxes collect, how much wealth left, and what do the new designs change?"
url: https://scholar4ai.com/ideas/wealth-taxes-return
markdown_url: https://scholar4ai.com/ideas/wealth-taxes-return.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "tax", "inequality"]
keywords: ["wealth tax", "inheritance tax"]
---

# The return of the wealth tax

Wealth and inheritance taxes appear on 19 recent papers in the economics journals, with 8 in the last quarter against about 4 per quarter before. Several countries are debating or reintroducing taxes on large fortunes. What did the earlier wealth taxes collect, how much wealth left, and what do the new designs change?

## What prompted this

Since October 2025, 19 papers carry a wealth-tax, inheritance-tax or estate-tax keyword, all in economics journals: "wealth tax" on 12, "inheritance tax" on 4. Eight of them were published in July–September 2026 against about 4 per quarter earlier. "Wealth inequality" appears on 41 papers.

## Questions

- In countries that kept or reintroduced wealth taxes (Switzerland, Spain, Norway, Colombia), how much did revenue and reported wealth respond, and how much of the response was avoidance rather than emigration?
- Do minimum taxes on very high incomes, as proposed in several countries, raise more than wealth taxes with the same rate?
- Do inheritance tax changes alter gifts and saving years before death?

## Data that could help

Tax records with wealth and migration information (Scandinavian registers, Spanish regional data), rich lists, and the dates of tax changes.

## Next step

Compare reported wealth and migration of top wealth holders in Norway around its 2022 wealth tax increase with those in Sweden, which has no wealth tax.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| wealth tax | 0 | – | 1, 1, 1, 0, 1, 2, 1, 0, 1, 3, 0, 1, 0 | 12 |
| inheritance tax | 0 | – | 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0 | 4 |


---

---
title: "Labels without labellers for financial documents"
description: "Pseudo-labeling, label noise and weakly supervised learning appear on nearly 500 recent papers, almost all in AI journals, and only 7 of them concern financial, legal or business documents. Banks and auditors have millions of unlabelled contracts, invoices and filings and few labelled ones. Can weak supervision from rules and models replace hand labelling, and what errors does it introduce?"
url: https://scholar4ai.com/ideas/weak-supervision-for-financial-documents
markdown_url: https://scholar4ai.com/ideas/weak-supervision-for-financial-documents.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "finance", "documents"]
keywords: ["weakly supervised learning", "label noise", "pseudo-labeling"]
---

# Labels without labellers for financial documents

Pseudo-labeling, label noise and weakly supervised learning appear on nearly 500 recent papers, almost all in AI journals, and only 7 of them concern financial, legal or business documents. Banks and auditors have millions of unlabelled contracts, invoices and filings and few labelled ones. Can weak supervision from rules and models replace hand labelling, and what errors does it introduce?

## What prompted this

Since October 2025, 489 papers carry a weak-supervision, pseudo-label, noisy-label or label-noise keyword (487 in AI journals): "pseudo-labeling" on 215, "label noise" on 99, "weakly supervised learning" on 62, "pseudo-label refinement" on 36. Only 7 of the 489 also carry a finance, document, text-classification, legal, contract, invoice, economics, accounting or news keyword.

## Questions

- For tasks such as classifying contract clauses, flagging unusual invoices and tagging risk factors in filings, how close does weak supervision from rules and large models get to hand-labelled accuracy, at what cost?
- Are the errors of weakly supervised models concentrated in the rare cases that matter most (fraud, non-standard clauses)?
- Do language models used as labellers reproduce their own biases in the labels, and how can that be audited?

## Data that could help

Public financial document datasets with partial labels (contract clause datasets, SEC filings), and a partner firm's documents with a small gold-labelled set.

## Next step

Label a contract clause dataset with rules and a language model, train on the weak labels, and compare accuracy on a gold set with a model trained on the same number of hand labels.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| weakly supervised learning | 10 | #104 | 2, 3, 5, 3, 2, 7, 6, 8, 2, 6, 7, 6, 9 | 57 |
| label noise | 4 | #458 | 5, 7, 8, 6, 7, 19, 9, 11, 7, 5, 7, 5, 4 | 96 |
| pseudo-labeling | 8 | #153 | 15, 10, 17, 17, 22, 29, 20, 20, 13, 10, 19, 18, 7 | 210 |


---

---
title: "Warehouse robots and the workers beside them"
description: "Warehouse automation, operations and order picking appear on 63 recent papers in the operations research and AI journals, but only 10 of them carry a labour, worker, human, injury or wage keyword. Warehouses are where robots meet the most people. Do robots raise pace and injuries for the remaining workers, and do wages follow productivity?"
url: https://scholar4ai.com/ideas/warehouse-robots-and-workers
markdown_url: https://scholar4ai.com/ideas/warehouse-robots-and-workers.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "labor", "automation"]
keywords: ["warehouse automation", "order picking", "warehouse operations"]
---

# Warehouse robots and the workers beside them

Warehouse automation, operations and order picking appear on 63 recent papers in the operations research and AI journals, but only 10 of them carry a labour, worker, human, injury or wage keyword. Warehouses are where robots meet the most people. Do robots raise pace and injuries for the remaining workers, and do wages follow productivity?

## What prompted this

Since October 2025, 63 papers carry a warehouse, order-picking, fulfilment-centre or distribution-centre keyword (43 in operations research journals, 18 in AI): "warehouse automation" on 13, "warehouse operations" on 10, "order picking" on 9, "distribution centers" on 8. Only 10 of them also carry a labour, worker, human, ergonomics, injury, wage or employment keyword, most of those about human-robot interaction.

## Questions

- After a warehouse installs mobile robots, do picks per worker-hour rise, and do injury rates rise with them, as some public injury data suggest?
- Do wages at automated warehouses rise with productivity, or do the jobs that remain pay the same for more pace?
- Do robot installations reduce total warehouse employment in a region, or shift it to larger sites?

## Data that could help

Facility-level injury filings (US OSHA data), warehouse employment and wages by establishment, and robot installation announcements.

## Next step

Match facility-level injury filings to warehouse automation announcements and compare injury rates before and after installation with non-automated sites.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| warehouse automation | 1 | #2550 | 1, 0, 1, 1, 2, 0, 1, 1, 1, 2, 1, 2, 0 | 13 |
| order picking | 0 | – | 0, 0, 0, 2, 1, 1, 0, 1, 1, 3, 0, 0, 0 | 9 |
| warehouse operations | 0 | – | 1, 1, 3, 2, 1, 1, 0, 1, 0, 0, 0, 0, 0 | 10 |


---

---
title: "What managers' voices reveal in earnings calls"
description: "Earnings calls are a staple of finance and accounting research, read almost always as transcripts. In the AI journals, multimodal fusion is one of the biggest topics. Do voice and video carry information about firms that the words miss?"
url: https://scholar4ai.com/ideas/voices-in-earnings-calls
markdown_url: https://scholar4ai.com/ideas/voices-in-earnings-calls.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "accounting", "ai"]
keywords: ["earnings conference calls", "earnings call transcripts", "multimodal fusion"]
---

# What managers' voices reveal in earnings calls

Earnings calls are a staple of finance and accounting research, read almost always as transcripts. In the AI journals, multimodal fusion is one of the biggest topics. Do voice and video carry information about firms that the words miss?

## What prompted this

Since October 2025, 50 papers in the archive carry an earnings-call keyword (20 in finance journals, 17 in accounting). None of them carries a multimodal, audio, voice, video or facial keyword. Meanwhile "multimodal fusion" appears on 502 papers, 495 of them in AI journals, and is the 11th most frequent keyword of the last 30 days. Earnings call transcripts are also among the most-mentioned data sources in finance on the [dataset ranking](/rankings/datasets).

## Questions

- Do executives' vocal cues (pitch, pauses, speech rate) predict earnings surprises, restatements or crash risk beyond what they say?
- Does the market price these cues during the call, or only later?
- Do managers learn to control them once analysts and machines listen?

## Data that could help

- Call recordings, which many firms post on their investor-relations sites, with timestamped transcripts.
- Speech-emotion models pretrained on public corpora such as IEMOCAP or RAVDESS, and intraday prices.
- A text-only baseline such as the Loughran-McDonald tone measure.

Voice analysis touches on privacy, so the study should use public recordings and report firm-level results only.

## Next step

Collect audio for one quarter of S&P 500 calls and test whether a pretrained speech-emotion score adds anything to a text-only tone measure.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| earnings conference calls | 5 | #342 | 2, 1, 1, 0, 2, 3, 2, 4, 2, 3, 4, 3, 4 | 27 |
| earnings call transcripts | 1 | #2550 | 0, 2, 0, 0, 2, 0, 2, 0, 2, 2, 0, 1, 1 | 11 |
| [multimodal fusion](https://scholar4ai.com/keywords/multimodal-fusion.md) | 30 | #9 | 32, 36, 34, 46, 46, 44, 41, 44, 46, 44, 41, 27, 24 | 481 |


---

---
title: "Virtual power plants and the households inside them"
description: "Demand response, distributed energy resources, virtual power plants and peer-to-peer energy trading appear on 94 recent papers, mostly in economics and AI, and the topic is fading, with 17 in the last quarter against about 23 per quarter before. Aggregators now bid household batteries and heat pumps into power markets. How much flexibility do households really deliver, and what do they get for it?"
url: https://scholar4ai.com/ideas/virtual-power-plants
markdown_url: https://scholar4ai.com/ideas/virtual-power-plants.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "energy", "households"]
keywords: ["virtual power plants", "demand response", "peer-to-peer energy trading"]
---

# Virtual power plants and the households inside them

Demand response, distributed energy resources, virtual power plants and peer-to-peer energy trading appear on 94 recent papers, mostly in economics and AI, and the topic is fading, with 17 in the last quarter against about 23 per quarter before. Aggregators now bid household batteries and heat pumps into power markets. How much flexibility do households really deliver, and what do they get for it?

## What prompted this

Since October 2025, 94 papers carry a virtual-power-plant, demand-response, microgrid, distributed-energy-resource or peer-to-peer-energy keyword (51 in economics journals, 32 in AI, 9 in operations research): "demand response" on 41, "distributed energy resources" on 11, "virtual power plants" on 10, "peer-to-peer energy trading" on 8, "microgrids" on 7. Seventeen were published in July–September 2026 against about 23 per quarter earlier.

## Questions

- When an aggregator calls on household batteries and heat pumps, what share of the contracted flexibility is actually delivered, and does it decline as households override the controls?
- What do households earn per year, and is it enough to keep them enrolled after the sign-up bonus?
- Do virtual power plants reduce the need for peak capacity, or mostly shift payments from utilities to aggregators?

## Data that could help

Aggregator dispatch and delivery records, household enrolment and churn data, and capacity market results.

## Next step

Obtain dispatch and delivered-flexibility data from one virtual power plant programme and measure the delivery ratio and household churn over two years.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| virtual power plants | 2 | #1148 | 2, 0, 0, 1, 0, 1, 3, 1, 0, 0, 0, 1, 1 | 9 |
| demand response | 5 | #342 | 3, 3, 2, 3, 2, 5, 4, 4, 5, 2, 1, 2, 5 | 36 |
| peer-to-peer energy trading | 0 | – | 0, 0, 2, 0, 1, 1, 0, 1, 0, 3, 0, 0, 0 | 8 |


---

---
title: "Virtual influencers and the disclosure of nobody"
description: "Virtual influencers, avatars and virtual humans appear on 33 recent papers in the management and AI journals, with 9 in the last quarter against about 7 per quarter before. Brands now run computer-generated personalities with millions of followers. Do they sell, do followers care that no one is there, and what should disclosure rules require?"
url: https://scholar4ai.com/ideas/virtual-influencers
markdown_url: https://scholar4ai.com/ideas/virtual-influencers.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "marketing", "ai"]
keywords: ["virtual influencers", "avatars"]
---

# Virtual influencers and the disclosure of nobody

Virtual influencers, avatars and virtual humans appear on 33 recent papers in the management and AI journals, with 9 in the last quarter against about 7 per quarter before. Brands now run computer-generated personalities with millions of followers. Do they sell, do followers care that no one is there, and what should disclosure rules require?

## What prompted this

Since October 2025, 33 papers carry a virtual-influencer, virtual-human, digital-human, avatar or virtual-idol keyword (17 in management journals, 11 in AI): "virtual influencers" on 12, "avatars" on 5, "virtual humans" on 4, "virtual idols" on 2. The quarter to September 2026 had 9 such papers against about 7 per quarter earlier.

## Questions

- In matched campaigns, do virtual influencers deliver the same engagement and sales as human influencers at lower cost and risk?
- Does disclosure that an influencer is synthetic reduce trust, and does the effect fade as audiences get used to them?
- Who is responsible when a virtual influencer makes a false claim or endorses a product that harms someone?

## Data that could help

Campaign performance data from brands and agencies, follower surveys, and platform disclosure policies.

## Next step

Compare engagement and conversion for matched product promotions by virtual and human influencers on one platform, with and without synthetic-disclosure labels.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| virtual influencers | 0 | – | 0, 0, 1, 2, 1, 0, 1, 1, 0, 2, 3, 1, 0 | 12 |
| avatars | 0 | – | 0, 2, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0 | 5 |


---

---
title: "Venture capital in the AI boom (research idea)"
description: "Venture capital is one of the fastest-growing keywords in the finance journals, and startups are a big topic in management and economics, but only 18 of these papers touch artificial intelligence. As more venture money goes to AI companies, are other startups crowded out, or does AI lower the cost of starting up?"
url: https://scholar4ai.com/ideas/venture-capital-in-the-ai-boom
markdown_url: https://scholar4ai.com/ideas/venture-capital-in-the-ai-boom.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "management", "entrepreneurship"]
keywords: ["venture capital", "startups", "generative AI"]
---

# Venture capital in the AI boom

Venture capital is one of the fastest-growing keywords in the finance journals, and startups are a big topic in management and economics, but only 18 of these papers touch artificial intelligence. As more venture money goes to AI companies, are other startups crowded out, or does AI lower the cost of starting up?

## What prompted this

In the finance journals, "venture capital" had 13 papers in July–September 2026 against about 5 expected from its earlier share (see the [finance dashboard](/categories/finance)). Since October 2025, 250 papers carry a venture-capital or startup keyword (106 in management journals, 80 in economics, 56 in finance). Only 18 of them also carry an AI keyword.

## Questions

- As AI deals take a growing share of venture funding, do non-AI startups raise less, later or at lower valuations?
- Do AI tools let founders build more with less capital, so that seed rounds shrink while later rounds grow?
- Do AI-heavy funds earn returns that differ from those of earlier waves such as the internet, mobile and clean tech?

## Data that could help

Deal data from PitchBook, Crunchbase, Preqin or VentureXpert (the M&A and private capital category of the [dataset ranking](/rankings/datasets)), with AI exposure classified from company descriptions.

## Next step

Classify startups as AI or non-AI from their descriptions and compare round sizes and the time between rounds before and after 2023.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| venture capital | 3 | #687 | 3, 5, 2, 2, 7, 13, 11, 9, 1, 8, 5, 7, 2 | 73 |
| startups | 8 | #153 | 4, 6, 3, 8, 10, 9, 10, 4, 9, 9, 3, 10, 7 | 85 |
| generative AI | 10 | #104 | 25, 33, 21, 31, 18, 29, 38, 23, 35, 30, 37, 25, 9 | 345 |


---

---
title: "Vaccine allocation for the next pandemic (research idea)"
description: "Vaccine distribution, allocation, uptake and hesitancy appear on 55 recent papers, and 18 of them, mostly in operations research, carry a supply-chain or allocation keyword. The COVID-19 roll-out produced data on who got vaccinated when, and at what cost. What did the allocation models get right, and what would they change for next time?"
url: https://scholar4ai.com/ideas/vaccine-allocation-for-the-next-pandemic
markdown_url: https://scholar4ai.com/ideas/vaccine-allocation-for-the-next-pandemic.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "healthcare", "policy"]
keywords: ["vaccine distribution", "vaccine allocation", "vaccine hesitancy"]
---

# Vaccine allocation for the next pandemic

Vaccine distribution, allocation, uptake and hesitancy appear on 55 recent papers, and 18 of them, mostly in operations research, carry a supply-chain or allocation keyword. The COVID-19 roll-out produced data on who got vaccinated when, and at what cost. What did the allocation models get right, and what would they change for next time?

## What prompted this

Since October 2025, 55 papers carry a vaccine, vaccination or immunization keyword (25 in economics journals, 16 in operations research, 10 in AI): "vaccine distribution" on 6, "COVID-19 vaccination" on 5, "childhood immunization" on 4, "vaccine uptake" on 4, "vaccine hesitancy" on 4, "vaccine allocation" on 3. Eighteen of the 55 also carry a supply-chain, distribution, allocation, cold-chain, logistics, inventory or optimization keyword.

## Questions

- Comparing the 2021 allocation rules of different countries (by age, by occupation, by region) with their outcomes, which rules saved the most lives per dose, and did the optimization models of the time predict that?
- How much did hesitancy and no-shows, which the models ignored, change the optimal allocation?
- Would an allocation that accounts for distribution capacity in poor countries have changed global outcomes more than one that optimizes within rich countries?

## Data that could help

Country-level vaccination timelines and mortality data, allocation rules by country, wastage and no-show records, and the models published in 2020–2021.

## Next step

Re-run three published allocation models with the actual uptake and delivery data from 2021 and compare their recommendations with what countries did.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| vaccine distribution | 0 | – | 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0 | 6 |
| vaccine allocation | 1 | #2550 | 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1 | 2 |
| vaccine hesitancy | 1 | #2550 | 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1 | 3 |


---

---
title: "Air taxis before the first passenger (research idea)"
description: "Urban air mobility, eVTOL aircraft and vertiports appear on 24 recent papers in the operations research and AI journals, with 10 in the last quarter against about 4 per quarter before. The first commercial air taxi services are being certified. Where do the routing and scheduling models get their demand numbers, and would anyone pay the fares the models assume?"
url: https://scholar4ai.com/ideas/urban-air-mobility
markdown_url: https://scholar4ai.com/ideas/urban-air-mobility.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "transport", "aviation"]
keywords: ["urban air mobility", "eVTOL"]
---

# Air taxis before the first passenger

Urban air mobility, eVTOL aircraft and vertiports appear on 24 recent papers in the operations research and AI journals, with 10 in the last quarter against about 4 per quarter before. The first commercial air taxi services are being certified. Where do the routing and scheduling models get their demand numbers, and would anyone pay the fares the models assume?

## What prompted this

Since October 2025, 24 papers carry an urban-air-mobility, eVTOL, air-taxi or vertiport keyword (20 in operations research journals, 4 in AI): "urban air mobility" on 20, "eVTOL" on 5, "eVTOL aircraft" on 4, "vertiports" on 2. The quarter to September 2026 had 10 such papers against about 4 per quarter earlier.

## Questions

- What demand assumptions do the vertiport location and fleet scheduling models use, and how do they compare with stated-preference surveys and the fares the operators have announced?
- At realistic battery, pilot and vertiport costs, which city pairs break even, and how many passengers per day do they need?
- Do the models account for noise and airspace constraints that have blocked earlier urban aviation?

## Data that could help

Operators' published fares and routes, stated-preference surveys on willingness to pay, helicopter service data as a benchmark, and cost estimates from certification filings.

## Next step

Collect the demand and cost assumptions from the recent urban air mobility optimization papers and compare them with the fares and costs disclosed by operators preparing to launch.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| urban air mobility | 1 | #2550 | 0, 2, 1, 1, 4, 0, 1, 0, 2, 3, 2, 3, 1 | 19 |
| eVTOL | 0 | – | 0, 0, 0, 0, 2, 0, 1, 0, 0, 1, 1, 0, 0 | 5 |


---

---
title: "University spin-offs in the AI era (research idea)"
description: "University spin-offs and university entrepreneurship are among the fastest-growing keywords in the management journals, while AI research shifts from universities to companies. Only 2 of 37 recent papers on academic entrepreneurship mention AI. Do universities still turn AI research into startups, or does industry absorb the talent first?"
url: https://scholar4ai.com/ideas/university-spin-offs-in-the-ai-era
markdown_url: https://scholar4ai.com/ideas/university-spin-offs-in-the-ai-era.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "ai", "innovation", "entrepreneurship"]
keywords: ["university spin-offs", "university entrepreneurship", "technology commercialization"]
---

# University spin-offs in the AI era

University spin-offs and university entrepreneurship are among the fastest-growing keywords in the management journals, while AI research shifts from universities to companies. Only 2 of 37 recent papers on academic entrepreneurship mention AI. Do universities still turn AI research into startups, or does industry absorb the talent first?

## What prompted this

In the management journals, "university spin-offs" had 6 papers in July–September 2026 against about 1 expected, "university entrepreneurship" 5 against 1, and "technology commercialization" 5 against 2 (see the [management dashboard](/categories/management)). Since October 2025, 37 papers carry a spin-off, university-entrepreneurship or technology-commercialization keyword (29 in management journals); only 2 of them carry an AI keyword.

## Questions

- Are AI spin-offs from universities fewer, later or smaller than spin-offs in other fields, given how fast AI researchers move to industry?
- Do universities with more computing resources keep more AI faculty and spin off more companies?
- Do technology-transfer offices treat AI, which often comes as open code and models, differently from patents?

## Data that could help

Startup databases (Crunchbase, PitchBook) linked to universities, faculty moves to industry, and university patents and software licences.

## Next step

Link university-founded startups in Crunchbase to their fields and count AI spin-offs per AI faculty member, by university, for 2015–2025.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| university spin-offs | 1 | #2550 | 1, 0, 1, 0, 0, 0, 1, 0, 2, 2, 2, 2, 0 | 11 |
| university entrepreneurship | 0 | – | 0, 2, 1, 0, 0, 0, 0, 1, 1, 2, 2, 1, 0 | 10 |
| technology commercialization | 1 | #2550 | 1, 1, 0, 1, 2, 2, 0, 0, 3, 1, 1, 4, 1 | 16 |


---

---
title: "Treasury market liquidity and the basis trade"
description: "Treasury yields and bonds appear on 39 recent papers in the finance and economics journals, but only 2 of them carry a liquidity, dealer, hedge-fund or market-functioning keyword. Hedge funds' leveraged basis trades and dealers' balance sheet limits have been blamed for Treasury market stress. How fragile is the market now, and would central clearing help?"
url: https://scholar4ai.com/ideas/treasury-market-liquidity-and-the-basis-trade
markdown_url: https://scholar4ai.com/ideas/treasury-market-liquidity-and-the-basis-trade.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "fixed income", "market microstructure"]
keywords: ["Treasury yields", "market liquidity"]
---

# Treasury market liquidity and the basis trade

Treasury yields and bonds appear on 39 recent papers in the finance and economics journals, but only 2 of them carry a liquidity, dealer, hedge-fund or market-functioning keyword. Hedge funds' leveraged basis trades and dealers' balance sheet limits have been blamed for Treasury market stress. How fragile is the market now, and would central clearing help?

## What prompted this

Since October 2025, 39 papers carry a Treasury keyword (28 in finance journals, 9 in economics): "Treasury yields" on 14, "Treasury bonds" on 4, "treasury bills" on 3. Only 2 of the 39 also carry a liquidity, basis-trade, hedge-fund, dealer, market-functioning or auction keyword, while "market liquidity" appears on 51 papers about other markets.

## Questions

- How large is the cash-futures basis trade, and how fast would it unwind if repo rates or futures margins jumped?
- Do dealers' balance sheet constraints explain why Treasury liquidity worsens in stress, and did the 2023 leverage-ratio relief change that?
- Would mandatory central clearing, now being phased in, reduce the stress or move it elsewhere?

## Data that could help

Treasury transaction data (TRACE for Treasuries), futures positioning reports, repo market statistics and dealer inventories from the Federal Reserve.

## Next step

Estimate the size of the basis trade from futures positioning and repo data for 2019–2025 and relate it to liquidity measures around stress episodes.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| Treasury yields | 0 | – | 1, 1, 0, 1, 1, 1, 3, 1, 1, 2, 0, 2, 0 | 14 |
| market liquidity | 3 | #687 | 2, 5, 2, 2, 5, 2, 2, 7, 8, 6, 1, 8, 1 | 50 |


---

---
title: "Transfer learning for credit across countries"
description: "Transfer learning, domain adaptation and domain generalization appear on nearly 700 recent papers, almost all in AI journals, and only 10 of them concern credit, lending, banking or markets. Lenders entering new countries, and credit bureaus in countries with little data, want to reuse models trained elsewhere. How much transfers, and what breaks?"
url: https://scholar4ai.com/ideas/transfer-learning-for-credit-across-countries
markdown_url: https://scholar4ai.com/ideas/transfer-learning-for-credit-across-countries.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "finance", "development"]
keywords: ["transfer learning", "domain adaptation", "domain generalization"]
---

# Transfer learning for credit across countries

Transfer learning, domain adaptation and domain generalization appear on nearly 700 recent papers, almost all in AI journals, and only 10 of them concern credit, lending, banking or markets. Lenders entering new countries, and credit bureaus in countries with little data, want to reuse models trained elsewhere. How much transfers, and what breaks?

## What prompted this

Since October 2025, 679 papers carry a transfer-learning, domain-adaptation or domain-generalization keyword (664 in AI journals): "domain adaptation" on 182, "domain generalization" on 178, "transfer learning" on 162, "unsupervised domain adaptation" on 76. Only 10 of the 679 also carry a credit, loan, bank, finance, default, microfinance, emerging-market, fraud or stock keyword.

## Questions

- Does a credit model trained in one country, adapted with a few thousand local loans, beat a model trained from scratch on the local data, and how does the answer depend on how different the countries' borrowers and institutions are?
- Which features transfer (payment behaviour, mobile phone data) and which do not (income, employment categories)?
- Does transferred scoring import the source country's biases against groups that do not exist in the target?

## Data that could help

Loan datasets from several countries (public lending datasets, microfinance data, partner lenders), and borrower features that are available in both.

## Next step

Adapt a credit model trained on one public lending dataset to another country's dataset with small local samples and compare it with local-only training at each sample size.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| transfer learning | 8 | #153 | 17, 15, 20, 17, 13, 7, 9, 9, 16, 12, 18, 5, 7 | 158 |
| domain adaptation | 14 | #55 | 18, 9, 15, 15, 12, 21, 15, 11, 22, 16, 11, 12, 9 | 177 |
| domain generalization | 9 | #129 | 11, 8, 15, 13, 14, 14, 22, 11, 22, 15, 15, 12, 8 | 172 |


---

---
title: "The missing economics of medical AI (research idea)"
description: "Clinical decision support appears on 94 recent papers and medical image segmentation on 188, among more than 1,300 medical AI papers in the AI journals, but only 65 of all medical papers carry a cost, reimbursement, deployment or workflow keyword, and cost-effectiveness appears on 27 papers in the whole archive. Which medical AI tools pay for themselves, and for whom?"
url: https://scholar4ai.com/ideas/the-missing-economics-of-medical-ai
markdown_url: https://scholar4ai.com/ideas/the-missing-economics-of-medical-ai.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "health", "economics"]
keywords: ["clinical decision support", "cost-effectiveness", "clinical workflow"]
---

# The missing economics of medical AI

Clinical decision support appears on 94 recent papers and medical image segmentation on 188, among more than 1,300 medical AI papers in the AI journals, but only 65 of all medical papers carry a cost, reimbursement, deployment or workflow keyword, and cost-effectiveness appears on 27 papers in the whole archive. Which medical AI tools pay for themselves, and for whom?

## What prompted this

Since October 2025, 1,607 papers carry a medical, clinical, diagnosis, radiology, healthcare or patient keyword (1,320 in AI journals): "medical image segmentation" on 188, "clinical decision support" on 94, "medical image classification" on 73, "computer-aided diagnosis" on 46. Only 65 of the 1,607 also carry a cost-effectiveness, reimbursement, economic-evaluation, deployment, implementation, adoption, cost-saving, workflow or clinical-practice keyword; "cost-effectiveness" appears on 27 papers in the archive and "clinical workflow" on 7.

## Questions

- For the medical AI tools that have been reimbursed (for example stroke detection and diabetic retinopathy screening), what did the cost per quality-adjusted life year turn out to be in practice?
- Do tools that save clinician time reduce costs, or do clinicians fill the time with more cases and the hospital bills more?
- Which of the hundreds of published segmentation and diagnosis models have reached any clinic, and what separated them?

## Data that could help

Reimbursement decisions and health technology assessments, hospital adoption records, claims data around adoption, and the literature's own deployment reports.

## Next step

Trace the twenty most-cited medical AI models of 2020–2022 to any clinical deployment and record the economic evidence that accompanied each.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| clinical decision support | 3 | #687 | 8, 2, 3, 8, 7, 10, 5, 9, 11, 8, 11, 9, 3 | 91 |
| cost-effectiveness | 1 | #2550 | 4, 0, 1, 2, 2, 1, 3, 4, 3, 3, 1, 3, 0 | 27 |
| clinical workflow | 0 | – | 0, 0, 0, 1, 1, 0, 2, 0, 1, 0, 1, 1, 0 | 7 |


---

---
title: "The language divide in AI (research idea)"
description: "Low-resource languages are a fast-growing topic in the AI journals, and the digital divide is rising in economics. Language models work worse in languages with little training text, which are spoken mostly in poorer countries. Does that slow AI adoption, and its economic gains, where those languages are spoken?"
url: https://scholar4ai.com/ideas/the-language-divide-in-ai
markdown_url: https://scholar4ai.com/ideas/the-language-divide-in-ai.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "economics", "inequality"]
keywords: ["low-resource languages", "digital divide", "large language models"]
---

# The language divide in AI

Low-resource languages are a fast-growing topic in the AI journals, and the digital divide is rising in economics. Language models work worse in languages with little training text, which are spoken mostly in poorer countries. Does that slow AI adoption, and its economic gains, where those languages are spoken?

## What prompted this

In the AI journals, "low-resource languages" had 16 papers in July–September 2026 against about 10 expected; in economics, "digital divide" grew too (11 against 4); see the [trends page](/trends). Since October 2025, all 46 papers with the low-resource-languages keyword are in AI journals, while "digital divide" appears on 70 papers (25 in economics, 23 in finance, 11 in management, 9 in AI). Only 8 of the digital-divide papers carry an AI keyword.

## Questions

- How much worse do common language models perform in each language, and does that predict AI use by country once income and internet access are held fixed?
- When a model gets better at a language, do firms and workers who use that language adopt AI faster?
- Do translation layers close the gap, or do they cost accuracy where it matters most, such as in health, law and finance?

## Data that could help

Multilingual benchmark scores by model and language, AI usage by country (app downloads, search interest), and data on the languages spoken in each country.

## Next step

Match multilingual benchmark scores to countries by main language and correlate them with AI app adoption, controlling for income and internet use.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| low-resource languages | 1 | #2550 | 1, 3, 2, 7, 3, 1, 4, 7, 2, 2, 9, 5, 0 | 46 |
| digital divide | 7 | #210 | 3, 7, 8, 1, 2, 6, 2, 3, 7, 8, 9, 5, 7 | 61 |
| [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 89 | #1 | 68, 94, 103, 90, 83, 129, 129, 126, 147, 111, 122, 110, 72 | 1,312 |


---

---
title: "The four-day week (research idea)"
description: "Working hours appear on 41 recent papers in the economics journals and working time reduction on 4, but the four-day week, now being trialled by hundreds of firms and by some governments, appears under no keyword of its own. Do firms that cut hours without cutting pay keep output, and who gains the hours?"
url: https://scholar4ai.com/ideas/the-four-day-week
markdown_url: https://scholar4ai.com/ideas/the-four-day-week.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "labor", "management"]
keywords: ["working hours", "working time reduction"]
---

# The four-day week

Working hours appear on 41 recent papers in the economics journals and working time reduction on 4, but the four-day week, now being trialled by hundreds of firms and by some governments, appears under no keyword of its own. Do firms that cut hours without cutting pay keep output, and who gains the hours?

## What prompted this

Since October 2025, 52 papers carry a working-hours, working-time or work-week keyword (45 in economics journals): "working hours" on 41, "working time reduction" on 4, "flexible working hours" on 3. No keyword in the archive names the four-day week, although trials in the UK, Iceland, Germany, Japan and Portugal have reported results.

## Questions

- In firm trials, did output per week hold up because of fewer meetings and less idle time, or because workers compressed the same hours?
- Did turnover, sick leave and hiring costs fall by enough to pay for the fifth day?
- Which jobs cannot compress, and would a general shift widen the gap between office and shift workers?

## Data that could help

Trial data from the pilot organizers, firm productivity and turnover records, and time-use surveys of participants.

## Next step

Collect the published results of the national and firm-level trials since 2021 and compare productivity and retention outcomes by sector.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| working hours | 3 | #687 | 3, 2, 3, 2, 2, 7, 3, 4, 5, 2, 2, 5, 2 | 40 |
| working time reduction | 0 | – | 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0 | 4 |


---

---
title: "The compute bill of AI papers (research idea)"
description: "Inference latency and resource-constrained deployment are among the fastest-growing keywords in the AI journals, yet only 15 of 226 such papers carry an energy, carbon or green-AI keyword. How much computing do the methods in AI journals cost to train and run, and would reporting it change which methods win?"
url: https://scholar4ai.com/ideas/the-compute-bill-of-ai-papers
markdown_url: https://scholar4ai.com/ideas/the-compute-bill-of-ai-papers.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "methods"]
keywords: ["inference latency", "resource-constrained deployment", "green AI"]
---

# The compute bill of AI papers

Inference latency and resource-constrained deployment are among the fastest-growing keywords in the AI journals, yet only 15 of 226 such papers carry an energy, carbon or green-AI keyword. How much computing do the methods in AI journals cost to train and run, and would reporting it change which methods win?

## What prompted this

In the AI journals, "inference latency" had 12 papers in July–September 2026 against about 5 expected and "resource-constrained deployment" 10 against 4 (see the [AI dashboard](/categories/ai)). Since October 2025, 226 papers carry an inference-latency, resource-constrained or edge-deployment keyword (199 in AI journals). Only 15 of them carry an energy, carbon, green-AI or power-consumption keyword, and "green AI" itself appears on 6.

## Questions

- What share of recent AI-journal papers report training compute, inference time or energy, and do they report them for the baselines too?
- If results were compared per unit of computing, how often would the winner change?
- Would a short reporting checklist (hardware, hours, energy) be adopted, as code release was?

## Possible approach

Code a sample of recent papers for what they report about computing, and re-rank the reported results by accuracy per GPU-hour where both are given.

## Next step

Code 100 recent papers from the AI journals for whether they report hardware, training time, inference time and energy use.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| inference latency | 4 | #458 | 0, 0, 2, 3, 0, 2, 3, 3, 1, 1, 6, 5, 4 | 26 |
| resource-constrained deployment | 2 | #1148 | 0, 0, 1, 1, 2, 2, 1, 2, 2, 3, 5, 2, 2 | 21 |
| green AI | 1 | #2550 | 0, 1, 0, 1, 0, 3, 0, 0, 0, 1, 0, 0, 1 | 6 |


---

---
title: "Who paid the 2025 tariffs? (research idea)"
description: "Tariffs and the trade war appear on 184 recent papers, mostly in the economics journals, but only 47 carry a price, pass-through, incidence or inflation keyword, and \"tariff pass-through\" on 3. The 2025 tariff rounds were larger and broader than those of 2018. Did foreign exporters cut prices this time, or did consumers pay again?"
url: https://scholar4ai.com/ideas/tariff-pass-through-in-the-2025-trade-war
markdown_url: https://scholar4ai.com/ideas/tariff-pass-through-in-the-2025-trade-war.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "trade", "macroeconomics"]
keywords: ["tariffs", "import tariffs", "trade war", "tariff pass-through"]
---

# Who paid the 2025 tariffs?

Tariffs and the trade war appear on 184 recent papers, mostly in the economics journals, but only 47 carry a price, pass-through, incidence or inflation keyword, and "tariff pass-through" on 3. The 2025 tariff rounds were larger and broader than those of 2018. Did foreign exporters cut prices this time, or did consumers pay again?

## What prompted this

Since October 2025, 184 papers carry a tariff or trade-war keyword (119 in economics journals, 43 in finance): "US-China trade war" on 48, "tariffs" on 39, "import tariffs" on 18, "trade war" on 12. Only 47 of them also carry a pass-through, consumer-price, price, incidence, inflation or welfare keyword, and "tariff pass-through" appears on 3.

## Questions

- Did import prices at the border fall after the 2025 tariffs, as they mostly did not in 2018, and did the answer differ by exporter country and product?
- How much of the tariff reached retail prices, and how quickly?
- Did importers reroute through third countries, and did that limit the pass-through?

## Data that could help

Monthly import price indices and customs data by product and origin, retail scanner prices, and the tariff schedules with their effective dates.

## Next step

Estimate border price changes by product for the 2025 tariff rounds using monthly customs unit values and compare them with the 2018 estimates.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| tariffs | 3 | #687 | 2, 2, 5, 4, 0, 3, 6, 3, 3, 1, 2, 5, 3 | 36 |
| import tariffs | 0 | – | 0, 2, 2, 1, 3, 2, 1, 1, 1, 3, 2, 0, 0 | 18 |
| trade war | 2 | #1148 | 2, 1, 0, 0, 0, 0, 1, 2, 3, 0, 1, 1, 1 | 11 |
| tariff pass-through | 0 | – | 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0 | 3 |


---

---
title: "Foundation models for tables (research idea)"
description: "Tabular data appears on 53 recent papers and tabular classification on 13, almost all in AI journals, and 16 tabular papers involve pretrained or language models. Most business data is tables: loans, transactions, patients, sales. Do pretrained tabular models beat gradient boosting on real business tables, and when?"
url: https://scholar4ai.com/ideas/tabular-foundation-models
markdown_url: https://scholar4ai.com/ideas/tabular-foundation-models.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "methods", "data"]
keywords: ["tabular data", "tabular data classification"]
---

# Foundation models for tables

Tabular data appears on 53 recent papers and tabular classification on 13, almost all in AI journals, and 16 tabular papers involve pretrained or language models. Most business data is tables: loans, transactions, patients, sales. Do pretrained tabular models beat gradient boosting on real business tables, and when?

## What prompted this

Since October 2025, 77 papers carry a tabular keyword (73 in AI journals): "tabular data" on 53, "tabular data classification" on 13, "tabular data generation" on 4. Sixteen of them also carry a foundation-model, pretraining, transformer, language-model, in-context or zero-shot keyword. Thirteen were published in July–September 2026 against about 20 per quarter earlier.

## Questions

- On credit, fraud, churn and insurance tables with their real sizes and missingness, do pretrained tabular models beat tuned gradient boosting, and by how much per unit of computing?
- Do the gains hold under the temporal distribution shift typical of business data, or only on random splits?
- Can a model pretrained on public tables transfer to a bank's private tables without leaking anything from the public ones?

## Data that could help

Public business tables with time stamps (lending, fraud, insurance datasets), and private tables from a partner firm with temporal splits.

## Next step

Benchmark two pretrained tabular models against tuned gradient boosting on five public business datasets using temporal rather than random splits.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| tabular data | 4 | #458 | 3, 4, 4, 6, 3, 4, 2, 8, 8, 3, 4, 1, 3 | 50 |
| tabular data classification | 1 | #2550 | 0, 1, 0, 2, 0, 2, 2, 2, 1, 1, 0, 1, 1 | 12 |


---

---
title: "Synthetic financial data from diffusion models"
description: "Diffusion models are among the largest topics in the AI journals, and synthetic data generation is growing with them. Yet only 6 of 119 recent synthetic-data papers carry a finance keyword. Could synthetic transaction and loan data let banks share data for research without exposing customers, and how close is it to the real thing?"
url: https://scholar4ai.com/ideas/synthetic-financial-data-from-diffusion-models
markdown_url: https://scholar4ai.com/ideas/synthetic-financial-data-from-diffusion-models.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "finance", "methods"]
keywords: ["synthetic data generation", "diffusion models", "differential privacy"]
---

# Synthetic financial data from diffusion models

Diffusion models are among the largest topics in the AI journals, and synthetic data generation is growing with them. Yet only 6 of 119 recent synthetic-data papers carry a finance keyword. Could synthetic transaction and loan data let banks share data for research without exposing customers, and how close is it to the real thing?

## What prompted this

Since October 2025, "diffusion models" appears on 361 papers (357 in AI journals), and 119 papers carry a synthetic-data keyword (114 in AI journals); only 6 of them carry a finance, stock, bank, trading, credit or fraud keyword. "Differential privacy" appears on 72 papers.

## Questions

- Do models trained on synthetic loan or transaction data perform on real data as well as models trained on the real thing?
- Do synthetic datasets keep the rare events that matter most, such as defaults, fraud and crashes?
- How well do they protect privacy against attacks that try to find real customers in the synthetic data?

## Data that could help

Public financial datasets (LendingClub loans, credit card transactions, stock returns) to train and test the generators, and standard privacy-attack benchmarks.

## Next step

Train a tabular diffusion model on LendingClub loans and compare default models trained on the synthetic data with models trained on the real data.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| synthetic data generation | 3 | #687 | 7, 6, 8, 7, 1, 9, 7, 7, 7, 9, 5, 2, 2 | 75 |
| diffusion models | 18 | #33 | 25, 25, 28, 35, 22, 26, 44, 32, 25, 31, 25, 28, 17 | 346 |
| differential privacy | 4 | #458 | 5, 9, 6, 4, 6, 7, 6, 4, 5, 6, 6, 6, 3 | 70 |


---

---
title: "Sustainability-linked loans: do the targets bite?"
description: "Green credit and green loans appear on 58 recent papers, most of them about China's green credit policy, while sustainability-linked loans, whose interest rate moves with the borrower's ESG targets, appear on only 3. Are the targets ambitious, are they met, and does the rate step actually change behaviour?"
url: https://scholar4ai.com/ideas/sustainability-linked-loans
markdown_url: https://scholar4ai.com/ideas/sustainability-linked-loans.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "banking", "esg"]
keywords: ["sustainability-linked loans", "green loans", "green credit"]
---

# Sustainability-linked loans: do the targets bite?

Green credit and green loans appear on 58 recent papers, most of them about China's green credit policy, while sustainability-linked loans, whose interest rate moves with the borrower's ESG targets, appear on only 3. Are the targets ambitious, are they met, and does the rate step actually change behaviour?

## What prompted this

Since October 2025, 58 papers carry a green-credit, green-loan or sustainability-linked keyword (34 in finance journals, 17 in economics): "green credit" on 27, "green loans" on 15, "green credit policy" on 12, "sustainability-linked loans" on 3 and "sustainability-linked bonds" on 2. The quarter to September 2026 had 15 such papers against about 13 per quarter earlier.

## Questions

- Are the key performance targets in sustainability-linked loans set below what the borrower was already on track to achieve?
- When targets are missed, is the rate step large enough to matter, and do lenders waive it?
- Do borrowers with these loans cut emissions faster than similar borrowers with ordinary loans?

## Data that could help

Loan terms from syndicated loan data (DealScan) and bond prospectuses, borrowers' reported emissions and targets, and later target outcomes.

## Next step

Collect the targets and rate steps of 200 sustainability-linked loans and compare the targets with the borrowers' pre-loan emission trends.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| sustainability-linked loans | 0 | – | 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0 | 3 |
| green loans | 0 | – | 1, 1, 3, 1, 3, 3, 1, 1, 0, 0, 1, 0, 0 | 15 |
| green credit | 3 | #687 | 3, 2, 4, 2, 1, 1, 3, 0, 1, 2, 1, 5, 2 | 25 |


---

---
title: "Surge pricing and the limits of what consumers accept"
description: "Dynamic pricing appears on 51 recent papers and price discrimination on 28, mostly in operations research and economics; 29 of 91 such papers touch consumer welfare, but price fairness appears on 3. Retailers and venues have faced backlash over surge prices, and some have retreated. Where is the line that consumers will not accept, and what does it cost firms to cross it?"
url: https://scholar4ai.com/ideas/surge-pricing-and-fairness
markdown_url: https://scholar4ai.com/ideas/surge-pricing-and-fairness.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "marketing"]
keywords: ["dynamic pricing", "price discrimination", "price fairness"]
---

# Surge pricing and the limits of what consumers accept

Dynamic pricing appears on 51 recent papers and price discrimination on 28, mostly in operations research and economics; 29 of 91 such papers touch consumer welfare, but price fairness appears on 3. Retailers and venues have faced backlash over surge prices, and some have retreated. Where is the line that consumers will not accept, and what does it cost firms to cross it?

## What prompted this

Since October 2025, 91 papers carry a dynamic-pricing, surge-pricing, personalized-pricing, price-discrimination or algorithmic-pricing keyword (36 in operations research journals, 34 in economics, 14 in finance, 5 in management): "dynamic pricing" on 51, "price discrimination" on 28, "personalized pricing" on 6, "surge pricing" on 4. Twenty-nine of them carry a consumer, welfare, fairness or trust keyword, but "price fairness" appears on only 3.

## Questions

- After a visible pricing backlash (concert tickets, fast food surge pricing, ride-hailing in emergencies), do sales and brand sentiment fall for long, and do rivals gain?
- Which forms of variation do consumers accept (time of day, demand) and which do they reject (personal data, emergencies)?
- Do laws against price gouging and personalized pricing change what firms do, or only what they call it?

## Data that could help

Price and sales data from firms that introduced dynamic pricing, social media sentiment around backlash episodes, and survey experiments on acceptance.

## Next step

Measure sales and search interest for a retailer before and after a publicized surge-pricing episode, and compare with a competitor.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| dynamic pricing | 2 | #1148 | 4, 4, 3, 2, 2, 6, 5, 4, 3, 7, 5, 5, 2 | 50 |
| price discrimination | 0 | – | 7, 3, 4, 2, 2, 0, 2, 1, 4, 2, 0, 1, 0 | 28 |
| price fairness | 1 | #2550 | 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1 | 2 |


---

---
title: "Supply chain finance after Greensill (research idea)"
description: "Supply chain finance appears on 101 recent papers, mostly in the finance and operations research journals, but only 32 of them carry a risk, default, disclosure, accounting or fintech keyword, and reverse factoring on one. Since the Greensill collapse, firms must disclose these programmes. Who bears the risk, and does disclosure change pricing?"
url: https://scholar4ai.com/ideas/supply-chain-finance-after-greensill
markdown_url: https://scholar4ai.com/ideas/supply-chain-finance-after-greensill.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "operations research", "supply chain"]
keywords: ["supply chain finance"]
---

# Supply chain finance after Greensill

Supply chain finance appears on 101 recent papers, mostly in the finance and operations research journals, but only 32 of them carry a risk, default, disclosure, accounting or fintech keyword, and reverse factoring on one. Since the Greensill collapse, firms must disclose these programmes. Who bears the risk, and does disclosure change pricing?

## What prompted this

Since October 2025, "supply chain finance" appears on 101 papers (59 in finance journals, 25 in operations research), and "reverse factoring" on 1. Only 32 of the 101 also carry a risk, default, bankruptcy, disclosure, accounting, fintech, blockchain or platform keyword, and most of those are about blockchain and credit risk models rather than about the programmes' failure modes.

## Questions

- Since the 2023 disclosure rules for supplier finance programmes, did firms that disclose large programmes pay more for debt, or see their payables reclassified?
- When a buyer's finance programme stops, how fast do its suppliers run into distress?
- Do fintech-run programmes price supplier risk, or the buyer's risk alone?

## Data that could help

The new supplier-finance disclosures in annual reports, supplier-customer links, suppliers' payment terms and distress events.

## Next step

Hand-collect the supplier finance disclosures of large US and European firms for 2023–2025 and relate programme size to suppliers' days of receivables.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| supply chain finance | 6 | #263 | 3, 12, 12, 7, 8, 9, 9, 4, 12, 10, 7, 4, 5 | 97 |


---

---
title: "Supply chain due diligence laws and the audits behind them"
description: "Child labour, forced labour, modern slavery and human rights due diligence appear on 41 recent papers, with 13 in the last quarter against about 9 per quarter before, and 12 of them carry a supply-chain or supplier keyword. Germany's supply chain law, the EU directive and import bans on forced-labour goods now require firms to check suppliers. Do the checks find anything, and do suppliers change?"
url: https://scholar4ai.com/ideas/supply-chain-due-diligence-laws
markdown_url: https://scholar4ai.com/ideas/supply-chain-due-diligence-laws.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "supply chain", "ethics"]
keywords: ["modern slavery", "forced labor", "human rights due diligence"]
---

# Supply chain due diligence laws and the audits behind them

Child labour, forced labour, modern slavery and human rights due diligence appear on 41 recent papers, with 13 in the last quarter against about 9 per quarter before, and 12 of them carry a supply-chain or supplier keyword. Germany's supply chain law, the EU directive and import bans on forced-labour goods now require firms to check suppliers. Do the checks find anything, and do suppliers change?

## What prompted this

Since October 2025, 41 papers carry a due-diligence, modern-slavery, forced-labour, human-rights or child-labour keyword (19 in economics journals, 6 in management, 6 in AI, 5 in finance): "child labor" on 13, "human rights" on 7, "modern slavery" on 6, "forced labor" on 5, "human rights due diligence" on 3. The quarter to September 2026 had 13 such papers against about 9 per quarter earlier.

## Questions

- Do social audits required by the laws detect forced labour, or do they certify what suppliers show on audit day?
- After import bans on goods from regions with forced labour, did trade reroute through third countries, and did the targeted goods' prices rise?
- Do firms covered by the laws cut suppliers in high-risk countries, and what happens to the workers there?

## Data that could help

Firms' due diligence reports, customs detention records under import bans, trade data by product and origin, and audit reports where available.

## Next step

Compare US imports of cotton goods and solar components by origin before and after the 2022 forced-labour import ban, and look for rerouting.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| modern slavery | 0 | – | 0, 1, 2, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0 | 6 |
| forced labor | 0 | – | 0, 0, 2, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0 | 5 |
| human rights due diligence | 0 | – | 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0 | 3 |


---

---
title: "Subscriptions, churn and the right to cancel"
description: "Customer retention and churn appear on 33 recent papers and subscription pricing and models on 10, across the AI, operations research and finance journals. Subscriptions now cover software, cars, media and razors, and regulators are forcing easy cancellation. How much of subscription revenue depends on people not cancelling, and what happens when they can?"
url: https://scholar4ai.com/ideas/subscription-churn
markdown_url: https://scholar4ai.com/ideas/subscription-churn.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "marketing", "regulation"]
keywords: ["subscription pricing", "customer churn", "customer retention"]
---

# Subscriptions, churn and the right to cancel

Customer retention and churn appear on 33 recent papers and subscription pricing and models on 10, across the AI, operations research and finance journals. Subscriptions now cover software, cars, media and razors, and regulators are forcing easy cancellation. How much of subscription revenue depends on people not cancelling, and what happens when they can?

## What prompted this

Since October 2025, 52 papers carry a subscription, churn or customer-retention keyword, spread across AI (13), operations research (12), finance (9), economics (9) and management (7): "customer retention" on 18, "customer churn" on 8, "customer churn prediction" on 7, "subscription pricing" on 6, "subscription models" on 4. Most churn papers are prediction exercises; the economics of inertia is rarely their subject.

## Questions

- When "click to cancel" rules took effect, how much did cancellations rise, and did firms respond with lower prices or more retention offers?
- How much of subscription revenue comes from subscribers who no longer use the service, by category?
- Do firms that make cancelling hard lose customers' later purchases and referrals?

## Data that could help

Transaction panels with recurring charges and usage, the dates of cancellation rules by jurisdiction, and firm disclosures of churn rates.

## Next step

Use bank transaction data to estimate the share of recurring charges with no matching usage and compare cancellation rates before and after a click-to-cancel rule.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| subscription pricing | 0 | – | 0, 0, 0, 0, 2, 1, 1, 1, 0, 0, 0, 1, 0 | 6 |
| customer churn | 0 | – | 0, 0, 1, 0, 2, 1, 0, 2, 0, 0, 1, 1, 0 | 8 |
| customer retention | 1 | #2550 | 2, 1, 0, 1, 1, 3, 1, 0, 3, 0, 4, 1, 1 | 17 |


---

---
title: "After the student loan pause (research idea)"
description: "Student loans and student debt appear on only 12 recent papers, none of them in the last quarter, even though US repayments restarted in 2023 after a three-year pause and delinquencies then jumped. Did the pause change borrowers' finances for good, and what happened when payments resumed?"
url: https://scholar4ai.com/ideas/student-loan-repayment-restart
markdown_url: https://scholar4ai.com/ideas/student-loan-repayment-restart.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "households", "education"]
keywords: ["student loans", "student debt"]
---

# After the student loan pause

Student loans and student debt appear on only 12 recent papers, none of them in the last quarter, even though US repayments restarted in 2023 after a three-year pause and delinquencies then jumped. Did the pause change borrowers' finances for good, and what happened when payments resumed?

## What prompted this

Since October 2025, 12 papers carry a student-loan or student-debt keyword (7 in economics journals, 3 in finance): "student loans" on 8, "student debt" on 7. None of the 12 was published in July–September 2026, against about 4 per quarter earlier, while household debt as a whole remains an active topic.

## Questions

- During the pause, did borrowers pay down other debt, save, or borrow more, and did those who kept paying differ?
- When payments resumed, how many borrowers fell behind, and did the new income-driven plans and their legal reversals change that?
- Did the pause and the forgiveness debates change enrolment or borrowing by new students?

## Data that could help

Credit bureau panels with student loan balances and payment status, the Department of Education's repayment statistics, and enrolment data.

## Next step

Use a credit bureau panel to follow borrowers' balances and delinquencies on all accounts from 2019 through 2025.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| student loans | 0 | – | 0, 6, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0 | 8 |
| student debt | 0 | – | 0, 2, 1, 0, 2, 0, 0, 1, 1, 0, 0, 0, 0 | 7 |


---

---
title: "Stock Connect ten years on (research idea)"
description: "Foreign investors and capital market liberalization are growing keywords in the finance journals, with 13 papers in July–September 2026 against about 7 per quarter before. More than ten years after Shanghai-Hong Kong Stock Connect opened, has foreign ownership changed how Chinese stocks are priced, and what happened when foreign money left?"
url: https://scholar4ai.com/ideas/stock-connect-ten-years-on
markdown_url: https://scholar4ai.com/ideas/stock-connect-ten-years-on.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "china", "asset pricing"]
keywords: ["Shanghai-Hong Kong Stock Connect", "capital market liberalization", "foreign investors"]
---

# Stock Connect ten years on

Foreign investors and capital market liberalization are growing keywords in the finance journals, with 13 papers in July–September 2026 against about 7 per quarter before. More than ten years after Shanghai-Hong Kong Stock Connect opened, has foreign ownership changed how Chinese stocks are priced, and what happened when foreign money left?

## What prompted this

Since October 2025, 36 papers carry a Stock Connect, capital-market-liberalization, capital-account-liberalization, financial-opening or foreign-investor keyword (26 in finance journals). "Foreign investors" appears on 17, "capital account liberalization" on 8, "capital market liberalization" on 7 and the two Stock Connect programmes on 7. The quarter to September 2026 had 13 such papers against about 7 per quarter earlier.

## Questions

- Are stocks eligible for Stock Connect priced more in line with their fundamentals, with less of the retail-driven noise found in A-shares?
- When foreign investors sold heavily in 2022–2024, did eligible stocks fall more than ineligible ones, and did domestic institutions step in?
- Did eligibility change firms' disclosure, dividends or governance?

## Data that could help

Daily northbound and southbound flows, eligibility lists and their changes, A-share and H-share prices, and firm data (CSMAR).

## Next step

Compare the return and volatility behaviour of stocks added to and removed from the eligible list around each list revision.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| Shanghai-Hong Kong Stock Connect | 0 | – | 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0 | 4 |
| capital market liberalization | 0 | – | 0, 0, 1, 0, 1, 1, 0, 0, 0, 2, 1, 1, 0 | 7 |
| foreign investors | 1 | #2550 | 0, 2, 0, 1, 0, 3, 2, 1, 1, 1, 3, 2, 1 | 16 |


---

---
title: "State-space models for limit order books (research idea)"
description: "Mamba and other selective state-space models are among the most frequent new architectures in the AI journals, built for long sequences. Limit order books produce exactly such sequences. Yet only 4 of 329 recent Mamba and state-space papers carry a finance keyword, all of them in AI journals. Can these models forecast short-term price moves better, and is the gain tradable after costs?"
url: https://scholar4ai.com/ideas/state-space-models-for-limit-order-books
markdown_url: https://scholar4ai.com/ideas/state-space-models-for-limit-order-books.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "finance", "market microstructure"]
keywords: ["Mamba", "limit order book", "high-frequency trading"]
---

# State-space models for limit order books

Mamba and other selective state-space models are among the most frequent new architectures in the AI journals, built for long sequences. Limit order books produce exactly such sequences. Yet only 4 of 329 recent Mamba and state-space papers carry a finance keyword, all of them in AI journals. Can these models forecast short-term price moves better, and is the gain tradable after costs?

## What prompted this

Since October 2025, "Mamba" appears on 293 papers, 290 of them in AI journals, and 329 papers carry a Mamba or state-space-model keyword; only 4 of them carry a finance, stock, trading, market or asset keyword. "Limit order book" appears on 13 papers and "high-frequency trading" on 21, mostly in finance journals.

## Questions

- On standard limit order book data, do state-space models predict mid-price moves better than convolutional and Transformer models at the same computing cost?
- Do the gains survive realistic trading costs and latency?
- Do they capture order-flow patterns that last longer than other models can see?

## Data that could help

Public limit order book benchmarks (for example the FI-2010 dataset or LOBSTER samples) and published baseline models.

## Next step

Train a Mamba model on a public limit order book benchmark, and compare its forecasts and simulated trading results with published baselines.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| Mamba | 10 | #104 | 14, 12, 24, 29, 22, 14, 31, 32, 29, 25, 30, 24, 9 | 286 |
| limit order book | 1 | #2550 | 0, 2, 1, 2, 0, 2, 0, 1, 1, 1, 1, 1, 1 | 12 |
| high-frequency trading | 0 | – | 4, 1, 1, 2, 2, 3, 1, 3, 3, 0, 0, 1, 0 | 21 |


---

---
title: "Stablecoins as a channel of monetary policy"
description: "Stablecoins and monetary policy transmission are both among the fastest-growing keywords, but the papers that join them sit only in finance journals. Now that issuers hold large amounts of Treasury bills, does policy pass through stablecoins into crypto credit, and do stablecoin flows move bill yields?"
url: https://scholar4ai.com/ideas/stablecoins-and-monetary-policy
markdown_url: https://scholar4ai.com/ideas/stablecoins-and-monetary-policy.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "economics", "crypto"]
keywords: ["stablecoins", "monetary policy transmission", "stablecoin regulation"]
---

# Stablecoins as a channel of monetary policy

Stablecoins and monetary policy transmission are both among the fastest-growing keywords, but the papers that join them sit only in finance journals. Now that issuers hold large amounts of Treasury bills, does policy pass through stablecoins into crypto credit, and do stablecoin flows move bill yields?

## What prompted this

On the [trends page](/trends), "stablecoins" is among the ten keywords that grew most across all journals: 22 papers in July–September 2026, against about 8 expected from its earlier share. "Monetary policy transmission" grew as well (39 against 24). Since October 2025, 51 papers in the archive carry a stablecoin keyword (42 in finance journals, 9 in economics). Nine of them also carry a monetary-policy, interest-rate or Treasury keyword, and all nine are in finance journals.

## Questions

- When policy rates move, do stablecoin issuance and on-chain lending rates follow, the way deposits and loan rates do at banks?
- In the other direction, do large stablecoin inflows push down short-term Treasury bill yields?
- Does the pass-through differ between issuers with audited reserves and the rest, and has new stablecoin regulation changed it?

## Data that could help

- Daily supply by issuer from on-chain records and market data (the cryptocurrency category of the [dataset ranking](/rankings/datasets)), plus issuers' reserve reports.
- Monetary policy surprises around central-bank announcements, and Treasury bill yields.
- A vector autoregression or local projections around the surprises (see the [equation ranking](/rankings/equations)).

## Next step

Read the nine finance papers that combine both topics and note which direction of transmission each one tests.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| stablecoins | 4 | #458 | 1, 5, 1, 3, 1, 5, 1, 4, 4, 5, 5, 12, 3 | 47 |
| monetary policy transmission | 10 | #104 | 5, 7, 9, 6, 9, 12, 7, 9, 9, 16, 8, 15, 8 | 112 |
| stablecoin regulation | 0 | – | 0, 2, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0 | 7 |


---

---
title: "Sports, where operations research meets money"
description: "Sports betting, sports analytics, football and tournament design appear on 120 recent papers across the AI, economics, finance and operations research journals. Leagues sell schedules to broadcasters, players are traded on expected value, and betting markets price every match. Do the optimized schedules and ratings survive the money, and do betting markets correct the analytics?"
url: https://scholar4ai.com/ideas/sports-scheduling-and-analytics
markdown_url: https://scholar4ai.com/ideas/sports-scheduling-and-analytics.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "economics"]
keywords: ["sports analytics", "sports scheduling", "sports betting"]
---

# Sports, where operations research meets money

Sports betting, sports analytics, football and tournament design appear on 120 recent papers across the AI, economics, finance and operations research journals. Leagues sell schedules to broadcasters, players are traded on expected value, and betting markets price every match. Do the optimized schedules and ratings survive the money, and do betting markets correct the analytics?

## What prompted this

Since October 2025, 120 papers carry a sports, football, soccer, basketball, baseball, Olympic, athlete or esports keyword (43 in AI journals, 30 in economics, 25 in finance, 13 in operations research): "sports betting" on 8, "sports analytics" on 7, "football" on 5, "European football leagues" on 4, "tournament design" on 4, "sports scheduling" on 3. The scheduling, analytics and betting strands rarely appear together.

## Questions

- When leagues let broadcasters shape the schedule, how much fairness (rest days, travel) do teams lose, and does it show in results?
- Do player ratings from tracking data predict transfer fees and contracts better than the market does, or has the market caught up?
- Do betting markets incorporate analytics quickly enough that published models no longer beat the odds?

## Data that could help

League schedules and broadcast contracts, player tracking and transfer data, and historical betting odds.

## Next step

Measure the forecasting edge of published match-prediction models against closing betting odds for the last five seasons of two leagues.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| sports analytics | 0 | – | 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0 | 7 |
| sports scheduling | 1 | #2550 | 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1 | 2 |
| sports betting | 1 | #2550 | 1, 0, 1, 0, 0, 1, 1, 2, 0, 0, 1, 0, 1 | 7 |


---

---
title: "The economics of legal sports betting (research idea)"
description: "Sports betting, gambling and lotteries appear on 42 recent papers in the finance and economics journals. Since 2018, most US states have legalized online sports betting, and app-based betting is spreading elsewhere. What has it done to household finances, bankruptcies and the stock trading of the same people?"
url: https://scholar4ai.com/ideas/sports-betting-legalization
markdown_url: https://scholar4ai.com/ideas/sports-betting-legalization.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "households"]
keywords: ["sports betting", "gambling culture", "lotteries"]
---

# The economics of legal sports betting

Sports betting, gambling and lotteries appear on 42 recent papers in the finance and economics journals. Since 2018, most US states have legalized online sports betting, and app-based betting is spreading elsewhere. What has it done to household finances, bankruptcies and the stock trading of the same people?

## What prompted this

Since October 2025, 42 papers carry a sports-betting, gambling, lottery, casino or betting keyword (20 in finance journals, 16 in economics): "sports betting" on 8, "gambling culture" on 5, "lotteries" on 6, "betting odds" on 4. Household debt and consumer credit are large topics, but the state-by-state legalization is an experiment that few of these papers use.

## Questions

- After a state legalizes online betting, do credit card balances, overdrafts and bankruptcies rise, and in which households?
- Does betting substitute for stock trading and lottery play, or add to them?
- Do tax revenues cover the costs that show up in credit markets and problem-gambling services?

## Data that could help

Credit bureau panels by state, legalization dates, betting handle and revenue reports, and brokerage trading data.

## Next step

Use the staggered legalization across US states to estimate changes in credit card delinquencies and bankruptcies in the two years after launch.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| sports betting | 1 | #2550 | 1, 0, 1, 0, 0, 1, 1, 2, 0, 0, 1, 0, 1 | 7 |
| gambling culture | 0 | – | 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0 | 5 |
| lotteries | 1 | #2550 | 0, 0, 0, 1, 1, 0, 0, 2, 0, 1, 0, 0, 1 | 5 |


---

---
title: "Spare parts, printed on demand (research idea)"
description: "Additive manufacturing and 3D printing appear on 31 recent papers, split among operations research, AI and management, and spare parts management on 2. Printing parts on demand could replace warehouses of slow-moving inventory. Which parts are worth printing, where should the printers be, and do certification rules allow it?"
url: https://scholar4ai.com/ideas/spare-parts-and-3d-printing
markdown_url: https://scholar4ai.com/ideas/spare-parts-and-3d-printing.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "manufacturing", "logistics"]
keywords: ["additive manufacturing", "3D printing", "spare parts management"]
---

# Spare parts, printed on demand

Additive manufacturing and 3D printing appear on 31 recent papers, split among operations research, AI and management, and spare parts management on 2. Printing parts on demand could replace warehouses of slow-moving inventory. Which parts are worth printing, where should the printers be, and do certification rules allow it?

## What prompted this

Since October 2025, 31 papers carry an additive-manufacturing, 3D-printing or spare-parts keyword (14 in operations research journals, 11 in AI, 5 in management): "additive manufacturing" on 13, "3D printing" on 11, "metal additive manufacturing" on 3, "spare parts management" on 2, "spare parts provisioning" on 1. Six were published in July–September 2026 against about 8 per quarter earlier.

## Questions

- For a given fleet (aircraft, ships, rail, machinery), which share of spare parts could be printed at acceptable cost and lead time, and how much inventory would that remove?
- Where should printing capacity sit, centrally or near the fleet, given demand uncertainty and the cost of idle printers?
- How much do certification and liability rules restrict which safety-relevant parts can be printed, and how are they changing?

## Data that could help

Spare parts demand and inventory records from a fleet operator, printing cost and lead time data by part type, and certification rules.

## Next step

Classify the spare parts catalogue of one fleet operator by printability and compute the inventory reduction from printing the eligible slow movers.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| additive manufacturing | 0 | – | 2, 1, 0, 1, 0, 0, 1, 3, 0, 0, 1, 4, 0 | 13 |
| 3D printing | 1 | #2550 | 1, 0, 1, 0, 1, 1, 0, 2, 1, 0, 0, 4, 0 | 11 |
| spare parts management | 0 | – | 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0 | 2 |


---

---
title: "Sovereign defaults with China as the main creditor"
description: "Sovereign default and sovereign debt crises appear on 42 recent papers in the economics and finance journals, but only one of them carries a creditor, China or Common Framework keyword. Recent restructurings in Zambia, Ghana and Sri Lanka involved China as a major lender. Do defaults take longer and resolve differently when the creditors are new?"
url: https://scholar4ai.com/ideas/sovereign-defaults-with-china-as-creditor
markdown_url: https://scholar4ai.com/ideas/sovereign-defaults-with-china-as-creditor.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "finance", "macroeconomics"]
keywords: ["sovereign default", "sovereign debt crisis"]
---

# Sovereign defaults with China as the main creditor

Sovereign default and sovereign debt crises appear on 42 recent papers in the economics and finance journals, but only one of them carries a creditor, China or Common Framework keyword. Recent restructurings in Zambia, Ghana and Sri Lanka involved China as a major lender. Do defaults take longer and resolve differently when the creditors are new?

## What prompted this

Since October 2025, 42 papers carry a sovereign-default, sovereign-debt-crisis, debt-restructuring or debt-relief keyword (21 in economics journals, 20 in finance). Only one of them also carries a China, Common Framework, official-creditor, bilateral or creditor keyword. "Sovereign default" appears on 14 papers and "sovereign debt crisis" on 11.

## Questions

- Have restructurings since 2020 taken longer, and given smaller haircuts, than earlier ones, and does the share of Chinese lending explain the difference?
- Do collateralized and confidential loans change how private bondholders are treated?
- Did the G20 Common Framework shorten the process, or add a step?

## Data that could help

Restructuring timelines and terms (the IMF and academic restructuring databases), creditor composition from AidData's Chinese lending data, and bond prices around key events.

## Next step

Compile the timeline of each restructuring since 2020 with its creditor composition and compare durations with restructurings of the 2000s.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| sovereign default | 3 | #687 | 2, 1, 1, 0, 4, 0, 0, 1, 2, 0, 1, 0, 3 | 12 |
| sovereign debt crisis | 2 | #1148 | 0, 0, 1, 1, 1, 3, 0, 1, 0, 1, 0, 2, 1 | 10 |


---

---
title: "Social media and adolescents, measured (research idea)"
description: "Social media appears on 402 recent papers across the tracked journals, mostly as text data in AI and management research; only 18 carry a mental-health, adolescent, well-being or children keyword. Several countries are banning social media for under-16s. What does the evidence actually show, and what will the bans reveal?"
url: https://scholar4ai.com/ideas/social-media-and-adolescents
markdown_url: https://scholar4ai.com/ideas/social-media-and-adolescents.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "health", "technology"]
keywords: ["social media use", "adolescent mental health", "digital well-being"]
---

# Social media and adolescents, measured

Social media appears on 402 recent papers across the tracked journals, mostly as text data in AI and management research; only 18 carry a mental-health, adolescent, well-being or children keyword. Several countries are banning social media for under-16s. What does the evidence actually show, and what will the bans reveal?

## What prompted this

Since October 2025, 402 papers carry a social-media, smartphone or screen-time keyword (180 in AI journals, 117 in management, 52 in finance). Only 18 of them also carry a mental-health, adolescent, teen, youth, well-being, depression, suicide, children or student keyword; "social media use" appears on 13 papers and "adolescent mental health" on 7.

## Questions

- Did adolescent mental health indicators change after Australia's under-16 ban and similar laws, compared with countries without them?
- Does the effect of use depend on what is used for what (passive scrolling versus messaging friends), and can that be measured without self-reports?
- Do the bans reduce use, or move it to platforms outside the law?

## Data that could help

Health surveys and clinical records by age and country, app usage data from device studies, and the dates and scope of bans.

## Next step

Compare trends in adolescent mental health and app usage in Australia with those in New Zealand and Canada around the ban's start.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| social media use | 1 | #2550 | 1, 0, 0, 0, 0, 1, 2, 1, 2, 1, 1, 3, 1 | 12 |
| adolescent mental health | 0 | – | 2, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0 | 7 |
| digital well-being | 0 | – | 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0 | 3 |


---

---
title: "Social enterprises when the money gets tight"
description: "Social enterprises, social entrepreneurship and hybrid organizations appear on 41 recent papers, mostly in management, and the topic is fading, with 7 papers in the last quarter against about 10 per quarter before. Grant funding and impact investment have shrunk. Which social enterprises survive without them, and do certified B Corporations behave differently from the rest?"
url: https://scholar4ai.com/ideas/social-enterprises-and-b-corps
markdown_url: https://scholar4ai.com/ideas/social-enterprises-and-b-corps.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "entrepreneurship", "esg"]
keywords: ["social enterprises", "social entrepreneurship"]
---

# Social enterprises when the money gets tight

Social enterprises, social entrepreneurship and hybrid organizations appear on 41 recent papers, mostly in management, and the topic is fading, with 7 papers in the last quarter against about 10 per quarter before. Grant funding and impact investment have shrunk. Which social enterprises survive without them, and do certified B Corporations behave differently from the rest?

## What prompted this

Since October 2025, 41 papers carry a social-enterprise, social-entrepreneurship, hybrid-organization, benefit-corporation or B Corp keyword (31 in management journals, 9 in economics): "social enterprises" on 19, "social entrepreneurship" on 16, "hybrid organizations" on 5, "benefit corporations" on 2, "B Corp certification" on 1. Seven were published in July–September 2026 against about 10 per quarter earlier.

## Questions

- When grants and impact funds dry up, do social enterprises close, drop their social activities, or find commercial revenue, and which ones manage the last?
- Do certified B Corporations pay workers more, pollute less or give more than uncertified peers of the same size, or does certification follow behaviour that was already there?
- Do customers pay a premium for certification, and in which markets?

## Data that could help

B Corp certification records with scores, firm accounts and employment data, and funding data from impact investors.

## Next step

Match B Corp certification records to firm accounts and compare wage levels and growth before and after certification with similar uncertified firms.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| social enterprises | 1 | #2550 | 2, 2, 1, 2, 1, 6, 1, 1, 1, 1, 0, 0, 1 | 18 |
| social entrepreneurship | 2 | #1148 | 0, 2, 4, 2, 1, 1, 0, 2, 0, 1, 1, 0, 2 | 14 |


---

---
title: "Small sellers under platform algorithms (research idea)"
description: "Business growth is a rising keyword across the tracked journals, and small and medium-sized enterprises are a large topic, but only 19 of 431 recent SME papers carry a platform, e-commerce or algorithm keyword. Millions of small firms now depend on marketplace search rankings. What happens to them when the algorithm changes?"
url: https://scholar4ai.com/ideas/small-sellers-under-platform-algorithms
markdown_url: https://scholar4ai.com/ideas/small-sellers-under-platform-algorithms.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "platforms"]
keywords: ["small and medium-sized enterprises", "e-commerce platforms", "platform dependence"]
---

# Small sellers under platform algorithms

Business growth is a rising keyword across the tracked journals, and small and medium-sized enterprises are a large topic, but only 19 of 431 recent SME papers carry a platform, e-commerce or algorithm keyword. Millions of small firms now depend on marketplace search rankings. What happens to them when the algorithm changes?

## What prompted this

"Business growth" grew across all journals (25 papers in July–September 2026 against 15 expected) and in management (see the [trends page](/trends)). Since October 2025, 431 papers carry an SME or small-business keyword (157 in management journals, 121 in economics, 117 in finance); only 19 of them carry a platform, e-commerce, algorithm or marketplace keyword, and "platform dependence" appears on 3.

## Questions

- When a marketplace changes its ranking or fee rules, how much do small sellers' sales move, and do they leave or adapt?
- Are sellers that sell through several channels more resilient?
- Do rules such as the EU's Digital Markets Act change how platforms treat small sellers?

## Data that could help

Seller-level sales and rankings from marketplace data providers, announced changes to algorithms and fees, and SME surveys.

## Next step

Collect announced ranking or fee changes on one large marketplace and track the sales ranks of a panel of small sellers around them.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| small and medium-sized enterprises | 15 | #45 | 22, 28, 26, 28, 26, 25, 26, 18, 14, 23, 23, 19, 12 | 278 |
| e-commerce platforms | 3 | #687 | 5, 2, 1, 4, 4, 2, 3, 6, 2, 4, 2, 3, 3 | 38 |
| platform dependence | 0 | – | 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0 | 3 |


---

---
title: "Small models for finance (research idea)"
description: "Knowledge distillation, model compression and small language models appear on more than 700 recent papers in AI journals, and only 12 of them concern finance, banking, legal or business tasks. Banks cannot send customer data to the largest models, and regulators want models they can inspect. How small can a model be and still do financial work, and what is lost in the shrinking?"
url: https://scholar4ai.com/ideas/small-models-for-finance
markdown_url: https://scholar4ai.com/ideas/small-models-for-finance.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "finance", "methods"]
keywords: ["knowledge distillation", "small language models", "model compression"]
---

# Small models for finance

Knowledge distillation, model compression and small language models appear on more than 700 recent papers in AI journals, and only 12 of them concern finance, banking, legal or business tasks. Banks cannot send customer data to the largest models, and regulators want models they can inspect. How small can a model be and still do financial work, and what is lost in the shrinking?

## What prompted this

Since October 2025, 763 papers carry a knowledge-distillation, model-compression, small-language-model, quantization, pruning or parameter-efficient keyword (748 in AI journals): "knowledge distillation" on 345, "model compression" on 114, "parameter-efficient fine-tuning" on 111, "small language models" on 17. Only 12 of the 763 also carry a finance, bank, stock, fraud, credit, economics, legal, accounting, business or customer keyword.

## Questions

- On tasks such as sentiment, entity extraction from filings and customer intent classification, how much accuracy is lost when a large model is distilled to one that runs on a bank's own servers?
- Do small models fine-tuned on a firm's data beat large general models on that firm's tasks, and for how long before the data drifts?
- Do compressed models fail in different places than their teachers, in ways that matter for compliance?

## Data that could help

Public financial NLP benchmarks (Financial PhraseBank, FiQA, FinQA), open models at several sizes, and a partner firm's task data.

## Next step

Distil a large model into three smaller ones on financial sentiment and filing extraction tasks, and map where the small models' errors differ from the teacher's.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| [knowledge distillation](https://scholar4ai.com/keywords/knowledge-distillation.md) | 23 | #17 | 21, 24, 29, 26, 24, 23, 30, 44, 27, 27, 24, 31, 17 | 330 |
| small language models | 1 | #2550 | 1, 2, 3, 0, 1, 0, 2, 2, 1, 2, 1, 2, 0 | 17 |
| model compression | 5 | #342 | 6, 12, 8, 4, 8, 7, 11, 14, 9, 14, 10, 8, 3 | 111 |


---

---
title: "Sin taxes on sugar, alcohol and tobacco (research idea)"
description: "Alcohol consumption, tobacco control and excise taxes appear on 46 recent papers in the economics journals, with 17 in the last quarter against about 9 per quarter before. Sugar taxes have spread to more than 100 jurisdictions and alcohol taxes are being rewritten. Do the taxes change health, and who bears them?"
url: https://scholar4ai.com/ideas/sin-taxes
markdown_url: https://scholar4ai.com/ideas/sin-taxes.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "health", "tax"]
keywords: ["alcohol consumption", "tobacco control", "excise tax"]
---

# Sin taxes on sugar, alcohol and tobacco

Alcohol consumption, tobacco control and excise taxes appear on 46 recent papers in the economics journals, with 17 in the last quarter against about 9 per quarter before. Sugar taxes have spread to more than 100 jurisdictions and alcohol taxes are being rewritten. Do the taxes change health, and who bears them?

## What prompted this

Since October 2025, 46 papers carry a sugar-tax, alcohol, tobacco or excise-tax keyword (39 in economics journals): "alcohol consumption" on 16, "tobacco control" on 7, "excise tax" on 4, "sugar-sweetened beverage tax" on 2. The quarter to September 2026 had 17 such papers against about 9 per quarter earlier.

## Questions

- Do sugar taxes change consumption enough to show up in weight and dental health, or do firms reformulate and consumers switch within the taxed category?
- Do minimum unit prices for alcohol, as in Scotland, reduce harm more than proportional taxes, and do they hit low-income drinkers hardest?
- Do large tobacco tax increases now mostly shift smokers to vaping, and what does that mean for the tax base?

## Data that could help

Scanner purchase data, health outcome records, product reformulation data and the dates and sizes of tax changes by jurisdiction.

## Next step

Compare sugar content and sales of soft drinks in jurisdictions with tiered sugar taxes and with flat volume taxes, before and after each tax.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| alcohol consumption | 0 | – | 1, 2, 1, 0, 0, 0, 2, 1, 5, 0, 1, 3, 0 | 16 |
| tobacco control | 0 | – | 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0 | 7 |
| excise tax | 0 | – | 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0 | 4 |


---

---
title: "Does macroprudential tightening push credit into shadow…"
description: "Macroprudential policy and financial intermediation are both rising in the finance journals, but papers that follow credit out of the regulated banking system are rare, and none of them is in an economics journal. When regulators tighten on banks, how much lending simply moves to shadow banks and private credit funds?"
url: https://scholar4ai.com/ideas/shadow-banks-and-macroprudential-leakage
markdown_url: https://scholar4ai.com/ideas/shadow-banks-and-macroprudential-leakage.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "economics", "banking"]
keywords: ["macroprudential policy", "shadow banking", "financial intermediation"]
---

# Does macroprudential tightening push credit into shadow banks?

Macroprudential policy and financial intermediation are both rising in the finance journals, but papers that follow credit out of the regulated banking system are rare, and none of them is in an economics journal. When regulators tighten on banks, how much lending simply moves to shadow banks and private credit funds?

## What prompted this

In the [finance dashboard](/categories/finance) trends, "macroprudential policy" had 23 papers in July–September 2026 against about 14 expected from its earlier share, and "financial intermediation" had 13 against about 3. Since October 2025, 129 papers carry a macroprudential keyword (90 in finance journals, 35 in economics). Only 7 of them also carry a shadow-banking, private-credit or non-bank keyword, all in finance journals. "Private credit" itself appears on just 4 papers.

## Questions

- After loan-to-value limits, countercyclical capital buffers or loan caps tighten, does credit to the same borrowers rise at non-banks?
- Is the credit that leaks out riskier, more expensive or shorter-term than the bank credit it replaces?
- Does the leakage undo the policy's effect on house prices and leverage, or only part of it?

## Data that could help

- A macroprudential policy database (for example the IMF's iMaPP) for the timing of measures.
- Syndicated loans (DealScan), private credit deals (Preqin, PitchBook) and bank lending from supervisory filings.

## Next step

Find borrowers in DealScan that borrow from both banks and non-banks, and compare the two kinds of borrowing around national macroprudential tightenings.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| macroprudential policy | 13 | #68 | 8, 3, 5, 6, 9, 10, 6, 9, 10, 16, 4, 12, 11 | 98 |
| shadow banking | 3 | #687 | 7, 6, 3, 4, 4, 3, 2, 2, 5, 3, 1, 3, 3 | 43 |
| financial intermediation | 4 | #458 | 2, 1, 2, 1, 1, 6, 1, 1, 3, 3, 4, 10, 3 | 35 |


---

---
title: "Second life or recycling for EV batteries? (research idea)"
description: "Remanufacturing and closed-loop supply chains are growing in the operations research journals, and the first large wave of electric-vehicle batteries is reaching the end of its first life. Should a used battery go to stationary storage, to remanufacturing or straight to recycling, and who should decide?"
url: https://scholar4ai.com/ideas/second-life-or-recycling-for-ev-batteries
markdown_url: https://scholar4ai.com/ideas/second-life-or-recycling-for-ev-batteries.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "energy", "supply chain"]
keywords: ["remanufacturing", "closed-loop supply chain", "battery recycling"]
---

# Second life or recycling for EV batteries?

Remanufacturing and closed-loop supply chains are growing in the operations research journals, and the first large wave of electric-vehicle batteries is reaching the end of its first life. Should a used battery go to stationary storage, to remanufacturing or straight to recycling, and who should decide?

## What prompted this

In the operations research journals, "remanufacturing" had 8 papers in July–September 2026 against about 4 expected and "closed-loop supply chain" 9 against 5 (see the [operations research dashboard](/categories/operations-research)). Since October 2025, 107 papers carry a remanufacturing, closed-loop or reverse-logistics keyword (69 in operations research journals). Twelve of them carry a battery keyword, and "second-life batteries" appears on just one paper.

## Questions

- Given a battery's state of health, when is second-life storage worth more than recycling, once testing, repurposing and the risk of failure are priced in?
- How do mineral prices and recycling mandates, such as the recycled-content targets of the EU battery regulation, change that choice?
- Who does better as the owner of used batteries: carmakers, battery makers or third parties?

## Data that could help

Battery ageing data such as the NASA and CALCE datasets (in the [dataset ranking](/rankings/datasets)'s list), mineral prices and storage-market prices.

## Next step

Build a decision model that routes used batteries by state of health, and compare its value under 2023 and 2026 lithium prices.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| remanufacturing | 2 | #1148 | 0, 3, 2, 1, 0, 1, 2, 3, 3, 4, 3, 1, 2 | 23 |
| closed-loop supply chain | 2 | #1148 | 0, 2, 5, 2, 2, 1, 3, 0, 3, 6, 2, 2, 2 | 28 |
| battery recycling | 0 | – | 0, 1, 3, 2, 0, 4, 0, 2, 1, 2, 0, 0, 0 | 15 |


---

---
title: "School choice mechanisms and the families who use them"
description: "School choice, market design, stable matching and deferred acceptance appear on 89 recent papers, mostly in economics. Dozens of cities now assign school places with matching algorithms. Do families understand the rules well enough to benefit, and do the assignments change segregation and achievement?"
url: https://scholar4ai.com/ideas/school-choice-and-matching
markdown_url: https://scholar4ai.com/ideas/school-choice-and-matching.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "economics", "education"]
keywords: ["school choice", "matching markets", "deferred acceptance"]
---

# School choice mechanisms and the families who use them

School choice, market design, stable matching and deferred acceptance appear on 89 recent papers, mostly in economics. Dozens of cities now assign school places with matching algorithms. Do families understand the rules well enough to benefit, and do the assignments change segregation and achievement?

## What prompted this

Since October 2025, 89 papers carry a school-choice, matching-market, stable-matching, deferred-acceptance, market-design or assignment-mechanism keyword (72 in economics journals): "school choice" on 21, "market design" on 18, "stable matching" on 9, "matching markets" on 9, "deferred acceptance" on 8. Seventeen were published in July–September 2026 against about 21 per quarter earlier.

## Questions

- In cities using deferred acceptance, what share of families still submit lists that are not in their true order, and who loses from it?
- Did the switch from older mechanisms change school segregation by income, and did it change achievement for students who got their preferred school?
- Do information interventions (ranked lists with admission chances) change choices and outcomes?

## Data that could help

Application lists and assignments from city school districts, student outcomes, and the dates of mechanism changes.

## Next step

Use application data from a district that switched to deferred acceptance to measure changes in list length, strategic ranking and segregation.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| school choice | 2 | #1148 | 2, 1, 2, 2, 3, 1, 1, 0, 1, 2, 1, 3, 2 | 19 |
| matching markets | 1 | #2550 | 2, 0, 0, 0, 0, 3, 0, 1, 1, 0, 1, 1, 0 | 9 |
| deferred acceptance | 4 | #458 | 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3 | 5 |


---

---
title: "Sanctions evasion through third countries (research idea)"
description: "Sanctions appear on 85 recent papers, with 23 in the last quarter against about 20 per quarter before, and 35 of them carry a Russia, enforcement or effectiveness keyword. Trade data show exports from Europe to Central Asia and the Caucasus surging after 2022. How much of the sanctioned trade simply took a detour, and what stops it?"
url: https://scholar4ai.com/ideas/sanctions-evasion-through-third-countries
markdown_url: https://scholar4ai.com/ideas/sanctions-evasion-through-third-countries.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "trade", "politics"]
keywords: ["economic sanctions", "sanctions", "financial sanctions", "sanctions enforcement"]
---

# Sanctions evasion through third countries

Sanctions appear on 85 recent papers, with 23 in the last quarter against about 20 per quarter before, and 35 of them carry a Russia, enforcement or effectiveness keyword. Trade data show exports from Europe to Central Asia and the Caucasus surging after 2022. How much of the sanctioned trade simply took a detour, and what stops it?

## What prompted this

Since October 2025, 85 papers carry a sanctions keyword (45 in economics journals, 21 in finance, 11 in management): "sanctions" on 18, "economic sanctions" on 15, "financial sanctions" on 11, "trade sanctions" on 9, "sanctions enforcement" on 6. Thirty-five of the 85 also carry an evasion, circumvention, third-country, re-export, Russia, enforcement or effectiveness keyword.

## Questions

- How much of the fall in direct exports of sanctioned goods to Russia reappears as exports to its neighbours, product by product?
- Which firms do the rerouting: new shell exporters, or existing distributors?
- Did secondary sanctions on banks in third countries in 2024 cut the detours, and at what cost to legitimate trade?

## Data that could help

Monthly bilateral trade data by product (UN Comtrade, Eurostat), customs records with exporter identities where available, and the dates of sanctions packages.

## Next step

Compare the growth of EU exports of dual-use goods to Kazakhstan, Kyrgyzstan, Armenia and the UAE with Russia's imports of the same goods since 2022.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| economic sanctions | 1 | #2550 | 1, 0, 0, 1, 1, 1, 0, 2, 2, 5, 1, 0, 1 | 14 |
| sanctions | 0 | – | 2, 2, 2, 2, 0, 2, 2, 0, 0, 3, 3, 0, 0 | 18 |
| financial sanctions | 1 | #2550 | 1, 0, 0, 0, 0, 4, 2, 0, 0, 1, 1, 1, 1 | 10 |
| sanctions enforcement | 0 | – | 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 2, 0 | 6 |


---

---
title: "Robots and industrial upgrading in Chinese manufacturing"
description: "Industrial upgrading is a large topic in the finance and economics journals, and China installs more industrial robots than any other country. Yet of 397 recent papers with a robot keyword, only 2 carry an industrial-upgrading keyword. Do robots move Chinese firms up the value chain, or just replace workers in the same tasks?"
url: https://scholar4ai.com/ideas/robots-and-industrial-upgrading
markdown_url: https://scholar4ai.com/ideas/robots-and-industrial-upgrading.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "management", "innovation"]
keywords: ["industrial robots", "industrial upgrading", "automation"]
---

# Robots and industrial upgrading in Chinese manufacturing

Industrial upgrading is a large topic in the finance and economics journals, and China installs more industrial robots than any other country. Yet of 397 recent papers with a robot keyword, only 2 carry an industrial-upgrading keyword. Do robots move Chinese firms up the value chain, or just replace workers in the same tasks?

## What prompted this

Since October 2025, "industrial upgrading" appears on 117 papers (56 in finance journals, 48 in economics) and "industrial robots" on 46 (26 in economics). Of the 397 papers with any robot keyword (272 of them in AI journals), only 2 carry an industrial-upgrading or industrial-structure keyword. In the management journals, "industrial upgrading" is rising (5 papers in July–September 2026 against about 2 expected).

## Questions

- Do cities and firms that adopt more robots shift toward higher value-added products and exports?
- Do they hire more engineers and fewer production workers, and what happens to wages?
- Do robot subsidies speed up upgrading, or only the automation of existing lines?

## Data that could help

Robot installations by industry and country (International Federation of Robotics), customs data on export unit values, firm data (Chinese industrial enterprise data, CSMAR) and job postings.

## Next step

Build a city-level robot exposure measure from industry robot data and local industry shares, and relate it to changes in export unit values for 2010–2023.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| industrial robots | 4 | #458 | 3, 6, 2, 7, 4, 2, 4, 0, 5, 2, 2, 6, 3 | 43 |
| industrial upgrading | 5 | #342 | 8, 9, 8, 9, 11, 9, 9, 11, 8, 15, 8, 9, 4 | 114 |
| automation | 6 | #263 | 3, 7, 6, 8, 6, 7, 2, 5, 8, 2, 8, 11, 3 | 73 |


---

---
title: "Robotaxis and the ride-hailing market (research idea)"
description: "Ride-hailing and ride-sharing appear on 62 recent papers, mostly in operations research, and 11 of them carry an autonomous-vehicle keyword, with robotaxis on 3. Driverless taxis now operate commercially in several cities. What happens to prices, waiting times and drivers' earnings where they compete with human-driven rides?"
url: https://scholar4ai.com/ideas/robotaxis-and-ride-hailing
markdown_url: https://scholar4ai.com/ideas/robotaxis-and-ride-hailing.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "transport", "labor"]
keywords: ["ride-hailing", "robotaxis", "autonomous vehicles"]
---

# Robotaxis and the ride-hailing market

Ride-hailing and ride-sharing appear on 62 recent papers, mostly in operations research, and 11 of them carry an autonomous-vehicle keyword, with robotaxis on 3. Driverless taxis now operate commercially in several cities. What happens to prices, waiting times and drivers' earnings where they compete with human-driven rides?

## What prompted this

Since October 2025, 62 papers carry a ride-hailing, ride-sharing, mobility-on-demand or taxi keyword (37 in operations research journals, 13 in economics): "ride-hailing" on 24, "ride-hailing platforms" on 14, "ride-sharing" on 13, "taxi drivers" on 3, "robotaxis" on 3. Eleven of the 62 also carry an autonomous, driverless, self-driving or robotaxi keyword; "autonomous vehicles" appears on 64 papers in the archive, mostly in AI journals.

## Questions

- In cities where robotaxis operate at scale, did ride prices and waiting times fall, and did human drivers' hours and earnings decline?
- Do robotaxis take the dense, profitable trips and leave the rest to drivers, and does that change the economics of the platforms?
- How do the fleet-sizing and dispatch models that assume all-autonomous fleets perform in mixed markets?

## Data that could help

Trip-level data released by city regulators (as in San Francisco and Chicago), robotaxi service areas and launch dates, and driver earnings surveys.

## Next step

Compare ride-hailing trip prices, waiting times and driver hours in the parts of a city where robotaxis operate with the parts where they do not, before and after launch.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| ride-hailing | 1 | #2550 | 3, 1, 1, 4, 3, 2, 2, 1, 1, 3, 2, 0, 1 | 23 |
| robotaxis | 1 | #2550 | 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1 | 2 |
| autonomous vehicles | 3 | #687 | 3, 4, 4, 4, 9, 5, 5, 4, 5, 5, 9, 4, 3 | 61 |


---

---
title: "Robo-voting and proxy advisors (research idea)"
description: "Fund families now vote at thousands of shareholder meetings a year, and many follow proxy advisors' recommendations almost automatically. Yet only 9 recent papers in the archive carry a shareholder-voting or proxy-advisor keyword, none of them about funds. Does robo-voting decide close votes, and has pass-through voting changed that?"
url: https://scholar4ai.com/ideas/robo-voting-and-proxy-advisors
markdown_url: https://scholar4ai.com/ideas/robo-voting-and-proxy-advisors.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "governance"]
keywords: ["shareholder voting", "proxy advisors"]
---

# Robo-voting and proxy advisors

Fund families now vote at thousands of shareholder meetings a year, and many follow proxy advisors' recommendations almost automatically. Yet only 9 recent papers in the archive carry a shareholder-voting or proxy-advisor keyword, none of them about funds. Does robo-voting decide close votes, and has pass-through voting changed that?

## What prompted this

Since October 2025, only 9 papers carry a proxy-advisor, shareholder-voting or proxy-voting keyword (4 in economics journals, 2 in accounting, 2 in finance, 1 in management), and none of them carries a passive-fund, index-fund, institutional-investor, mutual-fund or robo keyword. Board composition, by contrast, is one of the fastest-growing keywords across all journals ([trends page](/trends)).

## Questions

- How often do funds vote exactly as a proxy advisor recommends, within hours of the recommendation, and does that decide close votes?
- Do companies change their proposals to win the advisors' support?
- Has pass-through voting, which lets fund investors choose how their shares are voted, changed outcomes?

## Data that could help

Fund voting records (Form N-PX filings, ISS Voting Analytics), proxy advisors' recommendations, vote results and meeting dates.

## Next step

From N-PX filings, compute for each fund family the share of votes cast as ISS recommended and the time between the recommendation and the vote.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| shareholder voting | 0 | – | 1, 1, 0, 1, 1, 1, 0, 1, 0, 2, 0, 0, 0 | 8 |
| proxy advisors | 0 | – | 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0 | 1 |


---

---
title: "Robo-advisors after the language model (research idea)"
description: "Robo-advisors appear on 12 recent papers, and the topic is fading: 1 paper in July–September 2026 against about 5 per quarter before. Meanwhile people ask chatbots for financial advice. Does conversational advice change portfolios more than the old questionnaire-based robo-advice did, and for better or worse?"
url: https://scholar4ai.com/ideas/robo-advisors-after-the-llm
markdown_url: https://scholar4ai.com/ideas/robo-advisors-after-the-llm.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "households", "ai"]
keywords: ["robo-advisors", "household portfolio choice"]
---

# Robo-advisors after the language model

Robo-advisors appear on 12 recent papers, and the topic is fading: 1 paper in July–September 2026 against about 5 per quarter before. Meanwhile people ask chatbots for financial advice. Does conversational advice change portfolios more than the old questionnaire-based robo-advice did, and for better or worse?

## What prompted this

Since October 2025, 16 papers carry a robo-advice keyword (8 in finance journals), with "robo-advisors" on 12. Only one of them was published in July–September 2026, against about 5 per quarter over the nine months before. "Household portfolio choice" appears on 15 papers. Generative AI, by contrast, is rising in almost every category on the [trends page](/trends).

## Questions

- When households ask a chatbot about investing, do they get advice that fits their situation, and do they act on it more than on a robo-advisor's allocation?
- Does conversational advice reduce known mistakes (under-diversification, high fees, panic selling) or add new ones (overconfidence, chasing stories)?
- Should the same suitability rules that apply to advisers apply to chatbots?

## Data that could help

Experiments in which participants receive advice from a robo-advisor questionnaire, a chatbot or a human, with incentivized portfolio choices; brokerage data on users who adopt in-app AI assistants.

## Next step

Run an online experiment in which participants allocate a real bonus after advice from one of the three sources, and measure diversification and fees.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| robo-advisors | 1 | #2550 | 2, 2, 0, 0, 3, 0, 0, 2, 1, 1, 0, 0, 1 | 11 |
| household portfolio choice | 2 | #1148 | 1, 1, 0, 1, 1, 1, 0, 3, 1, 1, 1, 2, 2 | 13 |


---

---
title: "Return-to-office mandates and who leaves (research idea)"
description: "Remote and hybrid work appear on 53 recent papers, and 18 of them carry a productivity, turnover, retention or performance keyword. Large firms have ordered workers back to the office, some for five days a week. Do the mandates raise output, or mostly lose the workers with other options?"
url: https://scholar4ai.com/ideas/return-to-office-mandates
markdown_url: https://scholar4ai.com/ideas/return-to-office-mandates.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "labor", "human resources"]
keywords: ["hybrid work", "employee turnover"]
---

# Return-to-office mandates and who leaves

Remote and hybrid work appear on 53 recent papers, and 18 of them carry a productivity, turnover, retention or performance keyword. Large firms have ordered workers back to the office, some for five days a week. Do the mandates raise output, or mostly lose the workers with other options?

## What prompted this

Since October 2025, 53 papers carry a remote-work, working-from-home, hybrid-work or return-to-office keyword (25 in economics journals, 17 in management): "remote work" on 38, "working from home" on 18, "hybrid work" on 13. Eighteen of them also carry a turnover, retention, quit, attrition, productivity, mandate, performance or innovation keyword; "employee turnover" appears on 17 papers in the archive.

## Questions

- After a return-to-office mandate, who quits: senior staff, women, caregivers, the best-rated employees?
- Do measured output and promotion rates change for those who stay, and do the firms hire replacements at higher pay?
- Do firms that mandate five days differ from those that settle on two or three, and do stock markets react to the announcements?

## Data that could help

Worker-level tenure and exit data (Revelio Labs, LinkedIn), mandate announcement dates, firm reviews (Glassdoor) and job postings with location requirements.

## Next step

Compare exit rates by seniority and gender at firms that announced five-day mandates with matched firms that kept hybrid policies, over the following year.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| hybrid work | 1 | #2550 | 0, 0, 1, 1, 2, 1, 2, 1, 2, 1, 1, 0, 1 | 12 |
| employee turnover | 0 | – | 2, 3, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 0 | 17 |


---

---
title: "Paper mills and the integrity of the literature"
description: "Research integrity, plagiarism, research misconduct and retractions appear on 28 recent papers across the AI, finance and economics journals. Retractions have reached record numbers, and paper mills sell authorship and citations, with language models making fabrication cheaper. How much of the recent literature in these fields is affected, and can it be detected?"
url: https://scholar4ai.com/ideas/research-integrity-and-paper-mills
markdown_url: https://scholar4ai.com/ideas/research-integrity-and-paper-mills.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["science", "publishing", "methods"]
keywords: ["research integrity", "plagiarism", "research misconduct"]
---

# Paper mills and the integrity of the literature

Research integrity, plagiarism, research misconduct and retractions appear on 28 recent papers across the AI, finance and economics journals. Retractions have reached record numbers, and paper mills sell authorship and citations, with language models making fabrication cheaper. How much of the recent literature in these fields is affected, and can it be detected?

## What prompted this

Since October 2025, 28 papers carry a research-integrity, plagiarism, research-misconduct, retraction, paper-mill, predatory or citation-manipulation keyword (13 in AI journals, 7 in finance, 4 in economics): "research integrity" on 10, "plagiarism" on 5, "research misconduct" on 3, "retraction" on 3. The tracked journals publish more than 40,000 papers a year between them, so the integrity literature is tiny relative to the output it concerns.

## Questions

- What share of recent papers in the tracked journals show paper-mill signals (tortured phrases, impossible author affiliations, citation rings), by journal and country?
- Do special issues and guest-edited collections carry more such signals, and did journals that cut special issues see fewer?
- Can AI-fabricated results in empirical finance and economics be detected from their statistics, for example from the distribution of reported test statistics?

## Data that could help

The full text and metadata of recent papers, retraction records (Retraction Watch), and the published detectors for tortured phrases and citation anomalies.

## Next step

Run tortured-phrase and citation-anomaly detectors over the last two years of papers in the tracked journals and compare rates across journals and special issues.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| research integrity | 1 | #2550 | 0, 0, 1, 3, 0, 0, 2, 1, 0, 0, 1, 1, 1 | 9 |
| plagiarism | 0 | – | 0, 0, 0, 1, 0, 0, 0, 1, 2, 0, 0, 1, 0 | 5 |
| research misconduct | 1 | #2550 | 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1 | 2 |


---

---
title: "Renewable forecasts and what they are worth in the market"
description: "Wind power, photovoltaic power and solar irradiance forecasting appear on 52 recent papers, almost all in AI journals, and only 8 of them carry a market, bidding, price, imbalance or trading keyword. Forecasts are judged by error metrics, but wind and solar operators pay for forecast errors in imbalance charges. Which forecast improvements pay, and which do not?"
url: https://scholar4ai.com/ideas/renewable-forecasts-and-market-bidding
markdown_url: https://scholar4ai.com/ideas/renewable-forecasts-and-market-bidding.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "energy"]
keywords: ["wind power forecasting", "photovoltaic power forecasting", "renewable energy forecasting"]
---

# Renewable forecasts and what they are worth in the market

Wind power, photovoltaic power and solar irradiance forecasting appear on 52 recent papers, almost all in AI journals, and only 8 of them carry a market, bidding, price, imbalance or trading keyword. Forecasts are judged by error metrics, but wind and solar operators pay for forecast errors in imbalance charges. Which forecast improvements pay, and which do not?

## What prompted this

Since October 2025, 52 papers carry a wind-power, photovoltaic-power, solar-power or renewable-energy forecasting keyword (46 in AI journals, 5 in economics): "wind power forecasting" on 22, "photovoltaic power forecasting" on 13, "wind speed forecasting" on 11, "solar irradiance forecasting" on 5, "renewable energy forecasting" on 6. Only 8 of the 52 also carry a market, bidding, price, imbalance, trading, economics, cost or dispatch keyword.

## Questions

- For a wind or solar plant selling into a day-ahead market with imbalance charges, how much revenue does a given reduction in forecast error add, and does the value depend on the direction of errors?
- Do the deep learning forecasts in recent papers improve revenue relative to standard ensemble weather forecasts, or only improve metrics that do not map to revenue?
- Does better forecasting by many plants reduce system balancing costs, or shift them?

## Data that could help

Plant-level production and forecast data, day-ahead and imbalance prices (ENTSO-E, Nord Pool), and the forecast models of published papers.

## Next step

Compute the imbalance cost of forecast errors for a set of wind plants under standard and published deep learning forecasts using one year of market prices.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| wind power forecasting | 0 | – | 2, 3, 3, 1, 1, 2, 3, 0, 1, 1, 3, 2, 0 | 22 |
| photovoltaic power forecasting | 1 | #2550 | 1, 2, 1, 0, 1, 2, 0, 0, 2, 2, 1, 1, 0 | 13 |
| renewable energy forecasting | 0 | – | 0, 1, 2, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0 | 6 |


---

---
title: "Remote work and the hollowing of city centres"
description: "Remote and hybrid work appear on 53 recent papers across the economics and management journals, but only 12 of them carry a city, housing, commuting, office or rent keyword. Office vacancies are at record highs in many downtowns. Where did spending, rents and tax bases move, and are the shifts permanent?"
url: https://scholar4ai.com/ideas/remote-work-and-city-centres
markdown_url: https://scholar4ai.com/ideas/remote-work-and-city-centres.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "cities", "labor"]
keywords: ["remote work", "working from home"]
---

# Remote work and the hollowing of city centres

Remote and hybrid work appear on 53 recent papers across the economics and management journals, but only 12 of them carry a city, housing, commuting, office or rent keyword. Office vacancies are at record highs in many downtowns. Where did spending, rents and tax bases move, and are the shifts permanent?

## What prompted this

Since October 2025, 53 papers carry a remote-work, working-from-home, hybrid-work or telework keyword (25 in economics journals, 17 in management): "remote work" on 38, "working from home" on 18, "hybrid work" on 13. Only 12 of them also carry a city, urban, housing, commuting, downtown, office, rent or real-estate keyword.

## Questions

- How much of the fall in downtown spending moved to suburbs and how much disappeared, and did restaurants and shops follow the workers?
- Have office-to-residential conversions and rent falls in city centres brought residents back?
- What happens to city budgets that depend on office property taxes and commuter spending?

## Data that could help

Card transaction data by location, office vacancy and rent data, mobile phone mobility data, and municipal tax revenues.

## Next step

Compare the recovery of card spending in downtown and suburban areas across US cities by their share of remote-capable jobs, from 2020 to 2025.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| remote work | 1 | #2550 | 4, 2, 1, 1, 2, 1, 7, 4, 4, 3, 5, 3, 1 | 37 |
| working from home | 0 | – | 1, 2, 1, 3, 1, 1, 1, 1, 3, 1, 1, 2, 0 | 18 |


---

---
title: "Remittances when payments become instant (research idea)"
description: "Remittances appear on 50 recent papers, mostly in economics, but only 12 of them carry a cost, fee, digital, mobile, fintech or stablecoin keyword. Instant payment systems, mobile money links and stablecoins are cutting the cost of sending money home. Does cheaper sending raise the amount received, and who switches?"
url: https://scholar4ai.com/ideas/remittances-and-instant-payments
markdown_url: https://scholar4ai.com/ideas/remittances-and-instant-payments.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "finance", "development"]
keywords: ["remittances", "international remittances"]
---

# Remittances when payments become instant

Remittances appear on 50 recent papers, mostly in economics, but only 12 of them carry a cost, fee, digital, mobile, fintech or stablecoin keyword. Instant payment systems, mobile money links and stablecoins are cutting the cost of sending money home. Does cheaper sending raise the amount received, and who switches?

## What prompted this

Since October 2025, 50 papers carry a remittance keyword (33 in economics journals, 10 in finance), with "remittances" on 39 and "international remittances" on 5. Only 12 of the 50 also carry a cost, fee, instant-payment, digital, mobile, fintech, crypto or stablecoin keyword. The global average cost of sending remittances has fallen but remains above the 3% target.

## Questions

- When a corridor gets a cheaper channel (a mobile money link or a stablecoin route), do remittances rise by more than the fee saved, and does the money shift from informal channels?
- Who uses the new channels: younger migrants, those with bank accounts, those sending small amounts?
- Do recipients spend remittances differently when they arrive in digital wallets rather than cash?

## Data that could help

Remittance prices by corridor (the World Bank's Remittance Prices Worldwide), central bank remittance statistics, mobile money operator data and household surveys in recipient countries.

## Next step

Match corridor-level price changes since 2019 to remittance flows and test whether flows respond more where digital channels entered.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| remittances | 0 | – | 2, 6, 2, 2, 0, 5, 3, 4, 6, 4, 2, 3, 0 | 39 |
| international remittances | 0 | – | 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0 | 5 |


---

---
title: "Tunneling through related-party transactions"
description: "Related-party transactions appear on 23 recent papers and tunneling on 17, almost all in finance journals and mostly about Chinese business groups. As rules on these transactions tighten and disclosure moves to machine-readable formats, can tunneling be detected at scale, and did the rules reduce it?"
url: https://scholar4ai.com/ideas/related-party-transactions-and-tunneling
markdown_url: https://scholar4ai.com/ideas/related-party-transactions-and-tunneling.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "finance", "governance"]
keywords: ["related-party transactions", "tunneling"]
---

# Tunneling through related-party transactions

Related-party transactions appear on 23 recent papers and tunneling on 17, almost all in finance journals and mostly about Chinese business groups. As rules on these transactions tighten and disclosure moves to machine-readable formats, can tunneling be detected at scale, and did the rules reduce it?

## What prompted this

Since October 2025, 41 papers carry a related-party or tunneling keyword (27 in finance journals, 6 in economics, 4 in accounting): "related-party transactions" on 23 and "tunneling" on 17. Most study a single country, and few use the transaction-level disclosures that listed firms now file.

## Questions

- Can anomalous related-party transactions (prices far from market, timing before earnings announcements) be detected automatically from disclosures, and do the flagged firms later face enforcement?
- Did stricter approval rules for related-party transactions in China and India reduce their volume, or move them into unreported forms?
- Do minority shareholders price the expected tunneling into group-affiliated firms?

## Data that could help

Transaction-level related-party disclosures (CSMAR for China, stock exchange filings elsewhere), enforcement records and stock returns.

## Next step

Build a detector for abnormal related-party prices from Chinese listed firms' transaction disclosures and check whether flagged firms are later sanctioned.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| related-party transactions | 1 | #2550 | 1, 4, 2, 2, 0, 1, 2, 1, 4, 1, 1, 3, 1 | 22 |
| tunneling | 0 | – | 1, 2, 1, 2, 0, 1, 3, 1, 2, 1, 2, 1, 0 | 17 |


---

---
title: "Refugee and immigrant entrepreneurs (research idea)"
description: "Immigrant entrepreneurs and refugee entrepreneurship appear on 13 recent papers, while immigration as a topic is rising fast in the economics journals. Millions of recent refugees and migrants have started businesses. Do their firms grow, hire natives and survive, and which policies (work permits, credit, recognition of qualifications) make the difference?"
url: https://scholar4ai.com/ideas/refugee-and-immigrant-entrepreneurs
markdown_url: https://scholar4ai.com/ideas/refugee-and-immigrant-entrepreneurs.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "entrepreneurship", "migration"]
keywords: ["immigrant entrepreneurs", "immigrant entrepreneurship", "refugee entrepreneurship"]
---

# Refugee and immigrant entrepreneurs

Immigrant entrepreneurs and refugee entrepreneurship appear on 13 recent papers, while immigration as a topic is rising fast in the economics journals. Millions of recent refugees and migrants have started businesses. Do their firms grow, hire natives and survive, and which policies (work permits, credit, recognition of qualifications) make the difference?

## What prompted this

Since October 2025, "immigrant entrepreneurs" appears on 6 papers, "immigrant entrepreneurship" on 4 and "refugee entrepreneurship" on 3, while papers with any immigration keyword rose to 42 in July–September 2026 from about 30 per quarter earlier. Sixteen of the 73 papers with an immigrant or diaspora keyword also carry an entrepreneurship, startup, self-employment or founder keyword.

## Questions

- Do businesses started by recent refugees (Ukrainians in Europe, Syrians in Turkey and Germany, Venezuelans in Latin America) survive and grow at the same rate as natives' businesses of the same type?
- Does faster access to work permits and bank accounts raise business formation, and does it reduce informal work?
- Do immigrant-founded firms hire natives, and in which sectors?

## Data that could help

Business registers with founder nationality, refugee registration data, and employer-employee records.

## Next step

Compare business registrations by Ukrainian nationals across EU countries with different speeds of work permit access since 2022.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| immigrant entrepreneurs | 3 | #687 | 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 3, 1 | 5 |
| immigrant entrepreneurship | 1 | #2550 | 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 1 | 3 |
| refugee entrepreneurship | 1 | #2550 | 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0 | 3 |


---

---
title: "What recommendations do to the people who get them"
description: "Recommender systems appear on 111 recent papers and personalized recommendation on 54, almost all in AI journals; 45 recommendation papers carry a diversity, fairness or popularity-bias keyword, and welfare, consumer surplus and addiction appear in almost none. Recommendation decides what billions of people see and buy. Who gains, and by how much?"
url: https://scholar4ai.com/ideas/recommender-systems-and-welfare
markdown_url: https://scholar4ai.com/ideas/recommender-systems-and-welfare.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "economics", "platforms"]
keywords: ["recommender systems", "popularity bias", "recommendation diversity"]
---

# What recommendations do to the people who get them

Recommender systems appear on 111 recent papers and personalized recommendation on 54, almost all in AI journals; 45 recommendation papers carry a diversity, fairness or popularity-bias keyword, and welfare, consumer surplus and addiction appear in almost none. Recommendation decides what billions of people see and buy. Who gains, and by how much?

## What prompted this

Since October 2025, 513 papers carry a recommender or recommendation keyword (440 in AI journals): "recommender systems" on 111, "personalized recommendation" on 54, "sequential recommendation" on 47, "multimodal recommendation" on 38. Forty-five of them also carry a filter-bubble, diversity, polarization, fairness, welfare, popularity-bias or well-being keyword, led by "popularity bias" on 19 and "recommendation diversity" on 13.

## Questions

- In platform experiments, how much consumer surplus do recommendations create relative to search alone, and how is it split between consumers, sellers and the platform?
- Do recommendations that optimize engagement reduce the variety of what people consume over months, and does the variety loss show up in satisfaction?
- When platforms are required to offer a non-personalized feed, as the EU now does, who switches, and what changes?

## Data that could help

Platform experiments with randomized recommendation policies, consumption logs over long horizons, and the EU's transparency reports on feed options.

## Next step

Run or obtain a platform experiment that randomizes recommendation strength and measure purchases, variety and self-reported satisfaction over three months.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| recommender systems | 8 | #153 | 7, 9, 8, 9, 9, 7, 10, 8, 9, 8, 12, 10, 8 | 106 |
| popularity bias | 0 | – | 1, 2, 5, 2, 0, 2, 1, 2, 2, 0, 1, 2, 0 | 20 |
| recommendation diversity | 0 | – | 0, 1, 3, 0, 0, 1, 3, 2, 0, 1, 2, 0, 0 | 13 |


---

---
title: "Machines that read tables and charts (research idea)"
description: "Document understanding, table question answering and chart understanding appear on 17 recent papers, all in AI journals, with 7 in the last quarter against about 3 per quarter before. Financial information lives in tables, footnotes and charts that models still misread. How accurate are current models on real filings, and which errors would mislead an analyst?"
url: https://scholar4ai.com/ideas/reading-tables-and-charts
markdown_url: https://scholar4ai.com/ideas/reading-tables-and-charts.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "finance", "documents"]
keywords: ["document understanding", "table question answering", "chart understanding"]
---

# Machines that read tables and charts

Document understanding, table question answering and chart understanding appear on 17 recent papers, all in AI journals, with 7 in the last quarter against about 3 per quarter before. Financial information lives in tables, footnotes and charts that models still misread. How accurate are current models on real filings, and which errors would mislead an analyst?

## What prompted this

Since October 2025, 17 papers carry a document-understanding, table-question-answering, chart-understanding, document-layout or visually-rich-document keyword, all in AI journals: "document understanding" on 5, "table question answering" on 4, "document image analysis" on 3, "chart understanding" on 2. Seven were published in July–September 2026 against about 3 per quarter earlier.

## Questions

- On questions that require combining a table, a footnote and a chart from the same annual report, how often do multimodal models get the number right, and how often do they confidently get it wrong?
- Which document features cause errors (merged cells, units in headers, negative numbers in parentheses, restated columns)?
- Would a layer that extracts tables into structured data before the model reads them remove most of the errors?

## Data that could help

Annual reports with XBRL-tagged values as ground truth, public financial document question sets (FinQA, TAT-QA), and chart datasets built from earnings presentations.

## Next step

Build 500 questions whose answers are XBRL-tagged values in annual reports and measure the accuracy of three multimodal models on the PDF pages.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| document understanding | 0 | – | 0, 0, 1, 0, 1, 0, 0, 0, 0, 2, 0, 1, 0 | 5 |
| table question answering | 0 | – | 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0 | 4 |
| chart understanding | 0 | – | 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0 | 2 |


---

---
title: "R&D tax incentives and the relabeling problem"
description: "Tax incentives appear on 47 recent papers and R&D investment on 267, but R&D tax incentives on only 5, and 26 of 75 incentive papers touch innovation. Governments spend heavily on R&D tax relief. How much of the reported increase in R&D is real research, and how much is ordinary spending relabeled?"
url: https://scholar4ai.com/ideas/rd-tax-incentives-and-relabeling
markdown_url: https://scholar4ai.com/ideas/rd-tax-incentives-and-relabeling.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "economics", "innovation"]
keywords: ["R&D tax incentives", "tax incentives"]
---

# R&D tax incentives and the relabeling problem

Tax incentives appear on 47 recent papers and R&D investment on 267, but R&D tax incentives on only 5, and 26 of 75 incentive papers touch innovation. Governments spend heavily on R&D tax relief. How much of the reported increase in R&D is real research, and how much is ordinary spending relabeled?

## What prompted this

Since October 2025, 75 papers carry a tax-incentive, tax-credit, R&D-tax or super-deduction keyword (47 in economics journals, 16 in finance); "tax incentives" appears on 47 and "R&D tax incentives" on 5. Twenty-six of the 75 also carry an R&D, innovation or patent keyword. "R&D investment" itself appears on 267 papers, one of the most frequent topics in the archive.

## Questions

- When R&D tax relief becomes more generous, do patents, new products and researcher hiring rise in proportion to reported R&D, or does reported R&D rise alone?
- Which firms relabel: those near the eligibility threshold, those with weak auditors, those in sectors where the line between R&D and engineering is blurry?
- Did the US requirement to amortize R&D from 2022 change reported R&D the other way?

## Data that could help

Firm R&D spending and tax relief claims, patents and product launches, researcher employment from job postings, and the dates of policy changes.

## Next step

Compare the growth of reported R&D with the growth of R&D job postings for firms around a change in the generosity of relief.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| R&D tax incentives | 1 | #2550 | 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 2, 0 | 5 |
| tax incentives | 3 | #687 | 3, 3, 4, 0, 4, 8, 1, 1, 8, 4, 5, 3, 3 | 44 |


---

---
title: "Rail freight and the modal shift that does not come"
description: "Rail freight, railway operations and intermodal freight transport appear on 55 recent papers, with 14 in the last quarter against about 12 per quarter before. Governments want freight moved from trucks to rail for climate reasons, yet rail's share keeps falling in most of Europe and the US. Which bottlenecks (terminals, reliability, pricing) matter, and what would change shippers' choices?"
url: https://scholar4ai.com/ideas/rail-freight-modal-shift
markdown_url: https://scholar4ai.com/ideas/rail-freight-modal-shift.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "transport", "climate"]
keywords: ["rail freight", "railway operations", "intermodal freight transport"]
---

# Rail freight and the modal shift that does not come

Rail freight, railway operations and intermodal freight transport appear on 55 recent papers, with 14 in the last quarter against about 12 per quarter before. Governments want freight moved from trucks to rail for climate reasons, yet rail's share keeps falling in most of Europe and the US. Which bottlenecks (terminals, reliability, pricing) matter, and what would change shippers' choices?

## What prompted this

Since October 2025, 55 papers carry a railway, rail-freight, railroad, intermodal or rail-transport keyword (27 in operations research journals, 13 in economics, 11 in AI): "rail freight" on 6, "railway operations" on 5, "intermodal freight transport" on 4, "railway safety" on 3, "China Railway Express" on 2. The quarter to September 2026 had 14 such papers against about 12 per quarter earlier.

## Questions

- For which origin-destination pairs and goods does rail lose to trucks on cost and time, and how much of the gap is terminal handling rather than line haul?
- Do shippers respond to carbon prices on trucking, or do they need reliability improvements first?
- Did the China-Europe rail services survive the collapse of their subsidies and the war in Ukraine, and what did they carry?

## Data that could help

Freight flow data by mode (Eurostat, US Commodity Flow Survey), rail punctuality records, terminal capacity data and shipper surveys.

## Next step

Estimate a mode-choice model on recent freight flow data with terminal time and reliability as separate variables, and simulate a carbon price on trucking.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| rail freight | 1 | #2550 | 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1 | 5 |
| railway operations | 0 | – | 0, 1, 1, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0 | 5 |
| intermodal freight transport | 1 | #2550 | 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0 | 4 |


---

---
title: "Retrieval-augmented generation for finance, tax and audit"
description: "Retrieval-augmented generation appears on 165 recent papers, 171 of 178 such papers in AI journals, and only 12 touch finance, legal, tax, accounting, audit or compliance questions. Banks, tax advisers and auditors are deploying it over filings, rules and contracts. How often does it cite the right source, and what happens when the source changes?"
url: https://scholar4ai.com/ideas/rag-for-finance-and-tax
markdown_url: https://scholar4ai.com/ideas/rag-for-finance-and-tax.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "finance", "accounting"]
keywords: ["retrieval-augmented generation"]
---

# Retrieval-augmented generation for finance, tax and audit

Retrieval-augmented generation appears on 165 recent papers, 171 of 178 such papers in AI journals, and only 12 touch finance, legal, tax, accounting, audit or compliance questions. Banks, tax advisers and auditors are deploying it over filings, rules and contracts. How often does it cite the right source, and what happens when the source changes?

## What prompted this

Since October 2025, 178 papers carry a retrieval-augmented keyword (171 in AI journals, 5 in management): "retrieval-augmented generation" on 165, "graph-based retrieval-augmented generation" on 8. Only 12 of the 178 also carry a finance, bank, legal, regulation, accounting, tax, audit, compliance, economics or business keyword, and those few include legal question answering and tax return filing.

## Questions

- On questions about accounting standards, tax rules and loan agreements, how often does a retrieval system answer from the right passage, from a superseded version, or from nothing?
- When rules change, how long do deployed systems keep answering from the old text, and who notices?
- Do professionals check the retrieved sources, and does showing them reduce errors or only slow the work?

## Data that could help

Question sets with gold answers and sources built from accounting standards, tax codes and standard contracts, including versions over time.

## Next step

Build a test set of 300 questions over the current and prior versions of one accounting standard and measure how often retrieval systems cite the wrong version.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| retrieval-augmented generation | 14 | #55 | 9, 6, 11, 8, 12, 17, 20, 16, 19, 14, 12, 15, 8 | 159 |


---

---
title: "Quiet quitting and the measurement of engagement"
description: "Job satisfaction appears on 39 recent papers in the management and economics journals, employee engagement on 17, work engagement on 9 and quiet quitting on 2. Engagement surveys drive billions in consulting fees. Does measured engagement predict anything that firms can observe, such as output, errors or exits, and did it actually fall?"
url: https://scholar4ai.com/ideas/quiet-quitting-and-engagement
markdown_url: https://scholar4ai.com/ideas/quiet-quitting-and-engagement.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "human resources"]
keywords: ["employee engagement", "quiet quitting", "work engagement"]
---

# Quiet quitting and the measurement of engagement

Job satisfaction appears on 39 recent papers in the management and economics journals, employee engagement on 17, work engagement on 9 and quiet quitting on 2. Engagement surveys drive billions in consulting fees. Does measured engagement predict anything that firms can observe, such as output, errors or exits, and did it actually fall?

## What prompted this

Since October 2025, 88 papers carry an engagement, job-satisfaction or disengagement keyword (56 in management journals, 21 in economics): "job satisfaction" on 39, "employee engagement" on 17, "work engagement" on 9, "quiet quitting" on 2. Almost all measure engagement with surveys and relate it to other surveys.

## Questions

- Does survey engagement predict later output, errors, absences and exits in administrative records, and how much of the link survives once pay and job type are controlled for?
- Did engagement fall after 2020, as the quiet-quitting story claims, in data that are not surveys, such as hours logged and voluntary tasks?
- Do engagement interventions (recognition programmes, flexibility) change behaviour or only survey answers?

## Data that could help

Firms' engagement survey results linked to productivity, absence and exit records; time-use and software activity logs where available.

## Next step

Partner with a firm that runs regular engagement surveys and test whether survey scores predict exits and performance ratings over the following year.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| employee engagement | 0 | – | 2, 1, 0, 2, 2, 5, 1, 1, 0, 3, 0, 0, 0 | 17 |
| quiet quitting | 0 | – | 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0 | 2 |
| work engagement | 0 | – | 0, 0, 1, 0, 2, 1, 1, 0, 2, 1, 1, 0, 0 | 9 |


---

---
title: "Quantum optimization against the classical state of the…"
description: "Quantum computing, quantum machine learning and quantum annealing appear on 115 recent papers, mostly in AI journals, and 33 of them concern optimization. Firms in finance and logistics run quantum pilots. Do the quantum results beat the best classical solvers on the same instances, or only the classical baselines the papers chose?"
url: https://scholar4ai.com/ideas/quantum-optimization-benchmarks
markdown_url: https://scholar4ai.com/ideas/quantum-optimization-benchmarks.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "ai", "methods"]
keywords: ["quantum computing", "quantum annealing", "quantum machine learning"]
---

# Quantum optimization against the classical state of the art

Quantum computing, quantum machine learning and quantum annealing appear on 115 recent papers, mostly in AI journals, and 33 of them concern optimization. Firms in finance and logistics run quantum pilots. Do the quantum results beat the best classical solvers on the same instances, or only the classical baselines the papers chose?

## What prompted this

Since October 2025, 115 papers carry a quantum keyword (81 in AI journals, 15 in economics, 13 in operations research): "quantum machine learning" on 22, "quantum computing" on 14, "quantum neural networks" on 9, "hybrid quantum-classical models" on 8. Thirty-three of the 115 also carry an optimization, annealing, combinatorial, portfolio, scheduling, logistics or finance keyword; "quantum annealing" appears on 3 and "quantum optimization" on 2.

## Questions

- On portfolio, routing and scheduling instances used in the quantum papers, how do the reported solutions compare with Gurobi, CPLEX or a tuned metaheuristic given the same time?
- Do the hybrid methods' gains come from the quantum part or from the classical pre- and post-processing?
- At what instance sizes, if any, do current devices beat classical solvers on energy use rather than solution quality?

## Data that could help

The instances and results published in recent quantum optimization papers, and standard classical solvers.

## Next step

Re-solve the benchmark instances of ten recent quantum optimization papers with a commercial solver and a tuned local search under the same time limit.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| quantum computing | 1 | #2550 | 1, 1, 1, 2, 0, 0, 0, 0, 3, 2, 2, 2, 1 | 14 |
| quantum annealing | 0 | – | 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0 | 3 |
| quantum machine learning | 2 | #1148 | 2, 0, 0, 2, 2, 2, 1, 1, 3, 1, 3, 3, 2 | 20 |


---

---
title: "Public procurement, read by machine (research idea)"
description: "Public procurement appears on 53 recent papers, with 40 procurement papers in the last quarter against about 26 per quarter before, and 33 of 123 carry a corruption, digital, e-procurement or bidding keyword. Open contracting data now cover millions of tenders. Can red flags in the data predict corruption, and do governments act on them?"
url: https://scholar4ai.com/ideas/public-procurement-and-corruption-in-the-data
markdown_url: https://scholar4ai.com/ideas/public-procurement-and-corruption-in-the-data.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "economics", "public sector"]
keywords: ["public procurement", "e-procurement", "corruption"]
---

# Public procurement, read by machine

Public procurement appears on 53 recent papers, with 40 procurement papers in the last quarter against about 26 per quarter before, and 33 of 123 carry a corruption, digital, e-procurement or bidding keyword. Open contracting data now cover millions of tenders. Can red flags in the data predict corruption, and do governments act on them?

## What prompted this

Since October 2025, 123 papers carry a procurement keyword (52 in economics journals, 20 in finance, 20 in management, 20 in operations research): "public procurement" on 53, "procurement strategy" on 11, "green public procurement" on 4, "e-procurement" on 3. The quarter to September 2026 had 40 such papers against about 26 per quarter earlier. Thirty-three of the 123 also carry a corruption, e-procurement, digital, transparency, competition or bidding keyword; "corruption" alone appears on 87 papers.

## Questions

- Do red flags computed from open contracting data (single bidders, short deadlines, repeated winners) predict later audit findings and prosecutions?
- When tenders move to electronic platforms, do single-bid rates and prices fall, and do the effects fade as bidders learn the system?
- Do firms that win contracts with red flags perform worse on the contracts?

## Data that could help

Open contracting data (national procurement portals, the Open Contracting Partnership), audit and court records, and contract performance data.

## Next step

Compute standard red-flag indicators for one country's procurement portal and test whether they predict audit findings in the following years.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| public procurement | 3 | #687 | 3, 4, 3, 2, 1, 4, 9, 6, 3, 6, 6, 4, 2 | 51 |
| e-procurement | 0 | – | 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0 | 3 |
| corruption | 3 | #687 | 5, 7, 6, 7, 5, 11, 10, 10, 3, 10, 5, 6, 3 | 85 |


---

---
title: "Public debt when rates exceed growth (research idea)"
description: "Public debt, fiscal rules and debt sustainability appear on 215 recent papers in the economics and finance journals, but only 41 of them carry an interest-rate, debt-service or monetary keyword. For a decade, low rates made debt look free. With interest costs now the fastest-growing budget item in several countries, which governments adjust, and how?"
url: https://scholar4ai.com/ideas/public-debt-with-higher-rates
markdown_url: https://scholar4ai.com/ideas/public-debt-with-higher-rates.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "public finance", "macroeconomics"]
keywords: ["public debt", "fiscal rules", "debt sustainability"]
---

# Public debt when rates exceed growth

Public debt, fiscal rules and debt sustainability appear on 215 recent papers in the economics and finance journals, but only 41 of them carry an interest-rate, debt-service or monetary keyword. For a decade, low rates made debt look free. With interest costs now the fastest-growing budget item in several countries, which governments adjust, and how?

## What prompted this

Since October 2025, 215 papers carry a public-debt, debt-sustainability, government-debt, fiscal-sustainability, debt-to-GDP or fiscal-rule keyword (121 in economics journals, 87 in finance): "public debt" on 98, "fiscal rules" on 32, "fiscal sustainability" on 31, "debt sustainability" on 12. Only 41 of them also carry an interest-rate, debt-service, interest-burden or monetary keyword.

## Questions

- As interest costs rise, do governments cut investment, raise taxes, or let deficits widen, and does the response depend on fiscal rules?
- Do bond markets now punish deficits faster, as the UK's 2022 episode suggested, and which countries are most exposed?
- How much do rising rates on domestic debt redistribute from taxpayers to bondholders?

## Data that could help

Government budget data by category, interest payments and debt maturity (IMF, OECD), sovereign yields and the dates of fiscal rule changes.

## Next step

Decompose the change in primary balances across OECD countries since 2022 into spending and revenue responses, and relate them to each country's interest cost increase.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| public debt | 10 | #104 | 5, 9, 9, 7, 4, 7, 7, 8, 8, 10, 5, 11, 9 | 90 |
| fiscal rules | 3 | #687 | 6, 2, 4, 2, 2, 1, 3, 0, 1, 3, 1, 5, 2 | 30 |
| debt sustainability | 4 | #458 | 3, 0, 2, 0, 1, 0, 1, 0, 1, 0, 0, 0, 4 | 8 |


---

---
title: "Prompt injection against financial agents (research idea)"
description: "Prompt injection, jailbreak attacks and LLM security appear on 37 recent papers, all in AI journals. Banks and brokers are deploying agents that read emails, documents and web pages and then act. A hidden instruction in a document can make such an agent move money or leak data. How exposed are deployed financial agents, and which defences hold?"
url: https://scholar4ai.com/ideas/prompt-injection-in-financial-agents
markdown_url: https://scholar4ai.com/ideas/prompt-injection-in-financial-agents.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "security", "finance"]
keywords: ["prompt injection", "jailbreak attacks", "LLM security"]
---

# Prompt injection against financial agents

Prompt injection, jailbreak attacks and LLM security appear on 37 recent papers, all in AI journals. Banks and brokers are deploying agents that read emails, documents and web pages and then act. A hidden instruction in a document can make such an agent move money or leak data. How exposed are deployed financial agents, and which defences hold?

## What prompted this

Since October 2025, 37 papers carry a prompt-injection, jailbreak, adversarial-prompt, LLM-security, agent-security or red-teaming keyword, all in AI journals: "prompt injection" on 11, "jailbreak attacks" on 10, "LLM security" on 6, "adversarial prompts" on 5, "red teaming" on 4. Agentic AI and LLM agents are among the rising keywords in the AI journals, and none of the security papers concerns finance.

## Questions

- In a sandbox with a financial assistant that reads documents and can call payment and trading tools, what share of injected instructions in invoices, emails and web pages succeed?
- Which defences (tool permission layers, instruction hierarchies, human confirmation thresholds) reduce success without making the agent useless?
- How would an attack look in audit logs, and could existing fraud controls catch it?

## Data that could help

Open agent frameworks, a corpus of documents with injected instructions, and the control frameworks that banks use for payment authorization.

## Next step

Build a sandboxed financial agent with payment tools and measure the success rate of injected instructions in 1,000 documents under three defence configurations.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| prompt injection | 1 | #2550 | 0, 0, 2, 2, 1, 1, 0, 0, 2, 0, 0, 2, 1 | 10 |
| jailbreak attacks | 0 | – | 1, 1, 1, 0, 1, 2, 1, 1, 0, 1, 0, 1, 0 | 10 |
| LLM security | 0 | – | 0, 0, 1, 0, 2, 0, 0, 2, 1, 0, 0, 0, 0 | 6 |


---

---
title: "Returns, and the policies that invite them (research idea)"
description: "Reverse logistics, return policies and product returns appear on 53 recent papers, almost all in operations research, with 15 in the last quarter against about 12 per quarter before. Online return rates are several times those of stores, and retailers are starting to charge for returns. Which policies cut returns without cutting sales, and what happens to the returned goods?"
url: https://scholar4ai.com/ideas/product-returns-and-reverse-logistics
markdown_url: https://scholar4ai.com/ideas/product-returns-and-reverse-logistics.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "retail", "logistics"]
keywords: ["product returns", "return policy", "reverse logistics"]
---

# Returns, and the policies that invite them

Reverse logistics, return policies and product returns appear on 53 recent papers, almost all in operations research, with 15 in the last quarter against about 12 per quarter before. Online return rates are several times those of stores, and retailers are starting to charge for returns. Which policies cut returns without cutting sales, and what happens to the returned goods?

## What prompted this

Since October 2025, 53 papers carry a product-return, return-policy, returns-management or reverse-logistics keyword (48 in operations research journals): "reverse logistics" on 30, "return policy" on 10, "product returns" on 8, "returns management" on 3. The quarter to September 2026 had 15 such papers against about 12 per quarter earlier. Most model the network; few use data on what consumers do when policies change.

## Questions

- When a retailer introduces a return fee or shortens the return window, how much do returns fall, and how much do sales and repeat purchases fall with them?
- What share of returned items is resold as new, resold as used, or destroyed, and how does that vary by category?
- Do virtual try-on tools and better size information reduce returns more cheaply than fees?

## Data that could help

Retailer transaction and return data around policy changes, disposition records for returned goods, and consumer survey data.

## Next step

Compare return rates and repeat purchase rates at retailers that introduced return fees in 2023–2024 with those that did not, using consumer panel data.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| product returns | 0 | – | 0, 1, 0, 1, 2, 0, 0, 0, 1, 2, 1, 0, 0 | 8 |
| return policy | 1 | #2550 | 0, 0, 0, 3, 1, 0, 0, 1, 3, 0, 0, 2, 0 | 10 |
| reverse logistics | 2 | #1148 | 0, 1, 2, 4, 2, 2, 3, 3, 2, 2, 2, 5, 2 | 28 |


---

---
title: "Private equity in nursing homes (research idea)"
description: "Private equity is a small but rising keyword in the finance journals, and only 2 of 22 recent private-equity papers touch health care. Private equity firms now own a large share of nursing homes and physician practices in several countries. Do their cost cuts show up in quality of care, and who pays?"
url: https://scholar4ai.com/ideas/private-equity-in-nursing-homes
markdown_url: https://scholar4ai.com/ideas/private-equity-in-nursing-homes.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "health", "governance"]
keywords: ["private equity", "nursing homes"]
---

# Private equity in nursing homes

Private equity is a small but rising keyword in the finance journals, and only 2 of 22 recent private-equity papers touch health care. Private equity firms now own a large share of nursing homes and physician practices in several countries. Do their cost cuts show up in quality of care, and who pays?

## What prompted this

Since October 2025, 22 papers carry a private-equity keyword (16 in finance journals), with 8 in July–September 2026 against about 5 per quarter over the nine months before. Only 2 of them also carry a health, hospital, nursing or care keyword. "Nursing homes" appears on 4 papers.

## Questions

- After a private equity buyout, do staffing levels, inspection deficiencies and patient outcomes in nursing homes change?
- Do buyouts raise prices for insurers and patients, or cut costs that others were overpaying?
- Do the effects differ between funds that hold for a few years and those that hold for longer?

## Data that could help

Ownership records of nursing homes and practices (public ownership files in the US, company registers elsewhere), inspection and staffing data, and claims or cost data.

## Next step

Match nursing home ownership changes to staffing and inspection records in one country and compare homes bought by private equity with those bought by other operators.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| private equity | 1 | #2550 | 0, 1, 1, 1, 1, 3, 0, 1, 0, 3, 1, 1, 0 | 13 |
| nursing homes | 0 | – | 0, 2, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 | 4 |


---

---
title: "Privacy rules and the small advertisers (research idea)"
description: "Privacy concerns, GDPR, data protection and consumer privacy appear on 81 recent papers, mostly about what consumers feel. Few study what the rules did to the firms that advertise. When tracking became harder, large platforms kept their data and small advertisers lost theirs. Did privacy rules raise concentration?"
url: https://scholar4ai.com/ideas/privacy-rules-and-small-advertisers
markdown_url: https://scholar4ai.com/ideas/privacy-rules-and-small-advertisers.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "marketing", "regulation"]
keywords: ["GDPR", "data protection", "privacy concerns", "consumer privacy"]
---

# Privacy rules and the small advertisers

Privacy concerns, GDPR, data protection and consumer privacy appear on 81 recent papers, mostly about what consumers feel. Few study what the rules did to the firms that advertise. When tracking became harder, large platforms kept their data and small advertisers lost theirs. Did privacy rules raise concentration?

## What prompted this

Since October 2025, 81 papers carry a GDPR, privacy-regulation, data-protection, cookie, app-tracking or consumer-privacy keyword (28 in management journals, 23 in AI, 12 in operations research, 11 in finance): "privacy concerns" on 32, "GDPR" on 9, "data protection" on 9, "consumer privacy" on 8. Thirty-three of them carry an advertising, marketing, targeting, competition or platform keyword, but almost none concerns the firms that buy advertising.

## Questions

- After app tracking restrictions and cookie consent rules, did small advertisers' customer acquisition costs rise more than large advertisers', and did some stop advertising online?
- Did the rules move advertising spending toward the platforms with the most first-party data?
- Did new firms find it harder to launch products, and did venture investment in consumer apps fall?

## Data that could help

Advertising spending by firm size from advertising platforms and panels, customer acquisition costs from e-commerce data providers, and the dates of privacy changes.

## Next step

Compare the advertising cost per acquisition of small and large e-commerce sellers before and after the 2021 app tracking change, using data from an e-commerce analytics provider.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| GDPR | 1 | #2550 | 2, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1 | 8 |
| data protection | 0 | – | 0, 0, 1, 1, 0, 0, 2, 1, 1, 1, 1, 1, 0 | 9 |
| privacy concerns | 1 | #2550 | 2, 3, 1, 2, 2, 3, 2, 3, 3, 2, 7, 1, 1 | 31 |
| consumer privacy | 0 | – | 2, 2, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0 | 8 |


---

---
title: "Pricing cyber insurance (research idea)"
description: "Cybersecurity risks and cyberattacks appear on 226 recent papers, mostly in AI journals, and 56 of them carry a risk-management, insurance, investment or cost keyword, with cyber insurance on 9. Insurers struggle to price a risk that is correlated across firms and shaped by the insured's own choices. What do losses look like, and does insurance change security?"
url: https://scholar4ai.com/ideas/pricing-cyber-insurance
markdown_url: https://scholar4ai.com/ideas/pricing-cyber-insurance.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "finance", "cybersecurity"]
keywords: ["cyber insurance", "cyber risk management", "cybersecurity investment"]
---

# Pricing cyber insurance

Cybersecurity risks and cyberattacks appear on 226 recent papers, mostly in AI journals, and 56 of them carry a risk-management, insurance, investment or cost keyword, with cyber insurance on 9. Insurers struggle to price a risk that is correlated across firms and shaped by the insured's own choices. What do losses look like, and does insurance change security?

## What prompted this

Since October 2025, 226 papers carry a cybersecurity or cyber keyword (112 in AI journals, 49 in finance, 32 in management): "cybersecurity risks" on 39, "cybersecurity" on 35, "cyberattack detection" on 11, "cyberattacks" on 11, "cyber insurance" on 9, "cyber risk management" on 5, "cybersecurity investment" on 3. Fifty-six of the 226 carry an investment, game, insurance, optimization, cost or risk-management keyword.

## Questions

- How fat-tailed and correlated are cyber losses across firms, and how much capital does an insurer need to cover a widespread event such as a cloud outage or a supply chain attack?
- Do insured firms invest more in security to get coverage, or less because they are covered, and do insurers' security requirements change behaviour?
- Did ransomware claims and the exclusions written after them change who is insurable?

## Data that could help

Claims data from insurers or brokers, breach notification records, firms' security postures from external scans, and policy terms.

## Next step

Fit a loss distribution to public breach and ransomware records with firm size and sector, and estimate the capital required for a portfolio with a common-cause event.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| cyber insurance | 0 | – | 3, 0, 2, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0 | 9 |
| cyber risk management | 0 | – | 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0 | 5 |
| cybersecurity investment | 1 | #2550 | 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1 | 2 |


---

---
title: "Who sets the price in live-streaming commerce?"
description: "Live-streaming e-commerce and price competition are among the fastest-growing keywords in operations research, and influencer marketing is rising in management. OR papers model live commerce as a pricing game, management papers as persuasion. Who captures the margin: the platform, the streamer or the brand?"
url: https://scholar4ai.com/ideas/price-setting-in-live-streaming-commerce
markdown_url: https://scholar4ai.com/ideas/price-setting-in-live-streaming-commerce.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "management", "platforms"]
keywords: ["live streaming e-commerce", "influencer marketing", "price competition", "Stackelberg game"]
---

# Who sets the price in live-streaming commerce?

Live-streaming e-commerce and price competition are among the fastest-growing keywords in operations research, and influencer marketing is rising in management. OR papers model live commerce as a pricing game, management papers as persuasion. Who captures the margin: the platform, the streamer or the brand?

## What prompted this

On the [operations research dashboard](/categories/operations-research), "price competition" (11 papers in July–September 2026 against about 2 expected), "live streaming e-commerce" (7 against 2), "digital platforms" and "Stackelberg game" are all growing; in management, "influencer marketing" is too (10 against 5). Since October 2025, 48 papers carry a live-streaming keyword (23 in operations research, 17 in management). Thirteen of them also carry a game or pricing keyword: 11 in operations research and none in management.

## Questions

- When top streamers win "lowest price" deals, do brands lose margin, or win it back through volume and awareness?
- Do commissions and prices match Stackelberg models with the platform, the streamer or the brand as the leader?
- How do the often high return rates of live commerce change the optimal price?

## Data that could help

- Prices of the same products in live streams and in regular store listings, from third-party sales trackers.
- Financial reports of listed consumer brands that rely on live commerce.

## Next step

Collect paired live-stream and store prices for a few hundred products and compare the gap with what published pricing models predict.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| live streaming e-commerce | 3 | #687 | 2, 2, 3, 1, 1, 1, 0, 2, 3, 6, 2, 2, 3 | 25 |
| influencer marketing | 2 | #1148 | 3, 2, 1, 3, 2, 2, 2, 2, 3, 4, 5, 2, 2 | 31 |
| price competition | 5 | #342 | 8, 5, 2, 4, 1, 2, 3, 3, 2, 8, 4, 10, 3 | 52 |
| Stackelberg game | 1 | #2550 | 6, 5, 4, 16, 5, 10, 8, 7, 4, 16, 8, 2, 1 | 91 |


---

---
title: "Precision agriculture and the farmers who adopt it"
description: "Precision and smart agriculture, plant disease detection and agricultural technology adoption appear on 148 recent papers, split between AI and economics; 47 of them carry an adoption, farmer, cost or income keyword. AI papers detect disease on leaves; economics papers study adoption of older technologies. Do the new tools reach farms, and do they raise income?"
url: https://scholar4ai.com/ideas/precision-agriculture-adoption
markdown_url: https://scholar4ai.com/ideas/precision-agriculture-adoption.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "agriculture", "development"]
keywords: ["precision agriculture", "smart agriculture", "plant disease detection", "agricultural technology adoption"]
---

# Precision agriculture and the farmers who adopt it

Precision and smart agriculture, plant disease detection and agricultural technology adoption appear on 148 recent papers, split between AI and economics; 47 of them carry an adoption, farmer, cost or income keyword. AI papers detect disease on leaves; economics papers study adoption of older technologies. Do the new tools reach farms, and do they raise income?

## What prompted this

Since October 2025, 148 papers carry a precision-agriculture, smart-agriculture, plant-disease, crop-yield, agricultural-technology or digital-agriculture keyword (90 in AI journals, 45 in economics): "precision agriculture" on 28, "smart agriculture" on 26, "plant disease detection" on 18, "agricultural technology adoption" on 11, "crop yield prediction" on 3. Forty-seven of them also carry an adoption, farmer, smallholder, economics, cost, profit, income, deployment, welfare or policy keyword, almost all in the economics journals.

## Questions

- Of the plant disease and yield models published in AI journals, which have been deployed in apps that farmers use, and does their field accuracy match the published accuracy?
- For smallholders, do app-based diagnoses change input use and yields, and does the benefit cover a smartphone and data?
- Do large farms' gains from precision application widen the gap with small farms, and do subsidies narrow it?

## Data that could help

Usage data from agricultural advisory apps, farm surveys with technology use and yields, and field validation studies of published models.

## Next step

Validate three published plant disease models on images collected by farmers in the field through an advisory app, and compare accuracy with the published figures.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| precision agriculture | 1 | #2550 | 2, 6, 1, 5, 2, 1, 0, 2, 4, 1, 3, 0, 1 | 27 |
| smart agriculture | 1 | #2550 | 3, 3, 4, 3, 2, 2, 4, 1, 1, 1, 1, 0, 1 | 25 |
| plant disease detection | 2 | #1148 | 2, 1, 0, 2, 2, 0, 0, 1, 3, 3, 2, 1, 1 | 17 |
| agricultural technology adoption | 1 | #2550 | 2, 0, 1, 1, 1, 0, 2, 0, 2, 0, 1, 0, 1 | 10 |


---

---
title: "Populism and the trade shocks behind it (research idea)"
description: "Populism appears on 26 recent papers, mostly in the economics journals, with 7 in the last quarter against about 5 per quarter before. Only 2 of them carry a trade, globalization, tariff or automation keyword, although the \"China shock\" is the best-known economic explanation of populist votes. Do the newer shocks (inflation, immigration, AI) now matter more?"
url: https://scholar4ai.com/ideas/populism-and-trade
markdown_url: https://scholar4ai.com/ideas/populism-and-trade.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "politics", "trade"]
keywords: ["populism", "right-wing populism", "trade policy"]
---

# Populism and the trade shocks behind it

Populism appears on 26 recent papers, mostly in the economics journals, with 7 in the last quarter against about 5 per quarter before. Only 2 of them carry a trade, globalization, tariff or automation keyword, although the "China shock" is the best-known economic explanation of populist votes. Do the newer shocks (inflation, immigration, AI) now matter more?

## What prompted this

Since October 2025, 26 papers carry a populism keyword (15 in economics journals, 6 in management): "populism" on 15, "right-wing populism" on 6, "populist parties" on 4. Only 2 of the 26 also carry a trade, globalization, import, China-shock, tariff, protection, inequality or automation keyword. "Trade policy" and "globalization" appear on 46 and 41 papers each, but not in the same papers as populism.

## Questions

- In the elections of 2024–2026, did regions hit hardest by import competition still vote more populist, or did inflation and immigration exposure take over as predictors?
- Do populist governments deliver the trade protection they promise, and does protection then reduce their support?
- Does exposure to AI-related job losses already predict voting, as import competition did?

## Data that could help

Regional election results, trade exposure measures by region, local inflation and immigration data, and AI exposure by occupation.

## Next step

Regress regional vote shares in recent European and US elections on import competition, local inflation and AI exposure, and compare the coefficients with those from the 2010s.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| populism | 2 | #1148 | 0, 0, 3, 2, 0, 2, 0, 0, 1, 0, 2, 3, 2 | 13 |
| right-wing populism | 1 | #2550 | 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1 | 5 |
| trade policy | 2 | #1148 | 3, 4, 6, 5, 5, 2, 2, 3, 2, 4, 4, 6, 1 | 46 |


---

---
title: "Pollution havens along the Belt and Road (research idea)"
description: "Environmental degradation is one of the fastest-rising keywords in the economics journals, and the Belt and Road Initiative is a large topic in finance and economics. But the pollution haven hypothesis appears on only 14 recent papers. Did Chinese investment abroad move dirty production to countries with weaker rules?"
url: https://scholar4ai.com/ideas/pollution-havens-along-the-belt-and-road
markdown_url: https://scholar4ai.com/ideas/pollution-havens-along-the-belt-and-road.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "development", "trade"]
keywords: ["pollution haven hypothesis", "Belt and Road Initiative", "outward foreign direct investment"]
---

# Pollution havens along the Belt and Road

Environmental degradation is one of the fastest-rising keywords in the economics journals, and the Belt and Road Initiative is a large topic in finance and economics. But the pollution haven hypothesis appears on only 14 recent papers. Did Chinese investment abroad move dirty production to countries with weaker rules?

## What prompted this

In economics, "environmental degradation" had 13 papers in July–September 2026 against about 5 expected (see the [economics dashboard](/categories/economics)). Since October 2025, the Belt and Road Initiative appears on 48 papers and outward foreign direct investment on 52, but the pollution haven hypothesis and its variants on only 14, of which 5 also carry a Belt and Road or outward-investment keyword.

## Questions

- Do Chinese-financed plants abroad (power, steel, cement) emit more per unit of output than similar plants financed by others in the same countries?
- Did China's 2021 pledge to stop building new coal power plants abroad change the mix of projects?
- Does local enforcement, more than national rules, decide where dirty projects go?

## Data that could help

Databases of overseas development finance (for example Boston University's China's Global Power Database), plant-level emissions and satellite pollution measures, and indices of environmental regulation by country.

## Next step

Match Chinese-financed power plants abroad to satellite measures of nearby air pollution, and compare them with other plants of the same type and age.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| pollution haven hypothesis | 0 | – | 0, 0, 0, 0, 1, 0, 1, 3, 2, 1, 1, 0, 0 | 9 |
| Belt and Road Initiative | 1 | #2550 | 6, 4, 7, 2, 1, 1, 9, 6, 2, 2, 3, 4, 1 | 47 |
| outward foreign direct investment | 0 | – | 5, 4, 4, 7, 5, 4, 5, 4, 4, 2, 6, 2, 0 | 52 |


---

---
title: "Do political connections still pay after the…"
description: "Political connections are a large topic across the finance, economics and management journals, and a rising one in management. Yet only 10 of 106 recent papers on them carry a corruption keyword. More than a decade after China's anti-corruption campaign began, are connections still worth anything, and to whom?"
url: https://scholar4ai.com/ideas/political-connections-after-the-anti-corruption-campaign
markdown_url: https://scholar4ai.com/ideas/political-connections-after-the-anti-corruption-campaign.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "economics", "governance"]
keywords: ["political connections", "anti-corruption campaign"]
---

# Do political connections still pay after the anti-corruption campaign?

Political connections are a large topic across the finance, economics and management journals, and a rising one in management. Yet only 10 of 106 recent papers on them carry a corruption keyword. More than a decade after China's anti-corruption campaign began, are connections still worth anything, and to whom?

## What prompted this

In the management journals, "political connections" had 8 papers in July–September 2026 against about 4 expected (see the [management dashboard](/categories/management)). Since October 2025, 106 papers carry a political-connection keyword (46 in finance journals, 28 in economics, 23 in management, 8 in accounting). Only 10 of them carry an anti-corruption or corruption keyword, and "anti-corruption campaign" appears on 16 papers in all.

## Questions

- Did the value of connections, measured by stock reactions to officials' promotions or downfalls, fall after 2012, and has it recovered?
- Did firms swap personal ties for other channels, such as hiring retired officials as directors?
- Do connections still help with bank loans and subsidies, or mainly with avoiding scrutiny?

## Data that could help

Biographies of officials and executives (CSMAR executive data, CNRDS), the dates of announced investigations, and the bank loans and subsidies of listed firms.

## Next step

Build an event list of investigations of provincial officials since 2013 and measure the stock reactions of firms whose executives had ties to them.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| political connections | 5 | #342 | 9, 8, 15, 10, 9, 7, 7, 7, 4, 8, 9, 7, 5 | 100 |
| anti-corruption campaign | 1 | #2550 | 1, 1, 1, 1, 2, 0, 0, 3, 2, 2, 2, 0, 1 | 15 |


---

---
title: "Policing by camera and algorithm (research idea)"
description: "Policing, law enforcement and crime appear on 63 recent papers, with 18 in the last quarter against about 15 per quarter before, but only 12 carry a technology, surveillance, camera or algorithm keyword, and \"predictive policing\" on 3. Cameras, licence plate readers and risk scores are spreading fast. Do they cut crime, and at what cost to whom?"
url: https://scholar4ai.com/ideas/policing-technology
markdown_url: https://scholar4ai.com/ideas/policing-technology.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "policy", "technology"]
keywords: ["policing", "law enforcement", "predictive policing", "crime"]
---

# Policing by camera and algorithm

Policing, law enforcement and crime appear on 63 recent papers, with 18 in the last quarter against about 15 per quarter before, but only 12 carry a technology, surveillance, camera or algorithm keyword, and "predictive policing" on 3. Cameras, licence plate readers and risk scores are spreading fast. Do they cut crime, and at what cost to whom?

## What prompted this

Since October 2025, 63 papers carry a police, policing, law-enforcement, crime-prevention or criminal-justice keyword (33 in economics journals, 16 in AI): "law enforcement" on 13, "crime rates" on 8, "crime prevention" on 7, "policing" on 5, "predictive policing" on 3. Only 12 of the 63 also carry a technology, surveillance, camera, algorithm or AI keyword.

## Questions

- Do licence plate readers and camera networks reduce crime, displace it, or mainly raise clearance rates?
- Do algorithmic risk scores change who is stopped and charged, and do they reduce or reproduce disparities?
- Did body cameras change complaints and use of force in the long run, once the novelty wore off?

## Data that could help

Crime and arrest records at the beat level, dates of technology deployments by police department, and complaint records.

## Next step

Use the staggered installation of licence plate reader networks across US cities to estimate changes in vehicle theft and its displacement to neighbouring areas.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| policing | 1 | #2550 | 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 1, 1 | 4 |
| law enforcement | 0 | – | 1, 1, 1, 0, 2, 0, 2, 0, 1, 2, 1, 2, 0 | 13 |
| predictive policing | 0 | – | 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0 | 3 |
| crime | 4 | #458 | 3, 0, 0, 2, 1, 3, 2, 2, 2, 0, 1, 1, 4 | 17 |


---

---
title: "Place-based industrial policy and semiconductor clusters"
description: "Industrial policy and place-based policy are both rising in the economics journals, and governments now subsidize chip plants in specific towns, from Arizona to Kumamoto. Yet only 4 of 127 recent papers on such policies mention semiconductors. Do these plants create lasting local clusters, or expensive islands?"
url: https://scholar4ai.com/ideas/place-based-policy-and-chip-clusters
markdown_url: https://scholar4ai.com/ideas/place-based-policy-and-chip-clusters.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "innovation"]
keywords: ["industrial policy", "place-based policy", "semiconductor industry"]
---

# Place-based industrial policy and semiconductor clusters

Industrial policy and place-based policy are both rising in the economics journals, and governments now subsidize chip plants in specific towns, from Arizona to Kumamoto. Yet only 4 of 127 recent papers on such policies mention semiconductors. Do these plants create lasting local clusters, or expensive islands?

## What prompted this

In economics, "industrial policy" had 17 papers in July–September 2026 against 11 expected and "place-based policy" 10 against 4; across all journals "innovation policy" grew too (30 against 16); see the [trends page](/trends). Since October 2025, 90 papers carry an industrial-policy keyword (59 in economics journals), and 13 of them carry a place-based, cluster or regional keyword. Of the 127 papers with either kind of keyword, only 4 carry a semiconductor or chip keyword.

## Questions

- Around new subsidized fabs, do local suppliers, wages and new firms grow beyond the plant itself, and for how long?
- Do housing costs and labour shortages eat the gains, as in some earlier large-plant cases?
- Do the subsidies change where the next plants and suppliers locate?

## Data that could help

Plant announcements and subsidies (CHIPS Act awards in the US, Japan's support for fabs in Kumamoto and Hokkaido), local employment and business registers, land prices and patents.

## Next step

Compare the municipalities around the TSMC-led plant in Kumamoto with similar municipalities, before and after its 2021 announcement, on employment, new firms and land prices.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| industrial policy | 5 | #342 | 5, 4, 11, 4, 6, 9, 3, 3, 9, 9, 6, 8, 4 | 77 |
| place-based policy | 2 | #1148 | 1, 0, 1, 2, 1, 3, 3, 2, 1, 3, 2, 6, 2 | 25 |
| semiconductor industry | 1 | #2550 | 0, 0, 0, 1, 1, 1, 2, 1, 1, 3, 1, 1, 1 | 12 |


---

---
title: "Did the Phillips curve steepen after the pandemic?"
description: "The Phillips curve, monetary policy rules and labour market tightness are all rising keywords in the economics journals, after an inflation surge that flat Phillips curves did not predict. Only 7 of 27 recent Phillips-curve papers carry a pandemic, tightness or nonlinearity keyword. Is the curve steep when labour markets are tight, and what does that mean for policy rules?"
url: https://scholar4ai.com/ideas/phillips-curve-after-the-pandemic
markdown_url: https://scholar4ai.com/ideas/phillips-curve-after-the-pandemic.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "macroeconomics"]
keywords: ["Phillips curve", "monetary policy rules", "labor market tightness"]
---

# Did the Phillips curve steepen after the pandemic?

The Phillips curve, monetary policy rules and labour market tightness are all rising keywords in the economics journals, after an inflation surge that flat Phillips curves did not predict. Only 7 of 27 recent Phillips-curve papers carry a pandemic, tightness or nonlinearity keyword. Is the curve steep when labour markets are tight, and what does that mean for policy rules?

## What prompted this

In economics, "Phillips curve" (7 papers in July–September 2026 against about 2 expected), "monetary policy rules" (7 against 2) and "labor market tightness" (6 against 1) are all growing (see the [economics dashboard](/categories/economics)), and the New Keynesian Phillips curve is on the [equation ranking](/rankings/equations)'s list. Since October 2025, 27 papers carry a Phillips-curve keyword (20 in economics journals); 7 of them carry a labour-market-tightness, pandemic or nonlinear keyword.

## Questions

- Measured with vacancies per unemployed worker rather than unemployment, is the curve steep when labour markets are tight and flat otherwise?
- Does the same pattern hold across countries and across US cities, where tightness varied?
- Would policy rules that respond to tightness have kept inflation steadier?

## Data that could help

Vacancies and unemployment by country and metro area, inflation (FRED, OECD) and inflation expectation surveys.

## Next step

Estimate a Phillips curve with the vacancy-to-unemployment ratio and a tightness threshold on US metro data for 2000–2025.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| Phillips curve | 2 | #1148 | 1, 0, 2, 2, 1, 0, 3, 1, 2, 2, 4, 1, 2 | 19 |
| monetary policy rules | 0 | – | 1, 1, 0, 1, 2, 0, 1, 2, 1, 3, 2, 2, 0 | 16 |
| labor market tightness | 2 | #1148 | 0, 0, 0, 1, 1, 0, 0, 1, 0, 3, 1, 2, 2 | 9 |


---

---
title: "Pension funds in private markets (research idea)"
description: "Pension funds appear on only 19 recent papers, and 2 of them touch infrastructure, private equity or illiquid assets, although pension money is the main source of capital for private markets. As defined-contribution plans are allowed to hold private assets, are the returns worth the fees and the lock-up?"
url: https://scholar4ai.com/ideas/pension-funds-and-private-markets
markdown_url: https://scholar4ai.com/ideas/pension-funds-and-private-markets.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "pensions"]
keywords: ["pension funds", "infrastructure investment"]
---

# Pension funds in private markets

Pension funds appear on only 19 recent papers, and 2 of them touch infrastructure, private equity or illiquid assets, although pension money is the main source of capital for private markets. As defined-contribution plans are allowed to hold private assets, are the returns worth the fees and the lock-up?

## What prompted this

Since October 2025, 19 papers carry a pension-fund keyword (12 in finance journals), with "pension funds" on 10. Only 2 of them also carry an infrastructure, private-market, private-asset, illiquid, private-equity, private-credit or alternative keyword. "Infrastructure investment" appears on 35 papers, mostly in economics, about public spending rather than investors.

## Questions

- After fees, do pension funds' private equity and infrastructure holdings beat listed alternatives over full cycles, and do reported returns smooth the losses?
- When private assets are opened to defined-contribution savers, who chooses them, and do they understand the lock-up?
- Do funds that must sell private assets to pay benefits sell at discounts in downturns?

## Data that could help

Pension fund holdings and performance reports, private fund cash flows (Preqin, PitchBook), and secondary-market transaction prices for fund stakes.

## Next step

Compare the reported and the cash-flow-based returns of private holdings in the annual reports of large public pension funds for 2015–2025.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| pension funds | 1 | #2550 | 2, 2, 2, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1 | 9 |
| infrastructure investment | 0 | – | 1, 6, 5, 4, 2, 2, 3, 5, 1, 1, 2, 3, 0 | 35 |


---

---
title: "Pay transparency laws and the gender pay gap"
description: "The gender pay gap appears on 55 recent papers, mostly in economics, while pay transparency appears on 2. The EU pay transparency directive takes effect in 2026, and several US states now require salary ranges in job postings. Does transparency narrow the gap, compress pay, or move it into bonuses?"
url: https://scholar4ai.com/ideas/pay-transparency-laws
markdown_url: https://scholar4ai.com/ideas/pay-transparency-laws.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "economics", "labor"]
keywords: ["gender pay gap", "pay transparency"]
---

# Pay transparency laws and the gender pay gap

The gender pay gap appears on 55 recent papers, mostly in economics, while pay transparency appears on 2. The EU pay transparency directive takes effect in 2026, and several US states now require salary ranges in job postings. Does transparency narrow the gap, compress pay, or move it into bonuses?

## What prompted this

Since October 2025, 65 papers carry a gender-pay-gap, pay-gap or pay-transparency keyword (53 in economics journals): "gender pay gap" on 55, "executive-employee pay gap" on 6, "pay transparency" on 2. The policy is arriving faster than the research.

## Questions

- After salary ranges became mandatory in job postings, did posted ranges narrow, did firms post wider ranges to say nothing, and did the gender gap among new hires change?
- Under the EU directive's reporting and pay-audit rules, do firms with large gaps raise women's pay, slow men's pay growth or restructure jobs?
- Do transparency rules reduce individual bargaining, and does that help or hurt the workers who bargained least?

## Data that could help

Job postings with salary ranges (Lightcast), firm pay gap reports (UK and EU), and linked employer-employee data.

## Next step

Compare the gender gap in starting pay for jobs posted in US states with and without salary range laws, before and after each law.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| gender pay gap | 2 | #1148 | 4, 2, 2, 6, 4, 3, 2, 7, 11, 6, 4, 2, 2 | 53 |
| pay transparency | 0 | – | 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 | 2 |


---

---
title: "Passive fund flows and price impact (research idea)"
description: "Price impact and fund flows are among the fastest-growing keywords in the finance journals, yet none of the price-impact papers carries a fund-flow or passive-investing keyword. Now that passive funds hold so much of the market, how much do their flows move prices, and are markets as inelastic as some theories claim?"
url: https://scholar4ai.com/ideas/passive-flows-and-price-impact
markdown_url: https://scholar4ai.com/ideas/passive-flows-and-price-impact.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "market microstructure"]
keywords: ["price impact", "fund flows", "passive investing"]
---

# Passive fund flows and price impact

Price impact and fund flows are among the fastest-growing keywords in the finance journals, yet none of the price-impact papers carries a fund-flow or passive-investing keyword. Now that passive funds hold so much of the market, how much do their flows move prices, and are markets as inelastic as some theories claim?

## What prompted this

In the finance journals, "fund flows" had 7 papers in July–September 2026 against about 1 expected from its earlier share, and "price impact" had 12 against about 5 (see the [finance dashboard](/categories/finance)). Since October 2025, 30 papers carry a price-impact keyword (27 in finance journals), and none of them carries a fund-flow, passive, index-fund or ETF keyword.

## Questions

- How much does a dollar of passive inflow raise the prices of the stocks it buys, and does the effect revert?
- Has the price impact of flows grown with the passive share, as the inelastic markets hypothesis predicts?
- Who sells to passive funds when they buy: active funds, households or the firms themselves?

## Data that could help

Fund holdings and flows (CRSP Mutual Fund Database, Morningstar), ETF creations and redemptions, index reconstitution events and stock returns (CRSP).

## Next step

Estimate the price response to flow-induced trading around annual index reconstitutions, year by year from 2010 to 2025.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| price impact | 1 | #2550 | 2, 0, 0, 1, 2, 1, 5, 2, 3, 4, 3, 6, 0 | 29 |
| fund flows | 1 | #2550 | 0, 1, 0, 0, 2, 1, 2, 0, 0, 1, 5, 1, 1 | 13 |
| passive investing | 0 | – | 0, 2, 0, 2, 0, 0, 1, 1, 0, 0, 1, 0, 0 | 7 |


---

---
title: "Parametric insurance and catastrophe bonds (research idea)"
description: "Reinsurance, catastrophe bonds and parametric insurance appear on only 25 recent papers in the finance and operations research journals, although catastrophe bond issuance has reached records and parametric covers are spreading to poorer countries. Do payouts arrive when they are needed, and who bears the basis risk?"
url: https://scholar4ai.com/ideas/parametric-insurance-and-cat-bonds
markdown_url: https://scholar4ai.com/ideas/parametric-insurance-and-cat-bonds.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "climate", "insurance"]
keywords: ["catastrophe bonds", "parametric insurance", "reinsurance"]
---

# Parametric insurance and catastrophe bonds

Reinsurance, catastrophe bonds and parametric insurance appear on only 25 recent papers in the finance and operations research journals, although catastrophe bond issuance has reached records and parametric covers are spreading to poorer countries. Do payouts arrive when they are needed, and who bears the basis risk?

## What prompted this

Since October 2025, 25 papers carry a catastrophe-bond, insurance-linked, parametric-insurance or reinsurance keyword (20 in finance journals, 3 in operations research): "reinsurance" on 9, "parametric insurance" on 5, "catastrophe bonds" on 5. Natural disasters, by contrast, appear on 82 papers, and climate risk on 242.

## Questions

- How often do parametric triggers miss real losses (no payout after damage, or payout without damage), and does the basis risk fall on governments or households?
- Do catastrophe bond spreads price climate trends, or only the last few years of losses?
- Does the record issuance of catastrophe bonds lower reinsurance prices for primary insurers, and does that reach policyholders?

## Data that could help

Catastrophe bond terms and spreads (public issuance data), parametric programme payouts (for example the Caribbean and African risk pools), and loss data from disaster databases such as EM-DAT.

## Next step

Match the payouts of regional parametric risk pools since 2015 to reported disaster losses and compute the share of events that triggered no payout.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| catastrophe bonds | 0 | – | 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0 | 5 |
| parametric insurance | 0 | – | 0, 0, 3, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0 | 5 |
| reinsurance | 0 | – | 0, 1, 1, 2, 1, 2, 0, 1, 1, 0, 0, 0, 0 | 9 |


---

---
title: "Overtourism and the housing of residents (research idea)"
description: "Tourism appears on 130 recent papers across the finance, economics and management journals, but only 11 of them carry an overtourism, housing, short-term-rental, resident or tax keyword. Cities from Barcelona to Kyoto are capping short-term rentals and taxing visitors. Do the measures lower rents, and what do they cost in jobs?"
url: https://scholar4ai.com/ideas/overtourism-and-housing
markdown_url: https://scholar4ai.com/ideas/overtourism-and-housing.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "housing"]
keywords: ["tourism", "overtourism", "short-term rentals"]
---

# Overtourism and the housing of residents

Tourism appears on 130 recent papers across the finance, economics and management journals, but only 11 of them carry an overtourism, housing, short-term-rental, resident or tax keyword. Cities from Barcelona to Kyoto are capping short-term rentals and taxing visitors. Do the measures lower rents, and what do they cost in jobs?

## What prompted this

Since October 2025, 130 papers carry a tourism keyword (39 in finance journals, 36 in economics, 36 in management): "tourism" on 15, "sustainable tourism" on 10, "overtourism" on 5, "short-term rentals" on 4. Only 11 of the 130 also carry an overtourism, housing, resident, crowding, short-term-rental, Airbnb or tax keyword.

## Questions

- After a city bans or caps short-term rentals, how much housing returns to the long-term market, and do rents fall in tourist neighbourhoods?
- Do tourist taxes reduce visitor numbers, or only raise revenue, and where does the revenue go?
- Which residents gain and lose: tenants, owners who rented to tourists, hospitality workers?

## Data that could help

Short-term rental listings over time (scraped data such as Inside Airbnb), long-term rents, visitor statistics and the dates of regulations by city.

## Next step

Track short-term rental listings and long-term rents in Barcelona around the 2024 announcement that tourist flat licences will not be renewed.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| tourism | 2 | #1148 | 1, 4, 2, 0, 1, 0, 2, 1, 1, 1, 0, 1, 1 | 14 |
| overtourism | 2 | #1148 | 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1 | 4 |
| short-term rentals | 0 | – | 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0 | 4 |


---

---
title: "Operating room schedules that surgeons follow"
description: "Operating room scheduling and planning appear on 16 recent papers in the operations research journals, among 52 surgery-related papers, most of which are AI work on surgical images. Hospitals rarely implement the optimized schedules. What stops them, and do the schedules survive surgeons' preferences and emergencies?"
url: https://scholar4ai.com/ideas/operating-room-scheduling
markdown_url: https://scholar4ai.com/ideas/operating-room-scheduling.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "healthcare"]
keywords: ["operating room scheduling", "operating room planning"]
---

# Operating room schedules that surgeons follow

Operating room scheduling and planning appear on 16 recent papers in the operations research journals, among 52 surgery-related papers, most of which are AI work on surgical images. Hospitals rarely implement the optimized schedules. What stops them, and do the schedules survive surgeons' preferences and emergencies?

## What prompted this

Since October 2025, 52 papers carry a surgery or operating-room keyword (32 in AI journals, 14 in operations research): "operating room scheduling" on 13, "surgical navigation" on 7, "surgical planning" on 4, "operating room planning" on 3. The scheduling papers are methods papers; implementation and its obstacles are rarely their subject.

## Questions

- In hospitals that implemented optimized block schedules, how much did utilization and overtime change, and how much of the gain survived after a year?
- How do surgeon preferences, emergency cases and turnover times, which the models simplify, erode the plans?
- Would models that schedule for robustness against duration uncertainty be adopted more readily than those that maximize utilization?

## Data that could help

Operating room logs with planned and actual times from hospitals that adopted scheduling tools, and interviews with surgical managers.

## Next step

Partner with a hospital that adopted a scheduling tool and compare planned and actual utilization in the year before and after adoption.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| operating room scheduling | 1 | #2550 | 0, 1, 1, 0, 1, 1, 0, 2, 0, 0, 3, 3, 1 | 12 |
| operating room planning | 0 | – | 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0 | 3 |


---

---
title: "Open-weight models and the economics of open source"
description: "Open-source software appears on 13 recent papers and open-source language models on 6, while named open-weight models such as DeepSeek, Llama and Qwen appear on dozens of AI papers as tools; 78 of 126 open-source papers involve language models. Firms give away models worth hundreds of millions to train. Who adopts them, and what do the givers get?"
url: https://scholar4ai.com/ideas/open-weight-models-and-open-source
markdown_url: https://scholar4ai.com/ideas/open-weight-models-and-open-source.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "economics", "technology"]
keywords: ["open source software", "open-source LLMs", "DeepSeek"]
---

# Open-weight models and the economics of open source

Open-source software appears on 13 recent papers and open-source language models on 6, while named open-weight models such as DeepSeek, Llama and Qwen appear on dozens of AI papers as tools; 78 of 126 open-source papers involve language models. Firms give away models worth hundreds of millions to train. Who adopts them, and what do the givers get?

## What prompted this

Since October 2025, 126 papers carry an open-source, open-weight or named open model keyword (96 in AI journals, 13 in economics, 8 in management): "DeepSeek" on 12, "DeepSeek-R1" on 11, "Llama 3" on 10, "Qwen2.5" on 6, "open source software" on 13, "open-source LLMs" on 6. Seventy-eight of the 126 also carry a language-model, foundation-model or fine-tuning keyword. The economics of open source, on 41 papers with 9 touching firms or investment, has not caught up with models.

## Questions

- Which firms deploy open-weight models rather than paid APIs, and do they do so for cost, privacy, control or regulation?
- Do the firms that release open models gain in cloud revenue, hiring, standards or valuation, and did the release of strong open models from China change the strategy of US firms?
- Does the availability of open models lower prices for closed ones, as open-source software did for proprietary software?

## Data that could help

Model download and fine-tuning statistics (Hugging Face), job postings naming models, API price histories and firm disclosures.

## Next step

Track API prices of closed models around the release dates of major open-weight models and measure the price response.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| open source software | 0 | – | 1, 1, 1, 1, 0, 3, 1, 1, 1, 1, 0, 2, 0 | 13 |
| open-source LLMs | 0 | – | 0, 2, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 0 | 6 |
| DeepSeek | 0 | – | 1, 0, 2, 0, 1, 3, 0, 1, 0, 2, 1, 1, 0 | 12 |


---

---
title: "Open innovation when the crowd is a model (research idea)"
description: "Open innovation and crowdsourcing appear on 71 recent papers, mostly in management, but only 3 of them carry an AI keyword. Firms ran idea contests and crowdsourcing platforms to get ideas from outside. If a language model can generate thousands of ideas in an hour, what is the crowd still for, and how do firms tell good ideas from fluent ones?"
url: https://scholar4ai.com/ideas/open-innovation-with-generative-ai
markdown_url: https://scholar4ai.com/ideas/open-innovation-with-generative-ai.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "innovation", "ai"]
keywords: ["open innovation", "crowdsourcing"]
---

# Open innovation when the crowd is a model

Open innovation and crowdsourcing appear on 71 recent papers, mostly in management, but only 3 of them carry an AI keyword. Firms ran idea contests and crowdsourcing platforms to get ideas from outside. If a language model can generate thousands of ideas in an hour, what is the crowd still for, and how do firms tell good ideas from fluent ones?

## What prompted this

Since October 2025, 71 papers carry an open-innovation or crowdsourcing keyword (39 in management journals, 11 in economics): "open innovation" on 42, "crowdsourcing" on 13, "open innovation ecosystems" on 5. Only 3 of the 71 also carry an artificial-intelligence, generative-AI, language-model or machine-learning keyword.

## Questions

- In idea contests, how do ideas from language models compare with crowd ideas on novelty, feasibility and what firms actually implement?
- Do participants in crowdsourcing platforms now submit AI-generated ideas, and does that raise the volume and lower the diversity of submissions?
- Does the value of open innovation shift from generating ideas to testing them with outside users?

## Data that could help

Submissions and outcomes from innovation contests and platforms, with timestamps around 2023, and experiments that mix model-generated and human ideas for blind evaluation.

## Next step

Run a blind evaluation in which managers rate a mix of crowd-sourced and model-generated ideas for a real problem, and track which ones they implement.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| open innovation | 2 | #1148 | 5, 2, 6, 4, 4, 1, 2, 4, 5, 5, 1, 1, 2 | 40 |
| crowdsourcing | 1 | #2550 | 1, 1, 1, 0, 0, 1, 1, 1, 0, 2, 3, 1, 1 | 12 |


---

---
title: "Open data mandates and what gets shared (research idea)"
description: "Open data, data sharing and open science appear on 121 recent papers, with 36 in the last quarter against about 26 per quarter before, spread across every tracked field. Funders and journals now require data and code, and open government data portals have multiplied. Does the shared data get reused, and do mandates change what researchers study?"
url: https://scholar4ai.com/ideas/open-data-mandates
markdown_url: https://scholar4ai.com/ideas/open-data-mandates.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["science", "data", "policy"]
keywords: ["open data", "data sharing", "open science"]
---

# Open data mandates and what gets shared

Open data, data sharing and open science appear on 121 recent papers, with 36 in the last quarter against about 26 per quarter before, spread across every tracked field. Funders and journals now require data and code, and open government data portals have multiplied. Does the shared data get reused, and do mandates change what researchers study?

## What prompted this

Since October 2025, 121 papers carry an open-data, data-sharing, open-science, data-availability or replication-package keyword (32 in AI journals, 29 in economics, 27 in finance, 20 in management, 12 in operations research): "open data" on 35, "data sharing" on 24, "open data portals" on 9, "open science" on 9, "data availability" on 7. The quarter to September 2026 had 36 such papers against about 26 per quarter earlier.

## Questions

- Of the datasets and code deposited under journal mandates, what share can be downloaded and run years later, and what share is cited or reused?
- Do mandates push researchers toward questions that can be answered with shareable data and away from proprietary data such as bank records and firm microdata?
- Do open government data portals get used by firms and researchers in proportion to what they cost, and which datasets generate the use?

## Data that could help

Journal data repositories and their download and citation logs, portal usage statistics, and the data sources named in published papers over time.

## Next step

Sample 300 replication packages deposited under mandates in 2019–2021 and record whether each still downloads, runs and has been cited.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| open data | 3 | #687 | 0, 6, 1, 1, 6, 4, 1, 2, 1, 5, 4, 1, 3 | 32 |
| data sharing | 2 | #1148 | 3, 3, 0, 0, 1, 2, 2, 2, 2, 4, 2, 1, 2 | 22 |
| open science | 0 | – | 1, 1, 1, 0, 0, 2, 0, 0, 0, 1, 2, 1, 0 | 9 |


---

---
title: "Oil prices, inflation and the rise of electric vehicles"
description: "Oil prices are a rising keyword in the economics journals, and oil-price shocks remain a standard driver in inflation models. But none of the 189 recent oil-price papers carries an electric-vehicle keyword. As electric vehicles spread, is oil's grip on inflation weakening, and where first?"
url: https://scholar4ai.com/ideas/oil-prices-inflation-and-electric-vehicles
markdown_url: https://scholar4ai.com/ideas/oil-prices-inflation-and-electric-vehicles.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "energy", "macroeconomics"]
keywords: ["oil price shocks", "oil prices", "electric vehicle adoption"]
---

# Oil prices, inflation and the rise of electric vehicles

Oil prices are a rising keyword in the economics journals, and oil-price shocks remain a standard driver in inflation models. But none of the 189 recent oil-price papers carries an electric-vehicle keyword. As electric vehicles spread, is oil's grip on inflation weakening, and where first?

## What prompted this

In economics, "oil prices" had 16 papers in July–September 2026 against about 10 expected (see the [economics dashboard](/categories/economics)). Since October 2025, 189 papers carry an oil-price keyword (99 in economics journals, 78 in finance); 26 of them also carry an inflation keyword, and none carries an electric-vehicle keyword, while "electric vehicle adoption" appears on 20 papers.

## Questions

- Has the pass-through of oil-price shocks to consumer prices fallen where electric vehicles took a large share of new car sales, such as Norway, China or California?
- Does the change show up first in fuel spending, then in transport services, then in core inflation?
- What does it mean for how central banks should read oil-price spikes?

## Data that could help

Oil prices, national and regional consumer price indices, the electric share of car sales and of the car fleet, and household fuel spending.

## Next step

Estimate the pass-through of oil-price changes to consumer prices by country and year for 2010–2025, and relate it to the electric share of the car fleet.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| oil price shocks | 3 | #687 | 4, 2, 3, 8, 1, 4, 5, 4, 3, 3, 5, 3, 3 | 45 |
| oil prices | 2 | #1148 | 10, 5, 8, 6, 5, 10, 6, 6, 6, 8, 10, 6, 2 | 86 |
| electric vehicle adoption | 1 | #2550 | 1, 0, 0, 6, 3, 1, 2, 3, 1, 0, 2, 0, 1 | 19 |


---

---
title: "Nuclear restarts, small reactors and who pays"
description: "Nuclear energy appears on 26 recent papers and small modular reactors on 4, mostly in the economics journals. Technology firms now sign deals to restart closed plants and fund new reactors for data centres, and Japan has restarted reactors idle since 2011. What do these deals cost, who bears the risk, and does public acceptance follow?"
url: https://scholar4ai.com/ideas/nuclear-restarts-and-small-reactors
markdown_url: https://scholar4ai.com/ideas/nuclear-restarts-and-small-reactors.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "energy"]
keywords: ["nuclear energy", "small modular reactors", "nuclear energy policy"]
---

# Nuclear restarts, small reactors and who pays

Nuclear energy appears on 26 recent papers and small modular reactors on 4, mostly in the economics journals. Technology firms now sign deals to restart closed plants and fund new reactors for data centres, and Japan has restarted reactors idle since 2011. What do these deals cost, who bears the risk, and does public acceptance follow?

## What prompted this

Since October 2025, 46 papers carry a nuclear-energy keyword (34 in economics journals, 7 in finance): "nuclear energy" on 26, "small modular reactors" on 4, "nuclear energy policy" on 3. Twenty-two of them carry an acceptance, cost, investment or public-opinion keyword, but the new financing model, in which a single large buyer underwrites a plant, appears in none.

## Questions

- What price per megawatt-hour do data-centre operators pay in restart and new-build agreements, and how does it compare with renewables plus storage?
- Who bears cost overruns in these contracts, and do they shift risk to ratepayers through capacity markets?
- Did the restarts in Japan change local acceptance, and did electricity prices in those regions fall?

## Data that could help

Published power purchase agreements and regulatory filings for restarts, construction cost records for small modular reactors, regional electricity prices and public opinion surveys.

## Next step

Collect the disclosed terms of nuclear power purchase agreements signed by technology firms since 2023 and compare their prices with contemporaneous renewable contracts.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| nuclear energy | 2 | #1148 | 2, 1, 1, 0, 4, 2, 5, 1, 5, 2, 0, 1, 2 | 24 |
| small modular reactors | 0 | – | 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0 | 4 |
| nuclear energy policy | 1 | #2550 | 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1 | 2 |


---

---
title: "Nowcasting with the data firms already have"
description: "High-frequency data, big data and alternative data appear on 236 recent papers across the finance, AI and economics journals, but only 10 of them carry a card-transaction, mobility, search, satellite or electricity keyword, and nowcasting appears on 9. Banks, card networks and job boards hold daily measures of the economy. Which of them nowcast GDP, inflation and employment best, and could statistical offices use them?"
url: https://scholar4ai.com/ideas/nowcasting-with-alternative-data
markdown_url: https://scholar4ai.com/ideas/nowcasting-with-alternative-data.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "forecasting", "data"]
keywords: ["nowcasting", "alternative data", "high-frequency data", "Google Trends"]
---

# Nowcasting with the data firms already have

High-frequency data, big data and alternative data appear on 236 recent papers across the finance, AI and economics journals, but only 10 of them carry a card-transaction, mobility, search, satellite or electricity keyword, and nowcasting appears on 9. Banks, card networks and job boards hold daily measures of the economy. Which of them nowcast GDP, inflation and employment best, and could statistical offices use them?

## What prompted this

Since October 2025, 236 papers carry a nowcasting, alternative-data, high-frequency-data, big-data or real-time-data keyword (66 in finance journals, 62 in AI, 54 in economics): "high-frequency data" on 67, "big data" on 50, "big data analytics" on 28, "alternative data" on 11, "nowcasting" on 9. Only 10 of the 236 also carry a card-transaction, credit-card, mobility, Google Trends, search, payment, satellite, electricity or job-posting keyword, although "Google Trends" appears on 22 papers in the archive.

## Questions

- In real time, with the data available on each day, which alternative sources improved nowcasts of GDP, inflation and unemployment during 2020–2025, and did the gains survive after the pandemic?
- Do card transaction and job posting data nowcast regional and sectoral activity that official statistics report with long lags?
- What would it cost statistical offices to license the data, and do the providers' incentives bias what they release?

## Data that could help

Daily card spending indices published by banks and card networks, job posting counts, mobility data, electricity consumption and the official release calendars.

## Next step

Build a real-time nowcasting exercise for quarterly GDP in two countries with and without card spending and job posting data for 2019–2025.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| nowcasting | 0 | – | 2, 0, 1, 0, 1, 3, 0, 0, 1, 0, 1, 0, 0 | 9 |
| alternative data | 0 | – | 1, 1, 1, 1, 2, 1, 0, 2, 0, 1, 1, 0, 0 | 11 |
| high-frequency data | 3 | #687 | 9, 11, 6, 4, 9, 4, 5, 4, 3, 2, 4, 3, 3 | 64 |
| Google Trends | 1 | #2550 | 0, 3, 1, 2, 2, 3, 4, 2, 3, 1, 0, 0, 1 | 21 |


---

---
title: "Neural solvers for continuous-time finance (research idea)"
description: "Continuous-time models built on the Hamilton-Jacobi-Bellman equation are rising in the finance journals, while neural operators and physics-informed networks that solve such equations are growing in AI. All five recent papers that pair the two are in AI journals. Can these solvers make richer portfolio and asset-pricing models usable?"
url: https://scholar4ai.com/ideas/neural-solvers-for-continuous-time-finance
markdown_url: https://scholar4ai.com/ideas/neural-solvers-for-continuous-time-finance.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "ai", "methods"]
keywords: ["Hamilton-Jacobi-Bellman equation", "neural operators", "physics-informed neural networks"]
---

# Neural solvers for continuous-time finance

Continuous-time models built on the Hamilton-Jacobi-Bellman equation are rising in the finance journals, while neural operators and physics-informed networks that solve such equations are growing in AI. All five recent papers that pair the two are in AI journals. Can these solvers make richer portfolio and asset-pricing models usable?

## What prompted this

In the finance journals, "Hamilton-Jacobi-Bellman equation" had 8 papers in July–September 2026 against about 2 expected (see the [finance dashboard](/categories/finance)); the Bellman equation is also on the [equation ranking](/rankings/equations)'s list. In the AI journals, "neural operators" grew (9 against 3), and "physics-informed neural networks" appears on 84 papers since October 2025. Of the 70 papers with an HJB, portfolio-choice or optimal-investment keyword, 5 also carry a neural-network or machine-learning keyword, and all 5 are in AI journals.

## Questions

- Can neural solvers handle portfolio problems with many assets, frictions or stochastic volatility that grid methods cannot?
- How accurate are they where closed forms exist, and do the errors matter more for policies than for values?
- Can a solver trained once (an operator) let researchers estimate structural models that need to be solved thousands of times?

## Data that could help

Benchmark problems with known solutions, and calibrations from standard asset-pricing models.

## Next step

Solve a portfolio problem with stochastic volatility that has a known solution using a physics-informed network, and compare the optimal policies.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| Hamilton-Jacobi-Bellman equation | 1 | #2550 | 3, 0, 1, 3, 2, 2, 5, 3, 1, 3, 2, 4, 1 | 29 |
| neural operators | 0 | – | 0, 0, 1, 2, 0, 1, 1, 2, 2, 4, 0, 5, 0 | 18 |
| physics-informed neural networks | 7 | #210 | 4, 2, 9, 0, 10, 7, 6, 5, 10, 7, 11, 8, 6 | 79 |


---

---
title: "Net metering reform and rooftop solar (research idea)"
description: "Rooftop solar, prosumers and net metering appear on 50 recent papers, mostly in economics, and the topic is fading, with 7 papers in the last quarter against about 14 per quarter before. California and other markets have cut the payment for exported power. Did installations collapse, and did batteries take their place?"
url: https://scholar4ai.com/ideas/net-metering-reform-and-rooftop-solar
markdown_url: https://scholar4ai.com/ideas/net-metering-reform-and-rooftop-solar.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "energy", "households"]
keywords: ["rooftop solar", "net metering", "prosumers"]
---

# Net metering reform and rooftop solar

Rooftop solar, prosumers and net metering appear on 50 recent papers, mostly in economics, and the topic is fading, with 7 papers in the last quarter against about 14 per quarter before. California and other markets have cut the payment for exported power. Did installations collapse, and did batteries take their place?

## What prompted this

Since October 2025, 50 papers carry a rooftop-solar, residential-solar, net-metering, prosumer or distributed-generation keyword (39 in economics journals): "rooftop solar" on 15, "prosumers" on 13, "distributed generation" on 13, "net metering" on 6. Seven were published in July–September 2026 against about 14 per quarter earlier.

## Questions

- After the payment for exported power was cut, how far did new installations fall, and did the share with batteries rise?
- Did the reform reduce the cost shift from solar owners to other customers, as its supporters claimed, and by how much?
- Do solar owners change when they use electricity once exports pay less?

## Data that could help

Interconnection records with installation dates and battery pairing, utility tariff changes, and smart meter data.

## Next step

Compare monthly residential solar installations and battery attachment rates in California before and after the 2023 net billing change with those in neighbouring states.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| rooftop solar | 1 | #2550 | 0, 2, 1, 2, 3, 1, 2, 3, 0, 0, 0, 0, 1 | 14 |
| net metering | 1 | #2550 | 0, 0, 1, 2, 0, 1, 0, 0, 0, 0, 1, 0, 1 | 5 |
| prosumers | 0 | – | 1, 0, 1, 2, 0, 3, 2, 1, 0, 1, 1, 1, 0 | 13 |


---

---
title: "Negative electricity prices and the value of storage"
description: "Energy markets are one of the rising keywords in the finance and economics journals, and wholesale power prices now often turn negative when solar output peaks. Batteries profit from that spread, but they also erase it. How much is storage worth as it scales, and who should pay for it?"
url: https://scholar4ai.com/ideas/negative-electricity-prices-and-storage
markdown_url: https://scholar4ai.com/ideas/negative-electricity-prices-and-storage.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "operations research", "energy"]
keywords: ["electricity prices", "battery energy storage", "renewable energy integration"]
---

# Negative electricity prices and the value of storage

Energy markets are one of the rising keywords in the finance and economics journals, and wholesale power prices now often turn negative when solar output peaks. Batteries profit from that spread, but they also erase it. How much is storage worth as it scales, and who should pay for it?

## What prompted this

"Energy markets" grew across all journals (16 papers in July–September 2026 against about 8 expected) and in finance (11 against about 5); see the [trends page](/trends). Since October 2025, "electricity prices" appears on 39 papers, "battery energy storage" on 38 (31 in economics journals) and "renewable energy integration" on 69. Of the 93 papers with an electricity-price keyword, 17 also carry a storage or battery keyword.

## Questions

- How fast does the arbitrage value of batteries fall as more storage enters a market?
- Do capacity payments or other support for storage pay off for consumers, or mainly for owners?
- How should operations research models of battery dispatch account for the price effect of all batteries acting together?

## Data that could help

Hourly wholesale prices and generation by source (ENTSO-E, US grid operators, Nord Pool), battery capacity by market, and capacity-market results.

## Next step

For one market with fast battery growth, regress the daily spread between peak and off-peak prices on installed storage capacity, controlling for solar output.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| electricity prices | 1 | #2550 | 4, 1, 6, 4, 3, 3, 3, 1, 3, 4, 1, 5, 1 | 38 |
| battery energy storage | 1 | #2550 | 4, 5, 3, 2, 4, 1, 4, 5, 5, 1, 1, 3, 0 | 38 |
| renewable energy integration | 5 | #342 | 8, 6, 4, 9, 4, 6, 3, 5, 7, 7, 4, 3, 5 | 66 |


---

---
title: "CEO personality at scale (research idea)"
description: "Narcissism, CEO characteristics and CEO personality appear on 42 recent papers, mostly in the management and accounting journals, with measures built from signatures, photos and pronoun counts. Language models can now score personality from thousands of hours of speech. Do the old measures and the new ones agree, and does personality predict decisions out of sample?"
url: https://scholar4ai.com/ideas/narcissistic-ceos-from-text
markdown_url: https://scholar4ai.com/ideas/narcissistic-ceos-from-text.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "accounting", "ai"]
keywords: ["CEO narcissism", "narcissism", "CEO characteristics"]
---

# CEO personality at scale

Narcissism, CEO characteristics and CEO personality appear on 42 recent papers, mostly in the management and accounting journals, with measures built from signatures, photos and pronoun counts. Language models can now score personality from thousands of hours of speech. Do the old measures and the new ones agree, and does personality predict decisions out of sample?

## What prompted this

Since October 2025, 42 papers carry a narcissism, CEO-personality, CEO-trait, hubris or CEO-characteristics keyword (19 in management journals, 10 in accounting): "narcissism" on 14, "CEO characteristics" on 12, "CEO narcissism" on 7, "CEO personality" on 3. Eleven were published in July–September 2026, in line with earlier quarters.

## Questions

- Do narcissism scores from signatures, photo size and pronoun use agree with scores that language models assign from interviews and earnings calls, and which predicts acquisitions, risk taking and fraud better?
- Do the published effects of CEO narcissism replicate in new samples with the new measures?
- Do boards hire different personalities after a scandal, and can that be seen in the data?

## Data that could help

Earnings call transcripts and interviews, proxy statements with signatures and photos, deal and accounting data (SDC, Compustat, Audit Analytics).

## Next step

Score the same 500 CEOs with the signature-based measure and a language-model measure from their earnings calls, and compare the two against acquisition behaviour.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| CEO narcissism | 0 | – | 1, 4, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0 | 7 |
| narcissism | 0 | – | 1, 2, 3, 0, 0, 1, 0, 1, 3, 1, 1, 1, 0 | 14 |
| CEO characteristics | 0 | – | 1, 2, 1, 0, 0, 1, 2, 1, 2, 2, 0, 0, 0 | 12 |


---

---
title: "Thousands of papers, one dataset: multiple testing in…"
description: "\"Chinese listed companies\" is the third most frequent keyword across the tracked journals over the past year, and most of these papers draw on the same few databases. Yet none of the 1,392 recent papers with such a keyword carries a replication, multiple-testing or publication-bias keyword. How many of their findings would survive a correction for everyone testing on the same data?"
url: https://scholar4ai.com/ideas/multiple-testing-in-research-on-chinese-listed-firms
markdown_url: https://scholar4ai.com/ideas/multiple-testing-in-research-on-chinese-listed-firms.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "methods"]
keywords: ["Chinese listed companies", "publication bias", "replication study"]
---

# Thousands of papers, one dataset: multiple testing in research on Chinese listed firms

"Chinese listed companies" is the third most frequent keyword across the tracked journals over the past year, and most of these papers draw on the same few databases. Yet none of the 1,392 recent papers with such a keyword carries a replication, multiple-testing or publication-bias keyword. How many of their findings would survive a correction for everyone testing on the same data?

## What prompted this

On the [trends page](/trends), "Chinese listed companies" is the third most frequent keyword over the last 12 complete months, after "China" and "large language models". Since October 2025, 1,392 papers carry a Chinese-listed-company keyword (928 in finance journals, 216 in economics, 172 in management, 54 in accounting). None of them carries a multiple-testing, p-hacking, replication, publication-bias, data-mining or false-discovery keyword.

## Questions

- If every published test on the same panel of firms counts as one of many, how many reported effects clear a stricter threshold, such as a t-statistic of 3?
- Do the results hold out of sample, for example in later years or in firms listed after a paper's sample ends?
- Which outcomes, such as green innovation, ESG scores or digital transformation, attract the most tests?

## Possible approach

Collect the main reported t-statistics and sample periods from a large set of papers that use CSMAR or Wind data, and estimate the false discovery rate with the methods developed for the "factor zoo" in asset pricing.

## Next step

Extract the main coefficient and t-statistic from 200 recent papers on Chinese listed firms and plot their distribution around 1.96.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| [Chinese listed companies](https://scholar4ai.com/keywords/chinese-listed-companies.md) | 56 | #3 | 110, 122, 150, 111, 86, 99, 106, 89, 111, 95, 91, 104, 46 | 1,274 |
| publication bias | 2 | #1148 | 0, 0, 0, 0, 0, 0, 5, 0, 0, 3, 3, 0, 2 | 11 |
| replication study | 1 | #2550 | 0, 1, 2, 2, 2, 0, 2, 1, 0, 1, 0, 0, 1 | 11 |


---

---
title: "Seeing past the first tier (research idea)"
description: "Supply chain transparency, traceability and visibility appear on 83 recent papers, and the topic is fading, with 16 in the last quarter against about 19 per quarter before. Most disruptions and most forced-labour and emissions problems sit beyond the first tier of suppliers, where firms rarely look. How far can visibility be built, and does it pay?"
url: https://scholar4ai.com/ideas/multi-tier-supply-chain-visibility
markdown_url: https://scholar4ai.com/ideas/multi-tier-supply-chain-visibility.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "supply chain", "operations"]
keywords: ["supply chain transparency", "traceability", "supply chain visibility"]
---

# Seeing past the first tier

Supply chain transparency, traceability and visibility appear on 83 recent papers, and the topic is fading, with 16 in the last quarter against about 19 per quarter before. Most disruptions and most forced-labour and emissions problems sit beyond the first tier of suppliers, where firms rarely look. How far can visibility be built, and does it pay?

## What prompted this

Since October 2025, 83 papers carry a supply-chain-visibility, supply-chain-mapping, multi-tier, supply-chain-transparency or traceability keyword (24 in management journals, 20 in AI, 17 in operations research): "supply chain transparency" on 35, "traceability" on 15, "supply chain traceability" on 8, "supply chain visibility" on 5. Sixteen were published in July–September 2026 against about 19 per quarter earlier.

## Questions

- When firms map their suppliers' suppliers, how often do they find shared sub-tier suppliers that make their alternative sources less alternative than they thought?
- Do firms that mapped deeper before 2020 recover faster from disruptions, and do investors reward disclosed mapping?
- Can shipping manifests and customs data reconstruct sub-tier networks well enough for risk management, without suppliers' cooperation?

## Data that could help

Bill-of-lading and customs data (for example from Panjiva or ImportGenius), supplier disclosure lists, and disruption event records.

## Next step

Reconstruct the two-tier supply network of a set of electronics firms from shipping manifests and measure how concentrated the second tier is.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| supply chain transparency | 2 | #1148 | 2, 2, 6, 3, 2, 4, 2, 4, 3, 4, 1, 0, 2 | 33 |
| traceability | 2 | #1148 | 0, 0, 1, 0, 2, 3, 1, 1, 1, 2, 3, 0, 2 | 14 |
| supply chain visibility | 1 | #2550 | 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1 | 4 |


---

---
title: "Moving relief before the disaster (research idea)"
description: "Humanitarian logistics is an active topic in operations research, and climate shocks a rising one in economics. Aid agencies now release money and move supplies when a forecast crosses a threshold, before a flood or drought hits. Only 11 of 103 recent humanitarian-logistics papers carry a forecast, early-warning or climate keyword. When does acting early pay off?"
url: https://scholar4ai.com/ideas/moving-relief-before-the-disaster
markdown_url: https://scholar4ai.com/ideas/moving-relief-before-the-disaster.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "logistics", "development"]
keywords: ["humanitarian logistics", "emergency logistics", "disaster relief"]
---

# Moving relief before the disaster

Humanitarian logistics is an active topic in operations research, and climate shocks a rising one in economics. Aid agencies now release money and move supplies when a forecast crosses a threshold, before a flood or drought hits. Only 11 of 103 recent humanitarian-logistics papers carry a forecast, early-warning or climate keyword. When does acting early pay off?

## What prompted this

Since October 2025, 103 papers carry a humanitarian-logistics, disaster-relief, emergency-logistics or relief keyword (48 in operations research journals). Only 11 of them carry a forecast, anticipatory, early-warning, climate or pre-positioning keyword. "Climate shocks" appears on 44 papers, 32 of them in economics.

## Questions

- Given how good the forecasts are, at what probability of a flood or drought should supplies be moved, and how much is lost to false alarms?
- Does acting early reduce deaths, distress sales of assets and hunger more than the same money spent after the disaster?
- Where should warehouses be when forecasts are unsure where the shock will land?

## Data that could help

Archived forecasts and outcomes (for example river-flood forecasts and rainfall records), aid agencies' data on early actions, and household surveys after disasters.

## Next step

Build a two-stage stochastic model that moves supplies according to archived flood forecasts for one river basin, and compare it with responding after the flood.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| humanitarian logistics | 1 | #2550 | 1, 5, 2, 0, 0, 2, 1, 2, 2, 1, 2, 2, 1 | 20 |
| emergency logistics | 1 | #2550 | 0, 3, 0, 0, 0, 0, 2, 3, 0, 1, 1, 1, 1 | 11 |
| disaster relief | 0 | – | 0, 2, 0, 2, 1, 1, 2, 0, 0, 0, 2, 0, 0 | 10 |


---

---
title: "Moderators and mediators everywhere (research idea)"
description: "\"Moderating effect\" and \"mediating effect\" are among the most frequent keywords in finance and management papers, and mediation models rank high on the equation ranking. How robust are these interaction and mediation results, and what would good practice look like?"
url: https://scholar4ai.com/ideas/moderators-and-mediators-everywhere
markdown_url: https://scholar4ai.com/ideas/moderators-and-mediators-everywhere.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["methods", "finance", "management"]
keywords: ["moderating effect", "mediating effect", "moderated mediation"]
---

# Moderators and mediators everywhere

"Moderating effect" and "mediating effect" are among the most frequent keywords in finance and management papers, and mediation models rank high on the equation ranking. How robust are these interaction and mediation results, and what would good practice look like?

## What prompted this

Since October 2025, "moderating effect" appears on 430 papers (219 in finance journals, 146 in management) and "mediating effect" on 267 (171 in finance); 29 carry both. "Moderating effect" is the 36th most frequent keyword of the last 30 days, and it is growing in the accounting journals (8 papers in July–September 2026 against about 4 expected). On the [equation ranking](/rankings/equations), the mediation model is mentioned by 62 papers in 30 days, 29 of them in finance.

## Questions

- How many moderators does a typical paper test, and does it correct for testing many?
- How many mediation claims rest on the three-step regressions of Baron and Kenny, which causal mediation analysis has questioned, and how many would survive its assumptions?
- Are interactions reported with marginal effects and enough statistical power?

## Possible approach

Code a sample of recent papers for the number of moderators and mediators tested, the inference method, corrections for multiple testing and the availability of replication data.

## Next step

Draw the finance papers with a "moderating effect" keyword from [papers.json](/data/papers.json) and record how many moderators each one tests.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| moderating effect | 17 | #37 | 43, 58, 47, 28, 29, 36, 35, 26, 21, 28, 20, 22, 13 | 393 |
| mediating effect | 7 | #210 | 30, 36, 43, 19, 16, 24, 26, 14, 14, 19, 9, 13, 5 | 263 |
| moderated mediation | 1 | #2550 | 1, 6, 2, 4, 5, 3, 3, 4, 3, 2, 2, 1, 1 | 36 |


---

---
title: "Model collapse and the data that is left (research idea)"
description: "Synthetic data generation appears on 77 recent papers and synthetic data on 18, almost all in AI journals, while model collapse, the degradation of models trained on their predecessors' output, appears on 3. The web is filling with generated text and images that will train the next models. How fast does quality decay, and what does that do to the value of human data?"
url: https://scholar4ai.com/ideas/model-collapse-and-synthetic-data
markdown_url: https://scholar4ai.com/ideas/model-collapse-and-synthetic-data.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "data", "methods"]
keywords: ["model collapse", "synthetic data", "data quality"]
---

# Model collapse and the data that is left

Synthetic data generation appears on 77 recent papers and synthetic data on 18, almost all in AI journals, while model collapse, the degradation of models trained on their predecessors' output, appears on 3. The web is filling with generated text and images that will train the next models. How fast does quality decay, and what does that do to the value of human data?

## What prompted this

Since October 2025, 117 papers carry a synthetic-data, model-collapse, self-consuming or recursive-training keyword (111 in AI journals): "synthetic data generation" on 77, "synthetic data" on 18, "synthetic data augmentation" on 10, "model collapse" on 3. Eleven of the 117 also carry a collapse, degradation, recursive, distribution-shift or contamination keyword, and "data quality" appears on 30 papers in the archive.

## Questions

- When models are trained on mixtures of human and generated data at the shares now seen on the web, how fast do rare patterns and tails disappear, and does filtering by quality stop it?
- Does the decay differ for text, images and tabular data used in finance and economics?
- If human-written data becomes scarce, what is it worth, and who owns the archives that have it?

## Data that could help

Web crawl samples by year with AI-text detection, controlled training runs on mixed data, and licensing deals for data archives.

## Next step

Train a small model on successive generations of mixed human and generated data at web-realistic shares and measure the loss of tail vocabulary and rare facts per generation.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| model collapse | 0 | – | 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0 | 3 |
| synthetic data | 0 | – | 0, 0, 2, 0, 1, 2, 3, 4, 2, 3, 0, 1, 0 | 18 |
| data quality | 0 | – | 1, 0, 2, 5, 3, 6, 2, 3, 1, 3, 0, 4, 0 | 30 |


---

---
title: "Mobile money as insurance against climate shocks"
description: "Financial inclusion is a large topic in the finance journals, and climate shocks a growing one in economics, but only 14 of 367 recent inclusion and payments papers carry a climate or disaster keyword. When droughts and floods hit, does mobile money bring help fast enough to keep households from selling assets or taking children out of school?"
url: https://scholar4ai.com/ideas/mobile-money-as-climate-insurance
markdown_url: https://scholar4ai.com/ideas/mobile-money-as-climate-insurance.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "economics", "development"]
keywords: ["mobile money", "financial inclusion", "climate shocks"]
---

# Mobile money as insurance against climate shocks

Financial inclusion is a large topic in the finance journals, and climate shocks a growing one in economics, but only 14 of 367 recent inclusion and payments papers carry a climate or disaster keyword. When droughts and floods hit, does mobile money bring help fast enough to keep households from selling assets or taking children out of school?

## What prompted this

Since October 2025, 367 papers carry a financial-inclusion, digital-payment or mobile-money keyword (229 in finance journals, 88 in economics), and only 14 of them carry a climate, weather, drought, flood, disaster or shock keyword. "Climate shocks" appears on 44 papers, 32 of them in economics. In the management journals, "inclusive growth" (6 papers in July–September 2026 against about 1 expected) and "developing countries" are rising.

## Questions

- After droughts or floods, do households with mobile money receive more remittances, and faster?
- Does that protect their consumption, their assets (livestock, land) and their children's schooling?
- Does the effect hold where agent networks are thin, or does the shock knock out the network itself?

## Data that could help

Geocoded household surveys (LSMS, DHS), rainfall and flood data (ERA5, satellite flood maps), and mobile money agent locations and transactions where available.

## Next step

Merge household panels from one East African country with rainfall shocks and the households' mobile money use, and compare how much consumption falls after droughts.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| mobile money | 1 | #2550 | 2, 1, 0, 1, 1, 0, 1, 3, 5, 3, 2, 2, 1 | 21 |
| financial inclusion | 9 | #129 | 15, 25, 21, 15, 17, 14, 13, 16, 16, 21, 19, 15, 9 | 207 |
| climate shocks | 1 | #2550 | 3, 7, 3, 5, 3, 5, 3, 2, 3, 3, 3, 3, 1 | 43 |


---

---
title: "Mixture of experts as learned regime switching"
description: "Mixture-of-experts models are among the fastest-growing topics in the AI journals, and regime-switching models have a long history in finance and economics. A gate that picks experts works much like a regime switch, yet no finance or economics paper in the archive carries the keyword. Can it forecast better across market regimes?"
url: https://scholar4ai.com/ideas/mixture-of-experts-as-regime-switching
markdown_url: https://scholar4ai.com/ideas/mixture-of-experts-as-regime-switching.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "finance", "forecasting"]
keywords: ["mixture of experts", "regime switching", "Markov regime switching", "volatility forecasting"]
---

# Mixture of experts as learned regime switching

Mixture-of-experts models are among the fastest-growing topics in the AI journals, and regime-switching models have a long history in finance and economics. A gate that picks experts works much like a regime switch, yet no finance or economics paper in the archive carries the keyword. Can it forecast better across market regimes?

## What prompted this

"Mixture of experts" is among the top growing keywords across all journals on the [trends page](/trends) (47 papers in July–September 2026 against 31 expected). Since October 2025 it appears on 152 papers: 150 in AI journals, 2 in operations research, and none in finance or economics. Regime switching appears on 70 papers (33 in finance, 27 in economics), and only one of them also carries a deep-learning, neural-network or mixture-of-experts keyword. The Markov-switching model is on the [equation ranking](/rankings/equations), half of its papers in finance.

## Questions

- In volatility or inflation forecasting, does a mixture of experts with a learned gate beat a Markov-switching model, and do its experts look like regimes such as calm and crisis?
- Can a Markov-switching likelihood serve as a structured gate, giving a hybrid of the two?
- Do the regimes it finds line up with known events?

## Data that could help

Realized volatility from intraday returns, the VIX, and monthly macro series such as FRED-MD.

## Next step

Fit a two-expert mixture and a two-state Markov-switching model to the daily realized volatility of the S&P 500, and compare their forecasts and inferred regimes.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| mixture of experts | 11 | #85 | 7, 8, 8, 14, 6, 13, 13, 11, 14, 16, 19, 12, 10 | 141 |
| regime switching | 4 | #458 | 3, 2, 3, 5, 2, 6, 5, 2, 3, 4, 1, 5, 3 | 41 |
| Markov regime switching | 1 | #2550 | 0, 3, 3, 0, 3, 4, 3, 2, 1, 3, 0, 4, 0 | 26 |
| volatility forecasting | 8 | #153 | 6, 9, 8, 6, 6, 7, 8, 2, 8, 4, 10, 6, 7 | 80 |


---

---
title: "Mixed-ownership reform: does private capital change…"
description: "Non-state-owned enterprises are a rising keyword in the economics journals, and state ownership is one of the most common comparisons in research on Chinese firms. Yet \"mixed ownership reform\" appears on only 9 recent papers. When private investors buy into state firms, do governance and performance change, or only the shareholder list?"
url: https://scholar4ai.com/ideas/mixed-ownership-reform
markdown_url: https://scholar4ai.com/ideas/mixed-ownership-reform.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "economics", "governance"]
keywords: ["mixed ownership reform", "state-owned enterprises", "non-state-owned enterprises"]
---

# Mixed-ownership reform: does private capital change state firms?

Non-state-owned enterprises are a rising keyword in the economics journals, and state ownership is one of the most common comparisons in research on Chinese firms. Yet "mixed ownership reform" appears on only 9 recent papers. When private investors buy into state firms, do governance and performance change, or only the shareholder list?

## What prompted this

In economics, "non-state-owned enterprises" had 12 papers in July–September 2026 against 7 expected (see the [economics dashboard](/categories/economics)). Over the last 12 complete months "state-owned enterprises" appeared on 666 papers, and since October 2025 "non-state-owned enterprises" on 169 (107 in finance journals). "Mixed ownership reform" appears on just 9 papers and "mixed ownership" on 2.

## Questions

- When private investors take board seats in state firms, do investment efficiency, pay structures and profitability change?
- Does it matter whether the new investor is a strategic partner, a fund or another state-linked firm?
- Do the firms' banks and suppliers treat them differently afterwards?

## Data that could help

Ownership and board data of listed firms (CSMAR, CNRDS), announcements of mixed-ownership deals, and firm accounts.

## Next step

Collect announced mixed-ownership deals of listed state firms since 2015, and compare board composition and investment efficiency before and after.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| mixed ownership reform | 0 | – | 0, 1, 1, 1, 2, 0, 1, 2, 1, 0, 0, 0, 0 | 9 |
| state-owned enterprises | 19 | #27 | 63, 73, 71, 56, 34, 68, 61, 45, 47, 53, 48, 47, 18 | 666 |
| non-state-owned enterprises | 7 | #210 | 15, 16, 16, 11, 15, 12, 11, 14, 11, 13, 19, 10, 6 | 163 |


---

---
title: "Minimum wages and the machines that replace the…"
description: "The minimum wage appears on 34 recent papers in the economics and finance journals, but only 2 of 44 minimum-wage papers carry an automation, robot or technology keyword. Self-checkouts, kiosks and warehouse robots are cheapest where wages are highest. Do minimum wage increases speed up automation, and over what horizon?"
url: https://scholar4ai.com/ideas/minimum-wages-and-automation
markdown_url: https://scholar4ai.com/ideas/minimum-wages-and-automation.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "labor", "automation"]
keywords: ["minimum wage", "labor demand"]
---

# Minimum wages and the machines that replace the minimum-wage job

The minimum wage appears on 34 recent papers in the economics and finance journals, but only 2 of 44 minimum-wage papers carry an automation, robot or technology keyword. Self-checkouts, kiosks and warehouse robots are cheapest where wages are highest. Do minimum wage increases speed up automation, and over what horizon?

## What prompted this

Since October 2025, 44 papers carry a minimum-wage keyword (35 in economics journals, 8 in finance), with "minimum wage" on 34. Only 2 of them also carry an automation, robot, technology, capital-labour or self-service keyword, while "labor demand" appears on 32 papers and industrial robots are a rising topic elsewhere.

## Questions

- After large minimum wage increases, do firms in affected areas buy more automation equipment and post fewer low-wage jobs, with a lag of years rather than months?
- Which tasks go first, and do the remaining jobs pay more or require more skill?
- Do the employment effects found in short-run studies understate long-run effects that run through automation?

## Data that could help

Minimum wage changes by state and country, equipment investment and job postings by establishment, and robot or kiosk adoption from supplier data.

## Next step

Relate state minimum wage changes in the US to the share of restaurant and retail job postings that mention self-service technology over the following three years.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| minimum wage | 1 | #2550 | 0, 5, 4, 5, 2, 2, 1, 3, 5, 1, 2, 3, 1 | 33 |
| labor demand | 3 | #687 | 1, 2, 2, 0, 2, 2, 2, 4, 4, 4, 4, 2, 3 | 29 |


---

---
title: "Shared micromobility after the boom (research idea)"
description: "Bike sharing, electric scooters and shared micromobility appear on 19 recent papers, mostly in operations research, and the topic is fading, with 2 papers in the last quarter against about 6 per quarter before. Several operators have failed and cities have banned scooters. Which systems survived, and did they replace car trips or walking?"
url: https://scholar4ai.com/ideas/micromobility-after-the-boom
markdown_url: https://scholar4ai.com/ideas/micromobility-after-the-boom.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "transport", "cities"]
keywords: ["bike sharing", "electric scooters"]
---

# Shared micromobility after the boom

Bike sharing, electric scooters and shared micromobility appear on 19 recent papers, mostly in operations research, and the topic is fading, with 2 papers in the last quarter against about 6 per quarter before. Several operators have failed and cities have banned scooters. Which systems survived, and did they replace car trips or walking?

## What prompted this

Since October 2025, 19 papers carry a bike-sharing, micromobility, e-scooter or shared-mobility keyword (14 in operations research journals): "bike sharing" on 11, "electric scooters" on 3, "shared micromobility" on 2, "dockless bike-sharing" on 2. Only 2 were published in July–September 2026 against about 6 per quarter earlier.

## Questions

- Across cities, which system designs (docked, dockless, publicly subsidized, operator-funded) survived the operator failures of 2022–2024, and at what cost per trip?
- What share of micromobility trips replaced car trips, and did the share change as novelty wore off?
- Did the scooter bans in Paris and elsewhere shift trips to bikes, transit or cars?

## Data that could help

Trip data published by operators and cities, operator entries and exits by city, and travel surveys before and after bans.

## Next step

Compile the entry and exit of micromobility operators across 50 cities since 2018 and relate survival to the system design and subsidy model.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| bike sharing | 0 | – | 0, 0, 3, 1, 0, 1, 2, 2, 2, 0, 0, 0, 0 | 11 |
| electric scooters | 0 | – | 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0 | 3 |


---

---
title: "Microfinance meets digital lenders (research idea)"
description: "Microfinance and microcredit appear on 35 recent papers, and only 2 of them carry a regulation, digital, fintech or competition keyword. App-based lenders now compete with microfinance institutions in Africa and Asia, and several countries have capped rates. Do borrowers end up with cheaper credit, or more of it?"
url: https://scholar4ai.com/ideas/microfinance-meets-digital-lenders
markdown_url: https://scholar4ai.com/ideas/microfinance-meets-digital-lenders.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "development"]
keywords: ["microfinance", "microfinance institutions"]
---

# Microfinance meets digital lenders

Microfinance and microcredit appear on 35 recent papers, and only 2 of them carry a regulation, digital, fintech or competition keyword. App-based lenders now compete with microfinance institutions in Africa and Asia, and several countries have capped rates. Do borrowers end up with cheaper credit, or more of it?

## What prompted this

Since October 2025, 35 papers carry a microfinance or microcredit keyword (18 in finance journals, 10 in economics); "microfinance" appears on 17 and "microfinance institutions" on 14. Only 2 of the 35 also carry an interest-rate-cap, regulation, digital, fintech, competition or over-indebtedness keyword. Financial inclusion as a whole is a large topic, so the gap is specific to how microfinance institutions compete now.

## Questions

- When digital lenders enter a market, do microfinance institutions lose their best borrowers, cut rates or move up-market?
- Do interest rate caps push lenders out of rural areas, as earlier caps did, or do digital lenders' lower costs change that?
- Do borrowers with both app loans and microfinance loans become over-indebted?

## Data that could help

Microfinance institution financials (the MIX Market successor datasets), credit bureau data where digital lenders report, and the dates of rate caps by country.

## Next step

Compare microfinance institutions' loan growth and portfolio quality in Kenya, where digital lending is widespread, before and after the 2021 digital credit regulations.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| microfinance | 1 | #2550 | 0, 1, 3, 1, 0, 4, 0, 2, 1, 1, 2, 2, 0 | 17 |
| microfinance institutions | 1 | #2550 | 0, 1, 2, 2, 1, 0, 1, 3, 1, 0, 2, 1, 0 | 14 |


---

---
title: "Metaverse retail after the hype (research idea)"
description: "Virtual reality, the metaverse, augmented reality and immersive technologies appear on 123 recent papers, but the topic is fading, with 21 papers in the last quarter against about 33 per quarter before; 23 of the papers concern retail or consumers. Which immersive applications survived the hype cycle, and what do their users actually buy?"
url: https://scholar4ai.com/ideas/metaverse-retail-after-the-hype
markdown_url: https://scholar4ai.com/ideas/metaverse-retail-after-the-hype.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "marketing", "technology"]
keywords: ["metaverse", "virtual reality", "immersive technologies"]
---

# Metaverse retail after the hype

Virtual reality, the metaverse, augmented reality and immersive technologies appear on 123 recent papers, but the topic is fading, with 21 papers in the last quarter against about 33 per quarter before; 23 of the papers concern retail or consumers. Which immersive applications survived the hype cycle, and what do their users actually buy?

## What prompted this

Since October 2025, 123 papers carry a virtual-reality, metaverse, augmented-reality, immersive or extended-reality keyword (60 in AI journals, 44 in management): "virtual reality" on 37, "metaverse" on 34, "augmented reality" on 30, "immersive technologies" on 13. Twenty-one were published in July–September 2026 against about 33 per quarter earlier. Twenty-three of the 123 carry a retail, shopping, consumer, purchase, marketing, brand or tourism keyword.

## Questions

- Which uses (virtual try-on, showrooms, training, tourism previews) produced measurable sales or savings, and which were abandoned?
- Do consumers who try products in augmented reality return fewer goods?
- Did firms that invested heavily in metaverse projects in 2021–2022 write them off, and did markets punish or reward the retreat?

## Data that could help

Retailer data on augmented-reality feature use and returns, firms' project announcements and later disclosures, and app usage statistics.

## Next step

Compare return rates for products bought with and without virtual try-on at one retailer, controlling for product and customer.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| metaverse | 0 | – | 1, 2, 7, 2, 3, 2, 3, 6, 4, 1, 2, 1, 0 | 34 |
| virtual reality | 0 | – | 2, 3, 5, 3, 4, 4, 1, 3, 4, 4, 3, 1, 0 | 37 |
| immersive technologies | 0 | – | 0, 1, 1, 0, 1, 0, 0, 1, 2, 5, 1, 1, 0 | 13 |


---

---
title: "Merger review and what happens to the deals"
description: "Mergers and acquisitions appear on 228 recent papers, but only 31 of them carry a merger-control, competition-policy or enforcement keyword. Merger enforcement tightened in the US and Europe after 2021 and loosened again in places since. Did the threat of review stop deals, reshape them, or simply delay them?"
url: https://scholar4ai.com/ideas/merger-review-and-deal-outcomes
markdown_url: https://scholar4ai.com/ideas/merger-review-and-deal-outcomes.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "economics", "competition"]
keywords: ["mergers and acquisitions", "merger control", "competition policy"]
---

# Merger review and what happens to the deals

Mergers and acquisitions appear on 228 recent papers, but only 31 of them carry a merger-control, competition-policy or enforcement keyword. Merger enforcement tightened in the US and Europe after 2021 and loosened again in places since. Did the threat of review stop deals, reshape them, or simply delay them?

## What prompted this

Since October 2025, 228 papers carry a merger, acquisition or takeover keyword (111 in finance journals, 68 in economics, 24 in management), with "mergers and acquisitions" on 122. Only 31 of them also carry an antitrust, merger-control, competition-policy, enforcement, competition-law or market-power keyword; "merger control" appears on 11 and "competition policy" on 34.

## Questions

- After the US agencies' tougher guidelines in 2023, did the number of large horizontal deals fall, or did firms switch to deals below reporting thresholds?
- Do deals that are challenged and then completed with remedies deliver the price effects that the remedies were meant to prevent?
- Did acquirers' stock reactions to deal announcements change with the perceived enforcement regime?

## Data that could help

Deal data (SDC Platinum, Zephyr), enforcement actions and second requests, and product prices in affected markets.

## Next step

Count announced deals by size relative to the reporting threshold before and after the 2023 guideline change and look for bunching just below it.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| mergers and acquisitions | 10 | #104 | 12, 9, 3, 7, 10, 10, 10, 13, 9, 10, 13, 10, 7 | 116 |
| merger control | 0 | – | 5, 0, 2, 1, 2, 0, 0, 1, 0, 0, 0, 0, 0 | 11 |
| competition policy | 2 | #1148 | 3, 4, 2, 3, 4, 6, 0, 2, 4, 3, 1, 2, 1 | 34 |


---

---
title: "Measuring overconfidence from what executives say"
description: "CEO and managerial overconfidence appear on 98 recent papers, mostly measured from option-exercise behaviour or press coverage, as in the 2000s. Only 6 of them carry a text, language or speech keyword, and 4 of those are in AI journals. Can a language model score overconfidence from earnings calls, and does it predict the same decisions?"
url: https://scholar4ai.com/ideas/measuring-overconfidence-from-executive-speech
markdown_url: https://scholar4ai.com/ideas/measuring-overconfidence-from-executive-speech.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "management", "ai"]
keywords: ["CEO overconfidence", "managerial overconfidence"]
---

# Measuring overconfidence from what executives say

CEO and managerial overconfidence appear on 98 recent papers, mostly measured from option-exercise behaviour or press coverage, as in the 2000s. Only 6 of them carry a text, language or speech keyword, and 4 of those are in AI journals. Can a language model score overconfidence from earnings calls, and does it predict the same decisions?

## What prompted this

Since October 2025, 98 papers carry an overconfidence keyword (44 in finance journals, 24 in AI, 18 in economics): "overconfidence" on 51, "CEO overconfidence" on 22, "managerial overconfidence" on 15. Only 6 of the 98 also carry a text, language, machine-learning, speech, tone or linguistic keyword, and 4 of those 6 are in AI journals.

## Questions

- Does a language model's reading of CEOs' answers on earnings calls (certainty, dismissal of risks, forecast ranges) agree with the option-based measure, and where do they disagree?
- Which measure better predicts overinvestment, overpriced acquisitions and later restatements?
- Can the text measure be computed for private firms and non-US firms, where option data do not exist?

## Data that could help

Earnings call transcripts, option exercise data (ExecuComp), deal and investment data (Compustat, SDC).

## Next step

Score 1,000 earnings calls with a language model for overconfidence and compare the scores with the option-based measure for the same CEOs.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| CEO overconfidence | 0 | – | 2, 0, 0, 1, 4, 3, 4, 1, 2, 3, 0, 2, 0 | 22 |
| managerial overconfidence | 0 | – | 0, 0, 1, 1, 2, 5, 0, 3, 1, 0, 1, 1, 0 | 15 |


---

---
title: "Do word counts measure digital transformation?"
description: "Digital transformation is among the most frequent keywords in the finance and management journals, much of it about Chinese listed firms, and studies often score it by counting digital terms in annual reports. Do those scores track what firms actually invest and hire?"
url: https://scholar4ai.com/ideas/measuring-digital-transformation
markdown_url: https://scholar4ai.com/ideas/measuring-digital-transformation.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "management", "measurement"]
keywords: ["digital transformation", "enterprise digital transformation", "text analysis"]
---

# Do word counts measure digital transformation?

Digital transformation is among the most frequent keywords in the finance and management journals, much of it about Chinese listed firms, and studies often score it by counting digital terms in annual reports. Do those scores track what firms actually invest and hire?

## What prompted this

Since October 2025, "digital transformation" appears on 544 papers (211 in management journals, 204 in finance, 74 in economics), and "enterprise digital transformation" on 80 more; it is the 10th most frequent keyword of the last 30 days. Only 7 of the 634 papers with either keyword also carry a text-analysis keyword, although indices built from word frequencies in annual reports are a common way to measure it.

## Questions

- How well do text scores correlate with hard measures such as software and IT investment, digital job postings, cloud contracts and digital patents?
- How much of a score is boilerplate copied from year to year, or a response to policy campaigns?
- Do headline results on productivity, ESG or innovation hold when hard measures replace the text score?

## Data that could help

- Annual reports of Chinese listed companies, and the ready-made digital indices in CSMAR and CNRDS (the China financial and firm databases on the [dataset ranking](/rankings/datasets)).
- Online job postings, CNIPA patents, and the software items in fixed-asset notes.

## Next step

For one year of annual reports, correlate a common word-count index with each firm's digital job postings and software assets.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| [digital transformation](https://scholar4ai.com/keywords/digital-transformation.md) | 30 | #9 | 52, 49, 48, 42, 42, 52, 45, 42, 36, 39, 31, 41, 27 | 519 |
| enterprise digital transformation | 6 | #263 | 6, 8, 6, 8, 2, 4, 8, 11, 6, 8, 5, 3, 5 | 75 |
| text analysis | 3 | #687 | 3, 1, 1, 0, 3, 4, 1, 1, 3, 3, 1, 4, 3 | 25 |


---

---
title: "Marriage markets with too few brides (research idea)"
description: "Marriage, the marriage market, sex ratios at birth and bride prices appear on 59 recent papers, almost all in the economics journals. In China and India, decades of skewed sex ratios have left millions of men unable to marry. What does that do to saving, crime, migration and the price of a wedding?"
url: https://scholar4ai.com/ideas/marriage-markets-and-sex-ratios
markdown_url: https://scholar4ai.com/ideas/marriage-markets-and-sex-ratios.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "demography", "households"]
keywords: ["marriage market", "sex ratio at birth", "bride price"]
---

# Marriage markets with too few brides

Marriage, the marriage market, sex ratios at birth and bride prices appear on 59 recent papers, almost all in the economics journals. In China and India, decades of skewed sex ratios have left millions of men unable to marry. What does that do to saving, crime, migration and the price of a wedding?

## What prompted this

Since October 2025, 59 papers carry a sex-ratio, marriage-market, gender-imbalance, bride-price or marriage keyword (52 in economics journals): "marriage" on 12, "sex ratio at birth" on 9, "marriage market" on 8, "marriage timing" on 6, "delayed marriage" on 4, "bride price" on 4. The cohorts born under the most skewed ratios are now of marriage age.

## Questions

- In counties with the most skewed sex ratios, how much more do families with sons save, and does the saving show up in housing prices and bride prices?
- Do unmarried men migrate more, commit more crime, or take riskier jobs?
- As the ratio at birth normalizes, do bride prices and saving rates fall with it?

## Data that could help

Census and population registers with sex ratios by cohort and county, household panels with marriage and saving data (China Family Panel Studies, China Household Finance Survey), and local crime statistics.

## Next step

Relate county-level sex ratios of the 1990–2000 birth cohorts to current household saving rates and marriage payments in Chinese household survey data.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| marriage market | 1 | #2550 | 1, 0, 0, 0, 1, 1, 3, 0, 0, 0, 0, 1, 1 | 7 |
| sex ratio at birth | 0 | – | 0, 2, 0, 0, 0, 1, 1, 2, 0, 1, 1, 1, 0 | 9 |
| bride price | 1 | #2550 | 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1 | 3 |


---

---
title: "Markups and the inflation of 2021–2023 (research idea)"
description: "Market power, price setting and markups appear on 186 recent papers, but only 8 of them carry an inflation keyword. The claim that firms used the inflation surge to raise margins (\"greedflation\") shaped policy debates. Did markups rise, in which sectors, and did they fall back?"
url: https://scholar4ai.com/ideas/markups-and-inflation
markdown_url: https://scholar4ai.com/ideas/markups-and-inflation.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "macroeconomics", "competition"]
keywords: ["markups", "market power", "inflation"]
---

# Markups and the inflation of 2021–2023

Market power, price setting and markups appear on 186 recent papers, but only 8 of them carry an inflation keyword. The claim that firms used the inflation surge to raise margins ("greedflation") shaped policy debates. Did markups rise, in which sectors, and did they fall back?

## What prompted this

Since October 2025, 186 papers carry a markup, profit-margin, price-setting or market-power keyword (107 in economics journals, 35 in finance): "market power" on 87, "price setting" on 34, "markups" on 29. Only 8 of them also carry an inflation keyword, although "inflation" appears on 211 papers in the archive.

## Questions

- Using firm-level data, did markups rise during 2021–2023 beyond what cost pass-through explains, and did they fall when costs eased?
- Were margin increases concentrated in sectors with supply shortages, with few competitors, or with fast price changes?
- Did consumers' expectations of price increases make it easier for firms to raise prices in step?

## Data that could help

Firm accounts with cost and revenue (Compustat, Orbis), producer and consumer price indices by sector, and estimates of markups from production functions.

## Next step

Estimate quarterly markups for listed firms by sector for 2019–2025 and compare their path with input cost indices.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| markups | 4 | #458 | 1, 1, 1, 2, 5, 0, 2, 4, 4, 3, 2, 0, 4 | 25 |
| market power | 4 | #458 | 4, 4, 5, 11, 10, 8, 9, 7, 5, 6, 9, 7, 4 | 85 |
| inflation | 13 | #68 | 24, 21, 18, 11, 10, 22, 12, 17, 20, 18, 10, 21, 11 | 204 |


---

---
title: "Detecting market manipulation at machine speed"
description: "Market manipulation, stock market manipulation, price manipulation and pump-and-dump schemes appear on about 20 recent papers, split between the AI and finance journals. Spoofing, wash trading and coordinated pumps now happen in milliseconds and across venues, including crypto. Can detectors trained on known cases find new ones, and what do regulators miss?"
url: https://scholar4ai.com/ideas/market-manipulation-detection
markdown_url: https://scholar4ai.com/ideas/market-manipulation-detection.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "finance", "market microstructure"]
keywords: ["market manipulation", "stock market manipulation", "price manipulation"]
---

# Detecting market manipulation at machine speed

Market manipulation, stock market manipulation, price manipulation and pump-and-dump schemes appear on about 20 recent papers, split between the AI and finance journals. Spoofing, wash trading and coordinated pumps now happen in milliseconds and across venues, including crypto. Can detectors trained on known cases find new ones, and what do regulators miss?

## What prompted this

Since October 2025, "market manipulation" appears on 10 papers, "stock market manipulation" on 5, "price manipulation" on 5, "pump-and-dump schemes" on 2 and "wash trading" on 1, split between AI journals (which build detectors) and finance journals (which study episodes). High-frequency trading and limit order books appear on 34 papers between them.

## Questions

- Trained on prosecuted spoofing and pump-and-dump cases, how well do detectors flag new cases, and how many false alarms do they raise per true case at market scale?
- How much wash trading is there on crypto exchanges, by exchange, and does it respond to enforcement?
- Do coordinated pumps organized on social media leave signatures in order flow before prices move?

## Data that could help

Enforcement case records with dates and instruments, order book data (LOBSTER, exchange feeds), crypto trade data and social media archives.

## Next step

Train a detector on the order book patterns of prosecuted spoofing cases and measure its alarm rate on a month of ordinary trading in the same stocks.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| market manipulation | 0 | – | 1, 1, 0, 2, 3, 0, 0, 0, 0, 2, 1, 0, 0 | 10 |
| stock market manipulation | 0 | – | 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0 | 5 |
| price manipulation | 0 | – | 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0 | 5 |


---

---
title: "Slow ships, clean fuels and the port queue (research idea)"
description: "Port operations, maritime logistics, container shipping and port congestion appear on dozens of recent papers in the operations research journals, while ship emissions and maritime decarbonization appear on 9 and 8. Shipping now pays for carbon in Europe and faces a global fuel standard. Do slower ships and new fuels cut emissions, or shift them to ports and queues?"
url: https://scholar4ai.com/ideas/maritime-decarbonization
markdown_url: https://scholar4ai.com/ideas/maritime-decarbonization.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "transport", "climate"]
keywords: ["container shipping", "port operations", "maritime decarbonization", "ship emissions"]
---

# Slow ships, clean fuels and the port queue

Port operations, maritime logistics, container shipping and port congestion appear on dozens of recent papers in the operations research journals, while ship emissions and maritime decarbonization appear on 9 and 8. Shipping now pays for carbon in Europe and faces a global fuel standard. Do slower ships and new fuels cut emissions, or shift them to ports and queues?

## What prompted this

Since October 2025, "port operations" appears on 19 papers, "maritime logistics" on 11, "container shipping" on 10, "container terminals" on 10 and "port congestion" on 10, mostly in operations research journals. "Ship emissions" appears on 9 papers, "maritime decarbonization" on 8, "emission control areas" on 5 and "port decarbonization" on 4. The operations and the emissions literatures mostly sit in different papers.

## Questions

- Since shipping entered the EU emissions trading system in 2024, did ships slow down, reroute to transshipment ports just outside the covered area, or pay?
- Does slow steaming reduce total emissions once the extra ships needed for the same cargo are counted?
- Where will ammonia and methanol bunkering be available first, and how do routing models change when fuel is available at only a few ports?

## Data that could help

Ship position and speed data (AIS), port call records, emissions trading data for maritime, and bunkering infrastructure announcements.

## Next step

Compare average speeds and port calls of container ships on Europe-Asia routes before and after January 2024 using AIS data.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| container shipping | 3 | #687 | 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 2, 3 | 7 |
| port operations | 0 | – | 1, 0, 2, 1, 2, 0, 2, 0, 4, 1, 1, 5, 0 | 19 |
| maritime decarbonization | 0 | – | 0, 0, 1, 0, 0, 2, 0, 0, 2, 1, 1, 1, 0 | 8 |
| ship emissions | 1 | #2550 | 2, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1 | 8 |


---

---
title: "Management guidance in uncertain times (research idea)"
description: "Management earnings forecasts appear on 11 recent papers in the accounting and finance journals. Firms withdrew guidance in the pandemic and again around the 2025 tariff shocks. What do investors and analysts do when guidance disappears, and does the practice recover?"
url: https://scholar4ai.com/ideas/management-guidance-in-uncertain-times
markdown_url: https://scholar4ai.com/ideas/management-guidance-in-uncertain-times.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "finance"]
keywords: ["management earnings forecasts"]
---

# Management guidance in uncertain times

Management earnings forecasts appear on 11 recent papers in the accounting and finance journals. Firms withdrew guidance in the pandemic and again around the 2025 tariff shocks. What do investors and analysts do when guidance disappears, and does the practice recover?

## What prompted this

Since October 2025, 12 papers carry a management-forecast or management-guidance keyword (6 in accounting journals, 6 in finance), with "management earnings forecasts" on 11. Four of them were published in July–September 2026, against about 2 per quarter earlier. Economic policy uncertainty and trade policy uncertainty are among the most frequent keywords in the finance journals.

## Questions

- When firms withdraw guidance in a shock, does analyst forecast dispersion and stock volatility rise more for those firms than for similar firms that keep guiding?
- Do firms that stopped guiding during the pandemic ever restart, and are those that restarted different?
- Did the 2025 tariff shocks lead firms to switch from point forecasts to ranges or scenarios?

## Data that could help

Guidance records (I/B/E/S Guidance), analyst forecasts, stock volatility and the dates of shocks.

## Next step

Count guidance withdrawals by quarter for 2019–2026 and compare the analyst dispersion of withdrawing firms with matched firms.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| management earnings forecasts | 2 | #1148 | 0, 0, 0, 0, 2, 2, 0, 1, 1, 3, 1, 0, 2 | 10 |


---

---
title: "Who will do the caring? (research idea)"
description: "Elderly care, long-term care insurance and informal care appear on 75 recent papers, with 22 in the last quarter against about 17 per quarter before, and 24 carry a labour, worker or immigration keyword. Ageing countries need millions more care workers. Does paying more bring them, does immigration fill the gap, and what do robots change?"
url: https://scholar4ai.com/ideas/long-term-care-workforce
markdown_url: https://scholar4ai.com/ideas/long-term-care-workforce.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "health", "labor"]
keywords: ["elderly care", "long-term care insurance", "informal care"]
---

# Who will do the caring?

Elderly care, long-term care insurance and informal care appear on 75 recent papers, with 22 in the last quarter against about 17 per quarter before, and 24 carry a labour, worker or immigration keyword. Ageing countries need millions more care workers. Does paying more bring them, does immigration fill the gap, and what do robots change?

## What prompted this

Since October 2025, 75 papers carry a long-term-care, elderly-care, nursing-home or caregiving keyword (41 in economics journals, 16 in AI): "elderly care" on 19, "long-term care insurance" on 14, "long-term care" on 9, "informal care" on 8. Twenty-four of them also carry a labour, worker, wage, workforce, immigration, robot or informal-care keyword. The quarter to September 2026 had 22 such papers against about 17 per quarter earlier.

## Questions

- When care wages rise, as in Japan's and Germany's recent increases, how many workers enter from other sectors, and does turnover fall?
- Do care-worker visas raise the supply without lowering wages for existing workers?
- Do families cut informal care when formal care becomes available, and does that raise women's employment?

## Data that could help

Care sector employment and wages, long-term care insurance claims (Japan, Germany, Korea), visa statistics and household surveys with caregiving questions.

## Next step

Compare care-worker employment growth across Japanese prefectures after the national pay increases, using differences in the local wage gap to other sectors.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| elderly care | 4 | #458 | 3, 1, 0, 2, 0, 3, 0, 1, 0, 2, 2, 4, 2 | 18 |
| long-term care insurance | 0 | – | 2, 4, 1, 0, 1, 0, 0, 2, 1, 2, 0, 1, 0 | 14 |
| informal care | 2 | #1148 | 2, 1, 0, 0, 0, 0, 0, 0, 1, 0, 2, 1, 1 | 7 |


---

---
title: "The long-run costs of pandemic school closures"
description: "\"Long-term effects\" is a rising keyword in the economics journals, and COVID-19 is still one of the most frequent keywords overall. Yet only 22 of 778 recent pandemic papers carry a schooling or human-capital keyword. Five years on, do the students who lost the most school time show it in exams, graduation and early careers?"
url: https://scholar4ai.com/ideas/long-run-costs-of-pandemic-school-closures
markdown_url: https://scholar4ai.com/ideas/long-run-costs-of-pandemic-school-closures.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "education", "labor"]
keywords: ["school closures", "learning loss", "long-term effects"]
---

# The long-run costs of pandemic school closures

"Long-term effects" is a rising keyword in the economics journals, and COVID-19 is still one of the most frequent keywords overall. Yet only 22 of 778 recent pandemic papers carry a schooling or human-capital keyword. Five years on, do the students who lost the most school time show it in exams, graduation and early careers?

## What prompted this

In economics, "long-term effects" had 7 papers in July–September 2026 against about 2 expected (see the [economics dashboard](/categories/economics)). Since October 2025, 778 papers carry a COVID-19 or pandemic keyword (318 in finance journals, 296 in economics). Only 22 of them carry a school-closure, learning-loss, educational-attainment or human-capital keyword, 16 of those in economics.

## Questions

- Did students whose schools stayed closed longer score lower years later, graduate less often or go to weaker colleges?
- Did tutoring and catch-up programmes close the gap, and for whom?
- What do the first labour market outcomes of these cohorts show?

## Data that could help

School-level closure data, standardized test scores (state assessments, NAEP, PISA), college enrolment and early earnings records.

## Next step

Relate district-level weeks of remote schooling in 2020–2021 to the change in test scores through 2025 in US state assessment data.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| school closures | 2 | #1148 | 0, 1, 0, 0, 0, 1, 0, 0, 1, 2, 0, 0, 2 | 5 |
| learning loss | 0 | – | 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0 | 3 |
| long-term effects | 3 | #687 | 1, 2, 0, 0, 1, 2, 0, 0, 0, 1, 1, 5, 1 | 13 |


---

---
title: "Loan officers versus algorithms (research idea)"
description: "Soft information and relationship lending appear on 26 recent papers in the finance and economics journals, but only 5 of them carry an algorithm, machine-learning, fintech or automation keyword. As banks automate small-business lending, what is lost when the loan officer's judgment goes, and can models learn it?"
url: https://scholar4ai.com/ideas/loan-officers-versus-algorithms
markdown_url: https://scholar4ai.com/ideas/loan-officers-versus-algorithms.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "banking", "ai"]
keywords: ["soft information", "relationship lending"]
---

# Loan officers versus algorithms

Soft information and relationship lending appear on 26 recent papers in the finance and economics journals, but only 5 of them carry an algorithm, machine-learning, fintech or automation keyword. As banks automate small-business lending, what is lost when the loan officer's judgment goes, and can models learn it?

## What prompted this

Since October 2025, 26 papers carry a loan-officer, relationship-lending or soft-information keyword (15 in finance journals, 7 in economics): "soft information" on 19, "relationship lending" on 6. Only 5 of them also carry an algorithm, machine-learning, fintech, automation, digital or hard-information keyword.

## Questions

- Do loans approved by loan officers against the model's recommendation default more or less often than loans the model approved?
- Does automation cut lending to borrowers whose strengths are not in the data (new firms, immigrants, informal businesses), and does it reduce discrimination?
- Can text from loan applications and site visits give models the soft information officers use?

## Data that could help

Loan application files with both officer decisions and model scores from a lender, and loan performance.

## Next step

Partner with a lender that records officer overrides of model scores and compare the default rates of overridden and non-overridden loans.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| soft information | 1 | #2550 | 2, 3, 1, 2, 1, 4, 1, 0, 1, 1, 2, 0, 1 | 18 |
| relationship lending | 0 | – | 1, 0, 0, 0, 0, 0, 2, 0, 3, 0, 0, 0, 0 | 6 |


---

---
title: "Do language-model pricing agents learn to collude?"
description: "Price competition is rising steeply in the operations research journals and AI governance leads the AI journals, while collusion remains a small topic that is rarely tied to algorithms. When sellers let language-model agents set prices, do prices drift up without any agreement?"
url: https://scholar4ai.com/ideas/llm-pricing-agents-and-collusion
markdown_url: https://scholar4ai.com/ideas/llm-pricing-agents-and-collusion.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "operations research", "ai", "platforms"]
keywords: ["algorithmic collusion", "collusion", "price competition", "digital platforms"]
---

# Do language-model pricing agents learn to collude?

Price competition is rising steeply in the operations research journals and AI governance leads the AI journals, while collusion remains a small topic that is rarely tied to algorithms. When sellers let language-model agents set prices, do prices drift up without any agreement?

## What prompted this

On the [trends page](/trends), "price competition" rises more steeply than any other growing keyword in the operations research journals (11 papers in July–September 2026 against about 2 expected), and "AI governance" tops the growing keywords of the AI journals. Since October 2025, 46 papers carry a collusion keyword (28 in economics); only 7 also carry an algorithm, reinforcement-learning or AI keyword, and "algorithmic collusion" appears on 5. Q-learning, the algorithm behind the best-known simulations of algorithmic collusion, shows up on the [equation ranking](/rankings/equations) almost only in AI journals.

## Questions

- In repeated pricing games, do language-model agents reach prices above the competitive level, as Q-learning agents do in simulations?
- Does it depend on the prompt, on memory, or on seeing rivals' prices?
- What tests could a competition authority run on such agents before they are deployed?

## Possible approach

Simulate repeated Bertrand competition with differentiated products, with language-model agents facing each other and facing Q-learning agents, and compare their prices with the Nash equilibrium and the joint-profit benchmark.

## Next step

Run a two-firm simulation with off-the-shelf models and record average prices over many rounds.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| algorithmic collusion | 0 | – | 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0 | 5 |
| collusion | 3 | #687 | 1, 1, 2, 1, 4, 5, 3, 0, 5, 3, 2, 3, 2 | 30 |
| price competition | 5 | #342 | 8, 5, 2, 4, 1, 2, 3, 3, 2, 8, 4, 10, 3 | 52 |
| digital platforms | 5 | #342 | 7, 4, 5, 7, 3, 6, 7, 6, 4, 7, 7, 8, 5 | 71 |


---

---
title: "Does the model know when it is guessing? (research idea)"
description: "Confidence calibration and uncertainty calibration appear on 54 recent papers, mostly in AI journals, and 35 calibration papers involve language or vision-language models, with 94 calibration-related papers in the last quarter against about 57 per quarter before. When a model answers a financial or legal question, its stated confidence rarely matches its accuracy. Can calibration be fixed, and do users read it?"
url: https://scholar4ai.com/ideas/llm-confidence-calibration
markdown_url: https://scholar4ai.com/ideas/llm-confidence-calibration.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "methods", "decision-making"]
keywords: ["confidence calibration", "uncertainty calibration"]
---

# Does the model know when it is guessing?

Confidence calibration and uncertainty calibration appear on 54 recent papers, mostly in AI journals, and 35 calibration papers involve language or vision-language models, with 94 calibration-related papers in the last quarter against about 57 per quarter before. When a model answers a financial or legal question, its stated confidence rarely matches its accuracy. Can calibration be fixed, and do users read it?

## What prompted this

Since October 2025, 280 papers carry a calibration or overconfidence keyword (176 in AI journals, 53 in finance, 28 in economics): "confidence calibration" on 28, "uncertainty calibration" on 26, "model calibration" on 19, besides the behavioural overconfidence of executives and investors studied in finance. Thirty-five of the 280 also carry a language-model or vision-language keyword. The quarter to September 2026 had 94 such papers against about 57 per quarter earlier.

## Questions

- On financial, accounting and legal questions, how far are models' stated confidences from their accuracy, and does the gap grow for harder questions?
- Which methods (verbalized confidence, sampling consistency, trained calibrators) give usable confidence for decisions, and do they survive fine-tuning?
- When users are shown calibrated confidence, do they rely on the model more where it is right and less where it is wrong?

## Data that could help

Question sets with gold answers in finance and law, model outputs with confidence under several methods, and user experiments with confidence displays.

## Next step

Measure the calibration of three models on a financial analysis question set under verbalized and consistency-based confidence, and test whether showing it changes user reliance.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| confidence calibration | 0 | – | 0, 1, 2, 1, 3, 2, 4, 3, 0, 5, 4, 3, 0 | 28 |
| uncertainty calibration | 1 | #2550 | 2, 1, 0, 1, 0, 1, 3, 1, 6, 1, 4, 5, 1 | 25 |


---

---
title: "Uncertainty indices built by language models"
description: "Economic, climate, trade and monetary policy uncertainty indices appear on 423 recent papers in the finance and economics journals, almost all built from counts of newspaper words. Only 5 of them use language models, embeddings or text mining beyond word counts. Would an index that reads the articles agree with the word counts, and would it predict better?"
url: https://scholar4ai.com/ideas/llm-built-uncertainty-indices
markdown_url: https://scholar4ai.com/ideas/llm-built-uncertainty-indices.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "finance", "methods"]
keywords: ["economic policy uncertainty", "text mining", "natural language processing"]
---

# Uncertainty indices built by language models

Economic, climate, trade and monetary policy uncertainty indices appear on 423 recent papers in the finance and economics journals, almost all built from counts of newspaper words. Only 5 of them use language models, embeddings or text mining beyond word counts. Would an index that reads the articles agree with the word counts, and would it predict better?

## What prompted this

Since October 2025, 423 papers carry a policy-uncertainty, uncertainty-index or text-based keyword (243 in finance journals, 144 in economics): "economic policy uncertainty" on 161, "climate policy uncertainty" on 106, "policy uncertainty" on 68, "trade policy uncertainty" on 48, "monetary policy uncertainty" on 32. Only 5 of the 423 also carry a language-model, BERT, transformer, machine-learning, deep-learning, embedding, text-mining or natural-language-processing keyword.

## Questions

- Do indices built by a language model reading the full text (asking whether the article describes policy uncertainty and of what kind) track the word-count indices, and where do they diverge?
- Do the model-built indices predict investment, hiring and asset prices better, or does the simpler index already capture what matters?
- Can the same approach build indices for countries and languages where the newspaper word lists were never validated?

## Data that could help

Newspaper archives (Factiva, LexisNexis), the published uncertainty indices, and macro and market data.

## Next step

Score a year of articles from the newspapers used by one uncertainty index with a language model and compare the resulting monthly index with the published one.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| economic policy uncertainty | 5 | #342 | 12, 15, 17, 16, 15, 13, 14, 16, 10, 7, 11, 13, 5 | 159 |
| text mining | 1 | #2550 | 1, 1, 2, 0, 2, 2, 1, 1, 0, 1, 0, 3, 0 | 14 |
| natural language processing | 4 | #458 | 4, 0, 3, 8, 1, 5, 4, 3, 5, 5, 4, 1, 4 | 43 |


---

---
title: "When the judge is a model (research idea)"
description: "Evaluation metrics appear on 56 recent papers and LLM-as-a-judge on 16, all in AI journals, with 25 evaluation papers in the last quarter in line with earlier quarters. Models now grade other models' answers, and firms use them to score customer conversations and reports. How reliable and how biased are the model judges, and how far do the biases propagate?"
url: https://scholar4ai.com/ideas/llm-as-a-judge-reliability
markdown_url: https://scholar4ai.com/ideas/llm-as-a-judge-reliability.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "methods"]
keywords: ["LLM-as-a-judge", "evaluation metrics", "LLM evaluation"]
---

# When the judge is a model

Evaluation metrics appear on 56 recent papers and LLM-as-a-judge on 16, all in AI journals, with 25 evaluation papers in the last quarter in line with earlier quarters. Models now grade other models' answers, and firms use them to score customer conversations and reports. How reliable and how biased are the model judges, and how far do the biases propagate?

## What prompted this

Since October 2025, 106 papers carry an LLM-as-a-judge, automatic-evaluation, evaluation-metric or human-evaluation keyword (97 in AI journals): "evaluation metrics" on 56, "LLM-as-a-judge" on 16, "LLM evaluation" on 9, "benchmark evaluation" on 5, "human evaluation" on 3. The quarter to September 2026 had 25 such papers against about 26 per quarter earlier.

## Questions

- On tasks with expert ground truth in finance, accounting and law, how well do model judges agree with experts, and do they favour longer, more confident or more similar-sounding answers?
- When a model is trained on rewards from a model judge, do the judge's biases compound in the trained model?
- In business uses (scoring customer service calls, grading analyst notes), do the scores change behaviour in the direction the firm wants?

## Data that could help

Expert-graded answer sets in professional domains, judge outputs from several models, and training runs with model-judged rewards.

## Next step

Compare model-judge scores with expert grades on 300 answers to accounting and legal questions and measure length and self-preference biases.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| LLM-as-a-judge | 0 | – | 2, 0, 2, 1, 0, 0, 2, 2, 2, 0, 3, 2, 0 | 16 |
| evaluation metrics | 4 | #458 | 3, 7, 5, 6, 1, 4, 4, 3, 9, 1, 6, 4, 3 | 53 |
| LLM evaluation | 2 | #1148 | 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1 | 8 |


---

---
title: "Do language-model agents amplify the bullwhip effect?"
description: "Agent-based modelling is rising in the operations research journals, and firms are starting to let AI agents place orders. The bullwhip effect, where small swings in demand grow larger up the supply chain, appears on only 9 recent papers, and just one of them carries an agent or AI keyword. Do AI ordering agents dampen the bullwhip, or amplify it when they all react to the same signals?"
url: https://scholar4ai.com/ideas/llm-agents-and-the-bullwhip-effect
markdown_url: https://scholar4ai.com/ideas/llm-agents-and-the-bullwhip-effect.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "ai", "supply chain"]
keywords: ["bullwhip effect", "agent-based modeling", "LLM agents"]
---

# Do language-model agents amplify the bullwhip effect?

Agent-based modelling is rising in the operations research journals, and firms are starting to let AI agents place orders. The bullwhip effect, where small swings in demand grow larger up the supply chain, appears on only 9 recent papers, and just one of them carries an agent or AI keyword. Do AI ordering agents dampen the bullwhip, or amplify it when they all react to the same signals?

## What prompted this

In the operations research journals, "agent-based modeling" had 6 papers in July–September 2026 against about 3 expected (see the [operations research dashboard](/categories/operations-research)). Since October 2025, "agent-based modeling" appears on 81 papers (40 in economics journals, 16 in operations research) and "LLM agents" on 44, while "bullwhip effect" appears on 9, only one of which carries a language-model, agent or AI keyword.

## Questions

- In the classic beer distribution game, do language-model agents order more steadily than human players, or overreact?
- What happens when every tier of the supply chain uses the same model?
- Does sharing point-of-sale data with the agents remove the bullwhip, as it does for people?

## Possible approach

Run the beer game with a language-model agent at each tier, varying the information the agents share and whether all tiers use the same model, and measure how much the variance of orders grows from tier to tier.

## Next step

Implement a four-tier beer game with language-model agents and compare the order variance at each tier with published results for human players.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| bullwhip effect | 2 | #1148 | 1, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 2 | 8 |
| agent-based modeling | 1 | #2550 | 3, 5, 4, 9, 6, 6, 6, 12, 9, 10, 6, 5, 1 | 81 |
| LLM agents | 4 | #458 | 2, 3, 3, 4, 3, 3, 5, 3, 1, 6, 1, 7, 3 | 41 |


---

---
title: "Learning to decompose (research idea)"
description: "Benders decomposition, column generation, branch-and-cut and related methods appear on 240 recent papers, almost all in the operations research journals, and the topic is fading, with 42 papers in the last quarter against about 64 per quarter before. Only 16 of them use learning. Can learned cut selection, column pricing and branching make these methods faster on real problems?"
url: https://scholar4ai.com/ideas/learning-to-decompose
markdown_url: https://scholar4ai.com/ideas/learning-to-decompose.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "ai", "methods"]
keywords: ["Benders decomposition", "column generation", "branch-and-cut"]
---

# Learning to decompose

Benders decomposition, column generation, branch-and-cut and related methods appear on 240 recent papers, almost all in the operations research journals, and the topic is fading, with 42 papers in the last quarter against about 64 per quarter before. Only 16 of them use learning. Can learned cut selection, column pricing and branching make these methods faster on real problems?

## What prompted this

Since October 2025, 240 papers carry a Benders, column-generation, branch-and-price, branch-and-cut, cutting-plane or Lagrangian-relaxation keyword (218 in operations research journals, 22 in AI): "Benders decomposition" on 65, "column generation" on 46, "branch-and-cut" on 35, "logic-based Benders decomposition" on 30, "branch-and-price" on 28. Forty-two were published in July–September 2026 against about 64 per quarter earlier. Only 16 of the 240 also carry a machine-learning, reinforcement-learning, neural or data-driven keyword.

## Questions

- Does a model trained to select Benders cuts or price columns on past instances of a firm's problem cut solution times on new instances by more than careful engineering of the classical method?
- How much training data does it need, and does it break when the problem's data distribution shifts?
- Which parts of the pipeline (branching, cut selection, primal heuristics, column pricing) give the largest gains per unit of engineering?

## Data that could help

Standard instance libraries (MIPLIB, routing and scheduling benchmarks) and firms' instance histories for a repeated planning problem.

## Next step

Train a cut-selection model on one firm's repeated network design instances and compare solution times with a tuned classical Benders implementation on held-out instances.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| Benders decomposition | 4 | #458 | 5, 2, 4, 6, 6, 5, 8, 7, 6, 6, 2, 6, 3 | 63 |
| column generation | 0 | – | 4, 3, 3, 3, 5, 2, 5, 5, 5, 5, 3, 3, 0 | 46 |
| branch-and-cut | 0 | – | 3, 5, 1, 4, 1, 5, 7, 3, 2, 0, 2, 2, 0 | 35 |


---

---
title: "Can language models stand in for survey respondents?"
description: "Survey experiments are a staple of economics, and some researchers now simulate respondents with language models (\"silicon samples\"). Only 6 of 179 recent survey papers carry a language-model keyword, 5 of them in AI journals. Where do simulated answers match real people, and where would they mislead economic research?"
url: https://scholar4ai.com/ideas/language-models-as-survey-respondents
markdown_url: https://scholar4ai.com/ideas/language-models-as-survey-respondents.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "ai", "methods"]
keywords: ["survey experiment", "large language models"]
---

# Can language models stand in for survey respondents?

Survey experiments are a staple of economics, and some researchers now simulate respondents with language models ("silicon samples"). Only 6 of 179 recent survey papers carry a language-model keyword, 5 of them in AI journals. Where do simulated answers match real people, and where would they mislead economic research?

## What prompted this

Since October 2025, 179 papers carry a survey, questionnaire or respondent keyword (117 in economics journals, 37 in finance). Only 6 of them carry a language-model, ChatGPT or generative-AI keyword, and 5 of those are in AI journals. "Survey experiment" appears on 15 papers, 14 of them in economics.

## Questions

- For published survey experiments in economics, do simulated respondents reproduce the direction and size of the treatment effects?
- Do they capture differences between groups (income, age, country), or flatten them?
- Could they help design and size real surveys, even if they cannot replace them?

## Possible approach

Replicate published survey experiments with language-model respondents given the demographics of the original sample, and compare the effects with the published ones.

## Next step

Pick ten published survey experiments in economics, simulate them with two language models, and compare the estimated treatment effects.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| survey experiment | 1 | #2550 | 1, 1, 0, 1, 0, 3, 1, 0, 2, 2, 1, 2, 1 | 14 |
| [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 89 | #1 | 68, 94, 103, 90, 83, 129, 129, 126, 147, 111, 122, 110, 72 | 1,312 |


---

---
title: "Language models as designers of heuristics (research idea)"
description: "Matheuristics and local search are among the fastest-growing keywords in the operations research journals, and recent systems let language models write and evolve heuristic code. Only 2 of the 252 heuristics papers in OR journals carry a language-model keyword. Can generated heuristics beat hand-designed ones on standard routing and scheduling instances?"
url: https://scholar4ai.com/ideas/language-models-as-heuristic-designers
markdown_url: https://scholar4ai.com/ideas/language-models-as-heuristic-designers.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "ai", "methods"]
keywords: ["matheuristic", "local search", "large language models"]
---

# Language models as designers of heuristics

Matheuristics and local search are among the fastest-growing keywords in the operations research journals, and recent systems let language models write and evolve heuristic code. Only 2 of the 252 heuristics papers in OR journals carry a language-model keyword. Can generated heuristics beat hand-designed ones on standard routing and scheduling instances?

## What prompted this

In the operations research journals, "matheuristic" had 14 papers in July–September 2026 against about 8 expected, and "local search" 15 against 7 (see the [operations research dashboard](/categories/operations-research)). Since October 2025, 547 papers carry a heuristic, local-search or matheuristic keyword (254 in AI journals, 252 in operations research). Only 13 of them carry a language-model keyword, 2 of those in operations research journals.

## Questions

- On standard instances (Solomon, CVRPLIB, Taillard), do heuristics that language models write and refine beat a well-tuned adaptive large neighborhood search?
- Are the generated heuristics new ideas or recombinations of known operators, and can people read them?
- How much computing do they cost compared with what they gain?

## Data that could help

Benchmark instances from the optimization category of the [dataset ranking](/rankings/datasets), with their published best-known solutions.

## Next step

Let a language model evolve destroy and repair operators for an adaptive large neighborhood search on the Solomon instances, and compare the gaps to the best-known solutions.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| matheuristic | 2 | #1148 | 3, 5, 2, 4, 3, 2, 3, 5, 2, 5, 4, 5, 1 | 43 |
| local search | 1 | #2550 | 4, 4, 3, 4, 5, 5, 2, 5, 1, 6, 6, 6, 1 | 51 |
| [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 89 | #1 | 68, 94, 103, 90, 83, 129, 129, 126, 147, 111, 122, 110, 72 | 1,312 |


---

---
title: "Will workers teach the AI that could replace them?"
description: "Knowledge transfer, sharing and management appear on 259 recent papers across the management and AI journals, but only 48 of them carry an AI or digital keyword, and knowledge hiding on 3. Firms are building assistants trained on employees' documents and chats. Do employees share less when sharing feeds a machine?"
url: https://scholar4ai.com/ideas/knowledge-sharing-with-ai-at-work
markdown_url: https://scholar4ai.com/ideas/knowledge-sharing-with-ai-at-work.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "human resources", "ai"]
keywords: ["knowledge sharing", "knowledge hiding", "knowledge management"]
---

# Will workers teach the AI that could replace them?

Knowledge transfer, sharing and management appear on 259 recent papers across the management and AI journals, but only 48 of them carry an AI or digital keyword, and knowledge hiding on 3. Firms are building assistants trained on employees' documents and chats. Do employees share less when sharing feeds a machine?

## What prompted this

Since October 2025, 259 papers carry a knowledge-transfer, knowledge-sharing, knowledge-management or knowledge-hiding keyword (112 in management journals, 105 in AI): "knowledge transfer" on 114, "knowledge sharing" on 56, "knowledge management" on 43, "knowledge hiding" on 3. Forty-eight of them also carry an AI, generative-AI, language-model or digital keyword, most of those about tools rather than about people's willingness to share.

## Questions

- When a firm announces that an internal assistant will be trained on employees' work, does documentation and answering of colleagues' questions rise or fall?
- Do experts whose know-how is most codifiable hide more, and do incentives for sharing offset that?
- Does the assistant reduce the value of expert employees in pay and promotions, and do they see it coming?

## Data that could help

Internal collaboration logs (documents, chat answers, wiki edits) from firms that deployed assistants, with deployment dates, and employee surveys.

## Next step

Compare documentation and question-answering activity in internal tools before and after an AI assistant announcement at a firm that shares its logs.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| knowledge sharing | 0 | – | 4, 5, 4, 6, 8, 9, 2, 5, 5, 4, 2, 2, 0 | 56 |
| knowledge hiding | 0 | – | 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0 | 3 |
| knowledge management | 0 | – | 5, 5, 3, 3, 3, 3, 3, 4, 1, 7, 0, 6, 0 | 43 |


---

---
title: "Knowledge graphs and rules for compliance (research idea)"
description: "Knowledge graphs appear on 141 recent papers and neuro-symbolic AI on 14, almost all in AI journals; only 34 of 481 such papers concern accounting, tax, legal, regulatory, financial or audit uses. Compliance is a domain of explicit rules and entities, which language models alone handle badly. Do graphs and symbolic rules make them reliable enough for audits and tax?"
url: https://scholar4ai.com/ideas/knowledge-graphs-for-compliance
markdown_url: https://scholar4ai.com/ideas/knowledge-graphs-for-compliance.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "accounting", "law"]
keywords: ["knowledge graphs", "neuro-symbolic AI", "regulatory compliance"]
---

# Knowledge graphs and rules for compliance

Knowledge graphs appear on 141 recent papers and neuro-symbolic AI on 14, almost all in AI journals; only 34 of 481 such papers concern accounting, tax, legal, regulatory, financial or audit uses. Compliance is a domain of explicit rules and entities, which language models alone handle badly. Do graphs and symbolic rules make them reliable enough for audits and tax?

## What prompted this

Since October 2025, 481 papers carry a knowledge-graph, neuro-symbolic, symbolic-reasoning, rule-based, ontology, formal-verification or logical-reasoning keyword (451 in AI journals): "knowledge graphs" on 141, "knowledge graph completion" on 41, "temporal knowledge graphs" on 38, "neuro-symbolic AI" on 14, "symbolic reasoning" on 8. Only 34 of the 481 also carry an accounting, tax, legal, regulatory, compliance, finance, contract, audit, law, bank, fraud or anti-money-laundering keyword, although "regulatory compliance" appears on 60 papers in the archive.

## Questions

- On tasks such as checking a transaction against sanctions lists and ownership chains, or applying a tax rule to a set of facts, do systems that combine a language model with a knowledge graph and explicit rules make fewer errors than the model alone, and are their errors explainable?
- How much effort does it take to keep the rules and graphs current when regulations change, and who maintains them?
- Would auditors and regulators accept outputs with symbolic justifications where they reject model-only outputs?

## Data that could help

Public regulatory texts and sanctions lists, company ownership graphs (Open Ownership, Orbis), and test cases with known answers from tax and compliance training materials.

## Next step

Build a benchmark of 200 compliance cases (sanctions screening with ownership chains, tax rule application) and compare a language model alone with the same model backed by a graph and rules.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| knowledge graphs | 8 | #153 | 7, 18, 14, 8, 13, 11, 19, 8, 8, 11, 9, 10, 8 | 136 |
| neuro-symbolic AI | 2 | #1148 | 0, 0, 1, 2, 0, 1, 1, 4, 1, 1, 1, 0, 2 | 12 |
| regulatory compliance | 5 | #342 | 1, 5, 5, 6, 7, 6, 7, 5, 6, 7, 1, 1, 4 | 57 |


---

---
title: "Japan's wages after thirty years of deflation"
description: "Japan appears on 184 recent papers, with 56 in the last quarter against about 41 per quarter before, but only 21 of them carry a wage, inflation or deflation keyword. Japan's annual wage rounds have produced the largest raises in decades, and the Bank of Japan has ended negative rates. Is the wage-price cycle real, and does it reach small firms?"
url: https://scholar4ai.com/ideas/japan-wage-growth-and-inflation
markdown_url: https://scholar4ai.com/ideas/japan-wage-growth-and-inflation.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "macroeconomics"]
keywords: ["Japan", "wage growth", "deflation", "Bank of Japan"]
---

# Japan's wages after thirty years of deflation

Japan appears on 184 recent papers, with 56 in the last quarter against about 41 per quarter before, but only 21 of them carry a wage, inflation or deflation keyword. Japan's annual wage rounds have produced the largest raises in decades, and the Bank of Japan has ended negative rates. Is the wage-price cycle real, and does it reach small firms?

## What prompted this

Since October 2025, 184 papers carry a Japan keyword (117 in economics journals, 49 in finance): "Japan" on 81, "Japanese companies" on 18, "Bank of Japan" on 11, "Japanese yen" on 8. The quarter to September 2026 had 56 such papers against about 41 per quarter earlier. Only 21 of the 184 also carry a wage, labour-shortage, real-wage, inflation or deflation keyword; "wage growth" appears on 14 papers and "deflation" on 13.

## Questions

- Did the large raises at big firms in the 2024–2026 wage rounds pass to their suppliers and to small firms, where most people work?
- Have real wages turned positive for long enough to change household inflation expectations and spending?
- Did the labour shortage, rather than the central bank, end deflation, and what does that imply for countries now ageing as fast as Japan?

## Data that could help

Monthly labour survey wages by firm size, the wage round results, household expectation surveys and price data by sector.

## Next step

Compare wage growth at firms with fewer than 30 employees with that at large firms in Japan's monthly labour survey for 2022–2026.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| Japan | 3 | #687 | 0, 12, 5, 3, 9, 11, 4, 6, 7, 9, 6, 7, 2 | 79 |
| wage growth | 1 | #2550 | 0, 0, 1, 4, 0, 2, 1, 3, 1, 0, 0, 1, 1 | 13 |
| deflation | 0 | – | 1, 0, 0, 1, 2, 1, 2, 1, 1, 1, 3, 0, 0 | 13 |
| Bank of Japan | 0 | – | 0, 0, 2, 1, 1, 3, 0, 1, 1, 0, 2, 0, 0 | 11 |


---

---
title: "Is the technology acceptance model still useful for…"
description: "The technology acceptance model is one of the fastest-growing keywords across the tracked journals, and a quarter of recent TAM and UTAUT papers now study AI. But the model was built for workplace software decades ago. Do perceived usefulness and ease of use predict who actually uses generative AI, and what do they miss?"
url: https://scholar4ai.com/ideas/is-the-technology-acceptance-model-still-useful
markdown_url: https://scholar4ai.com/ideas/is-the-technology-acceptance-model-still-useful.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "ai", "methods"]
keywords: ["technology acceptance model", "UTAUT", "generative AI"]
---

# Is the technology acceptance model still useful for generative AI?

The technology acceptance model is one of the fastest-growing keywords across the tracked journals, and a quarter of recent TAM and UTAUT papers now study AI. But the model was built for workplace software decades ago. Do perceived usefulness and ease of use predict who actually uses generative AI, and what do they miss?

## What prompted this

"Technology acceptance model" grew across all journals (19 papers in July–September 2026 against about 10 expected) and in finance (8 against 2); see the [trends page](/trends). Since October 2025, 109 papers carry a TAM or UTAUT keyword (67 in management journals, 24 in finance); 26 of them also carry a generative-AI, ChatGPT, language-model or AI keyword.

## Questions

- Do intentions measured with TAM surveys predict actual use, as logged by the tools, weeks later?
- Do things the model leaves out, such as trust in accuracy, fear of replacement or the tool doing the whole task, explain more of who uses it?
- Do results replicate across countries and occupations, or reflect the student samples many studies use?

## Possible approach

Pair a standard TAM survey with usage logs from an AI tool deployed in a firm or university, and compare its predictive power with and without the added factors.

## Next step

Survey the users of one AI tool with a standard TAM questionnaire and match their answers to their usage over the following month.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| technology acceptance model | 4 | #458 | 4, 2, 2, 3, 5, 2, 3, 6, 2, 7, 5, 7, 4 | 48 |
| UTAUT | 1 | #2550 | 2, 1, 2, 3, 2, 1, 1, 8, 2, 2, 3, 2, 1 | 29 |
| generative AI | 10 | #104 | 25, 33, 21, 31, 18, 29, 38, 23, 35, 30, 37, 25, 9 | 345 |


---

---
title: "Is biodiversity risk priced? (research idea)"
description: "Sustainable investment is rising across the tracked journals, and biodiversity risk has become a finance keyword. Yet few of these papers carry a keyword about returns, bonds or the cost of capital. Do markets price firms' exposure to nature loss, and has nature-related disclosure changed that?"
url: https://scholar4ai.com/ideas/is-biodiversity-risk-priced
markdown_url: https://scholar4ai.com/ideas/is-biodiversity-risk-priced.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "esg"]
keywords: ["biodiversity risk", "sustainable investment", "biodiversity loss"]
---

# Is biodiversity risk priced?

Sustainable investment is rising across the tracked journals, and biodiversity risk has become a finance keyword. Yet few of these papers carry a keyword about returns, bonds or the cost of capital. Do markets price firms' exposure to nature loss, and has nature-related disclosure changed that?

## What prompted this

"Sustainable investment" grew across all journals (19 papers in July–September 2026 against about 11 expected; see the [trends page](/trends)). Since October 2025, "biodiversity risk" appears on 35 papers (28 in finance journals), and 116 papers carry some biodiversity keyword (45 in economics, 44 in finance). Only 5 of the 116 also carry a keyword about stock returns, asset pricing, the cost of capital, bonds, equity or portfolios.

## Questions

- Do firms more exposed to nature loss (land use, water, supply chains in sensitive areas) earn higher expected returns, or pay more for credit?
- Did investors start pricing it after the Kunming-Montreal biodiversity framework (2022) or the nature-related disclosure recommendations (TNFD, 2023)?
- Can biodiversity exposure be measured from firms' locations and supply chains rather than from what they report?

## Data that could help

Facility locations of listed firms, protected-area and species-range maps, text-based exposure measures, and stock and bond returns.

## Next step

Build a location-based exposure measure from facility locations and protected-area maps, and test whether it predicts returns or credit spreads.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| biodiversity risk | 3 | #687 | 5, 4, 3, 3, 0, 2, 1, 3, 1, 1, 2, 7, 3 | 32 |
| sustainable investment | 2 | #1148 | 7, 3, 1, 3, 1, 7, 3, 3, 5, 5, 6, 8, 1 | 52 |
| biodiversity loss | 1 | #2550 | 3, 0, 0, 1, 1, 1, 1, 2, 2, 3, 1, 1, 1 | 16 |


---

---
title: "Investment scams on social media (research idea)"
description: "Financial fraud appears on 48 recent papers, but most study fraud by firms or fraud detection in transactions; only 6 carry a social-media, crypto, elderly or victim keyword, and \"fraud victimization\" on 5. Investment scams run through social platforms now cost households billions. Who falls for them, and what stops them?"
url: https://scholar4ai.com/ideas/investment-scams-on-social-media
markdown_url: https://scholar4ai.com/ideas/investment-scams-on-social-media.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "households", "security"]
keywords: ["financial fraud", "fraud victimization", "investor protection"]
---

# Investment scams on social media

Financial fraud appears on 48 recent papers, but most study fraud by firms or fraud detection in transactions; only 6 carry a social-media, crypto, elderly or victim keyword, and "fraud victimization" on 5. Investment scams run through social platforms now cost households billions. Who falls for them, and what stops them?

## What prompted this

Since October 2025, 48 papers carry a scam, investment-fraud, financial-fraud, Ponzi or fraud-victim keyword (18 in finance journals, 16 in AI); "financial fraud" appears on 22 and "fraud victimization" on 5. Only 6 of the 48 also carry a social-media, crypto, elderly, older-adult, romance or victim keyword. "Investor protection" appears on 49 papers, mostly about securities law.

## Questions

- Which households lose money to investment scams, and do the losses cluster among people with high financial literacy and confidence rather than low?
- Do warnings from regulators and platforms reduce losses, or do scams simply move?
- How much scam money flows through crypto exchanges, and does exchange compliance change the flow?

## Data that could help

Consumer complaint databases (for example the FTC Consumer Sentinel and FBI IC3 reports), bank transaction data with fraud flags, and platform takedown records.

## Next step

Analyse the complaint records of one regulator for 2020–2025 by age, scam type and channel, and relate losses to platform enforcement announcements.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| financial fraud | 1 | #2550 | 2, 1, 4, 3, 3, 0, 3, 1, 1, 2, 0, 1, 1 | 21 |
| fraud victimization | 0 | – | 2, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0 | 5 |
| investor protection | 2 | #1148 | 3, 9, 1, 2, 3, 2, 5, 4, 6, 5, 4, 3, 2 | 47 |


---

---
title: "Interest rate risk on bank balance sheets (research idea)"
description: "Interest rate risk appears on just 11 recent papers in the finance journals, only one of them in July–September 2026, although unrealized losses on bank bond holdings triggered the 2023 bank failures. Which banks still carry large unrealized losses, and does supervision now see them?"
url: https://scholar4ai.com/ideas/interest-rate-risk-on-bank-balance-sheets
markdown_url: https://scholar4ai.com/ideas/interest-rate-risk-on-bank-balance-sheets.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "banking"]
keywords: ["interest rate risk", "bank failures"]
---

# Interest rate risk on bank balance sheets

Interest rate risk appears on just 11 recent papers in the finance journals, only one of them in July–September 2026, although unrealized losses on bank bond holdings triggered the 2023 bank failures. Which banks still carry large unrealized losses, and does supervision now see them?

## What prompted this

Since October 2025, 13 papers carry an interest-rate-risk, held-to-maturity or unrealized-loss keyword (9 in finance journals, 3 in economics), and only one of them was published in July–September 2026. "Bank failures" appears on 12 papers. Bank runs and deposit insurance are rising topics, but the balance-sheet side of the 2023 failures is barely studied.

## Questions

- How much of the banking system's unrealized losses remain after rates stayed high, and are they concentrated in banks with uninsured deposits?
- Did banks lengthen or shorten the duration of their bond holdings after 2023, and did the use of hedges change?
- Would reporting held-to-maturity securities at fair value have changed depositors' behaviour?

## Data that could help

Call Reports and FR Y-9C filings, which report securities by maturity bucket and accounting classification, deposit composition and bank stock prices.

## Next step

Estimate each US bank's unrealized losses from its Call Report maturity buckets for 2022–2025 and relate them to deposit outflows and equity prices.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| interest rate risk | 0 | – | 2, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0 | 11 |
| bank failures | 0 | – | 1, 1, 1, 0, 0, 1, 2, 2, 1, 2, 1, 0, 0 | 12 |


---

---
title: "When insurers leave (research idea)"
description: "Insurance appears on more than 400 recent papers in the finance and economics journals, but only 52 of them carry a climate or disaster keyword, and property insurance barely appears. As insurers withdraw from wildfire- and hurricane-prone regions, who holds the risk, and what happens to house prices and mortgages?"
url: https://scholar4ai.com/ideas/insurers-retreat-from-climate-risk
markdown_url: https://scholar4ai.com/ideas/insurers-retreat-from-climate-risk.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "economics", "climate"]
keywords: ["insurance demand", "natural disasters", "catastrophic risk"]
---

# When insurers leave

Insurance appears on more than 400 recent papers in the finance and economics journals, but only 52 of them carry a climate or disaster keyword, and property insurance barely appears. As insurers withdraw from wildfire- and hurricane-prone regions, who holds the risk, and what happens to house prices and mortgages?

## What prompted this

Since October 2025, 408 papers carry an insurance keyword (196 in finance journals, 174 in economics), the most frequent being health, life, unemployment and deposit insurance. Only 52 of them also carry a wildfire, flood, climate, disaster, hurricane or catastrophe keyword, and "property insurance" appears on a single paper. "Natural disasters" appears on 82 papers.

## Questions

- When private insurers stop writing policies in a region, how much do house prices and mortgage lending fall, and who buys the homes?
- Do state-backed insurers of last resort, which are growing fast, price risk, or hide it?
- Does the retreat change where people and businesses move, and how quickly?

## Data that could help

Insurer market-exit filings and non-renewal rates by county, state insurer-of-last-resort enrolment, house transactions and mortgage originations (HMDA).

## Next step

Build a county panel of insurer exits and non-renewals for California and Florida since 2018, and relate it to house price growth and mortgage volumes.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| insurance demand | 0 | – | 2, 2, 2, 2, 0, 1, 2, 0, 1, 2, 2, 1, 0 | 17 |
| natural disasters | 5 | #342 | 7, 9, 8, 5, 5, 6, 8, 3, 8, 8, 10, 3, 5 | 80 |
| catastrophic risk | 0 | – | 3, 1, 1, 1, 1, 0, 0, 0, 1, 2, 0, 0, 0 | 10 |


---

---
title: "Insider trading plans after the new rules (research idea)"
description: "Insider trading appears on 40 recent papers in the finance and accounting journals, and 17 of 45 carry a regulation or enforcement keyword, but \"trading plan\" appears on one. Since 2023, US executives' pre-scheduled trading plans must have cooling-off periods and be disclosed. Did the plans stop being a cover for informed trades?"
url: https://scholar4ai.com/ideas/insider-trading-plans
markdown_url: https://scholar4ai.com/ideas/insider-trading-plans.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "accounting", "governance"]
keywords: ["insider trading", "insider trading regulation"]
---

# Insider trading plans after the new rules

Insider trading appears on 40 recent papers in the finance and accounting journals, and 17 of 45 carry a regulation or enforcement keyword, but "trading plan" appears on one. Since 2023, US executives' pre-scheduled trading plans must have cooling-off periods and be disclosed. Did the plans stop being a cover for informed trades?

## What prompted this

Since October 2025, 45 papers carry an insider-trading keyword (32 in finance journals, 7 in accounting); "insider trading" appears on 40 and "insider trading regulation" on 5. Of the 45, 17 carry a plan, enforcement, regulation, detection or machine-learning keyword, but the keyword "trading plan" appears on a single paper.

## Questions

- Before the 2023 rule, did trades under pre-scheduled plans earn abnormal returns, especially plans adopted shortly before trades?
- After the cooling-off period and disclosure requirements, did the returns to plan trades fall, and did executives trade less under plans?
- Did firms with the most profitable plan trades before the rule change their behaviour most?

## Data that could help

Insider trading filings (Form 4) with plan indicators, plan adoption and modification disclosures from 10-Q and 10-K filings, and stock returns.

## Next step

Compare abnormal returns of plan and non-plan insider sales in the two years before and after the 2023 rule.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| insider trading | 2 | #1148 | 4, 1, 6, 3, 5, 1, 5, 3, 2, 5, 3, 0, 2 | 38 |
| insider trading regulation | 0 | – | 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0 | 5 |


---

---
title: "Inheritance and the new limits of mobility (research idea)"
description: "Intergenerational mobility and transmission appear on 142 recent papers, mostly in economics, and 33 of them carry a wealth, inheritance, housing or parental keyword. As house prices and inheritances grow relative to wages, does where you end up depend more on what your parents own than on what you earn?"
url: https://scholar4ai.com/ideas/inheritance-and-mobility
markdown_url: https://scholar4ai.com/ideas/inheritance-and-mobility.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "inequality", "households"]
keywords: ["intergenerational mobility", "intergenerational wealth transfer"]
---

# Inheritance and the new limits of mobility

Intergenerational mobility and transmission appear on 142 recent papers, mostly in economics, and 33 of them carry a wealth, inheritance, housing or parental keyword. As house prices and inheritances grow relative to wages, does where you end up depend more on what your parents own than on what you earn?

## What prompted this

Since October 2025, 142 papers carry an intergenerational-mobility, social-mobility or intergenerational-transmission keyword (122 in economics journals): "intergenerational transmission" on 26, "intergenerational mobility" on 18, "social mobility" on 12. Thirty-three of them also carry a wealth, inheritance, housing, gift, parental or bequest keyword, and "intergenerational wealth transfer" appears on 4.

## Questions

- For cohorts born since 1980, how much of the correlation between parents' and children's wealth runs through inheritances and gifts, and how much through housing help at the time of purchase?
- Has the share of first-time buyers who received family help risen, and does help predict where they live and whether they move for work?
- Do countries with large inheritance taxes show higher wealth mobility across generations?

## Data that could help

Linked parent-child wealth records (Scandinavian registers, the Panel Study of Income Dynamics), surveys on home purchase financing, and inheritance tax data.

## Next step

Estimate the share of first-home down payments financed by family gifts by cohort from household surveys that record the source of funds.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| intergenerational mobility | 2 | #1148 | 1, 1, 1, 0, 5, 1, 1, 0, 2, 2, 0, 3, 2 | 17 |
| intergenerational wealth transfer | 1 | #2550 | 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1 | 3 |


---

---
title: "Seeing the informal economy from above (research idea)"
description: "Aerial imagery is rising steeply in the AI journals, and economists already use night-time lights to measure growth. Yet none of the papers on the informal economy uses either. Can image models measure informal activity that surveys miss?"
url: https://scholar4ai.com/ideas/informal-economy-from-above
markdown_url: https://scholar4ai.com/ideas/informal-economy-from-above.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "ai", "measurement"]
keywords: ["informal economy", "nighttime lights", "aerial imagery", "satellite imagery"]
---

# Seeing the informal economy from above

Aerial imagery is rising steeply in the AI journals, and economists already use night-time lights to measure growth. Yet none of the papers on the informal economy uses either. Can image models measure informal activity that surveys miss?

## What prompted this

In the [AI dashboard](/categories/ai) trends, "aerial imagery" had 9 papers in July–September 2026 against about 1 expected from its earlier share. In economics, night-time lights are an established proxy for activity: 26 papers carry the keyword since October 2025 (24 in economics journals), none of them with a machine-learning keyword. And of the 55 papers with an informal-economy, informal-employment or informal-sector keyword, none carries a satellite, night-light, aerial or remote-sensing keyword.

## Questions

- Can models trained on high-resolution imagery (market stalls, rooftops, vehicles, building materials) predict informal employment where surveys exist, and fill in where they do not?
- Do such measures change growth or inequality estimates in countries with large informal sectors?
- Do the models carry over from one country to another? Cross-dataset generalization is another rising AI keyword.

## Data that could help

- Free Sentinel-2 and Landsat imagery, VIIRS night-time lights, and commercial imagery for a few cities.
- Geocoded household surveys (LSMS, DHS) as ground truth; both are in the [dataset ranking](/rankings/datasets)'s list.

## Next step

Pick one country with geocoded survey clusters and test whether image features add to night-time lights in predicting informal employment shares.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| informal economy | 2 | #1148 | 0, 4, 3, 3, 1, 0, 2, 2, 7, 2, 2, 5, 0 | 31 |
| nighttime lights | 1 | #2550 | 2, 3, 1, 4, 2, 2, 3, 0, 3, 1, 2, 1, 1 | 24 |
| aerial imagery | 1 | #2550 | 0, 1, 2, 0, 0, 0, 0, 1, 0, 6, 2, 1, 1 | 13 |
| satellite imagery | 2 | #1148 | 4, 1, 2, 4, 2, 1, 0, 3, 2, 2, 3, 4, 2 | 28 |


---

---
title: "Industry 4.0 for small manufacturers: adoption without…"
description: "Industry 4.0, Industry 5.0 and smart manufacturing appear on 121 recent papers, but only 26 of them carry a small-firm, adoption, barrier or maturity keyword. Large manufacturers have automated and connected their plants; most small ones have not. What holds them back, and which support programmes have moved the needle?"
url: https://scholar4ai.com/ideas/industry-4-for-smes
markdown_url: https://scholar4ai.com/ideas/industry-4-for-smes.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "operations", "entrepreneurship"]
keywords: ["Industry 4.0", "Industry 5.0", "smart manufacturing"]
---

# Industry 4.0 for small manufacturers: adoption without engineers

Industry 4.0, Industry 5.0 and smart manufacturing appear on 121 recent papers, but only 26 of them carry a small-firm, adoption, barrier or maturity keyword. Large manufacturers have automated and connected their plants; most small ones have not. What holds them back, and which support programmes have moved the needle?

## What prompted this

Since October 2025, 121 papers carry an Industry 4.0, Industry 5.0, smart-manufacturing or smart-factory keyword (37 in management journals, 31 in operations research, 28 in AI): "Industry 4.0" on 54, "smart manufacturing" on 35, "Industry 5.0" on 32. Only 26 of them also carry a small-firm, SME, adoption, barrier, readiness or maturity keyword.

## Questions

- Among small manufacturers, is the binding constraint financing, skills, the absence of an engineer on staff, or the lack of a clear return?
- Do subsidized adoption programmes (Germany's and Japan's digitalization grants, US manufacturing extension) raise productivity, or mostly fund equipment that would have been bought anyway?
- Do small suppliers adopt when a large customer requires data connections, and do they capture any of the gains?

## Data that could help

Firm surveys on technology adoption, programme participation records, firm accounts and supplier-customer links.

## Next step

Compare the productivity growth of small manufacturers that received digitalization grants with similar applicants that were rejected or waitlisted.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| Industry 4.0 | 2 | #1148 | 3, 6, 1, 8, 3, 5, 5, 5, 2, 8, 5, 1, 2 | 52 |
| Industry 5.0 | 1 | #2550 | 3, 0, 2, 4, 4, 5, 1, 0, 6, 3, 2, 2, 1 | 32 |
| smart manufacturing | 1 | #2550 | 2, 4, 5, 4, 5, 3, 3, 1, 2, 4, 0, 1, 1 | 34 |


---

---
title: "Index insurance for farmers, a decade of pilots later"
description: "Agricultural and index insurance appear on 20 recent papers, split between the finance and economics journals. Weather index insurance was meant to protect smallholders cheaply, but take-up stayed low and basis risk high. With satellite data and mobile payments, is it finally working, and where?"
url: https://scholar4ai.com/ideas/index-insurance-for-farmers
markdown_url: https://scholar4ai.com/ideas/index-insurance-for-farmers.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "finance", "agriculture"]
keywords: ["agricultural insurance", "index insurance"]
---

# Index insurance for farmers, a decade of pilots later

Agricultural and index insurance appear on 20 recent papers, split between the finance and economics journals. Weather index insurance was meant to protect smallholders cheaply, but take-up stayed low and basis risk high. With satellite data and mobile payments, is it finally working, and where?

## What prompted this

Since October 2025, 20 papers carry an index-insurance, crop-insurance, agricultural-insurance or weather-insurance keyword (9 in finance journals, 7 in economics): "agricultural insurance" on 16, "index insurance" on 3, "crop insurance" on 2. Climate shocks, by contrast, are one of the fastest-growing keywords in the economics journals.

## Questions

- Do satellite-based indices (vegetation, soil moisture) track farm losses better than rainfall at a weather station, and does better tracking raise take-up?
- Do bundled products (insurance with credit, seed or mobile money) reach farmers who would not buy insurance alone?
- When insured farmers are paid after a drought, do they invest more in the next season than uninsured neighbours?

## Data that could help

Insurance programme enrolment and payout records, satellite indices, farm surveys with yields and investment, and mobile money data.

## Next step

Compare the correlation of farm-level losses with rainfall-based and satellite-based indices in one programme that switched from one to the other.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| agricultural insurance | 1 | #2550 | 1, 1, 1, 3, 1, 1, 0, 1, 3, 0, 1, 2, 1 | 15 |
| index insurance | 0 | – | 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0 | 3 |


---

---
title: "Immigration and housing costs (research idea)"
description: "Immigration and migrant workers appear on 141 recent papers, mostly in economics, with 42 in July–September 2026 against about 30 per quarter before. Only 18 carry a housing, rent or house-price keyword. Housing costs are now the loudest argument in immigration politics. How much of rent growth do arrivals explain, and how much is a lack of building?"
url: https://scholar4ai.com/ideas/immigration-and-housing-costs
markdown_url: https://scholar4ai.com/ideas/immigration-and-housing-costs.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "housing", "migration"]
keywords: ["immigration", "immigration policy", "housing market"]
---

# Immigration and housing costs

Immigration and migrant workers appear on 141 recent papers, mostly in economics, with 42 in July–September 2026 against about 30 per quarter before. Only 18 carry a housing, rent or house-price keyword. Housing costs are now the loudest argument in immigration politics. How much of rent growth do arrivals explain, and how much is a lack of building?

## What prompted this

Since October 2025, 141 papers carry an immigration or migrant keyword (116 in economics journals): "immigration" on 19, "migrant workers" on 12, "immigration policy" on 8. The quarter to September 2026 had 42 such papers against about 30 per quarter over the nine months before. Only 18 of the 141 also carry a housing, rent, house-price or home-price keyword.

## Questions

- In cities with large recent inflows (Canada, Australia, Germany, the UK), how much of rent growth is attributable to immigration once construction, interest rates and incomes are held fixed?
- Do immigrants raise rents mainly where housing supply is constrained by regulation?
- Do policies that cut immigration to ease housing, such as Canada's 2024 caps, lower rents, and at what cost to construction labour?

## Data that could help

Regional migration flows, rents and house prices, building permits and housing supply elasticity estimates.

## Next step

Estimate the rent response to immigration inflows across regions, interacted with measures of housing supply constraints, for 2015–2025.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| immigration | 2 | #1148 | 1, 2, 2, 0, 1, 0, 1, 2, 3, 2, 0, 3, 2 | 17 |
| immigration policy | 0 | – | 0, 0, 1, 0, 0, 0, 1, 0, 3, 1, 1, 1, 0 | 8 |
| housing market | 3 | #687 | 2, 5, 7, 0, 1, 2, 7, 7, 4, 4, 8, 3, 3 | 50 |


---

---
title: "Human-AI collaboration in the audit (research idea)"
description: "Human-AI collaboration is rising in the management and AI journals, while audit quality remains a core accounting topic. Few papers connect them. When auditors work with AI tools, does audit quality improve, and who answers for the judgments the tools shape?"
url: https://scholar4ai.com/ideas/human-ai-collaboration-in-the-audit
markdown_url: https://scholar4ai.com/ideas/human-ai-collaboration-in-the-audit.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "management", "ai"]
keywords: ["audit quality", "human-AI collaboration", "audit fees"]
---

# Human-AI collaboration in the audit

Human-AI collaboration is rising in the management and AI journals, while audit quality remains a core accounting topic. Few papers connect them. When auditors work with AI tools, does audit quality improve, and who answers for the judgments the tools shape?

## What prompted this

"Human-AI collaboration" is among the fastest-growing keywords across all journals on the [trends page](/trends) (36 papers in July–September 2026 against about 20 expected), and in management. Since October 2025, 335 papers carry an audit keyword (135 in accounting journals). 32 of them also carry an AI keyword, but only 8 of those are in accounting journals.

## Questions

- After audit firms roll out AI tools, do restatements, missed going-concern opinions or audit fees change?
- Does AI pull auditors' attention toward what it flags and away from what it misses?
- How should audit committees and regulators assign responsibility for AI-assisted judgments?

## Data that could help

- Restatements, opinions and fees from Audit Analytics, and audit inspection reports.
- Dates of AI rollouts at large audit firms, from announcements and job postings, as a staggered treatment.
- Experiments with practising auditors on misstatements that the AI flags and ones it does not.

## Next step

Build a timeline of AI tool rollouts at the largest audit firms and check whether it can serve as a staggered treatment.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| audit quality | 3 | #687 | 6, 8, 6, 5, 11, 2, 7, 3, 8, 1, 6, 7, 2 | 70 |
| human-AI collaboration | 8 | #153 | 4, 7, 6, 7, 3, 6, 12, 6, 11, 10, 14, 12, 8 | 98 |
| audit fees | 3 | #687 | 1, 4, 4, 5, 4, 1, 10, 5, 4, 5, 1, 2, 3 | 46 |


---

---
title: "Hukou reform and the migrants who stay (research idea)"
description: "Household registration and hukou reform appear on 22 recent papers in the economics and finance journals, and rural-urban migration on 15. Chinese cities have loosened residence rules, and many smaller cities now grant local registration freely. Do migrants settle when they can, and do cities gain workers or welfare costs?"
url: https://scholar4ai.com/ideas/hukou-reform
markdown_url: https://scholar4ai.com/ideas/hukou-reform.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "migration", "china"]
keywords: ["hukou reform", "household registration", "rural-urban migration"]
---

# Hukou reform and the migrants who stay

Household registration and hukou reform appear on 22 recent papers in the economics and finance journals, and rural-urban migration on 15. Chinese cities have loosened residence rules, and many smaller cities now grant local registration freely. Do migrants settle when they can, and do cities gain workers or welfare costs?

## What prompted this

Since October 2025, 22 papers carry a hukou or household-registration keyword (14 in economics journals, 7 in finance): "household registration" on 14, "hukou reform" on 6. "Rural-urban migration" appears on 15 papers. The reforms differ sharply by city size, which few papers exploit.

## Questions

- In cities that removed registration limits, did migrants bring their families, buy homes and enrol children in local schools, and did their consumption rise?
- Did large cities that kept points-based systems lose workers to cities that opened, and did wages respond?
- What did local governments spend on schools and health care for new residents, and did the fiscal transfers follow the people?

## Data that could help

City-level reform dates and rules, migrant surveys (the China Migrants Dynamic Survey), housing transactions and municipal budgets.

## Next step

Use the staggered relaxation of registration rules across cities to compare migrants' family settlement and home purchases before and after.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| hukou reform | 0 | – | 0, 2, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0 | 6 |
| household registration | 1 | #2550 | 1, 0, 0, 2, 0, 1, 2, 3, 1, 0, 2, 2, 0 | 14 |
| rural-urban migration | 1 | #2550 | 1, 0, 1, 2, 2, 2, 1, 0, 1, 3, 1, 1, 1 | 15 |


---

---
title: "How fragile is the difference-in-differences boom?"
description: "Difference-in-differences is the most-mentioned method on the equation ranking, and treatment effect heterogeneity is one of the fastest-rising keywords in economics. A first check finds that about three in ten recent DiD papers with searchable full text name a heterogeneity-robust estimator. How many staggered-adoption findings would survive one?"
url: https://scholar4ai.com/ideas/how-fragile-is-the-did-boom
markdown_url: https://scholar4ai.com/ideas/how-fragile-is-the-did-boom.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "exploring"
tags: ["economics", "finance", "methods"]
keywords: ["treatment effect heterogeneity", "staggered difference-in-differences", "difference-in-differences"]
---

# How fragile is the difference-in-differences boom?

Difference-in-differences is the most-mentioned method on the equation ranking, and treatment effect heterogeneity is one of the fastest-rising keywords in economics. A first check finds that about three in ten recent DiD papers with searchable full text name a heterogeneity-robust estimator. How many staggered-adoption findings would survive one?

## What prompted this

On the [equation ranking](/rankings/equations), [difference-in-differences](/equations/difference-in-differences) is the most-mentioned method: 148 papers in the 30 days to 2026-10-23 (72 in economics journals, 53 in finance, 14 in management, 6 in accounting). Instrumental variables (112 papers), two-way fixed effects (69) and propensity score matching (60) follow. In economics, "treatment effect heterogeneity" had 14 papers in July–September 2026 against about 4 expected from its earlier share.

Two-way fixed-effects DiD can be badly biased when a policy starts at different times for different units and its effect varies. Newer estimators fix this: Callaway and Sant'Anna, Sun and Abraham, de Chaisemartin and D'Haultfœuille, and Borusyak, Jaravel and Spiess, with the Goodman-Bacon decomposition as a diagnostic.

## First check

OpenAlex has searchable full text for 55 of the 148 DiD papers. 17 of those 55 (31%) name at least one of these estimators or the decomposition: 11 of 20 in finance, 3 of 21 in economics, 2 of 9 in management and 1 of 4 in accounting. The samples are small and most papers have no searchable full text, so this is a first signal, not a finding.

## Questions

- Re-estimated with robust estimators, how often do recent staggered-adoption results change sign, size or significance?
- Which designs are most at risk: many adoption cohorts, long panels, or effects that build up over time?
- Do studies of Chinese pilot policies, a common setting in these journals, behave differently?

## Data that could help

Replication packages where journals require them; for city pilot studies, the public city panels most of them use, such as the China City Statistical Yearbook.

## Next step

Pick 20 recent staggered DiD papers with replication data and re-estimate each one with two robust estimators.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| treatment effect heterogeneity | 0 | – | 1, 2, 3, 1, 1, 4, 3, 1, 4, 6, 6, 5, 0 | 37 |
| staggered difference-in-differences | 0 | – | 0, 1, 0, 0, 1, 0, 2, 2, 1, 2, 2, 0, 0 | 11 |
| difference-in-differences | 0 | – | 1, 2, 1, 0, 1, 0, 1, 1, 0, 4, 0, 1, 0 | 12 |


---

---
title: "Housing costs and the fertility decline (research idea)"
description: "Fertility is one of the fastest-growing keywords in the economics journals, as birth rates hit record lows in East Asia and elsewhere. Housing prices are a large topic too, but only 5 of 94 recent fertility papers carry a housing keyword. How much of the fertility decline do housing costs explain, and do housing policies raise births?"
url: https://scholar4ai.com/ideas/housing-costs-and-the-fertility-decline
markdown_url: https://scholar4ai.com/ideas/housing-costs-and-the-fertility-decline.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "households"]
keywords: ["fertility", "fertility decline", "housing prices"]
---

# Housing costs and the fertility decline

Fertility is one of the fastest-growing keywords in the economics journals, as birth rates hit record lows in East Asia and elsewhere. Housing prices are a large topic too, but only 5 of 94 recent fertility papers carry a housing keyword. How much of the fertility decline do housing costs explain, and do housing policies raise births?

## What prompted this

On the [economics dashboard](/categories/economics), "fertility" had 15 papers in July–September 2026 against about 8 expected from its earlier share, and "housing market" is growing as well (13 against 8). Since October 2025, 94 papers carry a fertility keyword (87 in economics journals). Only 5 of them also carry a housing keyword, while "housing prices" appears on 123 papers.

## Questions

- Do couples in places where housing costs rose faster have fewer children, or the same number later?
- Do owners and renters respond differently to rising house prices (a wealth gain for one, a cost for the other)?
- Did housing support for families, such as subsidies and priority access in China, Korea or Japan, change births?

## Data that could help

Regional births and house prices, household panels such as the China Family Panel Studies (see the [dataset ranking](/rankings/datasets)), and the timing of family housing policies.

## Next step

Relate city-level birth rates in China to house-price growth for 2010–2023, separately for cities with mostly owners and cities with many renters.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| fertility | 3 | #687 | 0, 2, 3, 2, 2, 2, 4, 2, 7, 5, 3, 7, 3 | 39 |
| fertility decline | 0 | – | 2, 4, 1, 1, 2, 0, 4, 2, 2, 1, 1, 2, 0 | 22 |
| housing prices | 4 | #458 | 8, 13, 8, 12, 2, 8, 13, 15, 11, 14, 10, 6, 4 | 120 |


---

---
title: "Household debt when rates rise (research idea)"
description: "Household debt and borrowing appear on 53 recent papers in the economics and finance journals, but only 9 of them carry an interest-rate, tightening, variable-rate or refinancing keyword. The 2022–2024 rate rises hit households with floating-rate mortgages hard in some countries and barely at all in others. Where did consumption fall, and did defaults follow?"
url: https://scholar4ai.com/ideas/household-debt-when-rates-rise
markdown_url: https://scholar4ai.com/ideas/household-debt-when-rates-rise.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "finance", "households"]
keywords: ["household debt", "household borrowing"]
---

# Household debt when rates rise

Household debt and borrowing appear on 53 recent papers in the economics and finance journals, but only 9 of them carry an interest-rate, tightening, variable-rate or refinancing keyword. The 2022–2024 rate rises hit households with floating-rate mortgages hard in some countries and barely at all in others. Where did consumption fall, and did defaults follow?

## What prompted this

Since October 2025, 53 papers carry a household-debt, household-borrowing, household-leverage or mortgage-debt keyword (29 in economics journals, 24 in finance): "household debt" on 32, "household borrowing" on 11, "household leverage" on 7. Only 9 of them also carry an interest-rate, rate-hike, monetary-tightening, variable-rate, adjustable or refinancing keyword.

## Questions

- In countries with mostly floating-rate mortgages (Australia, Sweden, the UK), how much did consumption fall per point of rate increase, compared with fixed-rate countries (the US, France)?
- Did households that had refinanced at low fixed rates stop moving house, and what did that do to labour mobility?
- Did arrears rise where expected, or did savings built up in the pandemic absorb the shock?

## Data that could help

Loan-level mortgage data with rate types, household spending from bank transaction data, and arrears statistics by country.

## Next step

Compare quarterly consumption growth across countries by their share of floating-rate mortgages during the 2022–2024 tightening.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| household debt | 1 | #2550 | 5, 5, 0, 3, 0, 1, 1, 5, 4, 2, 4, 1, 1 | 31 |
| household borrowing | 1 | #2550 | 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 2, 3, 1 | 10 |


---

---
title: "Heat pumps and the retrofit gap (research idea)"
description: "Heat pumps appear on 12 recent papers, energy retrofits on 7 and building energy efficiency on 10, almost all in the economics journals. Heating is the hardest part of home decarbonization, and subsidies have had uneven results. Who installs, do the energy savings match the models, and why do many households not switch?"
url: https://scholar4ai.com/ideas/heat-pumps-and-retrofits
markdown_url: https://scholar4ai.com/ideas/heat-pumps-and-retrofits.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "energy", "households"]
keywords: ["heat pumps", "energy retrofit", "building energy efficiency"]
---

# Heat pumps and the retrofit gap

Heat pumps appear on 12 recent papers, energy retrofits on 7 and building energy efficiency on 10, almost all in the economics journals. Heating is the hardest part of home decarbonization, and subsidies have had uneven results. Who installs, do the energy savings match the models, and why do many households not switch?

## What prompted this

Since October 2025, 44 papers carry a heat-pump, building-electrification, residential-heating or energy-retrofit keyword (38 in economics journals): "heat pumps" on 12, "building energy efficiency" on 10, "energy retrofit" on 7. The quarter to September 2026 had 11 such papers against about 10 per quarter earlier, with no sign of a surge despite large new subsidy programmes.

## Questions

- Do measured energy savings after retrofits and heat pump installations match the engineering estimates, and where do they fall short?
- Which households take up subsidies, and do rebates reach renters and low-income owners?
- When electricity is taxed more heavily than gas, as in many European countries, do heat pumps save money for the household at all?

## Data that could help

Smart meter data before and after installation, subsidy programme records, housing stock surveys and energy prices.

## Next step

Match subsidy recipients' smart meter readings before and after heat pump installation and compare realized savings with the projected ones.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| heat pumps | 0 | – | 0, 1, 1, 2, 3, 0, 1, 0, 1, 3, 0, 0, 0 | 12 |
| energy retrofit | 0 | – | 0, 0, 2, 0, 0, 2, 0, 0, 1, 0, 0, 2, 0 | 7 |
| building energy efficiency | 2 | #1148 | 0, 0, 2, 0, 1, 1, 1, 0, 0, 2, 1, 0, 2 | 8 |


---

---
title: "Heat and the productivity of work (research idea)"
description: "Extreme heat and heat waves appear on 47 recent papers, and temperature keywords on 208, but only 29 of those carry a productivity, labour, worker or firm keyword. As summers get hotter, how much output is lost on hot days, in which industries, and does adaptation close the gap?"
url: https://scholar4ai.com/ideas/heat-and-labour-productivity
markdown_url: https://scholar4ai.com/ideas/heat-and-labour-productivity.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "climate", "labor"]
keywords: ["extreme heat", "heat waves", "labor productivity"]
---

# Heat and the productivity of work

Extreme heat and heat waves appear on 47 recent papers, and temperature keywords on 208, but only 29 of those carry a productivity, labour, worker or firm keyword. As summers get hotter, how much output is lost on hot days, in which industries, and does adaptation close the gap?

## What prompted this

Since October 2025, 208 papers carry an extreme-heat, heat-wave, heat-stress or temperature keyword (105 in economics journals, 69 in AI): "extreme heat" on 34, "heat waves" on 13. Only 29 of the 208 also carry a productivity, labour, worker, output, firm or absenteeism keyword; "labor productivity" alone appears on 94 papers, mostly about other causes.

## Questions

- Using daily plant-level data, how much does output fall on days above 35°C in manufacturing, construction and agriculture, and does the loss persist into the following days?
- Do firms in hotter regions adapt (shifts, cooling, automation) so that the marginal effect of heat falls over time?
- Who bears the loss: workers through pay, firms through profits, or customers through delays?

## Data that could help

Daily production or output records from firms, weather station and reanalysis data (ERA5), and worker-level time and attendance data.

## Next step

Match daily output data from a multi-plant manufacturer to local temperatures and estimate the output loss per hot day by plant type.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| extreme heat | 1 | #2550 | 4, 5, 1, 2, 1, 3, 6, 3, 4, 0, 1, 3, 1 | 33 |
| heat waves | 1 | #2550 | 0, 2, 1, 2, 1, 2, 1, 1, 1, 0, 1, 1, 0 | 13 |
| labor productivity | 3 | #687 | 9, 11, 10, 7, 8, 6, 4, 8, 7, 5, 8, 8, 3 | 91 |


---

---
title: "Greenwashing as the gap between talk and emissions"
description: "Greenwashing is one of the fastest-growing keywords in the finance journals, yet few papers measure it against emissions. Scored as the gap between what firms say about climate and what their emissions do, does greenwashing fool ESG raters, lenders or investors?"
url: https://scholar4ai.com/ideas/greenwashing-talk-versus-emissions
markdown_url: https://scholar4ai.com/ideas/greenwashing-talk-versus-emissions.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "accounting", "esg"]
keywords: ["greenwashing", "ESG ratings", "ESG disclosure", "corporate greenwashing"]
---

# Greenwashing as the gap between talk and emissions

Greenwashing is one of the fastest-growing keywords in the finance journals, yet few papers measure it against emissions. Scored as the gap between what firms say about climate and what their emissions do, does greenwashing fool ESG raters, lenders or investors?

## What prompted this

In the [finance dashboard](/categories/finance) trends, "greenwashing" had 24 papers in July–September 2026 against about 13 expected from its earlier share. Since October 2025, 189 papers carry a greenwashing keyword (105 in finance, 37 in economics, 32 in management). Only 19 of them also carry "ESG ratings", and only 10 carry an emissions keyword, so most measure greenwashing from text or ratings alone.

## Questions

- Does a firm-level "talk minus walk" score (the change in climate language minus the change in emissions intensity) predict later ESG rating changes, controversies or enforcement?
- Do ESG raters reward the talk? Do loan spreads and bond yields?
- Does the gap shrink after mandatory climate disclosure rules?

## Data that could help

- Climate language in annual and sustainability reports, scored with a language model.
- Emissions and ratings from the ESG and energy categories of the [dataset ranking](/rankings/datasets), for example Trucost, CDP, Refinitiv ESG or MSCI ESG, or the EPA Greenhouse Gas Reporting Program for US plants.
- Staggered disclosure mandates as a [difference-in-differences](/equations/difference-in-differences) design, estimated with heterogeneity-robust methods.

## Next step

Collect the 19 papers that pair greenwashing with ESG ratings and list how each one measures greenwashing.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| greenwashing | 4 | #458 | 9, 11, 10, 6, 9, 9, 12, 14, 7, 9, 17, 14, 4 | 127 |
| ESG ratings | 9 | #129 | 11, 6, 8, 15, 12, 19, 2, 18, 5, 14, 9, 10, 6 | 129 |
| ESG disclosure | 11 | #85 | 8, 10, 10, 14, 15, 5, 6, 9, 9, 12, 11, 13, 9 | 122 |
| corporate greenwashing | 3 | #687 | 2, 4, 2, 4, 3, 0, 3, 1, 4, 4, 0, 4, 2 | 31 |


---

---
title: "Green hydrogen between targets and costs (research idea)"
description: "Green hydrogen appears on 32 recent papers and hydrogen policy on 11, mostly in the economics journals, while projects are being cancelled as costs stay high. Which uses of hydrogen survive at realistic prices, and what do the subsidies buy?"
url: https://scholar4ai.com/ideas/green-hydrogen-costs
markdown_url: https://scholar4ai.com/ideas/green-hydrogen-costs.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "energy"]
keywords: ["green hydrogen", "hydrogen policy"]
---

# Green hydrogen between targets and costs

Green hydrogen appears on 32 recent papers and hydrogen policy on 11, mostly in the economics journals, while projects are being cancelled as costs stay high. Which uses of hydrogen survive at realistic prices, and what do the subsidies buy?

## What prompted this

Since October 2025, 100 papers carry a hydrogen keyword (60 in economics journals, 12 in AI, 12 in operations research, 11 in management): "green hydrogen" on 32, "hydrogen policy" on 11, "hydrogen storage" on 10. The count fell to 21 in July–September 2026 against about 25 per quarter earlier, as several flagship projects were shelved.

## Questions

- At electrolyser and electricity costs observed in 2025, which uses (fertilizer, steel, refining, shipping fuel, heating) break even, and which never will?
- How many announced projects reached final investment decision, and what distinguishes those that did?
- Do production subsidies such as the US hydrogen tax credit and the EU hydrogen bank lower costs over time, or mainly pay for projects that would exist anyway?

## Data that could help

Project databases (the IEA hydrogen project tracker), electrolyser price surveys, subsidy awards, and firm announcements.

## Next step

Compile the fate of every green hydrogen project announced in 2021–2023 and model the break-even hydrogen price for each end use at 2025 costs.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| green hydrogen | 4 | #458 | 4, 1, 3, 4, 5, 3, 2, 0, 2, 2, 1, 2, 4 | 29 |
| hydrogen policy | 1 | #2550 | 0, 1, 0, 0, 2, 4, 0, 0, 0, 1, 1, 1, 1 | 10 |


---

---
title: "Graph neural networks for financial contagion"
description: "Financial contagion and systemic risk are rising in the finance and economics journals, and graph neural networks are among the largest topics in the AI journals. The two have met in only five papers. Can a graph model learn the channels of contagion that network models fix in advance?"
url: https://scholar4ai.com/ideas/graph-neural-networks-for-financial-contagion
markdown_url: https://scholar4ai.com/ideas/graph-neural-networks-for-financial-contagion.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "ai", "systemic risk"]
keywords: ["financial contagion", "systemic risk", "graph neural networks"]
---

# Graph neural networks for financial contagion

Financial contagion and systemic risk are rising in the finance and economics journals, and graph neural networks are among the largest topics in the AI journals. The two have met in only five papers. Can a graph model learn the channels of contagion that network models fix in advance?

## What prompted this

"Financial contagion" is among the fastest-growing keywords across all journals on the [trends page](/trends) (31 papers in July–September 2026 against 16 expected), and "systemic risk" is growing in economics. Since October 2025, 392 papers carry a contagion or systemic-risk keyword; 66 of them also carry a network keyword, but only 5 carry a graph-neural-network keyword. Over the same period "graph neural networks" appears on 578 papers, 555 of them in AI journals, and graph convolution is near the top of the [equation ranking](/rankings/equations).

## Questions

- Given exposure networks between banks or firms, does a graph neural network forecast joint tail losses (CoVaR, marginal expected shortfall) better than cascade models with fixed rules?
- Which links carry stress: lending, common holdings, supply chains or news co-mentions?
- Can the model's attributions tell supervisors which institutions transmit stress, and are they stable enough to act on?

## Data that could help

- Common-holdings networks from 13F filings, supply-chain links, and bank exposures where supervisors publish them.
- Daily returns for tail-risk measures such as CoVaR.

## Next step

Rebuild one published common-holdings contagion network from 13F data and compare a simple graph convolution model with a CoVaR benchmark.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| financial contagion | 12 | #76 | 3, 4, 4, 9, 7, 7, 4, 5, 5, 10, 10, 11, 11 | 79 |
| systemic risk | 12 | #76 | 17, 18, 24, 19, 29, 23, 17, 19, 16, 22, 18, 22, 9 | 244 |
| [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 39 | #5 | 49, 32, 47, 44, 33, 50, 59, 47, 51, 54, 44, 41, 32 | 551 |


---

---
title: "Goodwill impairment under pressure (research idea)"
description: "Goodwill appears on 14 recent papers across the tracked journals, with 2 in the last quarter. After a decade of acquisitions at high prices, goodwill on corporate balance sheets is enormous, and standard setters have again debated bringing back amortization. When do firms impair, and does the market believe them?"
url: https://scholar4ai.com/ideas/goodwill-impairment-under-pressure
markdown_url: https://scholar4ai.com/ideas/goodwill-impairment-under-pressure.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "financial reporting"]
keywords: ["goodwill impairment", "goodwill"]
---

# Goodwill impairment under pressure

Goodwill appears on 14 recent papers across the tracked journals, with 2 in the last quarter. After a decade of acquisitions at high prices, goodwill on corporate balance sheets is enormous, and standard setters have again debated bringing back amortization. When do firms impair, and does the market believe them?

## What prompted this

Since October 2025, 14 papers carry a goodwill keyword (6 in accounting journals, 4 in finance): "goodwill" on 7, "goodwill impairment" on 6, "goodwill accounting" on 2. Only 2 were published in July–September 2026. Mergers and acquisitions, the source of goodwill, appear on more than 120 papers over the same period.

## Questions

- Do firms delay impairments until a CEO change or a bad year, so that the charge says more about management than about the acquired business?
- Do stock prices react to impairments, or have investors already written the goodwill off?
- Would amortization, as the FASB and IASB have considered, change acquisition prices or only reported earnings?

## Data that could help

Goodwill balances and impairment charges (Compustat), acquisition records (SDC), CEO turnover and stock returns.

## Next step

Measure how often large impairments follow CEO changes within a year, compared with the base rate, for US and European firms since 2015.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| goodwill impairment | 0 | – | 1, 0, 0, 1, 1, 0, 0, 0, 2, 0, 1, 0, 0 | 6 |
| goodwill | 0 | – | 0, 0, 0, 0, 1, 1, 3, 1, 0, 0, 1, 0, 0 | 7 |


---

---
title: "Weight-loss drugs and the economy around them"
description: "Obesity appears on 15 recent papers in the economics journals and GLP-1 receptor agonists on 2. Tens of millions of people now take drugs that cut appetite. What happens to food demand, health spending, labour supply and the firms that sell what people stop buying?"
url: https://scholar4ai.com/ideas/glp1-drugs-and-the-economy
markdown_url: https://scholar4ai.com/ideas/glp1-drugs-and-the-economy.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "health", "households"]
keywords: ["obesity", "GLP-1 receptor agonists"]
---

# Weight-loss drugs and the economy around them

Obesity appears on 15 recent papers in the economics journals and GLP-1 receptor agonists on 2. Tens of millions of people now take drugs that cut appetite. What happens to food demand, health spending, labour supply and the firms that sell what people stop buying?

## What prompted this

Since October 2025, "obesity" appears on 15 papers and "childhood obesity" on 5, almost all in economics journals; "GLP-1 receptor agonists" appears on 2 and "weight loss" on 2. The drugs' spread since 2023 is one of the largest shifts in consumer health behaviour in decades, and the economics of it is barely in the journals.

## Questions

- Do households that start the drugs cut grocery and restaurant spending, and on which products?
- Do users work more hours or earn more after a year, and does that offset the drug's cost for insurers and employers?
- How are food, beverage and restaurant firms repositioning, and does the stock market price the change?

## Data that could help

Prescription data linked to household spending panels, insurance claims with employment records, and firms' product and marketing changes.

## Next step

Use a consumer panel with pharmacy and grocery purchases to compare food spending of households before and after a member starts a GLP-1 drug.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| obesity | 1 | #2550 | 2, 0, 1, 2, 0, 1, 4, 0, 1, 1, 2, 0, 1 | 14 |
| GLP-1 receptor agonists | 0 | – | 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0 | 2 |


---

---
title: "The global minimum tax as a natural experiment"
description: "Tax avoidance and profit shifting are steady topics across the finance, accounting and economics journals, but the 15% global minimum tax, in force in many countries since 2024, barely appears; only 3 of 103 recent papers carry a minimum-tax keyword. Does it end profit shifting, or move it to other margins?"
url: https://scholar4ai.com/ideas/global-minimum-tax-and-profit-shifting
markdown_url: https://scholar4ai.com/ideas/global-minimum-tax-and-profit-shifting.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "economics", "public finance"]
keywords: ["global minimum tax", "profit shifting", "tax avoidance"]
---

# The global minimum tax as a natural experiment

Tax avoidance and profit shifting are steady topics across the finance, accounting and economics journals, but the 15% global minimum tax, in force in many countries since 2024, barely appears; only 3 of 103 recent papers carry a minimum-tax keyword. Does it end profit shifting, or move it to other margins?

## What prompted this

Since October 2025, 103 papers carry a tax-avoidance, profit-shifting or tax-haven keyword (36 in finance journals, 30 in accounting, 27 in economics). Only 3 of them also carry a minimum-tax, Pillar Two or BEPS keyword, and "global minimum tax" appears on 4 papers in all.

## Questions

- For multinationals above the €750 million revenue threshold, did effective tax rates in low-tax affiliates rise toward 15% from 2024, compared with groups just below it?
- Did profits stop moving to low-tax countries, or move into the tax credits and incentives the rules still allow?
- How did low-tax countries respond: domestic top-up taxes, new subsidies, or both?

## Data that could help

Public country-by-country reports, which the EU requires from large groups from 2025 onward, affiliate-level financials (Orbis), and the OECD's corporate tax statistics.

## Next step

Compare effective tax rates of groups just above and just below the €750 million threshold before and after 2024, using affiliate data.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| global minimum tax | 0 | – | 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0 | 4 |
| profit shifting | 0 | – | 0, 2, 0, 0, 0, 4, 1, 2, 1, 0, 1, 1, 0 | 12 |
| tax avoidance | 2 | #1148 | 5, 8, 3, 3, 3, 2, 3, 4, 2, 4, 3, 5, 2 | 45 |


---

---
title: "Social insurance for gig workers (research idea)"
description: "The gig and platform economy appears on 44 recent papers in the management, economics and AI journals, but only 2 of them carry a social-insurance, pension, health-insurance, labour-protection or classification keyword. Courts and laws in Europe, the UK and parts of the US are reclassifying platform workers. What happens to their earnings and hours when protections arrive?"
url: https://scholar4ai.com/ideas/gig-workers-and-social-insurance
markdown_url: https://scholar4ai.com/ideas/gig-workers-and-social-insurance.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "management", "labor"]
keywords: ["gig economy", "gig workers", "platform work"]
---

# Social insurance for gig workers

The gig and platform economy appears on 44 recent papers in the management, economics and AI journals, but only 2 of them carry a social-insurance, pension, health-insurance, labour-protection or classification keyword. Courts and laws in Europe, the UK and parts of the US are reclassifying platform workers. What happens to their earnings and hours when protections arrive?

## What prompted this

Since October 2025, 44 papers carry a gig, platform-work or platform-economy keyword (20 in management journals, 12 in economics): "platform economy" on 15, "gig economy" on 15, "gig workers" on 9, "platform work" on 8. Only 2 of them also carry a social-insurance, social-security, pension, health-insurance, labour-protection, regulation, employment-status or classification keyword.

## Questions

- After reclassification (Spain's rider law, the UK Uber ruling, the EU platform work directive), did platforms cut the number of workers, their hours or their flexibility?
- Did workers' total earnings rise with the protections, or fall with the hours?
- Which protections could be attached to the work rather than the status, such as portable benefit accounts, and have any been tried at scale?

## Data that could help

Platform earnings data from worker-side apps and audits, administrative social insurance records, and the dates of legal changes by country.

## Next step

Compare hours and earnings of delivery riders in Spain before and after the 2021 rider law with riders in Italy, where classification stayed contested.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| gig economy | 0 | – | 5, 3, 0, 1, 1, 0, 0, 1, 1, 0, 2, 1, 0 | 15 |
| gig workers | 0 | – | 2, 2, 1, 1, 0, 2, 0, 0, 1, 0, 0, 0, 0 | 9 |
| platform work | 1 | #2550 | 0, 1, 0, 1, 0, 3, 0, 0, 0, 0, 2, 0, 1 | 7 |


---

---
title: "Geopolitical risk and the re-routing of supply chains"
description: "Geopolitical risk is one of the most frequent keywords in the finance and economics journals, and supply chain resilience is a major topic in operations research, but the two rarely meet in OR journals. Customs data can show whether trade re-routes through third countries when geopolitical risk rises, and at what cost."
url: https://scholar4ai.com/ideas/geopolitical-risk-and-supply-chain-rerouting
markdown_url: https://scholar4ai.com/ideas/geopolitical-risk-and-supply-chain-rerouting.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "operations research", "trade"]
keywords: ["geopolitical risk", "supply chain resilience", "reshoring"]
---

# Geopolitical risk and the re-routing of supply chains

Geopolitical risk is one of the most frequent keywords in the finance and economics journals, and supply chain resilience is a major topic in operations research, but the two rarely meet in OR journals. Customs data can show whether trade re-routes through third countries when geopolitical risk rises, and at what cost.

## What prompted this

Since October 2025, 445 papers carry a geopolitical keyword (202 in finance journals, 167 in economics), and "supply chain resilience" appears on 233 (95 in operations research). Only 50 papers carry both kinds of keyword, and only 8 of them are in operations research journals. In the last 30 days "geopolitical risk" is the 31st most frequent keyword.

## Questions

- After geopolitical shocks such as sanctions or tariff rounds, how much trade moves to connector countries rather than disappearing?
- Does re-routing show up in costs and lead times, for example in unit values and shipping times?
- Which network-design models in the OR literature predict the re-routing that actually happens?

## Data that could help

- UN Comtrade or CEPII BACI flows, Chinese customs records and tariff data (the international trade category of the [dataset ranking](/rankings/datasets)).
- The Geopolitical Risk index of Caldara and Iacoviello.
- A gravity model with exporter-year, importer-year and pair fixed effects (see the [equation ranking](/rankings/equations)).

## Next step

Estimate a gravity model for 2015–2025 with the geopolitical risk index interacted with political alignment, and look for trade diverted to third countries.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| geopolitical risk | 17 | #37 | 15, 26, 25, 17, 24, 23, 26, 17, 29, 21, 21, 34, 13 | 278 |
| supply chain resilience | 13 | #68 | 19, 20, 20, 11, 20, 19, 18, 16, 23, 16, 17, 23, 13 | 222 |
| reshoring | 0 | – | 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0 | 6 |


---

---
title: "Generative AI as a tutor in accounting courses"
description: "Accounting education is one of the fastest-growing keywords in the accounting journals, and a quarter of those papers now involve generative AI. Does an AI tutor help students learn accounting, or only finish the homework?"
url: https://scholar4ai.com/ideas/generative-ai-tutors-in-accounting-education
markdown_url: https://scholar4ai.com/ideas/generative-ai-tutors-in-accounting-education.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "ai", "education"]
keywords: ["accounting education", "generative AI", "ChatGPT"]
---

# Generative AI as a tutor in accounting courses

Accounting education is one of the fastest-growing keywords in the accounting journals, and a quarter of those papers now involve generative AI. Does an AI tutor help students learn accounting, or only finish the homework?

## What prompted this

On the [accounting dashboard](/categories/accounting), "accounting education" had 10 papers in July–September 2026 against about 5 expected from its earlier share, and "generative AI" is growing there too. Since October 2025, 36 papers carry an accounting-education keyword (33 of them in accounting journals), and 9 of those also carry a generative-AI, ChatGPT, language-model or AI keyword.

## Questions

- When access to an AI tutor is assigned at random by section or assignment, do students score better on exams they take without it?
- Which topics gain (journal entries, consolidation, tax) and which suffer?
- Do weaker students gain more, as some studies of AI assistance at work suggest, or does the tutor widen the gaps?

## Possible approach

Randomize access across sections of an introductory course, measure learning with closed-book exams, and log the tutor conversations to see what students ask.

## Next step

Read the nine papers that combine both topics and note which ones measure learning rather than satisfaction or usage.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| accounting education | 2 | #1148 | 0, 1, 1, 1, 3, 2, 5, 4, 2, 6, 2, 2, 2 | 29 |
| generative AI | 10 | #104 | 25, 33, 21, 31, 18, 29, 38, 23, 35, 30, 37, 25, 9 | 345 |
| ChatGPT | 1 | #2550 | 12, 17, 5, 6, 1, 6, 5, 4, 11, 9, 9, 4, 1 | 89 |


---

---
title: "Is generative AI removing the bottom rung of careers?"
description: "Employment is rising as a keyword in the economics journals, and generative AI is growing there fast. Yet of nearly 1,200 recent papers on employment, wages and labour markets, only 14 carry a generative-AI keyword. Is AI taking the tasks that junior workers used to learn on, and what happens to their careers?"
url: https://scholar4ai.com/ideas/generative-ai-and-entry-level-jobs
markdown_url: https://scholar4ai.com/ideas/generative-ai-and-entry-level-jobs.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "management", "ai", "labor"]
keywords: ["young workers", "employment effects", "generative AI"]
---

# Is generative AI removing the bottom rung of careers?

Employment is rising as a keyword in the economics journals, and generative AI is growing there fast. Yet of nearly 1,200 recent papers on employment, wages and labour markets, only 14 carry a generative-AI keyword. Is AI taking the tasks that junior workers used to learn on, and what happens to their careers?

## What prompted this

In the economics journals, "employment" had 16 papers in July–September 2026 against about 8 expected, "labor market tightness" 6 against 1, and "generative AI" 9 against 3 (see the [economics dashboard](/categories/economics)). Since October 2025, 1,181 papers carry an employment, labour-market or wage keyword (918 in economics journals). Only 14 of them also carry a generative-AI, language-model or ChatGPT keyword, and "young workers" appears on 10.

## Questions

- In occupations most exposed to generative AI, has hiring of new graduates fallen since 2023 relative to hiring of experienced workers?
- Do firms that adopt AI change their training and promotion ladders?
- Do workers who started their careers in exposed jobs earn less years later?

## Data that could help

Online job postings (Lightcast), worker profiles (LinkedIn, Revelio), occupational task data (O*NET) to measure AI exposure, and payroll or administrative employment data.

## Next step

Compare monthly entry-level and experienced job postings by occupational AI exposure from 2021 to 2026.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| young workers | 1 | #2550 | 1, 1, 0, 1, 0, 0, 2, 0, 2, 0, 1, 1, 1 | 9 |
| employment effects | 3 | #687 | 2, 6, 6, 3, 8, 4, 3, 4, 4, 9, 3, 5, 3 | 57 |
| generative AI | 10 | #104 | 25, 33, 21, 31, 18, 29, 38, 23, 35, 30, 37, 25, 9 | 345 |


---

---
title: "Time-series foundation models versus professional…"
description: "Foundation models are rising fast in the AI journals, and time-series versions now forecast series they were never trained on. Economists forecast inflation and growth with surveys and small models. Do the new models beat the Survey of Professional Forecasters in real time?"
url: https://scholar4ai.com/ideas/foundation-models-versus-professional-forecasters
markdown_url: https://scholar4ai.com/ideas/foundation-models-versus-professional-forecasters.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "economics", "forecasting"]
keywords: ["foundation models", "inflation forecasting", "macroeconomic forecasting", "time series forecasting"]
---

# Time-series foundation models versus professional forecasters

Foundation models are rising fast in the AI journals, and time-series versions now forecast series they were never trained on. Economists forecast inflation and growth with surveys and small models. Do the new models beat the Survey of Professional Forecasters in real time?

## What prompted this

In the AI journals, "foundation models" had 27 papers in July–September 2026 against about 16 expected (see the [AI dashboard](/categories/ai)). Since October 2025, 1,714 papers carry a foundation-model or large-language-model keyword, but only 5 of them also carry an inflation, macroeconomic or GDP forecasting or nowcasting keyword. "Inflation forecasting" appears on 31 papers (22 in economics), and "time series forecasting" on 148 (117 in AI journals).

## Questions

- With real-time data only, do zero-shot time-series foundation models beat the median forecasts of the Survey of Professional Forecasters for inflation and GDP growth?
- Can pretraining leak the future into tests that look out of sample? Data vintages help check this.
- Where do the models fail: at turning points, at regime changes, or around data revisions?

## Data that could help

Real-time data vintages (the Philadelphia Fed's real-time data set or ALFRED), the Survey of Professional Forecasters, and FRED-MD.

## Next step

Run one open time-series foundation model on real-time CPI vintages for 2015–2025 and score it against the survey median.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| foundation models | 6 | #263 | 3, 2, 5, 4, 8, 5, 8, 6, 10, 6, 9, 12, 4 | 78 |
| inflation forecasting | 1 | #2550 | 2, 3, 0, 3, 4, 5, 0, 1, 5, 4, 4, 0, 1 | 31 |
| macroeconomic forecasting | 1 | #2550 | 1, 0, 0, 1, 2, 3, 1, 0, 2, 1, 3, 2, 1 | 16 |
| time series forecasting | 15 | #45 | 13, 15, 14, 10, 8, 17, 10, 12, 9, 7, 12, 10, 12 | 137 |


---

---
title: "What the aid cuts will show (research idea)"
description: "Foreign aid, humanitarian aid and aid effectiveness appear on 69 recent papers, mostly in the economics journals, with no rise despite the largest aid cuts in decades in 2025. The cuts are a natural experiment that the earlier aid-effectiveness debate never had. Which programmes' loss shows up first in health, schooling and conflict?"
url: https://scholar4ai.com/ideas/foreign-aid-cuts
markdown_url: https://scholar4ai.com/ideas/foreign-aid-cuts.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "development", "policy"]
keywords: ["foreign aid", "official development assistance", "humanitarian aid", "aid effectiveness"]
---

# What the aid cuts will show

Foreign aid, humanitarian aid and aid effectiveness appear on 69 recent papers, mostly in the economics journals, with no rise despite the largest aid cuts in decades in 2025. The cuts are a natural experiment that the earlier aid-effectiveness debate never had. Which programmes' loss shows up first in health, schooling and conflict?

## What prompted this

Since October 2025, 69 papers carry a foreign-aid, development-aid, humanitarian-aid, aid-effectiveness or donor keyword (45 in economics journals): "foreign aid" on 16, "humanitarian aid" on 12, "development aid" on 6, "aid effectiveness" on 6, "aid allocation" on 5. The quarter to September 2026 had 16 such papers, in line with earlier quarters.

## Questions

- In districts where health programmes lost funding in 2025, did child mortality and disease incidence rise within a year, and did governments or other donors replace the money?
- Did cash-based humanitarian aid, cut in several crises, show larger effects than in-kind aid when it stopped?
- Do the cuts change where people migrate from?

## Data that could help

Project-level aid data with locations (AidData, the OECD creditor reporting system), health surveillance and survey data, and conflict and migration records.

## Next step

Map the 2025 programme terminations to districts and compare health indicators in those districts with neighbouring ones that kept funding.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| foreign aid | 3 | #687 | 3, 1, 0, 1, 1, 1, 1, 1, 0, 2, 0, 4, 1 | 15 |
| official development assistance | 0 | – | 0, 0, 1, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0 | 4 |
| humanitarian aid | 0 | – | 0, 1, 0, 0, 1, 3, 2, 2, 0, 2, 1, 0, 0 | 12 |
| aid effectiveness | 1 | #2550 | 0, 0, 0, 0, 1, 0, 1, 1, 2, 0, 0, 1, 0 | 6 |


---

---
title: "Perishable inventory and the food that expires"
description: "Food waste reduction, perishable goods and perishable inventory appear on 69 recent papers, mostly in operations research, with the food waste problem studied separately in economics. Dynamic discounts, better forecasts and date labels all promise less waste. Which of them works at scale, and who captures the savings?"
url: https://scholar4ai.com/ideas/food-waste-and-perishables
markdown_url: https://scholar4ai.com/ideas/food-waste-and-perishables.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "food", "retail"]
keywords: ["food waste", "perishable inventory", "food waste reduction"]
---

# Perishable inventory and the food that expires

Food waste reduction, perishable goods and perishable inventory appear on 69 recent papers, mostly in operations research, with the food waste problem studied separately in economics. Dynamic discounts, better forecasts and date labels all promise less waste. Which of them works at scale, and who captures the savings?

## What prompted this

Since October 2025, 69 papers carry a food-waste, perishable, food-loss, shelf-life or expiry keyword (40 in operations research journals, 14 in economics): "food waste reduction" on 17, "perishable goods" on 14, "perishable inventory" on 12, "food waste" on 12, "shelf life" on 6. The inventory models and the policy studies rarely cite each other.

## Questions

- In retailers that introduced automatic discounts on products near expiry, how much waste fell, and did the discounts cannibalize full-price sales?
- Do AI demand forecasts reduce waste more in fresh categories than in packaged ones, and do stores trust the forecasts enough to order less?
- Did changes to date labels ("best before" versus "use by") reduce household waste, and did they raise safety incidents?

## Data that could help

Store-level waste and sales records from retailers, forecast system deployment dates, and household waste surveys around labelling changes.

## Next step

Compare waste rates in fresh categories at stores that adopted dynamic markdowns with matched stores in the same chain that had not yet adopted them.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| food waste | 0 | – | 2, 4, 1, 0, 1, 0, 2, 0, 0, 0, 1, 1, 0 | 12 |
| perishable inventory | 0 | – | 1, 1, 3, 1, 0, 0, 1, 1, 2, 1, 1, 0, 0 | 12 |
| food waste reduction | 0 | – | 3, 0, 4, 2, 1, 2, 1, 1, 1, 1, 1, 0, 0 | 17 |


---

---
title: "Food prices and unrest (research idea)"
description: "Food security, food prices and food inflation appear on 105 recent papers, mostly in economics, but only 6 of them carry a conflict, unrest, protest or violence keyword. Food price spikes preceded unrest in 2008 and 2011. With prices high again and aid budgets cut, where is the risk greatest, and does early warning work?"
url: https://scholar4ai.com/ideas/food-prices-and-unrest
markdown_url: https://scholar4ai.com/ideas/food-prices-and-unrest.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "food", "politics"]
keywords: ["food prices", "food security", "food inflation"]
---

# Food prices and unrest

Food security, food prices and food inflation appear on 105 recent papers, mostly in economics, but only 6 of them carry a conflict, unrest, protest or violence keyword. Food price spikes preceded unrest in 2008 and 2011. With prices high again and aid budgets cut, where is the risk greatest, and does early warning work?

## What prompted this

Since October 2025, 105 papers carry a food-price, food-inflation or food-security keyword (83 in economics journals): "food security" on 64, "food prices" on 19, "food inflation" on 14. Only 6 of them also carry a conflict, unrest, protest, riot, violence, instability or war keyword.

## Questions

- Do local food price spikes predict protests and violence at the district level, and is the link stronger where governments subsidize bread and fuel?
- Do food aid cuts, such as those of 2025, raise the risk in the places that lost the most aid?
- Can commodity futures and weather forecasts give months of warning, and do governments act on it?

## Data that could help

Local food prices (the World Food Programme's price database), conflict events (ACLED), aid flows and weather data.

## Next step

Estimate the relationship between district-level food price changes and protest events in Africa and the Middle East for 2015–2025, with aid cuts as an added shock.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| food prices | 0 | – | 1, 4, 0, 0, 2, 1, 2, 2, 4, 3, 0, 0, 0 | 19 |
| food security | 2 | #1148 | 6, 11, 3, 2, 4, 5, 8, 5, 6, 6, 2, 5, 2 | 63 |
| food inflation | 0 | – | 2, 3, 0, 2, 0, 2, 0, 0, 2, 1, 1, 1, 0 | 14 |


---

---
title: "Food delivery platforms and their couriers (research idea)"
description: "Online food delivery, delivery apps and on-demand delivery appear on 34 recent papers, mostly in operations research, and the topic is fading, with 5 papers in the last quarter against about 10 per quarter before. Platforms have consolidated, raised fees and face new labour rules. How do the dispatch models interact with courier pay, and who pays for faster delivery?"
url: https://scholar4ai.com/ideas/food-delivery-couriers
markdown_url: https://scholar4ai.com/ideas/food-delivery-couriers.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "platforms", "labor"]
keywords: ["online food delivery", "on-demand delivery", "food delivery apps"]
---

# Food delivery platforms and their couriers

Online food delivery, delivery apps and on-demand delivery appear on 34 recent papers, mostly in operations research, and the topic is fading, with 5 papers in the last quarter against about 10 per quarter before. Platforms have consolidated, raised fees and face new labour rules. How do the dispatch models interact with courier pay, and who pays for faster delivery?

## What prompted this

Since October 2025, 34 papers carry a food-delivery, on-demand-delivery, quick-commerce, delivery-platform or courier keyword (21 in operations research journals): "online food delivery" on 8, "food delivery apps" on 6, "on-demand food delivery" on 5, "on-demand delivery" on 5, "food delivery" on 4, "courier services" on 3. Five were published in July–September 2026 against about 10 per quarter earlier.

## Questions

- When platforms batch orders to cut courier kilometres, how much of the saving goes to couriers' earnings, to restaurants, or to the platform?
- Did minimum pay rules for couriers (New York, Seattle, Spain) change delivery times, fees and order volumes, and did couriers earn more per hour after fewer hours?
- Do the dispatch models' assumptions about courier availability hold when pay rules change who works and when?

## Data that could help

Order-level data released in regulatory proceedings, courier earnings data from worker-side apps, and platform fee histories.

## Next step

Compare delivery fees, order volumes and courier hourly earnings in New York before and after the 2023 minimum pay rule, using platform disclosures and worker-side data.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| online food delivery | 0 | – | 0, 2, 0, 1, 0, 2, 0, 1, 2, 0, 0, 0, 0 | 8 |
| on-demand delivery | 0 | – | 0, 0, 0, 0, 0, 0, 0, 2, 2, 1, 0, 0, 0 | 5 |
| food delivery apps | 0 | – | 1, 0, 0, 0, 0, 1, 2, 0, 0, 1, 1, 0, 0 | 6 |


---

---
title: "Flood risk in mortgage lending (research idea)"
description: "Climate risk is one of the most frequent keywords in the finance journals, yet only 5 of 82 recent mortgage papers carry a flood, climate-risk or disaster keyword. Do lenders price flood risk into mortgages, and do they pass the riskiest loans to government-backed buyers?"
url: https://scholar4ai.com/ideas/flood-risk-in-mortgage-lending
markdown_url: https://scholar4ai.com/ideas/flood-risk-in-mortgage-lending.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "banking", "climate"]
keywords: ["mortgage lending", "flood risk", "climate risk"]
---

# Flood risk in mortgage lending

Climate risk is one of the most frequent keywords in the finance journals, yet only 5 of 82 recent mortgage papers carry a flood, climate-risk or disaster keyword. Do lenders price flood risk into mortgages, and do they pass the riskiest loans to government-backed buyers?

## What prompted this

Since October 2025, 82 papers carry a mortgage keyword (54 in finance journals, 26 in economics), and only 5 of them also carry a flood, climate-risk, physical-risk, wildfire or natural-disaster keyword. "Climate risk" alone appears on 242 papers, so the two literatures exist side by side without meeting.

## Questions

- Do mortgages on homes in flood zones carry higher rates or lower loan-to-value ratios, after controlling for borrower risk?
- Do lenders keep low-risk loans and sell flood-exposed ones to Fannie Mae and Freddie Mac, shifting the risk to taxpayers?
- When flood maps are updated, do lending and house prices in newly mapped areas change?

## Data that could help

Loan-level mortgage data (HMDA, Fannie Mae and Freddie Mac loan files), flood zone maps and their revision dates, and house transaction records (CoreLogic, Zillow).

## Next step

Compare the share of mortgages sold to the government-sponsored enterprises inside and outside flood zones, before and after a flood map update in one state.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| mortgage lending | 1 | #2550 | 1, 1, 1, 2, 0, 1, 1, 3, 2, 4, 2, 1, 1 | 19 |
| flood risk | 1 | #2550 | 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1 | 4 |
| climate risk | 15 | #45 | 23, 25, 18, 16, 22, 14, 24, 17, 25, 16, 9, 22, 12 | 231 |


---

---
title: "Fiscal expansion and bank credit in the euro area"
description: "Government spending, borrowing costs and the euro area are all rising keywords in the finance journals. When governments borrow more, domestic banks often buy the bonds. Does that crowd out bank lending to firms, and which firms lose the most?"
url: https://scholar4ai.com/ideas/fiscal-expansion-and-bank-credit-in-the-euro-area
markdown_url: https://scholar4ai.com/ideas/fiscal-expansion-and-bank-credit-in-the-euro-area.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "economics", "banking", "macroeconomics"]
keywords: ["government spending", "euro area", "credit supply"]
---

# Fiscal expansion and bank credit in the euro area

Government spending, borrowing costs and the euro area are all rising keywords in the finance journals. When governments borrow more, domestic banks often buy the bonds. Does that crowd out bank lending to firms, and which firms lose the most?

## What prompted this

In the finance journals, "government spending" had 9 papers in July–September 2026 against about 1 expected, "borrowing costs" 9 against 3, "euro area" 9 against 3 and "credit supply" 7 against 2 (see the [finance dashboard](/categories/finance)). Since October 2025, 327 papers carry a government-spending, fiscal-policy, public-debt or sovereign-debt keyword (213 in economics journals, 105 in finance). Only 13 of them carry a credit-supply, bank-lending, crowding-out or sovereign-bank keyword.

## Questions

- When a government issues more debt, do the domestic banks that buy it lend less to firms than banks that do not?
- Are small firms, which depend on banks, hit harder than large firms that can issue bonds?
- Does central-bank bond buying, or its end, change the effect?

## Data that could help

Bank-level sovereign holdings (EBA transparency exercises), the euro area credit register AnaCredit where access allows, firm balance sheets (Orbis) and national debt issuance.

## Next step

Relate changes in banks' holdings of their own government's bonds to their lending to firms around large national issuance waves.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| government spending | 6 | #263 | 3, 6, 3, 7, 4, 3, 9, 2, 8, 5, 4, 4, 6 | 58 |
| euro area | 5 | #342 | 8, 6, 4, 3, 2, 5, 5, 4, 9, 8, 7, 4, 4 | 65 |
| credit supply | 2 | #1148 | 2, 0, 1, 1, 0, 0, 2, 1, 5, 3, 1, 4, 1 | 20 |


---

---
title: "Firm peers from contrastive embeddings (research idea)"
description: "Contrastive learning is one of the largest topics in the AI journals, yet only 6 of 749 recent contrastive-learning papers carry a finance or firm keyword. Finance research still defines peer firms mostly by industry codes. Would firm embeddings learned contrastively from filings, returns and supply chains give better peers for valuation, benchmarking and pay?"
url: https://scholar4ai.com/ideas/firm-peers-from-contrastive-embeddings
markdown_url: https://scholar4ai.com/ideas/firm-peers-from-contrastive-embeddings.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "finance", "measurement"]
keywords: ["contrastive learning", "peer firms", "industry classification"]
---

# Firm peers from contrastive embeddings

Contrastive learning is one of the largest topics in the AI journals, yet only 6 of 749 recent contrastive-learning papers carry a finance or firm keyword. Finance research still defines peer firms mostly by industry codes. Would firm embeddings learned contrastively from filings, returns and supply chains give better peers for valuation, benchmarking and pay?

## What prompted this

Since October 2025, "contrastive learning" appears on 548 papers (538 in AI journals), and 749 papers carry some contrastive-learning keyword; only 6 of them carry a finance, stock, firm, company or industry-classification keyword. "Peer firms" appears on 13 papers and "industry classification" on 4.

## Questions

- Do peers from contrastive embeddings explain a firm's returns, valuation multiples and earnings better than SIC or GICS peers, or text-based peers?
- Do they pick up a firm's move into new businesses sooner than industry codes do?
- Would they change results that rely on peers, such as relative performance evaluation in CEO pay?

## Data that could help

Business descriptions in 10-K filings, stock returns (CRSP), supply-chain links and industry codes (Compustat).

## Next step

Train a contrastive model in which a firm's filing text and its return co-movement in the next year form positive pairs, and compare the resulting peers with GICS peers.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| [contrastive learning](https://scholar4ai.com/keywords/contrastive-learning.md) | 27 | #12 | 31, 38, 48, 38, 45, 56, 58, 38, 46, 40, 49, 41, 21 | 528 |
| peer firms | 1 | #2550 | 0, 1, 3, 2, 2, 1, 1, 0, 1, 0, 1, 0, 1 | 12 |
| industry classification | 0 | – | 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0 | 4 |


---

---
title: "Finfluencers and the retail trade (research idea)"
description: "Retail investors are a large topic in the finance journals, but only 8 of 139 recent papers on them carry a social-media keyword, and \"finfluencers\" appears on just 2. Do the trades that follow a finance influencer's post earn anything, and who profits from the audience?"
url: https://scholar4ai.com/ideas/finfluencers-and-retail-trading
markdown_url: https://scholar4ai.com/ideas/finfluencers-and-retail-trading.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "market microstructure", "media"]
keywords: ["retail investor attention", "finfluencers", "social media"]
---

# Finfluencers and the retail trade

Retail investors are a large topic in the finance journals, but only 8 of 139 recent papers on them carry a social-media keyword, and "finfluencers" appears on just 2. Do the trades that follow a finance influencer's post earn anything, and who profits from the audience?

## What prompted this

Since October 2025, 139 papers carry a retail- or individual-investor keyword (109 in finance journals, 13 in accounting). Only 8 of them also carry a social-media, influencer, Reddit, Twitter, YouTube or TikTok keyword, and "finfluencers" appears on 2 papers. "Social media" itself appears on 115 papers, most of them in AI and management journals, where it is studied as text rather than as a source of trades.

## Questions

- After a popular finance influencer recommends a stock, how much retail order flow follows, and do the followers earn or lose money over the next weeks?
- Do influencers who are paid by brokers or issuers recommend worse stocks than those who are not?
- Did disclosure rules for paid promotion, now in force in several countries, change what influencers post and how markets react?

## Data that could help

Timestamped posts from TikTok, YouTube and X with view counts, retail order flow proxies such as off-exchange trades, and stock returns.

## Next step

Collect the posts of 50 finance influencers for one year, match the stocks they mention to retail trading volume and returns, and compare paid and unpaid posts.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| retail investor attention | 0 | – | 0, 2, 0, 0, 0, 1, 4, 1, 0, 2, 0, 0, 0 | 10 |
| finfluencers | 0 | – | 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0 | 2 |
| social media | 6 | #263 | 6, 8, 12, 7, 9, 12, 11, 15, 9, 8, 11, 4, 4 | 112 |


---

---
title: "Financial sentiment beyond English (research idea)"
description: "Investor sentiment and sentiment analysis appear on 490 recent papers across the AI and finance journals, but only 22 of them carry a multilingual, cross-lingual or non-English language keyword, and financial sentiment analysis on 6. Most of the world's markets are discussed in languages the standard tools were not built for. How well do the tools work there, and do they move markets?"
url: https://scholar4ai.com/ideas/financial-sentiment-beyond-english
markdown_url: https://scholar4ai.com/ideas/financial-sentiment-beyond-english.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "finance"]
keywords: ["financial sentiment analysis", "sentiment analysis", "investor sentiment"]
---

# Financial sentiment beyond English

Investor sentiment and sentiment analysis appear on 490 recent papers across the AI and finance journals, but only 22 of them carry a multilingual, cross-lingual or non-English language keyword, and financial sentiment analysis on 6. Most of the world's markets are discussed in languages the standard tools were not built for. How well do the tools work there, and do they move markets?

## What prompted this

Since October 2025, 490 papers carry a sentiment keyword (206 in AI journals, 193 in finance, 50 in economics): "investor sentiment" on 186, "sentiment analysis" on 127, "multimodal sentiment analysis" on 74, "news sentiment" on 19, "financial sentiment analysis" on 6. Only 22 of the 490 also carry a multilingual, cross-lingual, low-resource or specific non-English language keyword, led by "low-resource languages" on 9 and "multilingual sentiment analysis" on 6.

## Questions

- Do financial sentiment models fine-tuned on English lose accuracy on Japanese, Korean, Arabic or Indonesian financial news, and do translation layers recover it?
- Does news sentiment predict returns in markets with fewer analysts and more retail investors, more or less than in US markets?
- Do the sentiment measures used in published papers on emerging markets reflect the languages their investors read?

## Data that could help

Financial news and social media in several languages, local market returns, and multilingual financial sentiment datasets, which are few.

## Next step

Build a labelled financial sentiment set in three non-English languages and compare the accuracy of English-trained models, multilingual models and translation pipelines.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| financial sentiment analysis | 1 | #2550 | 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1 | 5 |
| sentiment analysis | 5 | #342 | 9, 12, 4, 12, 8, 15, 11, 19, 7, 9, 8, 9, 5 | 123 |
| investor sentiment | 5 | #342 | 14, 12, 19, 14, 17, 12, 16, 22, 10, 19, 12, 15, 4 | 182 |


---

---
title: "Financial misinformation and the markets it moves"
description: "Misinformation, fake news detection and rumor detection appear on 185 recent papers, 140 of them in AI journals, and only 10 carry a market, stock, investor or finance keyword. Fake earnings leaks, forged press releases and coordinated rumours now move prices within minutes. How much do they move, who profits, and can detection work at trading speed?"
url: https://scholar4ai.com/ideas/financial-misinformation
markdown_url: https://scholar4ai.com/ideas/financial-misinformation.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "finance", "media"]
keywords: ["misinformation", "fake news detection", "rumor detection"]
---

# Financial misinformation and the markets it moves

Misinformation, fake news detection and rumor detection appear on 185 recent papers, 140 of them in AI journals, and only 10 carry a market, stock, investor or finance keyword. Fake earnings leaks, forged press releases and coordinated rumours now move prices within minutes. How much do they move, who profits, and can detection work at trading speed?

## What prompted this

Since October 2025, 185 papers carry a misinformation, fake-news, disinformation or rumor keyword (140 in AI journals, 18 in economics, 17 in management): "misinformation" on 75, "fake news detection" on 42, "social media misinformation" on 21, "rumor detection" on 16, "misinformation detection" on 16. Only 10 of the 185 also carry an economics, finance, market, stock, firm, investor, crypto, bank or trading keyword.

## Questions

- For documented cases of false financial news (forged releases, fake regulatory filings, fabricated analyst notes), how large and how long-lived was the price move, and who traded before the correction?
- Do detectors trained on political misinformation transfer to financial rumours, which are shorter, numerical and time-critical?
- Do firms that are targeted by false news change their disclosure practices?

## Data that could help

A catalogue of false financial news incidents with timestamps, intraday prices and volumes, social media archives and enforcement actions.

## Next step

Build a catalogue of 100 documented false financial news incidents since 2018 and measure the price reaction and reversal around each.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| misinformation | 2 | #1148 | 5, 11, 3, 8, 4, 6, 5, 6, 10, 6, 6, 4, 2 | 74 |
| fake news detection | 6 | #263 | 0, 2, 2, 4, 2, 4, 9, 5, 3, 3, 3, 2, 4 | 39 |
| rumor detection | 1 | #2550 | 1, 2, 2, 1, 1, 1, 0, 3, 1, 0, 1, 3, 0 | 16 |


---

---
title: "Financial literacy and crypto speculation (research idea)"
description: "Financial literacy is one of the largest household finance topics, on 146 recent papers, but only 7 of 161 literacy papers carry a crypto, gambling or speculation keyword. Does knowing more about finance keep people out of speculative crypto trading, or make them more confident speculators?"
url: https://scholar4ai.com/ideas/financial-literacy-and-crypto-speculation
markdown_url: https://scholar4ai.com/ideas/financial-literacy-and-crypto-speculation.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "households", "crypto"]
keywords: ["financial literacy", "cryptocurrency"]
---

# Financial literacy and crypto speculation

Financial literacy is one of the largest household finance topics, on 146 recent papers, but only 7 of 161 literacy papers carry a crypto, gambling or speculation keyword. Does knowing more about finance keep people out of speculative crypto trading, or make them more confident speculators?

## What prompted this

Since October 2025, 161 papers carry a financial-literacy keyword (112 in finance journals, 24 in economics, 20 in management), with "financial literacy" alone on 146 and "digital financial literacy" on 16. Only 7 of the 161 also carry a cryptocurrency, Bitcoin, digital-asset, gambling or speculation keyword, while "cryptocurrency" appears on 76 papers.

## Questions

- Are financially literate people less likely to hold crypto, or more likely to hold it in small, diversified amounts?
- Does literacy protect against the worst outcomes (leveraged trading, scams, panic selling in crashes)?
- Do literacy programmes that now include crypto change behaviour, or only quiz scores?

## Data that could help

Household surveys that ask about crypto holdings and literacy (for example the Survey of Consumer Finances, the China Household Finance Survey and national financial capability surveys), and exchange account data where available.

## Next step

Use a household survey with both literacy questions and crypto holdings to compare holdings, trading frequency and losses across literacy levels.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| financial literacy | 4 | #458 | 12, 16, 11, 17, 12, 12, 10, 8, 11, 14, 7, 13, 4 | 143 |
| cryptocurrency | 6 | #263 | 5, 7, 5, 3, 9, 5, 4, 8, 8, 1, 3, 13, 5 | 71 |


---

---
title: "Fertilizer prices and the food they grow (research idea)"
description: "Fertilizer use and prices appear on 30 recent papers, almost all in the economics journals. The 2022 fertilizer price spike after Russia's invasion of Ukraine was one of the largest on record. How much did farmers cut application, what did it do to yields and food prices a year later, and did it speed up the shift to precision farming?"
url: https://scholar4ai.com/ideas/fertilizer-prices-and-food
markdown_url: https://scholar4ai.com/ideas/fertilizer-prices-and-food.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "agriculture", "food"]
keywords: ["fertilizer prices", "fertilizer use"]
---

# Fertilizer prices and the food they grow

Fertilizer use and prices appear on 30 recent papers, almost all in the economics journals. The 2022 fertilizer price spike after Russia's invasion of Ukraine was one of the largest on record. How much did farmers cut application, what did it do to yields and food prices a year later, and did it speed up the shift to precision farming?

## What prompted this

Since October 2025, 30 papers carry a fertilizer keyword (26 in economics journals): "fertilizer use" on 6, "fertilizer prices" on 5, "fertilizer management" on 4. Food prices and food security appear on far more papers, but the link from input prices to output prices is rarely traced through farms.

## Questions

- After the 2022 spike, by how much did farmers in different countries cut nitrogen application, and how much yield did they lose?
- Did the yield loss show up in 2023 food prices, and did importing countries with fertilizer subsidies fare better?
- Did high prices accelerate adoption of precision application, organic fertilizer or nitrogen-fixing crops, and did the change persist when prices fell?

## Data that could help

Fertilizer sales and prices by country (FAO, IFA), farm surveys with application rates, crop yields and food price indices.

## Next step

Relate country-level changes in nitrogen use in 2022 to the fertilizer price shock and to the following year's cereal yields.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| fertilizer prices | 0 | – | 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0 | 5 |
| fertilizer use | 1 | #2550 | 1, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 1, 0 | 6 |


---

---
title: "Federated learning against money laundering"
description: "Federated learning, which trains one model across organizations without pooling their data, is a large topic in the AI journals, yet only 7 of 398 recent papers on it carry a finance, banking, fraud or money-laundering keyword. Money launderers spread transactions across banks precisely because no single bank sees the whole pattern. Can banks catch them together without sharing customer data?"
url: https://scholar4ai.com/ideas/federated-learning-against-money-laundering
markdown_url: https://scholar4ai.com/ideas/federated-learning-against-money-laundering.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "finance", "banking"]
keywords: ["federated learning", "anti-money laundering", "money laundering"]
---

# Federated learning against money laundering

Federated learning, which trains one model across organizations without pooling their data, is a large topic in the AI journals, yet only 7 of 398 recent papers on it carry a finance, banking, fraud or money-laundering keyword. Money launderers spread transactions across banks precisely because no single bank sees the whole pattern. Can banks catch them together without sharing customer data?

## What prompted this

Since October 2025, 398 papers carry a federated-learning keyword (386 in AI journals), and only 7 of them carry a money-laundering, bank, finance, fraud or credit keyword. "Anti-money laundering" appears on 14 papers. In the AI journals, "decentralized learning" is rising (8 papers in July–September 2026 against about 2 expected).

## Questions

- On realistic synthetic data in which laundering chains cross banks, how much does a federated model improve detection over models that each bank trains alone?
- How much detection do privacy protections such as secure aggregation and differential privacy cost?
- Who should run and pay for such a system: the banks, a shared utility or the regulator?

## Data that could help

Synthetic anti-money-laundering datasets with transactions across banks, public transaction-graph benchmarks, and open federated learning frameworks.

## Next step

Split a synthetic money-laundering dataset across five simulated banks and compare federated, bank-by-bank and pooled models on detection and false alarms.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| [federated learning](https://scholar4ai.com/keywords/federated-learning.md) | 23 | #17 | 17, 29, 23, 36, 26, 22, 23, 19, 25, 22, 28, 21, 23 | 291 |
| anti-money laundering | 1 | #2550 | 0, 1, 0, 2, 2, 0, 2, 1, 0, 2, 1, 2, 1 | 13 |
| money laundering | 1 | #2550 | 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 2, 1, 0 | 7 |


---

---
title: "Fake reviews from language models (research idea)"
description: "Online reviews appear on 54 recent papers, and 17 of 73 review papers carry a fake, manipulation or detection keyword. Fake reviews were once cheap but detectable; language models make them cheap and fluent. Can platforms still tell, how much of the review stock is now synthetic, and do consumers still trust ratings?"
url: https://scholar4ai.com/ideas/fake-reviews-from-language-models
markdown_url: https://scholar4ai.com/ideas/fake-reviews-from-language-models.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "marketing", "ai"]
keywords: ["online reviews", "fake reviews", "fake review detection"]
---

# Fake reviews from language models

Online reviews appear on 54 recent papers, and 17 of 73 review papers carry a fake, manipulation or detection keyword. Fake reviews were once cheap but detectable; language models make them cheap and fluent. Can platforms still tell, how much of the review stock is now synthetic, and do consumers still trust ratings?

## What prompted this

Since October 2025, 73 papers carry an online-review, product-review, consumer-review or fake-review keyword (32 in AI journals, 21 in management, 12 in operations research): "online reviews" on 54, "fake review detection" on 5, "fake reviews" on 4, "review manipulation" on 3. Seventeen of the 73 carry a fake, manipulation, fraud, incentive or detection keyword.

## Questions

- What share of recent reviews on major platforms show the statistical fingerprints of language models, and did the share jump in 2023?
- Do detectors trained on older fake reviews catch model-written ones, and can platforms keep up?
- As fake reviews become undetectable, do consumers shift to verified purchases, video reviews or recommendations from people they know?

## Data that could help

Review text with timestamps (public review datasets, scraped samples), AI-text detectors, platform enforcement announcements and consumer surveys.

## Next step

Run an AI-text detector on time-stamped reviews from one platform for 2019–2025 and track the flagged share by month and product category.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| online reviews | 4 | #458 | 2, 7, 4, 7, 3, 6, 1, 6, 2, 2, 6, 4, 4 | 50 |
| fake reviews | 0 | – | 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0 | 4 |
| fake review detection | 0 | – | 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0 | 5 |


---

---
title: "Fairness audits of credit-scoring models (research idea)"
description: "Fairness and algorithmic bias are growing in the AI journals, and credit scoring is one of the areas where AI rules are strictest. Yet only 19 of 473 recent fairness and bias papers carry a credit or lending keyword. What would an audit that lenders and regulators can both use look like, and what does fairness cost in default risk?"
url: https://scholar4ai.com/ideas/fairness-audits-of-credit-scoring
markdown_url: https://scholar4ai.com/ideas/fairness-audits-of-credit-scoring.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "finance", "ethics"]
keywords: ["algorithmic fairness", "credit scoring", "algorithmic bias"]
---

# Fairness audits of credit-scoring models

Fairness and algorithmic bias are growing in the AI journals, and credit scoring is one of the areas where AI rules are strictest. Yet only 19 of 473 recent fairness and bias papers carry a credit or lending keyword. What would an audit that lenders and regulators can both use look like, and what does fairness cost in default risk?

## What prompted this

In the AI journals, "fairness" had 16 papers in July–September 2026 against about 10 expected and "algorithmic bias" 16 against about 11 (see the [AI dashboard](/categories/ai)). Since October 2025, 473 papers carry a fairness, algorithmic-bias or discrimination keyword (186 in AI journals, 155 in economics), and only 19 of them carry a credit-scoring, lending, loan or credit-risk keyword.

## Questions

- On public credit data, how do approval rates and default rates change across groups when common fairness constraints are imposed?
- Do the fairness fixes popular in AI papers hold up under the legal tests used in lending, such as disparate impact?
- Can an audit work without protected attributes, which lenders are often not allowed to collect?

## Data that could help

Mortgage applications with applicant demographics (HMDA), public credit datasets such as German Credit and LendingClub (the credit category of the [dataset ranking](/rankings/datasets)), and the models of recent papers.

## Next step

Train a standard credit model on LendingClub loans and on HMDA applications, apply three common fairness constraints, and report how approval gaps and predicted default rates change.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| algorithmic fairness | 2 | #1148 | 3, 4, 2, 4, 6, 5, 4, 5, 3, 6, 5, 5, 1 | 52 |
| credit scoring | 2 | #1148 | 2, 3, 1, 6, 4, 3, 1, 3, 5, 1, 0, 1, 2 | 30 |
| algorithmic bias | 0 | – | 5, 11, 4, 7, 5, 6, 8, 4, 3, 5, 10, 7, 0 | 75 |


---

---
title: "Fairness constraints and what they cost (research idea)"
description: "The equity-efficiency trade-off and fair division appear on 49 recent papers across the economics, operations research and AI journals. Routing, scheduling and allocation models increasingly add fairness constraints, for drivers, patients or regions. How much efficiency do the constraints cost in practice, and do the beneficiaries see them as fair?"
url: https://scholar4ai.com/ideas/fair-division-in-operations
markdown_url: https://scholar4ai.com/ideas/fair-division-in-operations.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "ethics", "methods"]
keywords: ["fair division", "equity-efficiency trade-off"]
---

# Fairness constraints and what they cost

The equity-efficiency trade-off and fair division appear on 49 recent papers across the economics, operations research and AI journals. Routing, scheduling and allocation models increasingly add fairness constraints, for drivers, patients or regions. How much efficiency do the constraints cost in practice, and do the beneficiaries see them as fair?

## What prompted this

Since October 2025, 49 papers carry a fair-allocation, fair-division, equitable, equity-efficiency, fairness-constraint or envy-free keyword (20 in economics journals, 17 in operations research, 9 in AI): "equity-efficiency trade-off" on 25, "fair division" on 15, "envy-freeness" on 2, "fairness constraints" on 2.

## Questions

- Across published models with fairness constraints, how large is the efficiency loss at the fairness level a decision maker would choose, and is the trade-off curve usually flat or steep?
- Do the people the constraints protect (drivers given balanced routes, regions given minimum service) recognize the outcome as fair, or do they judge fairness by a different measure?
- Which fairness definitions survive contact with operational data, where groups overlap and needs differ?

## Data that could help

The instances and results of recent fairness-constrained models, and surveys of affected groups in a real deployment.

## Next step

Re-solve ten published fairness-constrained models across the full range of the fairness parameter and plot the price of fairness for each.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| fair division | 2 | #1148 | 1, 2, 2, 0, 0, 1, 1, 0, 1, 2, 1, 2, 2 | 13 |
| equity-efficiency trade-off | 1 | #2550 | 0, 3, 2, 4, 1, 2, 3, 3, 2, 2, 2, 0, 1 | 24 |


---

---
title: "Do stock market anomalies die when everyone trades them?"
description: "The cross-section of stock returns, momentum and asset pricing anomalies appear on 210 recent papers in the finance and economics journals, but only 14 of them carry a crowding, capacity, decay, replication or out-of-sample keyword. As quantitative funds trade the same signals, do anomaly returns shrink, and can crowding be measured in time?"
url: https://scholar4ai.com/ideas/factor-crowding-and-anomaly-decay
markdown_url: https://scholar4ai.com/ideas/factor-crowding-and-anomaly-decay.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "asset pricing"]
keywords: ["factor investing", "momentum", "factor models", "stock return predictability"]
---

# Do stock market anomalies die when everyone trades them?

The cross-section of stock returns, momentum and asset pricing anomalies appear on 210 recent papers in the finance and economics journals, but only 14 of them carry a crowding, capacity, decay, replication or out-of-sample keyword. As quantitative funds trade the same signals, do anomaly returns shrink, and can crowding be measured in time?

## What prompted this

Since October 2025, 210 papers carry a factor-investing, factor-model, anomaly, momentum or cross-section-of-returns keyword (130 in finance journals, 38 in economics). Only 14 of them also carry a crowding, capacity, arbitrage-capital, decay, post-publication, replication or out-of-sample keyword. "Factor investing" appears on 8 papers and "stock return predictability" on 28.

## Questions

- Do anomaly returns fall after the strategies become available in cheap funds, beyond the decline after publication?
- Can crowding be measured from short interest, fund holdings and trading volume in the anomaly portfolios, and does it predict crashes such as the 2007 quant meltdown?
- Do the same anomalies decay in China's A-share market, where quantitative funds grew later?

## Data that could help

Anomaly portfolio returns (Open Source Asset Pricing, the Kenneth French library), fund holdings (13F filings), short interest and trading volume (CRSP).

## Next step

Build a crowding measure from 13F holdings for 20 anomalies and test whether it predicts their returns over the following year.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| factor investing | 0 | – | 1, 1, 0, 2, 0, 0, 0, 1, 0, 1, 2, 0, 0 | 8 |
| momentum | 1 | #2550 | 3, 1, 2, 0, 1, 0, 0, 1, 1, 3, 1, 1, 1 | 14 |
| factor models | 1 | #2550 | 1, 1, 0, 0, 0, 2, 2, 0, 2, 2, 1, 1, 1 | 12 |
| stock return predictability | 0 | – | 2, 3, 3, 1, 3, 3, 2, 3, 3, 2, 1, 2, 0 | 28 |


---

---
title: "Scheduling the world's most expensive factories"
description: "Semiconductor manufacturing appears on 14 recent papers and wafer fabrication on 3, split between the AI and operations research journals, while chip plants worth tens of billions are being built with public money. Fab scheduling is among the hardest in industry. Do the published methods reach the plants, and how much yield and throughput do they add?"
url: https://scholar4ai.com/ideas/fab-scheduling
markdown_url: https://scholar4ai.com/ideas/fab-scheduling.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "manufacturing"]
keywords: ["semiconductor manufacturing", "wafer fabrication"]
---

# Scheduling the world's most expensive factories

Semiconductor manufacturing appears on 14 recent papers and wafer fabrication on 3, split between the AI and operations research journals, while chip plants worth tens of billions are being built with public money. Fab scheduling is among the hardest in industry. Do the published methods reach the plants, and how much yield and throughput do they add?

## What prompted this

Since October 2025, 48 papers carry a semiconductor or wafer-fabrication keyword (20 in AI journals, 9 in operations research, 8 in economics): "semiconductor manufacturing" on 14, "semiconductor industry" on 12, "wafer fabrication" on 3, "semiconductor supply chain" on 3. Twenty-four of them carry a scheduling, dispatching, production, yield or capacity keyword. The economics of chip subsidies is a separate and larger literature.

## Questions

- Do dispatching rules learned by reinforcement learning, as in several recent papers, beat the rules fabs use, on fab-scale simulations with realistic tool failures?
- How much of a new fab's ramp-up time comes from scheduling and yield learning, and can it be shortened by methods from existing plants?
- As fabs multiply across countries with different labour costs, which scheduling constraints change?

## Data that could help

Public fab simulation models and datasets (for example the SMT2020 testbed), ramp-up timelines from company reports, and yield data where disclosed.

## Next step

Benchmark three recent learned dispatching methods against standard rules on a public fab simulation testbed with tool failures turned on.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| semiconductor manufacturing | 1 | #2550 | 1, 2, 1, 0, 2, 1, 1, 1, 0, 2, 1, 1, 1 | 13 |
| wafer fabrication | 1 | #2550 | 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1 | 2 |


---

---
title: "Explaining the optimizer's answer (research idea)"
description: "In the operations research journals, 1,178 recent papers carry an optimization keyword, and 36 of them carry an explanation or interpretability keyword, while interpretability appears on 379 papers in the archive, almost all about machine learning. Planners reject optimized schedules they cannot understand. What counts as an explanation of a solution, and do explanations raise adoption?"
url: https://scholar4ai.com/ideas/explainable-optimization
markdown_url: https://scholar4ai.com/ideas/explainable-optimization.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "methods"]
keywords: ["decision support system", "interpretability"]
---

# Explaining the optimizer's answer

In the operations research journals, 1,178 recent papers carry an optimization keyword, and 36 of them carry an explanation or interpretability keyword, while interpretability appears on 379 papers in the archive, almost all about machine learning. Planners reject optimized schedules they cannot understand. What counts as an explanation of a solution, and do explanations raise adoption?

## What prompted this

Since October 2025, 1,178 papers in the operations research journals carry an optimization, mathematical-programming or mixed-integer keyword, and 36 of them also carry an explanation, interpretability, counterfactual, transparency, human-in-the-loop or trust keyword. "Interpretability" appears on 379 papers in the archive, mostly in AI journals about models rather than decisions; "decision support system" appears on 70.

## Questions

- Which forms of explanation (which constraints bind, what would change the answer, why a nearby plan is worse) help planners accept or correct a solution, and which do they ignore?
- Do explanations change the solutions planners choose, and do the changes improve or worsen outcomes?
- Can explanations be generated for large mixed-integer models without solving many variants?

## Data that could help

Experiments with planners choosing between optimized plans with different explanations, and logs from decision support systems that record overrides.

## Next step

Run an experiment with professional planners comparing acceptance of the same optimized schedule presented with three kinds of explanation.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| decision support system | 3 | #687 | 7, 2, 3, 5, 3, 5, 7, 6, 9, 8, 8, 4, 3 | 67 |
| interpretability | 17 | #37 | 31, 36, 29, 22, 26, 37, 36, 33, 39, 34, 20, 20, 16 | 363 |


---

---
title: "Expected credit losses in their first real test"
description: "Loan loss provisions appear on 23 recent papers in the finance and accounting journals, IFRS 9 on 5 and CECL on 3. The expected-loss rules were designed after 2008 to make banks provision earlier. The 2022–2024 rate shock and the pandemic were their first real tests. Did provisions move earlier, or just more?"
url: https://scholar4ai.com/ideas/expected-credit-losses-in-the-first-real-test
markdown_url: https://scholar4ai.com/ideas/expected-credit-losses-in-the-first-real-test.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "banking"]
keywords: ["loan loss provisions", "IFRS 9", "CECL"]
---

# Expected credit losses in their first real test

Loan loss provisions appear on 23 recent papers in the finance and accounting journals, IFRS 9 on 5 and CECL on 3. The expected-loss rules were designed after 2008 to make banks provision earlier. The 2022–2024 rate shock and the pandemic were their first real tests. Did provisions move earlier, or just more?

## What prompted this

Since October 2025, 31 papers carry a loan-loss-provision, expected-credit-loss, IFRS 9 or CECL keyword (19 in finance journals, 11 in accounting): "loan loss provisions" on 23, "expected credit loss" on 6, "IFRS 9" on 5, "CECL" on 3. Twenty of the 31 carry a procyclicality, crisis, lending, capital or discretion keyword, so the question is on the table but with few papers.

## Questions

- Did banks under IFRS 9 and CECL raise provisions at the start of the pandemic and the rate shock, and release them later, in a way that stabilized or amplified lending?
- Do the forward-looking scenarios banks use for provisions differ systematically from consensus forecasts, in the direction of smoother earnings?
- Did the "day one" provisioning for new loans reduce lending to riskier borrowers, as critics predicted?

## Data that could help

Bank provisions by stage and scenario disclosures from annual reports, loan-level data, and macro forecasts at the time.

## Next step

Compare quarterly provision changes of IFRS 9 banks with those of banks still under incurred-loss rules during 2020 and 2022–2023.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| loan loss provisions | 3 | #687 | 1, 2, 0, 1, 3, 3, 3, 1, 1, 2, 2, 1, 3 | 20 |
| IFRS 9 | 0 | – | 0, 0, 1, 0, 1, 0, 0, 2, 0, 0, 1, 0, 0 | 5 |
| CECL | 1 | #2550 | 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1 | 2 |


---

---
title: "Evaluating fraud detection in money, not F1 scores"
description: "Class imbalance is one of the largest topics in the AI journals, and fraud detection is where it matters most for finance. But only 10 of 174 recent fraud papers carry a cost, profit or loss keyword; most report accuracy or F1 scores. Would ranking fraud models by the money they save change which ones win?"
url: https://scholar4ai.com/ideas/evaluating-fraud-detection-in-money
markdown_url: https://scholar4ai.com/ideas/evaluating-fraud-detection-in-money.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "finance", "methods"]
keywords: ["fraud detection", "class imbalance", "cost-sensitive learning"]
---

# Evaluating fraud detection in money, not F1 scores

Class imbalance is one of the largest topics in the AI journals, and fraud detection is where it matters most for finance. But only 10 of 174 recent fraud papers carry a cost, profit or loss keyword; most report accuracy or F1 scores. Would ranking fraud models by the money they save change which ones win?

## What prompted this

Since October 2025, "class imbalance" appears on 471 papers (427 in AI journals), and in the AI journals "label imbalance" is rising (7 papers in July–September 2026 against about 1 expected). Of the 174 papers with a fraud keyword (58 in finance journals, 57 in AI, 25 in economics, 16 in accounting), only 10 carry a cost-sensitive, profit, business-value, loss or cost keyword.

## Questions

- On public fraud datasets, how often is the model with the best F1 score not the one that saves the most money once losses and investigation costs are counted?
- Do the oversampling methods popular in AI papers still help when the score is money?
- How should the alert threshold be set when investigators can check only a fixed number of cases a day?

## Data that could help

Public fraud datasets with transaction amounts, such as the credit card fraud, PaySim and IEEE-CIS data in the [dataset ranking](/rankings/datasets)'s list, and the models of published papers.

## Next step

Re-evaluate five published fraud models on a public credit card dataset by the money they save under a fixed daily investigation budget.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| fraud detection | 3 | #687 | 6, 1, 2, 4, 6, 4, 4, 3, 1, 4, 3, 2, 3 | 40 |
| [class imbalance](https://scholar4ai.com/keywords/class-imbalance.md) | 22 | #20 | 36, 29, 36, 36, 34, 43, 44, 45, 40, 29, 47, 34, 19 | 453 |
| cost-sensitive learning | 1 | #2550 | 1, 0, 1, 2, 0, 2, 2, 0, 2, 3, 1, 0, 1 | 14 |


---

---
title: "The EU deforestation rule and the farms it reaches"
description: "Deforestation appears on 28 recent papers and the EU Deforestation Regulation on 4, with 10 deforestation papers in the last quarter against about 8 per quarter before. The rule requires proof that coffee, cocoa, soy, palm oil and other goods were not grown on deforested land. Will it change where forests fall, or only where goods are sold?"
url: https://scholar4ai.com/ideas/eu-deforestation-regulation
markdown_url: https://scholar4ai.com/ideas/eu-deforestation-regulation.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "trade", "environment"]
keywords: ["deforestation", "EU Deforestation Regulation"]
---

# The EU deforestation rule and the farms it reaches

Deforestation appears on 28 recent papers and the EU Deforestation Regulation on 4, with 10 deforestation papers in the last quarter against about 8 per quarter before. The rule requires proof that coffee, cocoa, soy, palm oil and other goods were not grown on deforested land. Will it change where forests fall, or only where goods are sold?

## What prompted this

Since October 2025, 36 papers carry a deforestation keyword (34 in economics journals): "deforestation" on 28, "EU Deforestation Regulation" on 4, "zero deforestation" on 2. Twelve of the 36 also carry a supply-chain, trade, regulation, commodity or due-diligence keyword. The rule's start was delayed to the end of 2025 and its scope narrowed, which makes its first years a moving target.

## Questions

- Do exporters divert goods from recently deforested land to markets without such rules, so that EU imports become clean while deforestation continues?
- Can smallholders provide the geolocation proof the rule requires, or are they dropped from supply chains?
- Does the rule's traceability requirement change the price premium for certified products?

## Data that could help

Trade data by product and origin, satellite forest loss (Global Forest Watch), supply chain traceability data (for example Trase), and farm surveys in producing countries.

## Next step

Compare forest loss near farms that supply the EU with loss near farms that supply other markets, using traceability data for cocoa or palm oil.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| deforestation | 3 | #687 | 0, 3, 2, 2, 2, 2, 2, 1, 3, 3, 5, 1, 3 | 26 |
| EU Deforestation Regulation | 1 | #2550 | 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1 | 3 |


---

---
title: "ESG investing under political backlash (research idea)"
description: "ESG investing appears on 56 recent papers in the finance journals, but only 3 of 81 papers on responsible investing carry a backlash, political or polarization keyword. Several US states now restrict ESG in public funds, and some asset managers have stopped using the label. Did fund flows, fees and holdings change?"
url: https://scholar4ai.com/ideas/esg-investing-under-political-backlash
markdown_url: https://scholar4ai.com/ideas/esg-investing-under-political-backlash.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "esg", "politics"]
keywords: ["ESG investing", "political polarization"]
---

# ESG investing under political backlash

ESG investing appears on 56 recent papers in the finance journals, but only 3 of 81 papers on responsible investing carry a backlash, political or polarization keyword. Several US states now restrict ESG in public funds, and some asset managers have stopped using the label. Did fund flows, fees and holdings change?

## What prompted this

Since October 2025, 81 papers carry an ESG-investing, responsible-investing or sustainable-fund keyword (60 in finance journals); "ESG investing" alone appears on 56. Only 3 of the 81 also carry a backlash, anti-ESG, political, polarization or partisan keyword, although "political polarization" appears on 26 papers in the archive.

## Questions

- After a state restricted ESG considerations in its public funds, did the funds' holdings and returns change, or only the words in their reports?
- Did asset managers that dropped the ESG label change their voting and engagement, or only their marketing?
- Did investors in states with restrictions pull money from ESG funds, and did fees change?

## Data that could help

Fund flows and holdings (Morningstar, CRSP Mutual Fund Database), state legislation dates, and fund voting records.

## Next step

List the US state anti-ESG laws with their dates and compare flows into ESG-labelled funds held by investors in those states with flows elsewhere.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| ESG investing | 8 | #153 | 1, 4, 1, 7, 6, 5, 1, 6, 7, 6, 2, 3, 8 | 49 |
| political polarization | 3 | #687 | 3, 3, 1, 0, 3, 1, 1, 4, 1, 1, 3, 3, 2 | 24 |


---

---
title: "Employee ownership and the firms that choose it"
description: "Profit sharing and employee ownership appear on 24 recent papers spread thinly across the tracked journals. As founders of the baby-boom generation retire, employee buyouts and ownership trusts are one way to pass on firms. Do employee-owned firms survive longer, pay better, and invest differently?"
url: https://scholar4ai.com/ideas/employee-ownership
markdown_url: https://scholar4ai.com/ideas/employee-ownership.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "finance", "governance"]
keywords: ["employee ownership", "profit sharing"]
---

# Employee ownership and the firms that choose it

Profit sharing and employee ownership appear on 24 recent papers spread thinly across the tracked journals. As founders of the baby-boom generation retire, employee buyouts and ownership trusts are one way to pass on firms. Do employee-owned firms survive longer, pay better, and invest differently?

## What prompted this

Since October 2025, 24 papers carry an employee-ownership, profit-sharing, ESOP or worker-cooperative keyword, spread across economics (6), finance (6), operations research (4), accounting (3), management (3) and AI (2): "profit sharing" on 11, "employee ownership" on 10, "worker cooperatives" on 1.

## Questions

- When a retiring founder sells to employees rather than to a competitor or a fund, does the firm keep more jobs and plants in place over the next decade?
- Do employee-owned firms pay more, invest less, or take fewer risks, and do their workers hold too much of their wealth in one firm?
- Do the tax incentives for employee ownership trusts, as in the UK and proposed elsewhere, raise the number of such sales?

## Data that could help

Ownership transition records, employee ownership registers, firm accounts (Orbis) and employment data.

## Next step

Compare survival, employment and wages of UK firms sold to employee ownership trusts since 2014 with firms of similar size sold to trade buyers.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| employee ownership | 0 | – | 2, 2, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0 | 10 |
| profit sharing | 2 | #1148 | 1, 1, 1, 0, 1, 1, 0, 1, 2, 2, 0, 0, 2 | 10 |


---

---
title: "Electric truck routing with charging queues"
description: "Electric trucks are entering freight fleets, and routing them is a growing problem in operations research. But most models assume a charger is free when the truck arrives; only 5 of 66 recent papers on electric-vehicle routing and charging carry a queue, waiting or congestion keyword. How much do charging queues change routes, fleet size and costs?"
url: https://scholar4ai.com/ideas/electric-truck-routing-with-charging-queues
markdown_url: https://scholar4ai.com/ideas/electric-truck-routing-with-charging-queues.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "logistics", "energy"]
keywords: ["electric vehicle routing problem", "charging stations", "electric trucks"]
---

# Electric truck routing with charging queues

Electric trucks are entering freight fleets, and routing them is a growing problem in operations research. But most models assume a charger is free when the truck arrives; only 5 of 66 recent papers on electric-vehicle routing and charging carry a queue, waiting or congestion keyword. How much do charging queues change routes, fleet size and costs?

## What prompted this

Since October 2025, 66 papers carry an electric-vehicle-routing, electric-truck, charging-station or charging-scheduling keyword (42 in operations research journals); only 5 of them carry a queue, waiting-time or congestion keyword. Vehicle routing is one of the most frequent keywords of the year in the [operations research journals](/categories/operations-research).

## Questions

- When chargers are shared and queues form at busy times, how much longer and costlier do the best routes get?
- Is it cheaper to reserve charging slots, add battery capacity or build depot chargers?
- How do hourly electricity prices change when trucks should charge?

## Possible approach

Add random queues at public chargers to a standard electric vehicle routing model with time windows, and solve it on benchmark instances with realistic numbers of chargers.

## Next step

Extend an electric vehicle routing benchmark with charging queues drawn from observed station occupancy, and compare the plans made with and without queues.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| electric vehicle routing problem | 0 | – | 3, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0 | 8 |
| charging stations | 0 | – | 1, 0, 1, 1, 0, 1, 2, 1, 1, 0, 0, 0, 0 | 8 |
| electric trucks | 0 | – | 0, 0, 0, 0, 1, 0, 1, 1, 2, 0, 0, 0, 0 | 5 |


---

---
title: "Inspecting bridges and power lines by drone"
description: "Industrial and infrastructure inspection appear on 139 recent papers, almost all in AI journals about detecting defects in images, and 25 of them involve drones or robots. Ageing bridges, pipelines and power lines need inspecting more often than inspectors can manage. How much do drone inspections cost and miss compared with people, and do they change maintenance decisions?"
url: https://scholar4ai.com/ideas/drone-inspection-of-infrastructure
markdown_url: https://scholar4ai.com/ideas/drone-inspection-of-infrastructure.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "ai", "transport"]
keywords: ["infrastructure inspection", "industrial inspection", "UAV inspection"]
---

# Inspecting bridges and power lines by drone

Industrial and infrastructure inspection appear on 139 recent papers, almost all in AI journals about detecting defects in images, and 25 of them involve drones or robots. Ageing bridges, pipelines and power lines need inspecting more often than inspectors can manage. How much do drone inspections cost and miss compared with people, and do they change maintenance decisions?

## What prompted this

Since October 2025, 139 papers carry an inspection keyword (106 in AI journals, 12 in operations research, 11 in economics): "industrial inspection" on 36, "industrial visual inspection" on 11, "infrastructure inspection" on 6, "automated inspection" on 6. Twenty-five of them also carry a drone, UAV, robot, autonomous or computer-vision keyword; "UAV inspection" appears on 3 papers.

## Questions

- On the same structures, how do drone-based defect detections compare with human inspections in what they find and what they miss, and does the mix of misses matter for safety?
- Do agencies that adopt drone inspection inspect more often, and do they then repair earlier or defer repairs more?
- What does an inspection cost by drone and by crew once flight permissions, data review and false alarms are counted?

## Data that could help

Inspection records with both methods from transport or utility agencies, maintenance decisions and costs, and defect ground truth from later repairs.

## Next step

Compare drone and crew inspection findings for a set of bridges inspected both ways, and track which findings led to repairs.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| infrastructure inspection | 0 | – | 1, 0, 0, 0, 1, 0, 2, 1, 0, 0, 1, 0, 0 | 6 |
| industrial inspection | 3 | #687 | 3, 4, 3, 3, 2, 1, 7, 2, 2, 4, 2, 0, 3 | 33 |
| UAV inspection | 0 | – | 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0 | 3 |


---

---
title: "Invoicing currencies and the limits of de-dollarization"
description: "The US dollar, invoicing currency and de-dollarization appear on 67 recent papers in the finance and economics journals, and the yuan's use in trade settlement has risen since 2022. Yet only 23 of those papers connect the dollar to trade invoicing or the renminbi. Is de-dollarization happening in the currency of contracts, or only in the headlines?"
url: https://scholar4ai.com/ideas/dollar-invoicing-and-de-dollarization
markdown_url: https://scholar4ai.com/ideas/dollar-invoicing-and-de-dollarization.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "finance", "international"]
keywords: ["US dollar", "invoicing currency", "de-dollarization"]
---

# Invoicing currencies and the limits of de-dollarization

The US dollar, invoicing currency and de-dollarization appear on 67 recent papers in the finance and economics journals, and the yuan's use in trade settlement has risen since 2022. Yet only 23 of those papers connect the dollar to trade invoicing or the renminbi. Is de-dollarization happening in the currency of contracts, or only in the headlines?

## What prompted this

Since October 2025, 67 papers carry a dollar, invoicing-currency, de-dollarization or currency-internationalization keyword (33 in finance journals, 32 in economics): "US dollar" on 21, "dollarization" on 6, "invoicing currency" on 5, "de-dollarization" on 4. Only 23 of the 67 also carry an invoicing, trade, pass-through, renminbi or reserve-currency keyword.

## Questions

- In countries that announced yuan settlement with China, what share of actual trade contracts switched currency, and did it change exchange rate pass-through to their import prices?
- Do exporters invoice in yuan when they can, or do they want dollars even for trade with China?
- Has the share of dollar invoicing in global trade changed measurably since 2022, outside of Russia?

## Data that could help

Customs data with invoicing currency (available for the EU, some Asian countries and Russia), SWIFT currency shares, and central bank reserve composition (IMF COFER).

## Next step

Compare invoicing currency shares in customs data for countries that signed yuan settlement agreements with those that did not, before and after the agreements.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| US dollar | 1 | #2550 | 1, 2, 3, 2, 3, 0, 5, 0, 1, 0, 1, 3, 0 | 21 |
| invoicing currency | 0 | – | 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0 | 5 |
| de-dollarization | 0 | – | 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0 | 4 |


---

---
title: "Do multi-criteria rankings agree? (research idea)"
description: "Supplier selection is one of the fastest-growing keywords in the operations research journals, and much of it relies on multi-criteria methods such as AHP, TOPSIS and the best-worst method. These methods can rank the same options differently. How often would a paper's conclusion change with the method, and what should a robustness check look like?"
url: https://scholar4ai.com/ideas/do-multi-criteria-rankings-agree
markdown_url: https://scholar4ai.com/ideas/do-multi-criteria-rankings-agree.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "methods"]
keywords: ["supplier selection", "TOPSIS", "multi-criteria decision making"]
---

# Do multi-criteria rankings agree?

Supplier selection is one of the fastest-growing keywords in the operations research journals, and much of it relies on multi-criteria methods such as AHP, TOPSIS and the best-worst method. These methods can rank the same options differently. How often would a paper's conclusion change with the method, and what should a robustness check look like?

## What prompted this

In the operations research journals, "supplier selection" had 11 papers in July–September 2026 against about 4 expected (see the [operations research dashboard](/categories/operations-research)). Since October 2025, 227 papers carry a multi-criteria keyword such as TOPSIS, AHP or the best-worst method (112 in AI journals, 74 in operations research). Only 15 of them carry a rank-reversal, sensitivity-analysis, robustness or comparative-analysis keyword. The formulas of these methods are on the [equation ranking](/rankings/equations)'s list.

## Questions

- In published case studies, how often does the top choice change when the weighting method, the normalization or the aggregation changes?
- How sensitive are the rankings to small changes in expert judgments?
- Would reporting the share of method variants that agree on the winner be a useful standard?

## Possible approach

Re-run the decision matrices reported in recent papers through a grid of method variants, and record how often the top choice and the full ranking agree.

## Next step

Collect the decision matrices of 20 recent supplier-selection papers and re-rank each one with TOPSIS, VIKOR and the best-worst method.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| supplier selection | 6 | #263 | 1, 1, 1, 2, 3, 5, 2, 2, 4, 3, 5, 5, 5 | 34 |
| TOPSIS | 1 | #2550 | 4, 2, 3, 5, 2, 4, 2, 4, 5, 2, 3, 2, 1 | 38 |
| multi-criteria decision making | 2 | #1148 | 16, 11, 6, 8, 18, 14, 7, 17, 22, 7, 8, 8, 2 | 142 |


---

---
title: "Do digital twins pay? (research idea)"
description: "Digital twins appear on 87 recent papers, 64 of them in AI journals and 22 in operations research, almost all about building them. Digital twin adoption appears on 2 papers. Firms are buying twins of plants, ports and cities. Which of them changed a decision, and what did they cost relative to what they saved?"
url: https://scholar4ai.com/ideas/do-digital-twins-pay
markdown_url: https://scholar4ai.com/ideas/do-digital-twins-pay.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "operations research", "technology"]
keywords: ["digital twin", "digital twin adoption"]
---

# Do digital twins pay?

Digital twins appear on 87 recent papers, 64 of them in AI journals and 22 in operations research, almost all about building them. Digital twin adoption appears on 2 papers. Firms are buying twins of plants, ports and cities. Which of them changed a decision, and what did they cost relative to what they saved?

## What prompted this

Since October 2025, 95 papers carry a digital-twin keyword (64 in AI journals, 22 in operations research, 7 in management): "digital twin" on 87, "human digital twin" on 3, "digital twin adoption" on 2. The quarter to September 2026 had 22 such papers against about 24 per quarter earlier. Almost all are about methods; the business case appears in almost none.

## Questions

- In firms that built digital twins of plants or logistics networks, which decisions were made differently because of the twin, and what were the savings?
- How much of the cost is data integration rather than modelling, and do the twins stay in use once the project team leaves?
- Do twins of public infrastructure (ports, cities) deliver benefits that justify public funding?

## Data that could help

Case records from firms and public agencies with costs and decisions, vendor contract data, and surveys of plant managers.

## Next step

Interview or survey managers at 30 firms that announced digital twin projects since 2021 about the decisions the twin changed and the project's cost and status.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| digital twin | 2 | #1148 | 13, 4, 8, 6, 3, 10, 5, 9, 8, 9, 8, 3, 2 | 86 |
| digital twin adoption | 0 | – | 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 | 2 |


---

---
title: "Do AI explanations agree with each other? (research idea)"
description: "Explainable AI is one of the largest topics in the AI journals and is spreading into management, finance and economics. Explanation fidelity (whether an explanation reflects what the model really does) is a fast-rising but still small keyword. When SHAP, LIME and attention maps disagree about the same prediction, which should a loan officer or a doctor believe?"
url: https://scholar4ai.com/ideas/do-ai-explanations-agree
markdown_url: https://scholar4ai.com/ideas/do-ai-explanations-agree.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "methods"]
keywords: ["explainable AI", "explanation fidelity", "faithfulness"]
---

# Do AI explanations agree with each other?

Explainable AI is one of the largest topics in the AI journals and is spreading into management, finance and economics. Explanation fidelity (whether an explanation reflects what the model really does) is a fast-rising but still small keyword. When SHAP, LIME and attention maps disagree about the same prediction, which should a loan officer or a doctor believe?

## What prompted this

Since October 2025, "explainable AI" appears on 419 papers (341 in AI journals, 28 in management, 20 in operations research, 15 each in finance and economics). "Explanation fidelity" appears on 11 papers and is rising in the AI journals (6 papers in July–September 2026 against about 1 expected).

## Questions

- How often do common explanation methods disagree about which features drive the same prediction?
- Which of them stay faithful when the model is changed in ways that should change the explanation?
- Do people decide better with explanations that are more faithful, or just with ones that look more confident?

## Possible approach

Compare SHAP, LIME, integrated gradients and attention on standard tabular and text tasks, using tests that check fidelity by perturbing the model or its inputs.

## Next step

Measure how often SHAP and LIME agree on the top features for one credit dataset and one medical dataset, across several model types.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| [explainable AI](https://scholar4ai.com/keywords/explainable-ai.md) | 26 | #13 | 35, 26, 31, 22, 41, 36, 38, 44, 35, 22, 35, 36, 22 | 401 |
| explanation fidelity | 1 | #2550 | 0, 0, 1, 1, 1, 0, 0, 0, 1, 2, 2, 2, 1 | 10 |
| faithfulness | 0 | – | 2, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0 | 6 |


---

---
title: "Disclosure written for machine readers (research idea)"
description: "Information transparency and corporate disclosure are rising in the accounting journals, and language models now read filings for investors. Yet few disclosure papers mention language models or XBRL. Is disclosure written for machines changing what transparency means?"
url: https://scholar4ai.com/ideas/disclosure-for-machine-readers
markdown_url: https://scholar4ai.com/ideas/disclosure-for-machine-readers.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "finance", "ai"]
keywords: ["information transparency", "corporate disclosure", "large language models"]
---

# Disclosure written for machine readers

Information transparency and corporate disclosure are rising in the accounting journals, and language models now read filings for investors. Yet few disclosure papers mention language models or XBRL. Is disclosure written for machines changing what transparency means?

## What prompted this

On the [accounting dashboard](/categories/accounting), "information transparency" (5 papers in July–September 2026 against about 1 expected) and "corporate disclosure" (7 against 3) are among the fastest-growing keywords. Since October 2025, 1,136 papers carry a disclosure or transparency keyword; only 43 of them also carry a language-model, generative-AI or XBRL keyword, and "XBRL" itself appears on 2.

## Questions

- Are filings that machines parse easily (structured tags, clean tables) priced faster after release?
- Earlier text-analysis work found that firms avoid the words that machine readers count as negative. Do language-model readers see through that?
- Does machine readability help small investors, or mostly the fastest traders?

## Data that could help

- SEC EDGAR filings with acceptance timestamps, inline XBRL data and intraday prices.
- The SEC's phase-in of inline XBRL (2019–2021) as a staggered change in machine readability.

## Next step

Measure how fast prices react to 10-K releases before and after each group of filers moved to inline XBRL.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| information transparency | 7 | #210 | 20, 14, 10, 11, 10, 15, 11, 9, 4, 14, 16, 5, 7 | 139 |
| corporate disclosure | 14 | #55 | 7, 5, 10, 5, 3, 8, 5, 6, 11, 9, 6, 10, 12 | 85 |
| [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 89 | #1 | 68, 94, 103, 90, 83, 129, 129, 126, 147, 111, 122, 110, 72 | 1,312 |


---

---
title: "Digital servitization and who owns the data"
description: "Servitization and digital servitization appear on 50 recent papers, mostly in management, with 15 in the last quarter against about 12 per quarter before. Manufacturers now sell uptime, outcomes and subscriptions instead of machines. Do the service contracts earn more than the products did, and what happens to the data the machines generate?"
url: https://scholar4ai.com/ideas/digital-servitization
markdown_url: https://scholar4ai.com/ideas/digital-servitization.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "operations"]
keywords: ["servitization", "digital servitization"]
---

# Digital servitization and who owns the data

Servitization and digital servitization appear on 50 recent papers, mostly in management, with 15 in the last quarter against about 12 per quarter before. Manufacturers now sell uptime, outcomes and subscriptions instead of machines. Do the service contracts earn more than the products did, and what happens to the data the machines generate?

## What prompted this

Since October 2025, 50 papers carry a servitization, product-service-system or as-a-service keyword (30 in management journals): "servitization" on 28, "digital servitization" on 10, "product-service systems" on 4. The quarter to September 2026 had 15 such papers against about 12 per quarter earlier.

## Questions

- Do manufacturers that shift to outcome-based contracts earn higher margins over the equipment's life, or do they take on risks they cannot price?
- Who owns and can use the operating data from connected equipment, and do customers pay for services with their data?
- Do service revenues make manufacturers' earnings steadier through downturns, as the strategy promises?

## Data that could help

Segment reporting of manufacturers that disclose service revenues, contract terms from case firms, and equipment telemetry agreements.

## Next step

Compare the margin and revenue volatility of manufacturers with high and low service revenue shares through the 2020 and 2022–2023 downturns.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| servitization | 0 | – | 2, 2, 1, 3, 0, 3, 1, 2, 7, 5, 2, 0, 0 | 28 |
| digital servitization | 0 | – | 2, 1, 1, 1, 1, 0, 1, 0, 2, 1, 0, 0, 0 | 10 |


---

---
title: "Digital public infrastructure and who it reaches"
description: "E-government appears on 28 recent papers and digital identity on 4, while India's identity, payment and data-sharing systems have become a model that dozens of countries are copying. What have the systems changed for welfare delivery, tax collection and the firms built on top of them, and who is left out?"
url: https://scholar4ai.com/ideas/digital-public-infrastructure
markdown_url: https://scholar4ai.com/ideas/digital-public-infrastructure.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "development", "technology"]
keywords: ["e-government", "digital identity"]
---

# Digital public infrastructure and who it reaches

E-government appears on 28 recent papers and digital identity on 4, while India's identity, payment and data-sharing systems have become a model that dozens of countries are copying. What have the systems changed for welfare delivery, tax collection and the firms built on top of them, and who is left out?

## What prompted this

Since October 2025, 66 papers carry an e-government, digital-identity, digital-government or digital-ID keyword (23 in AI journals, 19 in finance, 13 in economics): "e-government" on 28, "digital identity" on 4. Most study adoption and satisfaction with online services rather than the economic effects of the underlying systems.

## Questions

- Did biometric identity and direct benefit transfers cut leakage in welfare programmes without excluding people who fail authentication?
- Did instant payment systems raise formal transactions and tax revenue, and which firms grew on top of them?
- When a country copies the model, which components matter, and what happens when the identity system goes down?

## Data that could help

Programme administrative data (India's direct benefit transfer and UPI statistics), household surveys on exclusion, firm registrations and tax revenue.

## Next step

Compare welfare payment leakage and exclusion rates before and after biometric authentication in Indian states that adopted it at different times.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| e-government | 3 | #687 | 1, 2, 2, 2, 1, 7, 1, 2, 2, 1, 1, 3, 3 | 25 |
| digital identity | 0 | – | 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0 | 4 |


---

---
title: "Land rights on a server (research idea)"
description: "Property rights appear on 42 recent papers and land tenure and land rights on 13, mostly in economics. Countries are digitizing land registries and using satellite imagery to map parcels. Does a digital title change credit, investment and disputes more than a paper one, and who loses when the map is wrong?"
url: https://scholar4ai.com/ideas/digital-land-registries
markdown_url: https://scholar4ai.com/ideas/digital-land-registries.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "development", "housing"]
keywords: ["land tenure", "land rights", "property rights"]
---

# Land rights on a server

Property rights appear on 42 recent papers and land tenure and land rights on 13, mostly in economics. Countries are digitizing land registries and using satellite imagery to map parcels. Does a digital title change credit, investment and disputes more than a paper one, and who loses when the map is wrong?

## What prompted this

Since October 2025, 71 papers carry a land-titling, land-tenure, land-right, land-registration or property-rights keyword (56 in economics journals): "property rights" on 42, "land rights" on 7, "land tenure" on 6, "land registration" on 2. Few of them concern the digital registries and satellite-based mapping programmes now under way.

## Questions

- After a registry is digitized and parcels are mapped from imagery, do land transactions, mortgage lending and farm investment rise?
- Do disputes fall, or do mapping errors create new ones, especially for women and customary holders?
- Does digitization make land easier to tax, and do governments use it?

## Data that could help

Registry transaction records before and after digitization, mortgage data, court records on land disputes and household surveys.

## Next step

Use the district-by-district roll-out of a digital land registry in one country to compare land transactions and credit before and after.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| land tenure | 2 | #1148 | 0, 3, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 1 | 6 |
| land rights | 1 | #2550 | 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 2, 0 | 7 |
| property rights | 0 | – | 6, 6, 4, 2, 3, 3, 1, 2, 5, 4, 4, 2, 0 | 42 |


---

---
title: "Detecting earnings management beyond accrual models"
description: "Earnings management is a mainstay of the finance and accounting journals, still measured mostly with accrual regressions such as the modified Jones model. Only 4 of 156 recent papers on it carry a machine-learning or text keyword. Do learned models, including language models reading the filings, flag manipulation that accrual models miss?"
url: https://scholar4ai.com/ideas/detecting-earnings-management-beyond-accruals
markdown_url: https://scholar4ai.com/ideas/detecting-earnings-management-beyond-accruals.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "finance", "ai"]
keywords: ["earnings management", "accrual-based earnings management", "real earnings management"]
---

# Detecting earnings management beyond accrual models

Earnings management is a mainstay of the finance and accounting journals, still measured mostly with accrual regressions such as the modified Jones model. Only 4 of 156 recent papers on it carry a machine-learning or text keyword. Do learned models, including language models reading the filings, flag manipulation that accrual models miss?

## What prompted this

Since October 2025, 156 papers carry an earnings-management or accrual keyword (88 in finance journals, 47 in accounting). Only 4 of them also carry a machine-learning, deep-learning, language-model, text or neural-network keyword. Accrual models such as the modified Jones model, on the [equation ranking](/rankings/equations)'s list, remain the standard measure.

## Questions

- Against known cases (restatements, enforcement actions), do learned models built on raw financial statements and filing text beat accrual residuals?
- Do the measures disagree on which firms manage earnings, and do published results depend on the measure used?
- Can a language model explain why it flags a firm, in terms an auditor could check?

## Data that could help

Restatements and enforcement actions (Audit Analytics, SEC releases), financial statements (Compustat or CSMAR) and filing text (SEC EDGAR).

## Next step

Train a gradient-boosting model on raw accounting items to predict restatements, and compare its ranking of firms with modified-Jones residuals.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| earnings management | 6 | #263 | 8, 7, 11, 8, 8, 8, 9, 7, 13, 8, 8, 9, 6 | 104 |
| accrual-based earnings management | 1 | #2550 | 1, 1, 3, 1, 1, 2, 2, 1, 5, 2, 1, 1, 1 | 21 |
| real earnings management | 4 | #458 | 2, 2, 1, 1, 2, 4, 4, 4, 3, 3, 2, 4, 2 | 32 |


---

---
title: "Managing decline in Japan's shrinking regions"
description: "Population decline, shrinking cities and the aging society appear on 26 recent papers, but only 4 of them also concern Japan, the country furthest into the decline. Which local policies (consolidation of services, migration incentives, remote work) keep regions viable, and what does orderly decline cost compared with resisting it?"
url: https://scholar4ai.com/ideas/depopulation-and-shrinking-regions-in-japan
markdown_url: https://scholar4ai.com/ideas/depopulation-and-shrinking-regions-in-japan.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "demography", "cities"]
keywords: ["population decline", "aging society", "Japan"]
---

# Managing decline in Japan's shrinking regions

Population decline, shrinking cities and the aging society appear on 26 recent papers, but only 4 of them also concern Japan, the country furthest into the decline. Which local policies (consolidation of services, migration incentives, remote work) keep regions viable, and what does orderly decline cost compared with resisting it?

## What prompted this

Since October 2025, 26 papers carry a depopulation, shrinking-city, population-decline, rural-decline or aging-society keyword (21 in economics journals): "population decline" on 10, "aging society" on 9, "shrinking cities" on 2. Only 4 of them also carry a Japan keyword, although "Japan" appears on 81 papers in the archive.

## Questions

- Do municipal mergers and the concentration of services in "compact city" cores lower costs per resident without accelerating out-migration?
- Did the hometown tax donation scheme and relocation subsidies bring people and money to shrinking towns, or shift them between towns?
- Which towns stabilized after remote work spread, and what did they have in common?

## Data that could help

Municipal population, finance and service data (Japan's census and municipal finance statistics), the dates of mergers and compact city plans, and relocation subsidy records.

## Next step

Compare per-resident public spending and net migration in Japanese municipalities that adopted compact city plans with similar municipalities that did not.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| population decline | 0 | – | 0, 0, 0, 1, 1, 1, 0, 2, 3, 0, 0, 2, 0 | 10 |
| aging society | 0 | – | 2, 0, 1, 0, 0, 0, 1, 2, 0, 0, 1, 2, 0 | 9 |
| Japan | 3 | #687 | 0, 12, 5, 3, 9, 11, 4, 6, 7, 9, 6, 7, 2 | 79 |


---

---
title: "Demand forecasting with language models in the loop"
description: "Demand and sales forecasting appear on 66 recent papers in the operations research and AI journals, but only 9 of them carry a language-model, foundation-model or text keyword. Retailers now feed product descriptions, reviews, weather and news into forecasting systems. Does text improve forecasts for new products, where history is missing, and does it help where history is long?"
url: https://scholar4ai.com/ideas/demand-forecasting-with-llms
markdown_url: https://scholar4ai.com/ideas/demand-forecasting-with-llms.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "retail", "ai"]
keywords: ["demand forecasting", "sales forecasting"]
---

# Demand forecasting with language models in the loop

Demand and sales forecasting appear on 66 recent papers in the operations research and AI journals, but only 9 of them carry a language-model, foundation-model or text keyword. Retailers now feed product descriptions, reviews, weather and news into forecasting systems. Does text improve forecasts for new products, where history is missing, and does it help where history is long?

## What prompted this

Since October 2025, 66 papers carry a demand-forecasting, sales-forecasting or demand-prediction keyword (31 in operations research journals, 22 in AI): "demand forecasting" on 42, "sales forecasting" on 7, "retail demand forecasting" on 3. Only 9 of them also carry a language-model, foundation-model, transformer, deep-learning or text keyword.

## Questions

- For new products with no sales history, do embeddings of descriptions and images predict first-season demand better than analogue-based methods, and by how much?
- Does adding text (reviews, news, promotions) improve forecasts for established products, or add noise?
- Do the forecast gains translate into lower inventory and fewer stockouts once ordering rules use them?

## Data that could help

Retail sales data with product descriptions and images (public datasets such as the M5 and fashion retail datasets), and inventory records from a retailer.

## Next step

Compare new-product forecast errors from an embedding-based model and an analogue method on a fashion retail dataset with product images and text.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| demand forecasting | 6 | #263 | 6, 2, 1, 5, 1, 5, 3, 3, 4, 1, 4, 3, 6 | 38 |
| sales forecasting | 0 | – | 1, 0, 0, 0, 1, 0, 1, 0, 3, 0, 0, 1, 0 | 7 |


---

---
title: "Does delegating to AI make dishonesty easier?"
description: "Unethical behavior and moral disengagement are rising in the management journals, and AI tools now act on people's behalf. Recent experiments suggest people cheat more when a machine does the act for them. Does that happen at work, with sales targets, expense claims or performance reports?"
url: https://scholar4ai.com/ideas/delegating-to-ai-and-dishonesty
markdown_url: https://scholar4ai.com/ideas/delegating-to-ai-and-dishonesty.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "ai", "ethics"]
keywords: ["unethical behavior", "moral disengagement", "dishonesty"]
---

# Does delegating to AI make dishonesty easier?

Unethical behavior and moral disengagement are rising in the management journals, and AI tools now act on people's behalf. Recent experiments suggest people cheat more when a machine does the act for them. Does that happen at work, with sales targets, expense claims or performance reports?

## What prompted this

In management, "unethical behavior" had 6 papers in July–September 2026 against about 2 expected, and "moral disengagement" 6 against 3 (see the [management dashboard](/categories/management)). Since October 2025, 56 papers carry an unethical-behavior, dishonesty, moral-disengagement or cheating keyword (35 in management journals); only 6 of them carry an AI keyword.

## Questions

- When employees can hand a task to an AI agent with a vague goal ("hit the target"), do they choose goals that lead to dishonest outcomes more often than when they act themselves?
- Does the effect shrink when the AI asks for explicit confirmation or keeps logs?
- Whom do peers and managers blame when an AI agent misreports?

## Possible approach

Workplace experiments with AI assistants on incentivized tasks, and field data on reporting errors before and after AI tools were introduced.

## Next step

Design an online experiment in which participants delegate a self-reported performance task to an AI assistant, with goals of varying vagueness.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| unethical behavior | 1 | #2550 | 3, 3, 3, 0, 0, 0, 0, 1, 0, 3, 0, 3, 1 | 16 |
| moral disengagement | 2 | #1148 | 1, 2, 2, 2, 4, 0, 0, 1, 0, 2, 1, 3, 1 | 18 |
| dishonesty | 0 | – | 2, 2, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0 | 6 |


---

---
title: "Diversity programmes under backlash (research idea)"
description: "Diversity, equity and inclusion, inclusive leadership and affirmative action appear on 44 recent papers, with 17 in the last quarter against about 8 per quarter before, but only 5 carry a political, backlash or legal keyword. Firms in the US have cut or renamed diversity programmes after court rulings and political pressure. What changed in hiring and pay, and what only changed in name?"
url: https://scholar4ai.com/ideas/dei-under-backlash
markdown_url: https://scholar4ai.com/ideas/dei-under-backlash.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "human resources", "politics"]
keywords: ["diversity management", "workplace diversity", "affirmative action"]
---

# Diversity programmes under backlash

Diversity, equity and inclusion, inclusive leadership and affirmative action appear on 44 recent papers, with 17 in the last quarter against about 8 per quarter before, but only 5 carry a political, backlash or legal keyword. Firms in the US have cut or renamed diversity programmes after court rulings and political pressure. What changed in hiring and pay, and what only changed in name?

## What prompted this

Since October 2025, 44 papers carry a diversity-and-inclusion, diversity-management, workplace-diversity, inclusive-leadership or affirmative-action keyword (23 in management journals): "diversity, equity, and inclusion" on 12, "inclusive leadership" on 8, "diversity and inclusion" on 8, "workplace diversity" on 5, "affirmative action" on 5, "diversity management" on 4. The quarter to September 2026 had 17 such papers against about 8 per quarter earlier. Only 5 of the 44 also carry a backlash, political, legal, rollback or ideology keyword.

## Questions

- At firms that announced cuts to diversity programmes, did the demographic mix of new hires and promotions change within a year, or did only the language in reports change?
- Did firms that kept programmes under new names lose or gain customers and applicants?
- Did the end of race-conscious college admissions change the pipeline that corporate programmes draw on?

## Data that could help

Workforce demographic filings (EEO-1 reports), job postings, firms' diversity reports before and after 2024, and applicant data from hiring platforms.

## Next step

Compare the demographics of new hires in EEO-1 filings for firms that publicly cut diversity programmes in 2024–2025 with matched firms that did not.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| diversity management | 1 | #2550 | 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1 | 3 |
| workplace diversity | 1 | #2550 | 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1 | 4 |
| affirmative action | 0 | – | 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0 | 5 |


---

---
title: "Liquidation cascades in decentralized finance"
description: "Decentralized finance is a growing topic in the finance journals, but only 11 of 54 recent papers on it carry a liquidation, leverage, collateral or stablecoin keyword. Lending protocols liquidate collateral automatically when prices fall. Do these liquidations feed on themselves, and could they reach traditional markets?"
url: https://scholar4ai.com/ideas/defi-liquidation-cascades
markdown_url: https://scholar4ai.com/ideas/defi-liquidation-cascades.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "crypto", "systemic risk"]
keywords: ["decentralized finance", "cryptocurrency"]
---

# Liquidation cascades in decentralized finance

Decentralized finance is a growing topic in the finance journals, but only 11 of 54 recent papers on it carry a liquidation, leverage, collateral or stablecoin keyword. Lending protocols liquidate collateral automatically when prices fall. Do these liquidations feed on themselves, and could they reach traditional markets?

## What prompted this

Since October 2025, "decentralized finance" appears on 54 papers (33 in finance journals), with 15 in July–September 2026 against about 12 per quarter over the nine months before. Only 11 of the 54 also carry a liquidation, leverage, collateral or stablecoin keyword. Financial contagion, a fast-rising topic on the [trends page](/trends), is studied almost entirely in traditional markets.

## Questions

- When a token's price falls, how much of the next fall is caused by forced liquidations on lending protocols, and how fast do they propagate across protocols?
- Do liquidation bots and oracle delays make cascades worse at times when exchanges are congested?
- As tokenized Treasury bills and stablecoins become collateral, could a cascade spill into short-term funding markets?

## Data that could help

On-chain records of loans, collateral and liquidations from the main lending protocols, matched to exchange prices at high frequency.

## Next step

Rebuild the liquidation chain of one major crypto sell-off from on-chain data and measure how much of the price drop happened during liquidation waves.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| decentralized finance | 4 | #458 | 4, 5, 5, 3, 5, 5, 1, 5, 3, 5, 5, 5, 3 | 51 |
| cryptocurrency | 6 | #263 | 5, 7, 5, 3, 9, 5, 4, 8, 8, 1, 3, 13, 5 | 71 |


---

---
title: "Deepfake detection outside the lab (research idea)"
description: "Deepfake detection appears on 45 recent papers and AI-generated image detection on 13, almost all in AI journals, with 23 papers in the last quarter against about 21 per quarter before. Detectors score well on the datasets they were built with and fail on the next generator. How well do they work on real fraud, elections and markets, and who should run them?"
url: https://scholar4ai.com/ideas/deepfake-detection-in-the-wild
markdown_url: https://scholar4ai.com/ideas/deepfake-detection-in-the-wild.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "security", "media"]
keywords: ["deepfake detection", "AI-generated image detection"]
---

# Deepfake detection outside the lab

Deepfake detection appears on 45 recent papers and AI-generated image detection on 13, almost all in AI journals, with 23 papers in the last quarter against about 21 per quarter before. Detectors score well on the datasets they were built with and fail on the next generator. How well do they work on real fraud, elections and markets, and who should run them?

## What prompted this

Since October 2025, 86 papers carry a deepfake, face-forgery, synthetic-media or AI-generated-image keyword (78 in AI journals, 7 in management): "deepfake detection" on 45, "AI-generated image detection" on 13, "deepfakes" on 13, "synthetic media" on 7, "face forgery detection" on 6. The quarter to September 2026 had 23 such papers against about 21 per quarter earlier.

## Questions

- On deepfakes collected from actual fraud cases, political campaigns and market rumours, rather than from research datasets, what is the detection rate of published detectors?
- How quickly does accuracy fall after a new generator is released, and can detectors be updated faster than generators?
- Do labels from platform detectors change how people react to a video, or do people believe what they expected to believe?

## Data that could help

Verified deepfake collections from fact-checkers and fraud investigations, detector code from published papers, and platform labelling experiments.

## Next step

Assemble 500 deepfakes verified by fact-checking organizations and measure the accuracy of five published detectors on them.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| deepfake detection | 1 | #2550 | 3, 3, 3, 5, 3, 1, 9, 1, 4, 4, 5, 3, 1 | 44 |
| AI-generated image detection | 0 | – | 0, 1, 0, 1, 0, 1, 1, 3, 1, 1, 1, 3, 0 | 13 |


---

---
title: "Deep reinforcement learning for portfolios, replicated"
description: "Reinforcement learning appears on more than 700 recent papers, and 41 of them apply it to portfolios, trading or hedging, almost all in AI and operations research journals. The results are rarely reproduced and rarely compared with simple rules after costs. Which of the reported gains survive replication on new data?"
url: https://scholar4ai.com/ideas/deep-rl-for-portfolios-reproducibility
markdown_url: https://scholar4ai.com/ideas/deep-rl-for-portfolios-reproducibility.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "finance", "methods"]
keywords: ["deep reinforcement learning", "portfolio optimization", "portfolio management"]
---

# Deep reinforcement learning for portfolios, replicated

Reinforcement learning appears on more than 700 recent papers, and 41 of them apply it to portfolios, trading or hedging, almost all in AI and operations research journals. The results are rarely reproduced and rarely compared with simple rules after costs. Which of the reported gains survive replication on new data?

## What prompted this

Since October 2025, 738 papers carry a reinforcement-learning keyword (577 in AI journals, 120 in operations research): "reinforcement learning" on 255, "deep reinforcement learning" on 191, "multi-agent reinforcement learning" on 97. Forty-one of them also carry a portfolio, trading, stock, asset-allocation, hedging, market-making, finance or crypto keyword; "portfolio optimization" appears on 153 papers in the archive and "portfolio management" on 18.

## Questions

- When the published agents are retrained on the same data with different random seeds and then run on the two years after each paper's sample, how often do they beat equal weights and mean-variance after transaction costs?
- How much of the reported performance comes from look-ahead in data preparation and from tuning on the test period?
- Does the hedging use case, with a clearer objective, replicate better than the return-maximizing one?

## Data that could help

The code and data of recent papers, daily prices for the post-sample period, and realistic transaction cost models.

## Next step

Replicate ten published deep reinforcement learning portfolio papers with multiple seeds and evaluate them on the two years after each sample, after costs.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| deep reinforcement learning | 14 | #55 | 13, 11, 16, 17, 13, 10, 17, 17, 22, 11, 14, 19, 11 | 180 |
| portfolio optimization | 12 | #76 | 16, 13, 10, 10, 10, 15, 14, 13, 5, 11, 12, 14, 11 | 143 |
| portfolio management | 1 | #2550 | 1, 2, 0, 4, 0, 1, 3, 1, 2, 3, 0, 1, 0 | 18 |


---

---
title: "Data leakage into the benchmarks (research idea)"
description: "Data leakage appears on 16 recent papers and memorization on 3, almost all in AI journals, while thousands of papers report results on public benchmarks that language models may have seen in training. How much of reported progress is contamination, and can finance and economics benchmarks be protected?"
url: https://scholar4ai.com/ideas/data-leakage-in-benchmarks
markdown_url: https://scholar4ai.com/ideas/data-leakage-in-benchmarks.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "methods"]
keywords: ["data leakage", "memorization"]
---

# Data leakage into the benchmarks

Data leakage appears on 16 recent papers and memorization on 3, almost all in AI journals, while thousands of papers report results on public benchmarks that language models may have seen in training. How much of reported progress is contamination, and can finance and economics benchmarks be protected?

## What prompted this

Since October 2025, 29 papers carry a data-leakage, memorization, contamination or benchmark-leakage keyword (23 in AI journals): "data leakage" on 16, "memorization" on 3, "training data leakage" on 2. Benchmark results, by contrast, appear in a large share of the 17,000 AI-journal papers in the archive, and in the finance papers that test language models on public financial question sets.

## Questions

- For the benchmarks most used in the AI journals, how much do scores fall on freshly written items of the same kind, and does the fall differ between open and closed models?
- Have the financial and economic test sets used to evaluate language models (exam questions, sentiment sets) been published online before the models' training cutoffs?
- Can rolling benchmarks built from new filings, news and data releases give uncontaminated tests, and would journals require them?

## Data that could help

The release dates of benchmark items and of model training cutoffs, fresh item sets written after the cutoffs, and the results tables of recent papers.

## Next step

Rewrite 200 items from a widely used financial question benchmark with the same structure and new numbers, and compare model scores on the old and new items.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| data leakage | 2 | #1148 | 1, 1, 2, 3, 2, 0, 0, 1, 2, 0, 3, 0, 2 | 15 |
| memorization | 0 | – | 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0 | 3 |


---

---
title: "Data centres and the grid (research idea)"
description: "Data centres appear on just 16 recent papers across the tracked journals, though the count is rising, and electricity demand on 17. AI training and inference are driving the first sustained growth in electricity demand in rich countries for decades. Who pays for the new capacity, and do data centres crowd out electrification elsewhere?"
url: https://scholar4ai.com/ideas/data-centres-and-the-grid
markdown_url: https://scholar4ai.com/ideas/data-centres-and-the-grid.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "energy", "ai"]
keywords: ["data centers", "electricity demand"]
---

# Data centres and the grid

Data centres appear on just 16 recent papers across the tracked journals, though the count is rising, and electricity demand on 17. AI training and inference are driving the first sustained growth in electricity demand in rich countries for decades. Who pays for the new capacity, and do data centres crowd out electrification elsewhere?

## What prompted this

Since October 2025, 16 papers carry a data-centre keyword (7 in AI journals, 4 in finance, 4 in economics), with 7 in July–September 2026 against about 3 per quarter earlier. "Electricity demand" appears on 17 papers. Most data-centre papers in the AI journals are about cooling and scheduling, not about the grid.

## Questions

- In regions with data-centre booms (Virginia, Ireland, Texas), how much have wholesale and retail electricity prices risen, and how much of the new capacity cost falls on households?
- Do data centres delay the connection of other new loads, such as factories and heat pumps, and the retirement of fossil plants?
- Do power purchase agreements with data centres bring new clean capacity, or claim capacity that would have been built anyway?

## Data that could help

Grid connection queues, data-centre capacity by region, electricity prices and generation mix (ENTSO-E, US grid operators), and power purchase agreement records.

## Next step

Relate changes in regional retail electricity prices since 2020 to data-centre capacity additions, controlling for fuel prices and other demand.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| data centers | 0 | – | 0, 0, 0, 1, 0, 2, 0, 1, 0, 0, 0, 0, 0 | 4 |
| electricity demand | 0 | – | 1, 1, 0, 3, 1, 2, 1, 3, 1, 4, 0, 0, 0 | 17 |


---

---
title: "Four days to disclose a cyberattack (research idea)"
description: "Cybersecurity appears on 159 recent papers, most of them in AI journals about detection; only 22 carry a disclosure, reporting or investor keyword, and \"cybersecurity disclosure\" on 6. Since 2023, US firms must report material cyber incidents within four business days. What do they report, and what do markets learn?"
url: https://scholar4ai.com/ideas/cybersecurity-incident-disclosure
markdown_url: https://scholar4ai.com/ideas/cybersecurity-incident-disclosure.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "finance", "cybersecurity"]
keywords: ["cybersecurity disclosure", "data breaches", "cybersecurity risks"]
---

# Four days to disclose a cyberattack

Cybersecurity appears on 159 recent papers, most of them in AI journals about detection; only 22 carry a disclosure, reporting or investor keyword, and "cybersecurity disclosure" on 6. Since 2023, US firms must report material cyber incidents within four business days. What do they report, and what do markets learn?

## What prompted this

Since October 2025, 159 papers carry a cybersecurity, cyber-risk, data-breach or cyberattack keyword (58 in AI journals, 47 in finance, 26 in management, 12 in accounting): "cybersecurity risks" on 39, "data breaches" on 10, "cybersecurity disclosure" on 6. Only 22 of the 159 also carry a disclosure, SEC, reporting, internal-control, investor or firm-value keyword.

## Questions

- Since the four-day rule, how many incidents are disclosed as material, how quickly, and what do firms say about the financial effect?
- Do stock prices react more to the first disclosure or to later updates, and do firms that disclose faster lose less value?
- Has the rule changed cyber insurance pricing or firms' investment in security?

## Data that could help

Item 1.05 disclosures from 8-K filings since December 2023, breach notification records held by state attorneys general, stock returns and cyber insurance rates.

## Next step

Collect all 8-K cyber incident disclosures since the rule took effect, code their timing and content, and measure the stock reactions.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| cybersecurity disclosure | 0 | – | 1, 0, 0, 1, 0, 0, 0, 0, 1, 2, 1, 0, 0 | 6 |
| data breaches | 1 | #2550 | 3, 0, 2, 0, 0, 0, 0, 3, 0, 0, 1, 0, 1 | 9 |
| cybersecurity risks | 3 | #687 | 1, 3, 1, 5, 2, 4, 2, 5, 5, 2, 2, 5, 2 | 37 |


---

---
title: "The first reports under the EU sustainability directive"
description: "The Corporate Sustainability Reporting Directive appears on 9 recent papers and double materiality on 3, while sustainability reporting as a whole appears on 57. The first reports under the directive were published in 2025, and the rules were then scaled back. What did the first wave disclose, and what did the rollback remove?"
url: https://scholar4ai.com/ideas/csrd-first-reports
markdown_url: https://scholar4ai.com/ideas/csrd-first-reports.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "esg", "regulation"]
keywords: ["Corporate Sustainability Reporting Directive", "double materiality", "sustainability reporting"]
---

# The first reports under the EU sustainability directive

The Corporate Sustainability Reporting Directive appears on 9 recent papers and double materiality on 3, while sustainability reporting as a whole appears on 57. The first reports under the directive were published in 2025, and the rules were then scaled back. What did the first wave disclose, and what did the rollback remove?

## What prompted this

Since October 2025, 12 papers carry a CSRD, European Sustainability Reporting Standards or double-materiality keyword (4 in finance journals, 4 in management, 3 in economics, 1 in accounting): "Corporate Sustainability Reporting Directive" on 9 and "double materiality" on 3. "Sustainability reporting" appears on 57 papers.

## Questions

- In the first reports, which topics did firms declare material, and does the double-materiality assessment differ across countries and auditors?
- Did firms that reported for the first time change their emissions targets or capital spending, or only their disclosure?
- After the 2025 simplification removed most mid-sized firms from the scope, did those firms stop reporting, or keep reporting voluntarily because their customers ask?

## Data that could help

The first-wave reports (machine-readable from 2025), materiality assessments, firms' earlier voluntary reports, and the scope changes by firm size.

## Next step

Code the material topics declared by 300 first-wave reporters and compare them across countries and sectors.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| Corporate Sustainability Reporting Directive | 0 | – | 0, 2, 0, 1, 1, 0, 0, 2, 0, 1, 1, 1, 0 | 9 |
| double materiality | 0 | – | 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0 | 3 |
| sustainability reporting | 4 | #458 | 1, 4, 4, 3, 5, 5, 5, 8, 7, 2, 3, 8, 3 | 55 |


---

---
title: "Crowdshipping, if the crowd shows up (research idea)"
description: "Crowdshipping and crowdsourced delivery appear on 10 recent papers in the operations research journals, with 4 in the last quarter against about 2 per quarter before. The models route parcels through ordinary travellers who happen to be going the same way. Do enough people accept such tasks, and at what pay, for the routes to work?"
url: https://scholar4ai.com/ideas/crowdshipping
markdown_url: https://scholar4ai.com/ideas/crowdshipping.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "logistics"]
keywords: ["crowdshipping", "crowdsourced delivery"]
---

# Crowdshipping, if the crowd shows up

Crowdshipping and crowdsourced delivery appear on 10 recent papers in the operations research journals, with 4 in the last quarter against about 2 per quarter before. The models route parcels through ordinary travellers who happen to be going the same way. Do enough people accept such tasks, and at what pay, for the routes to work?

## What prompted this

Since October 2025, 10 papers carry a crowdshipping, crowdsourced-delivery or crowd-logistics keyword (9 in operations research journals): "crowdshipping" on 6, "crowdsourced delivery" on 4. Four of them were published in July–September 2026 against about 2 per quarter earlier.

## Questions

- In the platforms that actually run crowdshipping, what share of offered tasks are accepted, by whom, and at what compensation relative to professional couriers?
- Do the routing models' assumptions about the supply of occasional drivers match observed acceptance rates, and how much of the modelled savings survive when they do not?
- Does crowdshipping reduce vehicle kilometres, or add detours by drivers who were not going there anyway?

## Data that could help

Task offer and acceptance logs from a crowdshipping platform, driver surveys, and GPS traces of accepted deliveries.

## Next step

Obtain acceptance and detour data from one crowdshipping platform and recompute a published model's savings with the observed acceptance rates.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| crowdshipping | 0 | – | 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 2, 0, 0 | 6 |
| crowdsourced delivery | 0 | – | 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0 | 4 |


---

---
title: "Critical-mineral export controls and the energy transition"
description: "Critical minerals rose from almost nothing to one of the fastest-growing keywords across the tracked journals this year. Export controls on gallium, germanium, graphite and some rare earths since 2023 are natural experiments. Do they raise costs for clean-energy producers, speed up substitution and new supply, or both?"
url: https://scholar4ai.com/ideas/critical-mineral-export-controls
markdown_url: https://scholar4ai.com/ideas/critical-mineral-export-controls.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "energy", "trade"]
keywords: ["critical minerals", "rare earth elements", "lithium"]
---

# Critical-mineral export controls and the energy transition

Critical minerals rose from almost nothing to one of the fastest-growing keywords across the tracked journals this year. Export controls on gallium, germanium, graphite and some rare earths since 2023 are natural experiments. Do they raise costs for clean-energy producers, speed up substitution and new supply, or both?

## What prompted this

Across all journals, "critical minerals" had 9 papers in July–September 2026 against about 1 expected from its earlier share (see the [trends page](/trends)). Since October 2025 it appears on 14 papers, "rare earth elements" on 12 and "lithium" on 11, most of them in economics and finance journals.

## Questions

- After an export control is announced, how do prices outside the controlling country, and the share prices of the firms that use the mineral, respond?
- Do controls speed up substitution (new materials and chemistries) and new mines elsewhere, and how fast?
- Which clean-energy supply chains are most exposed, by product and country?

## Data that could help

Export-control announcement dates, trade flows by product (UN Comtrade, China Customs), mineral prices, firms' supplier links, and patents on substitute materials.

## Next step

Build a list of critical-mineral export controls since 2023 and run an event study of the share prices of firms that use the minerals.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| critical minerals | 2 | #1148 | 0, 0, 0, 0, 0, 0, 1, 2, 0, 3, 3, 3, 2 | 12 |
| rare earth elements | 3 | #687 | 0, 0, 0, 1, 0, 1, 1, 1, 1, 2, 0, 2, 3 | 9 |
| lithium | 1 | #2550 | 1, 0, 0, 1, 2, 1, 1, 1, 1, 0, 1, 1, 1 | 10 |


---

---
title: "Counterfeits, superfakes and luxury resale (research idea)"
description: "Luxury brands, luxury consumption and counterfeit goods appear on 32 recent papers, mostly in management, with 5 in the last quarter against about 8 per quarter before. High-quality counterfeits and a booming resale market have blurred what authenticity means. Do counterfeits erode brand value, and does authentication technology restore it?"
url: https://scholar4ai.com/ideas/counterfeits-and-luxury-resale
markdown_url: https://scholar4ai.com/ideas/counterfeits-and-luxury-resale.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "marketing"]
keywords: ["luxury brands", "counterfeit goods"]
---

# Counterfeits, superfakes and luxury resale

Luxury brands, luxury consumption and counterfeit goods appear on 32 recent papers, mostly in management, with 5 in the last quarter against about 8 per quarter before. High-quality counterfeits and a booming resale market have blurred what authenticity means. Do counterfeits erode brand value, and does authentication technology restore it?

## What prompted this

Since October 2025, 32 papers carry a counterfeit or luxury keyword (20 in management journals): "luxury brands" on 8, "counterfeit goods" on 4, "luxury consumption" on 3, "counterfeiting" on 2. Thirteen of them also carry a counterfeit, authentication, fake, resale or secondhand keyword. Five were published in July–September 2026 against about 8 per quarter earlier.

## Questions

- Do consumers who buy high-quality counterfeits later buy the genuine product, or do they stop valuing the brand?
- Does authentication on resale platforms raise prices for genuine secondhand goods and reduce demand for new ones?
- Do brands' own resale programmes capture value from the secondhand market, or cannibalize new sales?

## Data that could help

Resale platform listings and prices with authentication status, customs seizure records, consumer panel data and brand sales.

## Next step

Compare resale prices of the same handbag models on platforms with and without authentication guarantees, and track them around counterfeit scandals.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| luxury brands | 0 | – | 0, 2, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0 | 8 |
| counterfeit goods | 1 | #2550 | 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1 | 3 |


---

---
title: "Corporate venture capital in the AI cycle (research idea)"
description: "Corporate venture capital is a small but rising keyword, with 7 papers in July–September 2026 against about 3 per quarter before. Large technology firms now invest in AI startups that also buy their cloud and chips. Is this venture investing, customer financing, or a way to buy talent without a merger?"
url: https://scholar4ai.com/ideas/corporate-venture-capital-in-the-ai-cycle
markdown_url: https://scholar4ai.com/ideas/corporate-venture-capital-in-the-ai-cycle.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "management", "innovation"]
keywords: ["corporate venture capital", "open innovation"]
---

# Corporate venture capital in the AI cycle

Corporate venture capital is a small but rising keyword, with 7 papers in July–September 2026 against about 3 per quarter before. Large technology firms now invest in AI startups that also buy their cloud and chips. Is this venture investing, customer financing, or a way to buy talent without a merger?

## What prompted this

Since October 2025, "corporate venture capital" appears on 16 papers (8 in finance journals, 5 in economics, 3 in management), 7 of them in July–September 2026 against about 3 per quarter over the nine months before. "Open innovation" appears on 42 papers. Venture capital itself is one of the fastest-growing keywords in the finance journals ([trends page](/trends)).

## Questions

- How much of corporate investment in AI startups comes back to the investor as cloud and chip revenue, and does that circular flow inflate valuations?
- Do startups backed by a corporate investor get acquired by it later at a discount, or by its rivals at a premium?
- Do "acqui-hire" deals, in which the team is hired and the company licensed, escape merger review that a purchase would face?

## Data that could help

Deal data (PitchBook, Crunchbase) with investor identities, startups' cloud providers from job postings and technical documentation, and later exits.

## Next step

List AI startups with corporate investors since 2022 and check what share of their reported spending goes to the investor's products.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| corporate venture capital | 0 | – | 1, 2, 1, 0, 1, 1, 1, 0, 2, 2, 3, 2, 0 | 16 |
| open innovation | 2 | #1148 | 5, 2, 6, 4, 4, 1, 2, 4, 5, 5, 1, 1, 2 | 40 |


---

---
title: "Did corporate purpose statements change anything?"
description: "Stakeholder theory appears on 88 recent papers in the management and finance journals, but corporate purpose on 4 and stakeholder capitalism on 2. Hundreds of large firms signed statements putting stakeholders before shareholders in 2019. Several years on, did the signatories treat workers, suppliers and communities differently?"
url: https://scholar4ai.com/ideas/corporate-purpose-statements
markdown_url: https://scholar4ai.com/ideas/corporate-purpose-statements.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "governance", "esg"]
keywords: ["stakeholder theory", "corporate purpose"]
---

# Did corporate purpose statements change anything?

Stakeholder theory appears on 88 recent papers in the management and finance journals, but corporate purpose on 4 and stakeholder capitalism on 2. Hundreds of large firms signed statements putting stakeholders before shareholders in 2019. Several years on, did the signatories treat workers, suppliers and communities differently?

## What prompted this

Since October 2025, 101 papers carry a stakeholder-theory, stakeholder-orientation, corporate-purpose or stakeholder-capitalism keyword (55 in management journals, 29 in finance): "stakeholder theory" on 88, "stakeholder orientation" on 6, "corporate purpose" on 4, "stakeholder capitalism" on 2. The theory is well cited; the statements that invoked it are rarely tested.

## Questions

- Did signatory firms differ from non-signatories in layoffs, supplier payment terms, environmental fines or executive pay in the years after 2019?
- Did they use the purpose language in shareholder disputes, and did courts or investors take it into account?
- Did the statements change which employees and customers chose the firm?

## Data that could help

The list of signatories, firm-level employment, supplier payment data, enforcement actions and executive pay (Compustat, ExecuComp).

## Next step

Compare layoff announcements and supplier payment days of Business Roundtable signatories with matched non-signatories for 2017–2025.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| stakeholder theory | 6 | #263 | 7, 6, 5, 10, 6, 5, 6, 8, 9, 9, 6, 7, 5 | 84 |
| corporate purpose | 0 | – | 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0 | 4 |


---

---
title: "Does misconduct spread through corporate networks?"
description: "Corporate misconduct, reputational risk and corporate scandals appear on 90 recent papers across the management, finance and accounting journals, mostly one firm at a time. Firms share directors, auditors, suppliers and bankers. Does misconduct travel along those links, and can network position predict who is next?"
url: https://scholar4ai.com/ideas/corporate-misconduct-contagion
markdown_url: https://scholar4ai.com/ideas/corporate-misconduct-contagion.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "governance"]
keywords: ["corporate misconduct", "reputational risk", "corporate scandals"]
---

# Does misconduct spread through corporate networks?

Corporate misconduct, reputational risk and corporate scandals appear on 90 recent papers across the management, finance and accounting journals, mostly one firm at a time. Firms share directors, auditors, suppliers and bankers. Does misconduct travel along those links, and can network position predict who is next?

## What prompted this

Since October 2025, 90 papers carry a scandal, corporate-misconduct, corporate-social-irresponsibility, reputation-damage, crisis-communication or misconduct keyword (31 in management journals, 30 in finance, 11 in economics, 10 in accounting): "corporate misconduct" on 22, "reputational risk" on 21, "corporate social irresponsibility" on 9, "crisis communication" on 8, "corporate scandals" on 5.

## Questions

- Are firms that share a director or auditor with a firm caught in misconduct more likely to be caught later, and does the link run through practices or through enforcement attention?
- When a scandal hits one firm, do customers and investors punish its peers, and do the peers change behaviour?
- Can network measures improve the prediction of enforcement actions beyond firm characteristics?

## Data that could help

Enforcement actions and restatements (Audit Analytics, SEC releases), director networks (BoardEx), auditor identities and supply chain links.

## Next step

Build the director network of listed firms and test whether a board link to a sanctioned firm predicts later sanctions, controlling for industry and size.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| corporate misconduct | 0 | – | 4, 1, 0, 2, 2, 3, 2, 1, 1, 2, 1, 3, 0 | 22 |
| reputational risk | 2 | #1148 | 0, 4, 0, 4, 2, 2, 2, 0, 1, 0, 2, 3, 2 | 20 |
| corporate scandals | 0 | – | 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0 | 5 |


---

---
title: "Who lobbies on AI rules, and what they get (research idea)"
description: "Lobbying, corporate political activity and campaign contributions appear on 49 recent papers, mostly about tax, trade and environmental policy, while AI regulation appears on 53 papers as a question of design. The EU AI Act and dozens of US state bills attracted record lobbying by technology firms. What did the money change in the texts?"
url: https://scholar4ai.com/ideas/corporate-lobbying-on-ai
markdown_url: https://scholar4ai.com/ideas/corporate-lobbying-on-ai.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "politics", "ai"]
keywords: ["lobbying", "corporate political activity", "campaign contributions"]
---

# Who lobbies on AI rules, and what they get

Lobbying, corporate political activity and campaign contributions appear on 49 recent papers, mostly about tax, trade and environmental policy, while AI regulation appears on 53 papers as a question of design. The EU AI Act and dozens of US state bills attracted record lobbying by technology firms. What did the money change in the texts?

## What prompted this

Since October 2025, 49 papers carry a lobbying, corporate-political-activity or campaign-contribution keyword (23 in economics journals, 11 in management, 8 in accounting, 7 in finance): "lobbying" on 20, "corporate political activity" on 14, "campaign contributions" on 7, "corporate lobbying" on 6. "AI regulation" appears on 53 papers and "EU AI Act" on 25, in a separate literature on how the rules should look.

## Questions

- Which provisions of the EU AI Act changed between the Commission's draft and the final text, and do the changes line up with the positions filed by the largest lobbyists?
- In US states, do bills with the most technology-industry lobbying die, get delayed or get amended in predictable ways?
- Do firms that lobby on AI also publish "responsible AI" principles, and do the two say the same thing?

## Data that could help

Lobbying registers (the EU Transparency Register, US federal and state filings), legislative texts across drafts, and firms' public policy statements.

## Next step

Code the changes between the drafts and the final EU AI Act article by article and match them to the positions in lobby meeting records.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| lobbying | 0 | – | 0, 0, 1, 4, 1, 4, 2, 3, 1, 3, 0, 1, 0 | 20 |
| corporate political activity | 1 | #2550 | 0, 4, 3, 0, 2, 0, 0, 1, 1, 1, 1, 0, 1 | 13 |
| campaign contributions | 0 | – | 1, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 2, 0 | 7 |


---

---
title: "Corporate bond ETFs and the liquidity they promise"
description: "Corporate bonds appear on 54 recent papers in the finance and economics journals, but only 7 of them carry an ETF, fund or liquidity keyword. Exchange-traded funds now hold a large share of corporate bonds and trade far more than the bonds themselves. Do they add liquidity, or hide the lack of it until a stress?"
url: https://scholar4ai.com/ideas/corporate-bond-etfs-and-liquidity
markdown_url: https://scholar4ai.com/ideas/corporate-bond-etfs-and-liquidity.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "fixed income", "market microstructure"]
keywords: ["corporate bonds", "exchange-traded funds"]
---

# Corporate bond ETFs and the liquidity they promise

Corporate bonds appear on 54 recent papers in the finance and economics journals, but only 7 of them carry an ETF, fund or liquidity keyword. Exchange-traded funds now hold a large share of corporate bonds and trade far more than the bonds themselves. Do they add liquidity, or hide the lack of it until a stress?

## What prompted this

Since October 2025, 54 papers carry a corporate-bond keyword (41 in finance journals); only 7 of them also carry an ETF, exchange-traded, liquidity or fund keyword. "Exchange-traded funds" appears on 29 papers, mostly about equity funds.

## Questions

- When bond ETFs trade at large discounts to their holdings, as in March 2020, do the discounts reflect the true price of the bonds or a dislocation in the ETF itself?
- Do bonds held heavily by ETFs become more liquid in normal times and less liquid in stress?
- Do the authorized participants that create and redeem ETF shares step back exactly when they are needed?

## Data that could help

Bond transactions (TRACE), ETF holdings, prices and creation and redemption data, and dealer inventories.

## Next step

Measure the price impact of trades in bonds with high and low ETF ownership across calm and stress periods using TRACE data.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| corporate bonds | 3 | #687 | 2, 1, 2, 3, 0, 1, 2, 2, 1, 2, 1, 4, 3 | 21 |
| exchange-traded funds | 2 | #1148 | 1, 5, 1, 6, 2, 2, 4, 3, 2, 0, 1, 0, 2 | 27 |


---

---
title: "Copyright, creators and the training data (research idea)"
description: "Copyright protection, data ownership, content creators and the creative industries appear on 96 recent papers across the AI, management and economics journals, and 37 of them involve generative AI. Courts are deciding whether training on copyrighted work is fair use, and licensing deals are being signed. What are creators losing or gaining, and what would a licensing market look like?"
url: https://scholar4ai.com/ideas/copyright-and-creators-in-the-ai-era
markdown_url: https://scholar4ai.com/ideas/copyright-and-creators-in-the-ai-era.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "law", "media"]
keywords: ["copyright", "copyright protection", "data ownership", "content creators"]
---

# Copyright, creators and the training data

Copyright protection, data ownership, content creators and the creative industries appear on 96 recent papers across the AI, management and economics journals, and 37 of them involve generative AI. Courts are deciding whether training on copyrighted work is fair use, and licensing deals are being signed. What are creators losing or gaining, and what would a licensing market look like?

## What prompted this

Since October 2025, 96 papers carry a copyright, data-licensing, fair-use, data-ownership, creative-industries or content-creator keyword (49 in AI journals, 25 in management, 10 in economics): "copyright protection" on 19, "data ownership" on 11, "copyright" on 10, "cultural and creative industries" on 10, "creative industries" on 9, "content creators" on 8. Thirty-seven of the 96 also carry a generative-AI, language-model, diffusion-model or AI-generated keyword.

## Questions

- Since image and text generators became widespread, how have earnings of illustrators, stock photographers, translators and voice actors changed, and which work was lost first?
- What are the terms of the licensing deals between AI firms and publishers, and how is the money shared with the authors whose work is licensed?
- Would a collective licensing scheme, like those for music, be workable for training data, and what price would it set?

## Data that could help

Freelance platform earnings by occupation, licensing deal announcements and terms, court filings, and royalty data from collecting societies.

## Next step

Track earnings and job volumes of illustrators, translators and voice actors on freelance platforms from 2021 to 2025 relative to occupations less exposed to generators.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| copyright | 0 | – | 1, 0, 0, 2, 0, 0, 3, 0, 4, 0, 0, 0, 0 | 10 |
| copyright protection | 1 | #2550 | 1, 1, 2, 1, 0, 2, 3, 3, 2, 1, 1, 1, 1 | 18 |
| data ownership | 1 | #2550 | 0, 1, 0, 2, 1, 2, 0, 1, 0, 1, 2, 1, 0 | 11 |
| content creators | 1 | #2550 | 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1 | 7 |


---

---
title: "Coopetition in AI (research idea)"
description: "Coopetition, cooperation between competitors, appears on 31 recent papers split between the management and operations research journals, with 7 in the last quarter, in line with earlier quarters. AI firms compete fiercely while sharing open models, benchmarks and safety research, and renting computing from each other. When does sharing help a rival more than oneself?"
url: https://scholar4ai.com/ideas/coopetition-in-ai
markdown_url: https://scholar4ai.com/ideas/coopetition-in-ai.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "ai"]
keywords: ["coopetition"]
---

# Coopetition in AI

Coopetition, cooperation between competitors, appears on 31 recent papers split between the management and operations research journals, with 7 in the last quarter, in line with earlier quarters. AI firms compete fiercely while sharing open models, benchmarks and safety research, and renting computing from each other. When does sharing help a rival more than oneself?

## What prompted this

Since October 2025, "coopetition" appears on 31 papers, 14 in management journals and 14 in operations research, mostly on supply chains and platform ecosystems. Open-source models, shared benchmarks and cloud partnerships among AI firms are among the clearest current cases, and none of the 31 papers names them.

## Questions

- Do firms release open models to set standards, to recruit developers or to commoditize a rival's product, and do the stated and revealed motives match?
- When a firm both sells computing to a rival and competes with it in models, how does it price, and does the rival hedge by building its own?
- Does participation in shared safety and benchmark efforts correlate with market position, and does it change when a firm leads?

## Data that could help

Model release histories and licences, cloud partnership announcements and contract terms, and benchmark and safety consortium memberships.

## Next step

Build a timeline of open model releases and cloud partnerships among the main AI firms and relate release decisions to each firm's position in the market at the time.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| coopetition | 2 | #1148 | 2, 0, 4, 6, 3, 1, 2, 0, 4, 1, 4, 2, 2 | 29 |


---

---
title: "Supply chain contracts that cut Scope 3 emissions"
description: "Supply chain coordination is growing in the operations research journals, along with emissions trading and carbon pricing. For most firms, the Scope 3 emissions of their supply chains dwarf their own, yet \"Scope 3 emissions\" appears on only 9 recent papers. Which contracts get suppliers to decarbonize, and who pays?"
url: https://scholar4ai.com/ideas/contracts-that-cut-scope-3-emissions
markdown_url: https://scholar4ai.com/ideas/contracts-that-cut-scope-3-emissions.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "esg", "supply chain"]
keywords: ["supply chain coordination", "Scope 3 emissions", "emissions trading"]
---

# Supply chain contracts that cut Scope 3 emissions

Supply chain coordination is growing in the operations research journals, along with emissions trading and carbon pricing. For most firms, the Scope 3 emissions of their supply chains dwarf their own, yet "Scope 3 emissions" appears on only 9 recent papers. Which contracts get suppliers to decarbonize, and who pays?

## What prompted this

In the operations research journals, "supply chain coordination" had 19 papers in July–September 2026 against 11 expected, "emissions trading" 7 against 3 and "carbon pricing" 5 against 3 (see the [operations research dashboard](/categories/operations-research)). Since October 2025, 77 papers carry a supply-chain-coordination or supply-chain-contract keyword (58 in operations research journals); 16 of them carry an emission, carbon or Scope 3 keyword, and "Scope 3 emissions" appears on 9 papers in all.

## Questions

- Which contract forms (cost sharing, price premiums for low-carbon inputs, long-term purchase commitments) lead suppliers to invest in abatement, and at what cost to the buyer?
- How do carbon prices on the supplier's side, such as the EU ETS and the EU's border carbon adjustment, change the best contract?
- Can buyers verify supplier emissions well enough to write contracts on them?

## Data that could help

Supplier-level emissions (the CDP supply chain programme), carbon prices, and purchasing contracts from case firms.

## Next step

Model a buyer and a supplier that can invest in abatement under a carbon price, and compare cost sharing with a price premium in a two-stage Stackelberg game.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| supply chain coordination | 8 | #153 | 2, 3, 6, 9, 7, 6, 4, 6, 4, 7, 8, 8, 6 | 70 |
| Scope 3 emissions | 1 | #2550 | 0, 0, 0, 1, 2, 0, 2, 1, 1, 1, 0, 0, 1 | 8 |
| emissions trading | 15 | #45 | 9, 14, 9, 13, 14, 11, 8, 11, 14, 11, 9, 10, 14 | 133 |


---

---
title: "Congestion pricing in New York (research idea)"
description: "Congestion pricing, low emission zones and road tolls appear on just 10 recent papers, with one in the last quarter. New York's congestion charge began in January 2025, the first in the US, after years of delay. What did it do to traffic, transit, business and air quality, and who paid?"
url: https://scholar4ai.com/ideas/congestion-pricing-in-new-york
markdown_url: https://scholar4ai.com/ideas/congestion-pricing-in-new-york.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "cities", "transport"]
keywords: ["congestion pricing", "low emission zones"]
---

# Congestion pricing in New York

Congestion pricing, low emission zones and road tolls appear on just 10 recent papers, with one in the last quarter. New York's congestion charge began in January 2025, the first in the US, after years of delay. What did it do to traffic, transit, business and air quality, and who paid?

## What prompted this

Since October 2025, 10 papers carry a congestion-pricing, congestion-charge, road-pricing, low-emission-zone or toll keyword (5 in economics journals, 4 in operations research): "congestion pricing" on 5, "low emission zones" on 4. Only one of them was published in July–September 2026. The evidence from London, Stockholm and Milan is old; New York's charge is a new, large and well-measured experiment.

## Questions

- How much did vehicle entries, travel times and bus speeds change in the charging zone, and did traffic rise just outside it?
- Did retail sales and restaurant visits in the zone fall, as opponents predicted, or rise with easier access?
- Who pays the charge by income and residence, and did transit ridership and the revenue reach the promised investments?

## Data that could help

Traffic counts and travel times (the transit authority's public data, mapping services), card spending by location, transit ridership and air quality monitors.

## Next step

Compare weekday vehicle entries, bus speeds and card spending in the zone with matched areas elsewhere in the city for the year before and after January 2025.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| congestion pricing | 0 | – | 0, 0, 0, 2, 0, 0, 2, 0, 1, 0, 0, 0, 0 | 5 |
| low emission zones | 0 | – | 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0 | 4 |


---

---
title: "Concept drift in credit and fraud models (research idea)"
description: "Distribution shift, concept drift and non-stationary time series appear on 371 recent papers, almost all in AI journals, and only 27 of them concern credit, fraud, banking or markets. Credit and fraud models decay as borrowers, fraudsters and the economy change. How fast, how should it be detected, and when should a model be retrained rather than recalibrated?"
url: https://scholar4ai.com/ideas/concept-drift-in-credit-and-fraud-models
markdown_url: https://scholar4ai.com/ideas/concept-drift-in-credit-and-fraud-models.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "finance", "methods"]
keywords: ["concept drift", "distribution shift", "non-stationary time series"]
---

# Concept drift in credit and fraud models

Distribution shift, concept drift and non-stationary time series appear on 371 recent papers, almost all in AI journals, and only 27 of them concern credit, fraud, banking or markets. Credit and fraud models decay as borrowers, fraudsters and the economy change. How fast, how should it be detected, and when should a model be retrained rather than recalibrated?

## What prompted this

Since October 2025, 371 papers carry a concept-drift, non-stationarity, distribution-shift, covariate-shift or dataset-shift keyword (347 in AI journals): "distribution shift" on 170, "concept drift" on 65, "non-stationary time series" on 42, "non-stationarity" on 22. Only 27 of the 371 also carry a credit, finance, fraud, bank, loan, stock, market, economics or trading keyword.

## Questions

- In credit scoring models deployed through the pandemic and the rate rises, how much did discrimination and calibration decay per year, and did the decay come from borrower mix or from the relationship between features and default?
- Which drift detectors raise the alarm early enough to matter and rarely enough to be trusted?
- When fraudsters adapt to a model, is the drift adversarial in a way that standard detectors miss?

## Data that could help

Loan performance data over 2018–2025 (LendingClub, mortgage loan-level data), fraud transaction data with time stamps, and model monitoring logs from a lender.

## Next step

Train a credit model on 2018–2019 loans and track its monthly discrimination and calibration through 2025, attributing the decay to feature drift and relationship drift.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| concept drift | 6 | #263 | 7, 2, 9, 8, 3, 6, 2, 5, 3, 3, 7, 6, 4 | 61 |
| distribution shift | 8 | #153 | 3, 9, 12, 9, 14, 20, 16, 15, 18, 17, 13, 16, 8 | 162 |
| non-stationary time series | 3 | #687 | 5, 3, 4, 2, 2, 2, 5, 2, 1, 1, 8, 4, 3 | 39 |


---

---
title: "Code generation and the work of software (research idea)"
description: "Code generation, program synthesis, vulnerability detection and software development appear on 75 recent papers, mostly in AI journals, and 35 of them touch productivity, quality or security. Coding assistants are the most widely adopted generative AI tool at work. What happened to developer output, code quality and the junior developer's job?"
url: https://scholar4ai.com/ideas/code-generation-and-software-work
markdown_url: https://scholar4ai.com/ideas/code-generation-and-software-work.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "labor", "technology"]
keywords: ["code generation", "software development", "vulnerability detection", "code quality"]
---

# Code generation and the work of software

Code generation, program synthesis, vulnerability detection and software development appear on 75 recent papers, mostly in AI journals, and 35 of them touch productivity, quality or security. Coding assistants are the most widely adopted generative AI tool at work. What happened to developer output, code quality and the junior developer's job?

## What prompted this

Since October 2025, 75 papers carry a code-generation, program-synthesis, coding-assistant, software-development, code-quality or vulnerability-detection keyword (60 in AI journals, 8 in management): "code generation" on 18, "program synthesis" on 6, "smart contract vulnerability detection" on 5, "software development" on 4, "code quality" on 3. Thirty-five of them also carry a productivity, developer, quality, security, bug or labour keyword, but almost none uses data on real teams.

## Questions

- In firms that adopted coding assistants, did output per developer rise, and did defect rates, security vulnerabilities and code churn rise with it?
- Did hiring of junior developers fall relative to senior ones, and did pay for review and architecture skills rise?
- Do open-source projects show more contributions and more low-quality pull requests since 2023?

## Data that could help

Firm engineering metrics around assistant roll-outs, open-source repository histories, job postings for software roles by seniority, and developer surveys.

## Next step

Measure pull request volume, acceptance rates and post-merge bug fixes in a large sample of open-source repositories before and after 2023.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| code generation | 0 | – | 0, 1, 2, 5, 2, 0, 4, 1, 0, 1, 2, 0, 0 | 18 |
| software development | 0 | – | 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0 | 4 |
| vulnerability detection | 0 | – | 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0 | 3 |
| code quality | 0 | – | 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0 | 3 |


---

---
title: "Coal phase-out and local labour markets (research idea)"
description: "A \"just transition\" is now a common theme in the economics journals, and many coal plants and mines have announced closure dates. Closures known years in advance are a rare chance to see whether workers and towns can prepare. What happens to coal workers' earnings, and to the towns around them?"
url: https://scholar4ai.com/ideas/coal-phase-out-and-local-labour-markets
markdown_url: https://scholar4ai.com/ideas/coal-phase-out-and-local-labour-markets.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "energy", "labor"]
keywords: ["just transition", "coal-fired power plants"]
---

# Coal phase-out and local labour markets

A "just transition" is now a common theme in the economics journals, and many coal plants and mines have announced closure dates. Closures known years in advance are a rare chance to see whether workers and towns can prepare. What happens to coal workers' earnings, and to the towns around them?

## What prompted this

Since October 2025, "just transition" appears on 38 papers, 36 of them in economics journals, and "coal-fired power plants" on 12. Energy topics are rising on the [trends page](/trends), from energy markets to critical minerals.

## Questions

- When a coal plant or mine closes, how much do its workers' earnings fall, and for how long, compared with workers at plants that stay open?
- Do announced closure dates let workers and towns adjust earlier, softening the blow?
- Do transition funds, such as the EU's Just Transition Fund, reach the affected workers and towns, and do they help?

## Data that could help

Plant closure dates (Global Coal Plant Tracker, national registers), linked employer-employee data or local labour statistics, and transition fund allocations.

## Next step

Compile the announced and actual closure dates of coal plants in one country and compare employment in their counties with similar counties without closures.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| just transition | 0 | – | 2, 4, 4, 1, 5, 5, 4, 2, 2, 4, 3, 2, 0 | 38 |
| coal-fired power plants | 1 | #2550 | 1, 0, 1, 2, 3, 1, 0, 1, 2, 0, 0, 0, 1 | 11 |


---

---
title: "Do climate stress tests change lending? (research idea)"
description: "Stress testing appears on 40 recent papers in the finance journals, and climate stress testing on 12. Central banks in Europe and Asia have run climate scenarios on their banks. Did banks that fared badly in the scenarios cut lending to exposed borrowers, or only write longer reports?"
url: https://scholar4ai.com/ideas/climate-stress-tests-and-lending
markdown_url: https://scholar4ai.com/ideas/climate-stress-tests-and-lending.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "banking", "climate"]
keywords: ["stress testing", "climate stress testing"]
---

# Do climate stress tests change lending?

Stress testing appears on 40 recent papers in the finance journals, and climate stress testing on 12. Central banks in Europe and Asia have run climate scenarios on their banks. Did banks that fared badly in the scenarios cut lending to exposed borrowers, or only write longer reports?

## What prompted this

Since October 2025, 57 papers carry a stress-testing keyword (40 in finance journals), and 17 of them also carry a climate keyword; "climate stress testing" appears on 12 papers. Climate risk itself appears on 242 papers, so the stress-test exercises are a small corner of a large literature.

## Questions

- After the European Central Bank's 2022 climate stress test, did banks with larger projected losses reduce loans to carbon-intensive or flood-exposed borrowers?
- Did borrowers simply switch to banks that were not tested, or to bond markets?
- Do the scenarios' long horizons and lack of capital consequences mean that banks treat them as reporting exercises?

## Data that could help

Published stress-test results by bank, loan-level data where available (AnaCredit, syndicated loans), and borrowers' emissions and locations.

## Next step

Compare the syndicated lending of banks with high and low projected climate losses to carbon-intensive borrowers before and after the 2022 exercise.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| stress testing | 2 | #1148 | 3, 3, 4, 2, 4, 4, 3, 2, 1, 6, 4, 3, 1 | 39 |
| climate stress testing | 0 | – | 0, 3, 1, 1, 2, 1, 0, 3, 0, 0, 0, 1, 0 | 12 |


---

---
title: "Climate migration, measured (research idea)"
description: "Migration keywords appear on 283 recent papers, with 85 in the last quarter against about 61 per quarter before, but only 20 of them carry a climate, weather, drought, flood or disaster keyword, and \"climate-induced migration\" on 3. Do people move because of climate shocks, how far, and does the move help?"
url: https://scholar4ai.com/ideas/climate-migration
markdown_url: https://scholar4ai.com/ideas/climate-migration.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "migration", "climate"]
keywords: ["internal migration", "climate-induced migration", "migration"]
---

# Climate migration, measured

Migration keywords appear on 283 recent papers, with 85 in the last quarter against about 61 per quarter before, but only 20 of them carry a climate, weather, drought, flood or disaster keyword, and "climate-induced migration" on 3. Do people move because of climate shocks, how far, and does the move help?

## What prompted this

Since October 2025, 283 papers carry a migration, emigration, migrant, refugee or displaced keyword (235 in economics journals): "internal migration" on 27, "immigration" on 19, "migration" on 17, "rural-urban migration" on 15. The quarter to September 2026 had 85 such papers against about 61 per quarter earlier. Only 20 of the 283 also carry a climate, weather, drought, flood, temperature, sea-level, disaster or environmental keyword.

## Questions

- After droughts and floods, do people move within their country, mostly to nearby cities, rather than across borders, as earlier evidence suggests, and does that still hold for the largest recent shocks?
- Do the poorest households, who are most exposed, lack the means to move, so that shocks trap them instead?
- Do migrants who left after a shock earn more than those who stayed, and do they send money back that helps those who stayed adapt?

## Data that could help

Census and survey data on migration, gridded weather and flood data, mobile phone mobility data, and remittance flows.

## Next step

Match district-level migration flows from censuses to drought and flood exposure for several countries and compare the response of poor and non-poor households.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| internal migration | 2 | #1148 | 2, 2, 4, 2, 4, 2, 1, 0, 1, 2, 5, 1, 2 | 26 |
| climate-induced migration | 0 | – | 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0 | 3 |
| migration | 0 | – | 2, 1, 0, 0, 2, 2, 3, 1, 3, 1, 1, 1, 0 | 17 |


---

---
title: "Mandatory climate disclosure, three ways (research idea)"
description: "Climate risk disclosure appears on 19 recent papers, carbon disclosure on 10 and TCFD on 7; only 14 of 53 climate-disclosure papers carry a mandate, rule or regulation keyword. The US SEC rule was stayed, California's law survived, and the ISSB standards are being adopted by dozens of countries. Which regime changes what firms report, and what investors do with it?"
url: https://scholar4ai.com/ideas/climate-disclosure-mandates
markdown_url: https://scholar4ai.com/ideas/climate-disclosure-mandates.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "esg", "regulation"]
keywords: ["climate risk disclosure", "carbon disclosure", "TCFD"]
---

# Mandatory climate disclosure, three ways

Climate risk disclosure appears on 19 recent papers, carbon disclosure on 10 and TCFD on 7; only 14 of 53 climate-disclosure papers carry a mandate, rule or regulation keyword. The US SEC rule was stayed, California's law survived, and the ISSB standards are being adopted by dozens of countries. Which regime changes what firms report, and what investors do with it?

## What prompted this

Since October 2025, 53 papers carry a climate-disclosure, TCFD, carbon-disclosure or emission-disclosure keyword (27 in finance journals, 11 in accounting, 9 in economics): "climate risk disclosure" on 19, "carbon disclosure" on 10, "carbon emission disclosure" on 10, "TCFD" on 7. Only 14 of the 53 also carry a mandatory, rule, regulation, law, SEC or requirement keyword.

## Questions

- Do firms report different numbers under voluntary TCFD reporting, California's law and ISSB-based rules, for the same emissions?
- Did mandatory disclosure in the UK and New Zealand change investors' holdings of high-emitting firms, or lenders' terms?
- Do firms covered by California's law but not by any federal rule change their US operations or legal structure?

## Data that could help

Disclosed emissions by regime (CDP, annual reports), adoption dates of mandates by country, institutional holdings and loan terms.

## Next step

Compare the emissions reported by multinationals under the UK mandate with those reported voluntarily to CDP for the same years.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| climate risk disclosure | 1 | #2550 | 0, 0, 0, 1, 4, 3, 0, 5, 1, 2, 1, 1, 1 | 18 |
| carbon disclosure | 0 | – | 0, 1, 3, 2, 1, 1, 0, 0, 0, 2, 0, 0, 0 | 10 |
| TCFD | 0 | – | 1, 0, 0, 2, 0, 0, 0, 1, 1, 1, 0, 1, 0 | 7 |


---

---
title: "City logistics after the vans (research idea)"
description: "Urban logistics, urban freight transport and urban delivery appear on 46 recent papers, almost all in operations research, and the topic is fading, with 8 in the last quarter against about 13 per quarter before. Cities are restricting vans and funding cargo bikes and micro-hubs. Which combinations cut kilometres and cost, and do couriers and retailers adopt them without mandates?"
url: https://scholar4ai.com/ideas/city-logistics-and-cargo-bikes
markdown_url: https://scholar4ai.com/ideas/city-logistics-and-cargo-bikes.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "logistics", "cities"]
keywords: ["urban logistics", "cargo bikes", "urban freight transport"]
---

# City logistics after the vans

Urban logistics, urban freight transport and urban delivery appear on 46 recent papers, almost all in operations research, and the topic is fading, with 8 in the last quarter against about 13 per quarter before. Cities are restricting vans and funding cargo bikes and micro-hubs. Which combinations cut kilometres and cost, and do couriers and retailers adopt them without mandates?

## What prompted this

Since October 2025, 46 papers carry an urban-logistics, urban-freight, urban-delivery, cargo-bike, micro-hub or consolidation keyword (41 in operations research journals): "urban logistics" on 29, "urban freight transport" on 8, "urban delivery" on 6, "cargo bikes" on 2, "urban consolidation centers" on 1. Eight were published in July–September 2026 against about 13 per quarter earlier.

## Questions

- In cities that restricted delivery vans in central zones, did couriers switch to cargo bikes and micro-hubs, and what happened to delivery cost and times?
- Do micro-hubs reduce total vehicle kilometres once the trips to stock them are counted?
- Which consolidation schemes survived without public subsidy, and what distinguished them from the many that closed?

## Data that could help

Courier fleet and route data, city regulation dates, micro-hub operator records and delivery performance data.

## Next step

Compare delivery costs and vehicle kilometres per parcel in central Paris or London before and after van restrictions, using courier data.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| urban logistics | 0 | – | 1, 6, 4, 2, 2, 0, 3, 4, 2, 2, 2, 1, 0 | 29 |
| cargo bikes | 0 | – | 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0 | 2 |
| urban freight transport | 0 | – | 2, 0, 2, 0, 2, 0, 0, 0, 1, 1, 0, 0, 0 | 8 |


---

---
title: "Circular textiles and the fast fashion they compete with"
description: "The textile and apparel industries, fashion retail and fast fashion appear on dozens of recent papers across the AI, management and operations research journals, and 18 of them carry a circular, recycling, waste or resale keyword. The EU will require separate textile collection and producer responsibility. Can collection, sorting and recycling be made to pay, and does resale cut new production?"
url: https://scholar4ai.com/ideas/circular-textiles
markdown_url: https://scholar4ai.com/ideas/circular-textiles.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "management", "esg"]
keywords: ["textile industry", "fast fashion", "apparel industry"]
---

# Circular textiles and the fast fashion they compete with

The textile and apparel industries, fashion retail and fast fashion appear on dozens of recent papers across the AI, management and operations research journals, and 18 of them carry a circular, recycling, waste or resale keyword. The EU will require separate textile collection and producer responsibility. Can collection, sorting and recycling be made to pay, and does resale cut new production?

## What prompted this

Since October 2025, 87 papers carry a textile, apparel, fashion, garment or clothing keyword (32 in AI journals, 30 in management, 11 in economics, 11 in operations research): "textile industry" on 12, "fashion retail" on 8, "apparel industry" on 7, "fast fashion" on 5. Eighteen of the 87 also carry a circular, recycling, waste, sustainability, secondhand, resale, rental or fast-fashion keyword.

## Questions

- At current prices for recycled fibres, which sorting and recycling technologies cover their costs, and how large must collection volumes be?
- Does secondhand resale reduce purchases of new clothing, or add to total consumption?
- Who pays under producer responsibility fees, and do fees change what fast fashion firms design?

## Data that could help

Textile collection and sorting data from the countries that already collect separately, resale platform sales, apparel import volumes and producer responsibility fee schedules.

## Next step

Build a cost model of a textile sorting and recycling chain from published plant data and compute the collection volume needed to break even at 2025 fibre prices.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| textile industry | 0 | – | 1, 0, 3, 2, 0, 2, 1, 0, 1, 1, 0, 1, 0 | 12 |
| fast fashion | 1 | #2550 | 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0 | 5 |
| apparel industry | 1 | #2550 | 0, 1, 0, 0, 0, 2, 1, 1, 0, 1, 0, 0, 1 | 6 |


---

---
title: "China raises its retirement age (research idea)"
description: "Retirement, retirement age and delayed retirement appear on 94 recent papers in the economics and finance journals, and 38 of them touch China, pension reform or labour supply. China's statutory retirement age began rising in 2025, from some of the lowest ages in the world. Who works longer, who cares for grandchildren less, and what happens to youth hiring?"
url: https://scholar4ai.com/ideas/chinas-retirement-age-reform
markdown_url: https://scholar4ai.com/ideas/chinas-retirement-age-reform.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "labor", "pensions"]
keywords: ["retirement age", "delayed retirement", "retirement"]
---

# China raises its retirement age

Retirement, retirement age and delayed retirement appear on 94 recent papers in the economics and finance journals, and 38 of them touch China, pension reform or labour supply. China's statutory retirement age began rising in 2025, from some of the lowest ages in the world. Who works longer, who cares for grandchildren less, and what happens to youth hiring?

## What prompted this

Since October 2025, 94 papers carry a retirement keyword (54 in economics journals, 29 in finance): "retirement" on 19, "retirement age" on 12, "retirement planning" on 12, "delayed retirement" on 4. Thirty-eight of them also carry a China, labour-supply, employment, health or reform keyword, and "pension reform" appears on 10 of those. The 2025 reform raises the retirement age gradually over fifteen years, which makes it a long natural experiment.

## Questions

- Do workers affected by the first steps of the increase keep working, or leave the labour force without a pension?
- Does later retirement reduce grandparents' childcare and thereby lower their children's labour supply and fertility?
- Do firms hire fewer young workers when older workers stay, as the "lump of labour" argument claims, or not?

## Data that could help

Household panels with retirement and childcare data (China Health and Retirement Longitudinal Study, China Family Panel Studies), firm employment records and the reform's cohort schedule.

## Next step

Compare labour force participation and childcare hours of cohorts just above and just below the first retirement age steps in the 2025–2026 survey waves.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| retirement age | 0 | – | 0, 1, 1, 0, 1, 1, 1, 1, 2, 1, 2, 1, 0 | 12 |
| delayed retirement | 1 | #2550 | 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 1, 0 | 4 |
| retirement | 1 | #2550 | 3, 2, 1, 1, 0, 2, 1, 0, 3, 2, 1, 2, 1 | 18 |


---

---
title: "China's local debt swap and the firms that wait to be paid"
description: "Local government finance is rising in the economics journals, and local government debt is a large topic in finance as China's property slump cut land revenue. In late 2024 China launched a large swap of local governments' hidden debt. Did it get suppliers paid and credit flowing, or just move risk to banks?"
url: https://scholar4ai.com/ideas/china-local-debt-swap
markdown_url: https://scholar4ai.com/ideas/china-local-debt-swap.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "finance", "public finance"]
keywords: ["local government debt", "land finance", "local government finance"]
---

# China's local debt swap and the firms that wait to be paid

Local government finance is rising in the economics journals, and local government debt is a large topic in finance as China's property slump cut land revenue. In late 2024 China launched a large swap of local governments' hidden debt. Did it get suppliers paid and credit flowing, or just move risk to banks?

## What prompted this

In economics, "local government finance" had 8 papers in July–September 2026 against 3 expected (see the [economics dashboard](/categories/economics)). Since October 2025, 77 papers carry a local-government-debt, land-finance or local-government-finance keyword (42 in finance journals, 31 in economics); 16 of them also carry a housing, real-estate, land or property keyword.

## Questions

- Did provinces with larger swap quotas pay overdue bills to firms sooner, and did those firms then invest or hire more?
- Did the swap lower borrowing costs of local government financing vehicles, and did the banks that bought the new bonds cut other lending?
- Does replacing hidden debt with official bonds change how local governments spend?

## Data that could help

Swap quotas and bond issuance by province, bonds of local government financing vehicles (Wind, CSMAR), listed firms' receivables from government-linked customers, and bank balance sheets.

## Next step

Collect provincial swap quotas for 2024–2026 and relate them to changes in listed firms' receivables from government-linked customers.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| local government debt | 3 | #687 | 5, 6, 3, 5, 1, 6, 4, 5, 3, 3, 2, 2, 2 | 45 |
| land finance | 1 | #2550 | 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1 | 5 |
| local government finance | 3 | #687 | 0, 2, 0, 2, 2, 1, 2, 3, 2, 5, 2, 1, 3 | 22 |


---

---
title: "Chatbots, disclosure and customer satisfaction"
description: "Customer satisfaction and customer engagement are among the fastest-growing keywords in the management journals, and firms keep replacing service agents with chatbots. The EU AI Act requires telling people when they are talking to an AI. Does disclosure lower satisfaction, and can good service recovery make up for it?"
url: https://scholar4ai.com/ideas/chatbots-and-customer-satisfaction
markdown_url: https://scholar4ai.com/ideas/chatbots-and-customer-satisfaction.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "ai", "marketing"]
keywords: ["chatbots", "customer satisfaction", "customer engagement"]
---

# Chatbots, disclosure and customer satisfaction

Customer satisfaction and customer engagement are among the fastest-growing keywords in the management journals, and firms keep replacing service agents with chatbots. The EU AI Act requires telling people when they are talking to an AI. Does disclosure lower satisfaction, and can good service recovery make up for it?

## What prompted this

In the management journals, "customer satisfaction" had 17 papers in July–September 2026 against about 6 expected, and "customer engagement" 13 against 8 (see the [management dashboard](/categories/management)). Since October 2025, 90 papers carry a chatbot keyword (54 in AI journals, 29 in management), and only 7 of them also carry a satisfaction, service-quality, customer-service or service-recovery keyword.

## Questions

- Do customers rate the same service lower once they learn it came from a chatbot, and does the gap shrink as they get used to it?
- When a chatbot fails, does handing over to a human repair satisfaction better than an apology from the bot?
- Do disclosure rules change which firms deploy chatbots, and for which tasks?

## Data that could help

Customer reviews that mention chatbots (Yelp, TripAdvisor, app stores), firms' chatbot launch dates, and field experiments with service providers.

## Next step

Collect app-store reviews of banking apps that mention a chatbot, and compare ratings before and after each app began telling users that its assistant is an AI.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| chatbots | 4 | #458 | 8, 4, 6, 7, 6, 2, 3, 4, 5, 4, 6, 2, 4 | 57 |
| customer satisfaction | 4 | #458 | 12, 8, 4, 5, 8, 3, 4, 3, 5, 8, 6, 10, 1 | 76 |
| customer engagement | 2 | #1148 | 2, 3, 1, 5, 0, 5, 4, 8, 3, 7, 6, 5, 1 | 49 |


---

---
title: "Pay ratio disclosure and the pay of everyone else"
description: "Executive compensation appears on 114 recent papers, yet only 14 of 144 pay papers carry a disclosure, pay-ratio, say-on-pay or transparency keyword. Firms in the US and UK now publish the ratio of CEO pay to median worker pay. Did it change CEO pay, worker pay or neither?"
url: https://scholar4ai.com/ideas/ceo-pay-ratio-disclosure
markdown_url: https://scholar4ai.com/ideas/ceo-pay-ratio-disclosure.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "accounting", "governance"]
keywords: ["executive compensation", "CEO compensation"]
---

# Pay ratio disclosure and the pay of everyone else

Executive compensation appears on 114 recent papers, yet only 14 of 144 pay papers carry a disclosure, pay-ratio, say-on-pay or transparency keyword. Firms in the US and UK now publish the ratio of CEO pay to median worker pay. Did it change CEO pay, worker pay or neither?

## What prompted this

Since October 2025, 144 papers carry an executive-compensation, CEO-compensation or executive-pay keyword (73 in finance journals, 30 in accounting, 22 in management); "executive compensation" appears on 114 and "CEO compensation" on 33. Only 14 of the 144 also carry a pay-ratio, disclosure, say-on-pay or transparency keyword, and "executive compensation disclosure" on 2.

## Questions

- After pay ratio disclosure began, did firms with the highest ratios slow CEO pay growth, raise median worker pay, or outsource their lowest-paid jobs?
- Do investors vote against pay packages more often at high-ratio firms, and do customers or job applicants react?
- Do firms manage the ratio itself, for example by changing which employees count?

## Data that could help

Pay ratio disclosures from proxy statements, ExecuComp, say-on-pay vote results and workforce data from annual reports.

## Next step

Collect pay ratios for S&P 1500 firms for 2018–2025 and test whether a firm's ratio in one year predicts changes in median worker pay in the next.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| executive compensation | 7 | #210 | 8, 4, 9, 6, 12, 19, 8, 7, 12, 10, 7, 5, 7 | 107 |
| CEO compensation | 0 | – | 3, 3, 0, 6, 5, 4, 2, 2, 1, 7, 0, 0, 0 | 33 |


---

---
title: "Why central banks are buying gold (research idea)"
description: "Gold and safe-haven assets appear on 172 recent papers in the finance and economics journals, almost all about gold as a hedge for investors. Only 5 carry a central-bank, reserve or sanctions keyword. Central banks have been the largest buyers of gold since 2022. Is it insurance against sanctions, and does it move the price?"
url: https://scholar4ai.com/ideas/central-banks-buying-gold
markdown_url: https://scholar4ai.com/ideas/central-banks-buying-gold.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "economics", "monetary policy"]
keywords: ["gold", "safe-haven assets", "gold prices"]
---

# Why central banks are buying gold

Gold and safe-haven assets appear on 172 recent papers in the finance and economics journals, almost all about gold as a hedge for investors. Only 5 carry a central-bank, reserve or sanctions keyword. Central banks have been the largest buyers of gold since 2022. Is it insurance against sanctions, and does it move the price?

## What prompted this

Since October 2025, 172 papers carry a gold or safe-haven keyword (99 in finance journals, 50 in economics): "gold" on 58, "safe-haven assets" on 49, "gold prices" on 24. Only 5 of the 172 also carry a central-bank, reserve, de-dollarization, sanctions or official keyword, and "gold reserves" appears on 2 papers.

## Questions

- Did central banks of countries at risk of sanctions buy more gold after the freezing of Russia's reserves in 2022, and did they move gold home?
- How much of the gold price rise since 2022 can be attributed to official buying rather than investor demand?
- Does gold in reserves reduce a country's borrowing costs or exchange rate volatility?

## Data that could help

Official reserve data (IMF, World Gold Council), customs data on gold flows, gold prices and measures of geopolitical alignment.

## Next step

Relate quarterly changes in each central bank's gold holdings since 2022 to its country's political distance from the US and its exposure to sanctions.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| gold | 2 | #1148 | 5, 3, 4, 8, 6, 3, 8, 3, 4, 3, 6, 3, 2 | 56 |
| safe-haven assets | 3 | #687 | 3, 4, 7, 7, 6, 3, 5, 1, 2, 1, 4, 3, 3 | 46 |
| gold prices | 1 | #2550 | 2, 4, 1, 2, 1, 1, 0, 2, 2, 3, 3, 3, 0 | 24 |


---

---
title: "Can plain-language central bank messages reach households?"
description: "Central bank communication is one of the rising keywords across the tracked journals, but few of those papers look at households. Language models can now rewrite a policy statement for any reader. Would households who receive such messages update their inflation expectations?"
url: https://scholar4ai.com/ideas/central-bank-messages-for-households
markdown_url: https://scholar4ai.com/ideas/central-bank-messages-for-households.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "finance", "households"]
keywords: ["central bank communication", "forward guidance", "inflation expectations"]
---

# Can plain-language central bank messages reach households?

Central bank communication is one of the rising keywords across the tracked journals, but few of those papers look at households. Language models can now rewrite a policy statement for any reader. Would households who receive such messages update their inflation expectations?

## What prompted this

"Central bank communication" grew across all journals (16 papers in July–September 2026 against about 9 expected) and in finance (10 against 4); see the [trends page](/trends). Since October 2025, 58 papers carry a central-bank-communication or forward-guidance keyword (32 in economics, 26 in finance). Only 13 of them also carry a household, consumer, survey, expectations or public keyword, while "inflation expectations" appears on 86 papers.

## Questions

- In a randomized survey, do households who read a plain-language or personalized version of a policy statement change their inflation expectations more than those who read the original?
- Does the effect last, and does it change spending plans?
- Do people trust a message less if they know a machine wrote it?

## Data that could help

- Household expectation surveys, such as the Michigan Surveys of Consumers or central banks' own consumer surveys, with an experiment embedded.
- Policy statements and the layered, plain-language versions that several central banks already publish.

## Next step

Draft three versions of one recent policy statement (the original, a plain-language version and one personalized by a language model) and pilot them in an online survey with an expectations question.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| central bank communication | 9 | #129 | 2, 5, 4, 1, 3, 5, 2, 1, 3, 6, 2, 8, 7 | 42 |
| forward guidance | 1 | #2550 | 0, 2, 1, 2, 2, 1, 2, 0, 0, 1, 2, 1, 1 | 14 |
| inflation expectations | 7 | #210 | 6, 18, 6, 6, 4, 7, 8, 3, 8, 3, 6, 5, 7 | 80 |


---

---
title: "Would a digital currency drain bank deposits?"
description: "Central bank digital currencies appear on 56 recent papers in the finance and economics journals, but only 14 of them carry a deposit, bank-lending, bank-run or financial-stability keyword. Pilots now run in China, Europe and elsewhere. If people can hold central bank money directly, how much would leave banks, and when?"
url: https://scholar4ai.com/ideas/cbdc-and-bank-deposits
markdown_url: https://scholar4ai.com/ideas/cbdc-and-bank-deposits.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "banking", "monetary policy"]
keywords: ["central bank digital currency", "CBDC adoption", "bank deposits"]
---

# Would a digital currency drain bank deposits?

Central bank digital currencies appear on 56 recent papers in the finance and economics journals, but only 14 of them carry a deposit, bank-lending, bank-run or financial-stability keyword. Pilots now run in China, Europe and elsewhere. If people can hold central bank money directly, how much would leave banks, and when?

## What prompted this

Since October 2025, 56 papers carry a central-bank-digital-currency keyword (46 in finance journals); "CBDC adoption" appears on 11. Only 14 of the 56 also carry a deposit, disintermediation, bank-lending, bank-run or financial-stability keyword. "Bank deposits" appears on 8 papers.

## Questions

- In the pilots in China, Nigeria and the Bahamas, did adopters move balances out of bank deposits, and did banks respond with higher deposit rates?
- Would holding limits and no interest, as the digital euro design proposes, keep deposits in place during a bank stress?
- How does a digital currency compare with stablecoins and money market funds as a drain on deposits?

## Data that could help

Pilot usage statistics published by central banks, bank-level deposit data, and survey evidence on who adopted the digital currency and why.

## Next step

Compare deposit growth at banks in pilot cities with banks elsewhere during China's e-CNY roll-out.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| central bank digital currency | 6 | #263 | 0, 3, 5, 2, 6, 8, 5, 3, 6, 4, 4, 5, 5 | 51 |
| CBDC adoption | 0 | – | 0, 0, 1, 0, 2, 2, 0, 2, 1, 2, 0, 1, 0 | 11 |
| bank deposits | 1 | #2550 | 1, 1, 2, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1 | 7 |


---

---
title: "The carbon border adjustment and the exporters it hits"
description: "Carbon leakage appears on 28 recent papers and the EU's carbon border adjustment on a handful, with the topic fading (5 papers in the last quarter against about 8 per quarter before) just as the mechanism starts charging in 2026. Which exporters pay, do they clean up or divert trade, and does leakage actually fall?"
url: https://scholar4ai.com/ideas/cbam-and-carbon-leakage
markdown_url: https://scholar4ai.com/ideas/cbam-and-carbon-leakage.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "trade", "climate"]
keywords: ["carbon leakage", "CBAM", "border carbon adjustment"]
---

# The carbon border adjustment and the exporters it hits

Carbon leakage appears on 28 recent papers and the EU's carbon border adjustment on a handful, with the topic fading (5 papers in the last quarter against about 8 per quarter before) just as the mechanism starts charging in 2026. Which exporters pay, do they clean up or divert trade, and does leakage actually fall?

## What prompted this

Since October 2025, 30 papers carry a carbon-leakage or border-carbon-adjustment keyword (24 in economics journals): "carbon leakage" on 28, "border carbon adjustment" on 4. Five were published in July–September 2026 against about 8 per quarter earlier. The keyword "CBAM" appears on 38 papers, but part of that count is an attention module in AI journals that shares the acronym.

## Questions

- For steel, cement, aluminium and fertilizer exporters to the EU, how much of the charge falls on them, and do they lower prices, cut emissions or sell elsewhere?
- Do exporters ship their cleanest production to Europe and dirtier output to other markets, so that measured leakage falls while global emissions do not?
- Do trading partners respond with their own carbon prices, as the mechanism was designed to encourage?

## Data that could help

EU import data by product and origin with CBAM declarations, exporters' plant-level emissions, and carbon pricing changes in partner countries.

## Next step

Track EU imports of the covered goods by origin country before and after January 2026 and compare them with those countries' exports to other destinations.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| carbon leakage | 0 | – | 1, 3, 3, 5, 2, 2, 2, 2, 4, 2, 2, 0, 0 | 28 |
| CBAM | 2 | #1148 | 2, 0, 3, 2, 5, 3, 2, 6, 8, 0, 3, 2, 2 | 36 |
| border carbon adjustment | 0 | – | 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0 | 4 |


---

---
title: "Causal machine learning for deciding who gets the policy"
description: "Double machine learning, causal forests, conditional average treatment effects and policy learning appear on 50 recent papers, split between the AI and economics journals, with 14 in the last quarter against about 11 per quarter before. The methods promise to find who benefits most from a programme. Do the targeting rules they produce hold up in new samples, and are they fair?"
url: https://scholar4ai.com/ideas/causal-machine-learning-for-targeting
markdown_url: https://scholar4ai.com/ideas/causal-machine-learning-for-targeting.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "economics", "methods"]
keywords: ["double machine learning", "causal forest", "conditional average treatment effect"]
---

# Causal machine learning for deciding who gets the policy

Double machine learning, causal forests, conditional average treatment effects and policy learning appear on 50 recent papers, split between the AI and economics journals, with 14 in the last quarter against about 11 per quarter before. The methods promise to find who benefits most from a programme. Do the targeting rules they produce hold up in new samples, and are they fair?

## What prompted this

Since October 2025, 50 papers carry a double-machine-learning, causal-forest, heterogeneous-treatment, uplift, conditional-average-treatment-effect or policy-learning keyword (19 in AI journals, 19 in economics, 8 in finance): "double machine learning" on 13, "causal forest" on 11, "conditional average treatment effect" on 8, "policy learning" on 5. The quarter to September 2026 had 14 such papers against about 11 per quarter earlier.

## Questions

- When targeting rules estimated from one experiment are applied to a new experiment of the same programme, how much of the predicted gain over uniform treatment is realized?
- Do the rules concentrate treatment on groups that are already better off, and how much is lost by adding fairness constraints?
- Are the heterogeneity estimates stable across estimators, or do causal forests and double machine learning disagree about who benefits?

## Data that could help

Public experimental datasets with multiple waves or sites (job training, microfinance and cash transfer experiments), and replication code of the estimators.

## Next step

Estimate targeting rules on one site of a multi-site experiment with two estimators and evaluate them on the other sites.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| double machine learning | 2 | #1148 | 0, 2, 1, 0, 0, 2, 2, 0, 1, 1, 1, 1, 2 | 11 |
| causal forest | 0 | – | 0, 1, 0, 1, 0, 2, 2, 1, 1, 0, 1, 2, 0 | 11 |
| conditional average treatment effect | 0 | – | 0, 1, 0, 1, 0, 1, 2, 0, 1, 1, 0, 1, 0 | 8 |


---

---
title: "Do cash transfers raise local prices? (research idea)"
description: "Cash transfers appear on 43 recent papers in the economics journals, but only 4 of them carry a price, inflation, multiplier or spillover keyword. If a programme gives cash to a large share of a village, shopkeepers may raise prices and non-recipients may gain or lose. How large are these effects, and when do they reverse the programme's purpose?"
url: https://scholar4ai.com/ideas/cash-transfers-and-local-prices
markdown_url: https://scholar4ai.com/ideas/cash-transfers-and-local-prices.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "development", "welfare"]
keywords: ["cash transfers", "conditional cash transfers"]
---

# Do cash transfers raise local prices?

Cash transfers appear on 43 recent papers in the economics journals, but only 4 of them carry a price, inflation, multiplier or spillover keyword. If a programme gives cash to a large share of a village, shopkeepers may raise prices and non-recipients may gain or lose. How large are these effects, and when do they reverse the programme's purpose?

## What prompted this

Since October 2025, 43 papers carry a cash-transfer keyword (42 in economics journals): "cash transfers" on 30, "conditional cash transfers" on 11, "unconditional cash transfers" on 4. Only 4 of them also carry a price, inflation, local-economy, multiplier, spillover or general-equilibrium keyword.

## Questions

- In villages where most households receive transfers, how much do food and rent prices rise, and for how long?
- Do non-recipients gain from the extra demand (as sellers and workers) or lose (as buyers)?
- Do mobile money transfers, which can be spent beyond the village, reduce the price effects?

## Data that could help

Transfer programme rosters with saturation by village, local price surveys, and household surveys of recipients and non-recipients.

## Next step

Use a programme with randomized saturation across villages to estimate price changes and the net effect on non-recipients.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| cash transfers | 1 | #2550 | 4, 3, 3, 3, 2, 3, 0, 0, 4, 5, 1, 1, 1 | 29 |
| conditional cash transfers | 1 | #2550 | 0, 1, 1, 0, 2, 0, 2, 2, 0, 3, 0, 0, 1 | 11 |


---

---
title: "Which carbon credits are real? (research idea)"
description: "Carbon credits and offsetting appear on 22 recent papers, with 9 in the last quarter against about 4 per quarter before, but only 3 carry a quality, additionality, integrity or greenwashing keyword. After investigations found that many forest credits avoided no emissions, the market shrank and new integrity labels appeared. Do buyers now pay for quality?"
url: https://scholar4ai.com/ideas/carbon-credit-quality
markdown_url: https://scholar4ai.com/ideas/carbon-credit-quality.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "esg", "climate"]
keywords: ["carbon credits", "carbon offsetting"]
---

# Which carbon credits are real?

Carbon credits and offsetting appear on 22 recent papers, with 9 in the last quarter against about 4 per quarter before, but only 3 carry a quality, additionality, integrity or greenwashing keyword. After investigations found that many forest credits avoided no emissions, the market shrank and new integrity labels appeared. Do buyers now pay for quality?

## What prompted this

Since October 2025, 22 papers carry a carbon-credit, carbon-offset or voluntary-carbon keyword (14 in economics journals): "carbon credits" on 6, "carbon offsetting" on 5, "carbon credit trading" on 4. Nine of them were published in July–September 2026 against about 4 per quarter earlier. Only 3 of the 22 also carry a quality, additionality, integrity, rating or greenwashing keyword.

## Questions

- Do credits with integrity labels (for example the Core Carbon Principles) trade at a premium, and does the premium reflect measured additionality?
- Which firms buy the lowest-quality credits, and do their ESG ratings reward the purchase anyway?
- Can satellite data verify forest credits well enough to price them?

## Data that could help

Registry data on credit issuance and retirement (Verra, Gold Standard), credit prices by project type, buyers' sustainability reports, and satellite forest cover.

## Next step

Match retired credits in the public registries to the buyers' reported offset use and compare the project types chosen by firms with high and low ESG ratings.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| carbon credits | 0 | – | 0, 1, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 0 | 6 |
| carbon offsetting | 0 | – | 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0 | 5 |


---

---
title: "Carbon-aware computing (research idea)"
description: "Edge and cloud computing appear on 151 recent papers, with 43 in the last quarter against about 34 per quarter before, and 46 of them carry an energy, carbon or green keyword, mostly about device energy. Carbon-aware scheduling, which moves computing jobs to times and places where the grid is clean, appears on one paper. How much carbon could it save, and who would pay for the delay?"
url: https://scholar4ai.com/ideas/carbon-aware-computing
markdown_url: https://scholar4ai.com/ideas/carbon-aware-computing.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "ai", "energy"]
keywords: ["cloud computing", "edge computing", "green data centers", "energy-efficient computing"]
---

# Carbon-aware computing

Edge and cloud computing appear on 151 recent papers, with 43 in the last quarter against about 34 per quarter before, and 46 of them carry an energy, carbon or green keyword, mostly about device energy. Carbon-aware scheduling, which moves computing jobs to times and places where the grid is clean, appears on one paper. How much carbon could it save, and who would pay for the delay?

## What prompted this

Since October 2025, 151 papers carry a cloud-computing, edge-computing, data-centre, job-scheduling, task-offloading or serverless keyword (111 in AI journals, 14 in economics, 11 in finance, 9 in operations research): "edge computing" on 63, "cloud computing" on 22, "task offloading" on 14, "green data centers" on 4, "energy-efficient computing" on 11. The quarter to September 2026 had 43 such papers against about 34 per quarter earlier. Forty-six of the 151 carry an energy, carbon, renewable or green keyword, but "carbon-aware scheduling" appears on a single paper.

## Questions

- For training and batch inference jobs that can wait hours or move between regions, how much carbon can be cut at current grid carbon intensities, and at what cost in delay and transfer?
- Do cloud providers' carbon accounting methods (hourly matching versus annual) change which schedules look clean?
- Would a carbon price on computing move jobs enough to matter for the grid, and where?

## Data that could help

Hourly grid carbon intensity by region (ENTSO-E, electricityMap), cloud job traces released by providers, and electricity prices.

## Next step

Simulate shifting a public cloud job trace across regions and hours with 2025 grid carbon data and compute the carbon saved per hour of added delay.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| cloud computing | 1 | #2550 | 4, 1, 2, 1, 2, 2, 1, 3, 1, 4, 0, 0, 1 | 21 |
| edge computing | 4 | #458 | 3, 3, 4, 3, 5, 5, 8, 6, 5, 8, 6, 5, 4 | 61 |
| green data centers | 0 | – | 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 1, 0, 0 | 4 |
| energy-efficient computing | 0 | – | 1, 3, 1, 1, 0, 1, 2, 0, 1, 1, 0, 0, 0 | 11 |


---

---
title: "Capital flows after the fastest Fed tightening in decades"
description: "International capital flows appear on 96 recent papers, and 24 of them carry a Federal Reserve, global financial cycle or spillover keyword. The 2022–2023 tightening was the fastest in forty years, yet the sudden stops predicted for emerging markets mostly did not happen. What was different, and will it last?"
url: https://scholar4ai.com/ideas/capital-flows-and-fed-tightening
markdown_url: https://scholar4ai.com/ideas/capital-flows-and-fed-tightening.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "finance", "international"]
keywords: ["international capital flows", "global financial cycle", "capital flows"]
---

# Capital flows after the fastest Fed tightening in decades

International capital flows appear on 96 recent papers, and 24 of them carry a Federal Reserve, global financial cycle or spillover keyword. The 2022–2023 tightening was the fastest in forty years, yet the sudden stops predicted for emerging markets mostly did not happen. What was different, and will it last?

## What prompted this

Since October 2025, 96 papers carry a capital-flow, portfolio-flow, sudden-stop, capital-flight or global-financial-cycle keyword (65 in finance journals, 28 in economics): "international capital flows" on 37, "capital flows" on 29, "global financial cycle" on 8, "sudden stops" on 6. Twenty-four of them also carry a Federal Reserve, US-monetary, dollar, spillover or monetary-policy keyword.

## Questions

- Which emerging markets avoided outflows in 2022–2023, and did early rate hikes, local-currency borrowing or reserves explain it?
- Did outflows show up in bond funds and ETFs rather than in bank lending, and does the channel matter for crises?
- Does the resilience hold for frontier markets that lost market access, such as those that defaulted?

## Data that could help

Fund flow data (EPFR), balance of payments statistics, reserve data and policy rate changes by country.

## Next step

Classify emerging markets by the timing of their rate hikes relative to the Fed and compare their portfolio flows during 2022–2023.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| international capital flows | 3 | #687 | 2, 2, 2, 1, 4, 3, 2, 9, 3, 3, 0, 3, 3 | 34 |
| global financial cycle | 0 | – | 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 3, 0 | 8 |
| capital flows | 3 | #687 | 2, 3, 4, 2, 3, 3, 0, 1, 2, 3, 0, 4, 3 | 27 |


---

---
title: "Call centres with AI agents on the line (research idea)"
description: "Customer service and call centres appear on 25 recent papers, and 10 of them carry a chatbot, automation or language-model keyword. Firms now route calls through voice agents that handle part of each conversation and hand the rest to people. How should queues, staffing and handovers be designed when the first responder is a model?"
url: https://scholar4ai.com/ideas/call-centres-with-ai-agents
markdown_url: https://scholar4ai.com/ideas/call-centres-with-ai-agents.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "operations", "ai"]
keywords: ["customer service", "call centers", "customer service automation"]
---

# Call centres with AI agents on the line

Customer service and call centres appear on 25 recent papers, and 10 of them carry a chatbot, automation or language-model keyword. Firms now route calls through voice agents that handle part of each conversation and hand the rest to people. How should queues, staffing and handovers be designed when the first responder is a model?

## What prompted this

Since October 2025, 25 papers carry a call-centre, contact-centre or customer-service keyword (11 in management journals, 4 in operations research): "customer service" on 12, "customer service automation" on 2, "customer service employees" on 2, "call centers" on 2. Ten of them also carry a queue, staffing, chatbot, AI, automation, language-model or routing keyword. The queueing models that once dominated call-centre research have not yet absorbed AI agents.

## Questions

- When an AI agent resolves a share of calls and escalates the rest, how do waiting times and staffing needs change, and does the escalated traffic become harder per call?
- What is the optimal point to hand over to a person, and does the answer differ for customers who want to leave?
- Do AI-first systems lower costs more than they lower satisfaction and retention?

## Data that could help

Call logs with routing, handover and resolution data from contact centres that deployed AI agents, and customer retention records.

## Next step

Fit a queueing model with an AI first stage to call logs from one contact centre and test its staffing predictions against observed waiting times.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| customer service | 2 | #1148 | 0, 2, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 2 | 10 |
| call centers | 0 | – | 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 | 2 |
| customer service automation | 0 | – | 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0 | 2 |


---

---
title: "Calibrated AI uncertainty inside robust optimization"
description: "Distributionally robust optimization is one of the fastest-growing methods in operations research, and uncertainty estimation is rising in the AI journals, yet only two papers in the archive touch both. Can calibrated uncertainty from machine learning decide how much robustness an optimization model buys?"
url: https://scholar4ai.com/ideas/calibrated-uncertainty-in-robust-optimization
markdown_url: https://scholar4ai.com/ideas/calibrated-uncertainty-in-robust-optimization.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "ai", "methods"]
keywords: ["distributionally robust optimization", "conformal prediction", "uncertainty quantification", "uncertainty estimation"]
---

# Calibrated AI uncertainty inside robust optimization

Distributionally robust optimization is one of the fastest-growing methods in operations research, and uncertainty estimation is rising in the AI journals, yet only two papers in the archive touch both. Can calibrated uncertainty from machine learning decide how much robustness an optimization model buys?

## What prompted this

On the [trends page](/trends), "distributionally robust optimization" is growing in the operations research journals (22 papers in July–September 2026 against about 15 expected) and "uncertainty estimation" in the AI journals (39 against 23). Since October 2025, 195 papers carry a robust-optimization keyword (158 in operations research), but only 2 of them also carry a conformal-prediction, uncertainty-quantification, uncertainty-estimation or calibration keyword. Uncertainty quantification (105 papers) and uncertainty estimation (117) stay almost entirely in the AI journals, while "demand uncertainty" is the second most frequent operations research keyword of the year.

## Questions

- If the size of the ambiguity set (say, a Wasserstein radius) comes from conformal prediction intervals of a demand forecast, does it beat a cross-validated radius out of sample?
- How much cost does too much robustness waste in inventory problems such as the newsvendor model?
- What happens under distribution shift, when the forecast's calibration breaks?

## Data that could help

Public retail demand data such as the M5 competition data, and standard inventory and network-design instances.

## Next step

Solve a newsvendor problem on M5 data with conformal intervals and with a cross-validated Wasserstein radius, and compare cost and service level.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| distributionally robust optimization | 8 | #153 | 10, 7, 2, 4, 3, 9, 6, 4, 8, 7, 13, 7, 7 | 80 |
| conformal prediction | 2 | #1148 | 3, 2, 1, 1, 0, 0, 0, 3, 2, 4, 3, 1, 2 | 20 |
| uncertainty quantification | 7 | #210 | 3, 9, 12, 7, 2, 8, 11, 12, 8, 10, 5, 12, 6 | 99 |
| uncertainty estimation | 9 | #129 | 6, 2, 4, 9, 9, 11, 6, 9, 14, 14, 10, 17, 7 | 111 |


---

---
title: "Share buybacks under the excise tax (research idea)"
description: "Share repurchases appear on 28 recent papers in the finance journals, and only one of all 35 buyback papers carries a tax keyword. The US has taxed buybacks at 1% since 2023, and a higher rate has been proposed. Did the tax move payouts to dividends, and did it change investment?"
url: https://scholar4ai.com/ideas/buybacks-under-the-excise-tax
markdown_url: https://scholar4ai.com/ideas/buybacks-under-the-excise-tax.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "tax"]
keywords: ["share repurchases", "buybacks"]
---

# Share buybacks under the excise tax

Share repurchases appear on 28 recent papers in the finance journals, and only one of all 35 buyback papers carries a tax keyword. The US has taxed buybacks at 1% since 2023, and a higher rate has been proposed. Did the tax move payouts to dividends, and did it change investment?

## What prompted this

Since October 2025, 35 papers carry a share-repurchase or buyback keyword (29 in finance journals), with "share repurchases" on 28. Only one of them also carries a tax keyword. The literature on payout policy still mostly asks why firms buy back shares, not what a tax on it does.

## Questions

- After the 1% excise tax, did firms shift from buybacks to dividends, or did buybacks simply continue?
- Did firms with large buyback programmes invest or hire more, as supporters of the tax claimed?
- Do the responses predict what a 4% rate would do?

## Data that could help

Quarterly repurchase and dividend data (Compustat), announcement dates of buyback programmes, and firms' investment and employment.

## Next step

Compare buybacks and dividends of US firms before and after 2023 with those of Canadian firms, which faced a 2% tax from 2024, and with firms in countries without such a tax.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| share repurchases | 1 | #2550 | 5, 4, 2, 3, 2, 2, 2, 1, 1, 1, 3, 1, 1 | 27 |
| buybacks | 0 | – | 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0 | 3 |


---

---
title: "Buy now, pay later: credit for whom? (research idea)"
description: "\"Buy now, pay later\" appears on just 8 recent papers across the tracked journals, although it has become one of the fastest-growing forms of consumer credit and is now being brought into credit reporting. Who uses it, does it crowd out or add to other borrowing, and does it lead to missed payments elsewhere?"
url: https://scholar4ai.com/ideas/buy-now-pay-later
markdown_url: https://scholar4ai.com/ideas/buy-now-pay-later.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "households"]
keywords: ["buy now pay later", "consumer credit"]
---

# Buy now, pay later: credit for whom?

"Buy now, pay later" appears on just 8 recent papers across the tracked journals, although it has become one of the fastest-growing forms of consumer credit and is now being brought into credit reporting. Who uses it, does it crowd out or add to other borrowing, and does it lead to missed payments elsewhere?

## What prompted this

Since October 2025, "buy now pay later" appears on 8 papers in the archive, spread across finance, economics, management, operations research and AI journals, with 3 of them in July–September 2026. "Consumer credit" appears on 13. Credit scoring and household debt, by contrast, are well-established topics.

## Questions

- Do households that start using instalment plans at checkout spend more, and do they fall behind on other bills?
- Does access help people who are shut out of credit cards, or mainly people who already have them?
- Now that the main providers report to credit bureaus, do scores of frequent users rise or fall?

## Data that could help

Transaction-level data from banks or account aggregators, credit bureau records that now include instalment plans, and the dates of regulatory changes in the UK, Australia and the US.

## Next step

Use bank transaction data to identify new users of instalment plans and track their spending and late fees in the following six months.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| buy now pay later | 0 | – | 0, 0, 0, 0, 3, 1, 0, 0, 1, 2, 1, 0, 0 | 8 |
| consumer credit | 1 | #2550 | 1, 0, 3, 2, 0, 1, 1, 0, 0, 1, 1, 3, 1 | 13 |


---

---
title: "Board gender quotas: new faces or new decisions?"
description: "Board composition is one of the fastest-growing keywords across the tracked journals, and board gender diversity is a large topic in finance, accounting and management. But only 7 of 135 such papers carry a quota, mandate or regulation keyword, just as the EU's quota directive reaches its target date. Do quotas change how boards decide, or only who sits on them?"
url: https://scholar4ai.com/ideas/board-gender-quotas
markdown_url: https://scholar4ai.com/ideas/board-gender-quotas.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "finance", "governance"]
keywords: ["board gender diversity", "female directors", "board composition"]
---

# Board gender quotas: new faces or new decisions?

Board composition is one of the fastest-growing keywords across the tracked journals, and board gender diversity is a large topic in finance, accounting and management. But only 7 of 135 such papers carry a quota, mandate or regulation keyword, just as the EU's quota directive reaches its target date. Do quotas change how boards decide, or only who sits on them?

## What prompted this

"Board composition" grew across all journals (13 papers in July–September 2026 against about 4 expected; see the [trends page](/trends)). Since October 2025, 135 papers carry a board-gender-diversity, female-director or board-diversity keyword (51 in finance journals, 37 in accounting, 30 in management, 17 in economics). Only 7 of them also carry a quota, mandate, regulation, law or legislation keyword.

## Questions

- When a quota forces new appointments, do board decisions change (risk taking, payouts, CEO pay, sustainability), or do boards move decisions to committees that stay as they were?
- Do quota appointees differ from voluntary ones in experience and networks, and does that explain the results?
- Does the EU directive, with its June 2026 target, repeat the results of Norway's earlier quota, or do newer quotas work differently?

## Data that could help

Director-level data (BoardEx), quota laws and their dates by country, and firm outcomes (Compustat Global, Orbis).

## Next step

Code the quota laws and target dates across EU member states and count how many listed firms were below target the year before.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| board gender diversity | 3 | #687 | 5, 5, 2, 4, 6, 5, 6, 6, 9, 4, 5, 8, 3 | 65 |
| female directors | 0 | – | 2, 3, 1, 2, 3, 5, 2, 5, 6, 4, 1, 4, 0 | 38 |
| board composition | 0 | – | 2, 2, 0, 0, 1, 3, 0, 0, 3, 4, 3, 6, 0 | 24 |


---

---
title: "Tax enforcement by algorithm (research idea)"
description: "Tax compliance and enforcement appear on 118 recent papers, and 23 of them carry a digital, big-data or AI keyword, led by China's digitalized tax administration. Tax authorities now match invoices, bank data and platform records automatically. Which taxpayers change behaviour, and does automated enforcement crowd out voluntary compliance?"
url: https://scholar4ai.com/ideas/big-data-tax-enforcement
markdown_url: https://scholar4ai.com/ideas/big-data-tax-enforcement.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "economics", "tax"]
keywords: ["tax enforcement", "tax compliance", "digitalization of tax administration"]
---

# Tax enforcement by algorithm

Tax compliance and enforcement appear on 118 recent papers, and 23 of them carry a digital, big-data or AI keyword, led by China's digitalized tax administration. Tax authorities now match invoices, bank data and platform records automatically. Which taxpayers change behaviour, and does automated enforcement crowd out voluntary compliance?

## What prompted this

Since October 2025, 118 papers carry a tax-enforcement, tax-compliance, tax-administration or tax-audit keyword (53 in economics journals, 40 in finance, 15 in accounting): "tax compliance" on 49, "tax enforcement" on 34, "digitalization of tax administration" on 9, "Golden Tax Project" on 6. Twenty-three of the 118 also carry a digital, big-data, machine-learning or e-invoicing keyword.

## Questions

- When electronic invoicing becomes mandatory, how much does reported revenue rise, and does the effect fade as firms learn the system's blind spots?
- Does algorithmic audit selection target the right taxpayers, and does it create perceptions of unfairness that reduce voluntary compliance?
- Do firms respond to data-driven enforcement by moving to cash, to informality or across borders?

## Data that could help

Firm-level tax data around the phase-in of electronic invoicing (China's Golden Tax Phase IV, e-invoicing mandates in Italy, India and Latin America), audit selection records where available.

## Next step

Use the staggered roll-out of mandatory e-invoicing across regions in one country to measure changes in reported revenue and VAT.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| tax enforcement | 1 | #2550 | 1, 3, 2, 3, 4, 4, 4, 5, 3, 1, 2, 1, 1 | 33 |
| tax compliance | 4 | #458 | 2, 5, 5, 0, 1, 3, 5, 7, 6, 7, 2, 2, 4 | 45 |
| digitalization of tax administration | 1 | #2550 | 0, 1, 0, 0, 1, 0, 1, 2, 1, 1, 0, 2, 0 | 9 |


---

---
title: "Beneficial ownership registers after the EU court ruling"
description: "Corporate transparency is a rising keyword in the management journals, while beneficial ownership barely appears in the archive. In November 2022 the EU Court of Justice struck down general public access to the registers of companies' beneficial owners. Did the return of anonymity bring back shell companies and opaque property purchases?"
url: https://scholar4ai.com/ideas/beneficial-ownership-registers-after-the-eu-ruling
markdown_url: https://scholar4ai.com/ideas/beneficial-ownership-registers-after-the-eu-ruling.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "finance", "governance"]
keywords: ["corporate transparency", "beneficial ownership", "anti-money laundering"]
---

# Beneficial ownership registers after the EU court ruling

Corporate transparency is a rising keyword in the management journals, while beneficial ownership barely appears in the archive. In November 2022 the EU Court of Justice struck down general public access to the registers of companies' beneficial owners. Did the return of anonymity bring back shell companies and opaque property purchases?

## What prompted this

In the management journals, "corporate transparency" had 5 papers in July–September 2026 against about 2 expected, and in economics "transparency" grew too (9 against 4); see the [trends page](/trends). Since October 2025, "corporate transparency" appears on 50 papers (29 in finance journals), but "beneficial ownership" on just 2 and "anti-money laundering" on 14.

## Questions

- After several EU countries closed or restricted public access to their registers, did new companies with opaque ownership become more common?
- Did purchases of property through companies change in countries that closed their registers, compared with countries that kept them open?
- Do journalists, banks and tax authorities find fewer red flags without public access?

## Data that could help

Company registers and ownership data (Orbis ownership, Open Ownership), property transactions with corporate buyers, and the dates on which each country restricted access.

## Next step

List which EU countries closed, restricted or kept public access to their registers after the ruling, with dates, and count new company formations by legal form around those dates.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| corporate transparency | 1 | #2550 | 5, 4, 3, 9, 3, 4, 4, 2, 5, 5, 4, 2, 0 | 50 |
| beneficial ownership | 0 | – | 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 | 2 |
| anti-money laundering | 1 | #2550 | 0, 1, 0, 2, 2, 0, 2, 1, 0, 2, 1, 2, 1 | 13 |


---

---
title: "Benchmark inertia in AI journals (research idea)"
description: "The dataset ranking shows CIFAR and MNIST still among the most-mentioned image benchmarks in AI journal papers, while cross-dataset generalization and reproducibility are rising keywords. Do gains on these old benchmarks carry over to harder, shifted data?"
url: https://scholar4ai.com/ideas/benchmark-inertia-in-ai-journals
markdown_url: https://scholar4ai.com/ideas/benchmark-inertia-in-ai-journals.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "methods"]
keywords: ["benchmark dataset", "cross-dataset generalization", "reproducibility"]
---

# Benchmark inertia in AI journals

The dataset ranking shows CIFAR and MNIST still among the most-mentioned image benchmarks in AI journal papers, while cross-dataset generalization and reproducibility are rising keywords. Do gains on these old benchmarks carry over to harder, shifted data?

## What prompted this

On the [dataset ranking](/rankings/datasets), CIFAR-10/100 and MNIST are each named in the titles or abstracts of 7 AI journal papers in the 30 days to 2026-10-23, more than ImageNet (3). On the [AI dashboard](/categories/ai), "cross-dataset generalization" (24 papers in July–September 2026 against about 13 expected) and "reproducibility" (15 against 7) are growing. Since October 2025, 385 papers carry a benchmark keyword (287 in AI journals), and 28 of them also carry a reproducibility or generalization keyword.

## Questions

- For methods published with gains on CIFAR or MNIST, how much of the gain survives on newer or distribution-shifted datasets?
- How much of a reported gain comes from tuning effort rather than from the method?
- Should journals ask for at least one harder benchmark per paper?

## Possible approach

Rerun released code from recent papers on a shifted benchmark, such as one of the domain-adaptation suites, with the same tuning budget for the method and its baselines.

## Next step

Take ten recent AI-journal papers that report CIFAR gains and release their code, and rerun them on one distribution-shift benchmark.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| benchmark dataset | 6 | #263 | 3, 5, 8, 4, 6, 7, 5, 10, 6, 3, 6, 3, 6 | 66 |
| cross-dataset generalization | 2 | #1148 | 4, 1, 5, 5, 7, 3, 4, 4, 8, 5, 9, 10, 2 | 65 |
| reproducibility | 8 | #153 | 2, 3, 3, 4, 5, 2, 1, 1, 4, 4, 7, 7, 7 | 43 |


---

---
title: "What the basic income pilots found (research idea)"
description: "Universal basic income appears on 6 recent papers and unconditional cash transfers on 4, although large pilots in the US, Germany, Kenya and elsewhere have reported results in the last two years. What do the pilots agree on, where do they disagree, and what would scale change?"
url: https://scholar4ai.com/ideas/basic-income-pilots
markdown_url: https://scholar4ai.com/ideas/basic-income-pilots.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "welfare", "labor"]
keywords: ["universal basic income", "unconditional cash transfers"]
---

# What the basic income pilots found

Universal basic income appears on 6 recent papers and unconditional cash transfers on 4, although large pilots in the US, Germany, Kenya and elsewhere have reported results in the last two years. What do the pilots agree on, where do they disagree, and what would scale change?

## What prompted this

Since October 2025, 12 papers carry a basic-income, guaranteed-income or unconditional-cash keyword (11 in economics journals): "universal basic income" on 6, "unconditional cash transfers" on 4, "guaranteed income" on 2. The recent large pilots, with thousands of recipients and multi-year payments, have produced results that barely appear in these journals yet.

## Questions

- Across the pilots, how much did recipients reduce paid work, and did the hours go to care, education or job search?
- Did health, housing stability and children's outcomes improve by enough to offset the cost?
- What would differ at scale: prices, labour supply of non-recipients, and financing through taxes?

## Data that could help

Published pilot data (the US OpenResearch study, the German Pilotprojekt Grundeinkommen, GiveDirectly's Kenya experiment), and administrative records where pilots were linked to them.

## Next step

Tabulate the labour supply, income and well-being effects reported by each pilot on a common scale and compare them with the predictions of standard models.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| universal basic income | 2 | #1148 | 0, 0, 0, 1, 0, 1, 1, 0, 2, 0, 0, 0, 2 | 5 |
| unconditional cash transfers | 0 | – | 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0 | 4 |


---

---
title: "Bank runs at the speed of social media (research idea)"
description: "Deposit insurance and market discipline are rising topics after bank failures in which deposits left within hours. Yet only 3 recent papers on bank runs or deposits carry a social-media or digital-banking keyword. Does online talk now set the speed of runs, and should deposit insurance and liquidity rules change with it?"
url: https://scholar4ai.com/ideas/bank-runs-at-social-media-speed
markdown_url: https://scholar4ai.com/ideas/bank-runs-at-social-media-speed.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "banking"]
keywords: ["bank runs", "deposit insurance", "market discipline"]
---

# Bank runs at the speed of social media

Deposit insurance and market discipline are rising topics after bank failures in which deposits left within hours. Yet only 3 recent papers on bank runs or deposits carry a social-media or digital-banking keyword. Does online talk now set the speed of runs, and should deposit insurance and liquidity rules change with it?

## What prompted this

In the finance journals, "deposit insurance" had 7 papers in July–September 2026 against fewer than 2 expected from its earlier share, and "market discipline" is growing across all journals (12 against about 3); see the [trends page](/trends). Since October 2025, 37 papers carry a bank-run, deposit-insurance or bank-deposit keyword (23 in finance journals, 14 in economics). Only 3 of them carry a social-media, digital-banking or fintech keyword.

## Questions

- Do banks whose depositors talk more about them on social media lose deposits faster under stress, for the same share of uninsured deposits?
- Does online sentiment about a bank predict outflows days ahead, early enough for supervisors to act?
- Does app-based banking, where moving money takes minutes, make liquidity rules written for slower runs too lax?

## Data that could help

- Bank-level deposits from regulatory filings (Call Reports) and branch deposits (Summary of Deposits).
- Social media posts that mention banks, and each bank's share of deposits above the insurance limit.
- The 2023 US regional bank failures as a stress episode.

## Next step

Build a weekly count of social media posts per US bank for 2022–2024 and relate it to deposit flows around March 2023.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| bank runs | 2 | #1148 | 1, 1, 3, 2, 0, 0, 0, 0, 0, 3, 0, 2, 2 | 12 |
| deposit insurance | 0 | – | 1, 1, 1, 2, 0, 1, 1, 1, 2, 3, 3, 1, 0 | 17 |
| market discipline | 1 | #2550 | 1, 0, 0, 0, 1, 3, 1, 2, 2, 1, 5, 6, 1 | 22 |


---

---
title: "Decarbonizing aviation, flight by flight (research idea)"
description: "Airline, air traffic and aviation keywords appear on 142 recent papers, and the topic is fading, with 24 papers in the last quarter against about 38 per quarter before. Sustainable aviation fuel mandates began in Europe in 2025, and emissions trading now covers flights. Do the operations models and the climate policies meet, and what do the mandates cost per tonne?"
url: https://scholar4ai.com/ideas/aviation-decarbonization-operations
markdown_url: https://scholar4ai.com/ideas/aviation-decarbonization-operations.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "aviation", "climate"]
keywords: ["airline industry", "aviation emissions", "sustainable aviation fuel"]
---

# Decarbonizing aviation, flight by flight

Airline, air traffic and aviation keywords appear on 142 recent papers, and the topic is fading, with 24 papers in the last quarter against about 38 per quarter before. Sustainable aviation fuel mandates began in Europe in 2025, and emissions trading now covers flights. Do the operations models and the climate policies meet, and what do the mandates cost per tonne?

## What prompted this

Since October 2025, 142 papers carry an airline, aviation, flight-delay, air-traffic, air-transport, airport or aircraft keyword (56 in operations research journals, 39 in AI, 24 in economics): "airline industry" on 18, "air traffic management" on 12, "airline operations" on 6, "aviation emissions" on 5, "flight delays" on 5, "sustainable aviation fuel" on 4. Seventy-three of the 142 carry a pricing, scheduling, network, fuel, emission, delay or decarbonization keyword, but only a handful connect operations to the new mandates.

## Questions

- At the mandated blending shares for 2025–2030, what does sustainable aviation fuel cost per tonne of CO2 avoided compared with fleet renewal, routing changes and contrails avoidance?
- Do airlines respond to fuel mandates and emissions trading by changing networks, for example by refuelling outside the covered area?
- Do the scheduling and routing models that minimize fuel also minimize climate impact once contrails are included?

## Data that could help

Flight-level data (ADS-B traces, schedules), fuel prices and mandate schedules, emissions trading data for aviation, and contrail models.

## Next step

Estimate the cost per tonne avoided of the 2025 EU sustainable aviation fuel mandate from fuel price differentials and compare it with the cost of contrail avoidance on the same routes.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| airline industry | 1 | #2550 | 0, 4, 1, 2, 3, 0, 0, 3, 2, 1, 1, 0, 1 | 17 |
| aviation emissions | 0 | – | 0, 2, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0 | 5 |
| sustainable aviation fuel | 0 | – | 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0 | 4 |


---

---
title: "Autonomous driving and the economics of blame"
description: "Autonomous driving and autonomous vehicles appear on 314 recent papers, 270 of them in AI journals about perception and control, and only 29 carry a liability, insurance, regulation, acceptance or ethics keyword, with liability on 2. Driverless cars are now on public roads. When one crashes, who pays, and does the answer shape what gets deployed?"
url: https://scholar4ai.com/ideas/autonomous-driving-liability
markdown_url: https://scholar4ai.com/ideas/autonomous-driving-liability.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "law", "transport"]
keywords: ["autonomous driving", "autonomous driving safety", "connected and automated vehicles"]
---

# Autonomous driving and the economics of blame

Autonomous driving and autonomous vehicles appear on 314 recent papers, 270 of them in AI journals about perception and control, and only 29 carry a liability, insurance, regulation, acceptance or ethics keyword, with liability on 2. Driverless cars are now on public roads. When one crashes, who pays, and does the answer shape what gets deployed?

## What prompted this

Since October 2025, 314 papers carry an autonomous-driving, autonomous-vehicle, self-driving or automated-vehicle keyword (270 in AI journals, 31 in operations research): "autonomous driving" on 152, "autonomous vehicles" on 64, "autonomous driving perception" on 45, "connected and automated vehicles" on 15, "autonomous driving safety" on 10. Only 29 of the 314 also carry a liability, insurance, accident, regulation, acceptance, trust, ethics, law or policy keyword, and "liability" appears on 2 papers.

## Questions

- In crashes involving driverless vehicles so far, how was fault assigned between the operator, the manufacturer and other road users, and how did insurers settle?
- Do strict manufacturer liability regimes slow deployment or improve safety, compared with regimes that treat the vehicle like a driver?
- How do crash rates per mile of driverless fleets compare with human drivers on the same roads, once reporting differences are corrected?

## Data that could help

Crash reports filed with regulators (the US standing general order data, state DMV reports), court and settlement records, and fleet mileage disclosures.

## Next step

Compile the crash reports of driverless fleets since 2021 with fault determinations and compare injury rates per mile with human-driven ride-hailing in the same cities.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| autonomous driving | 3 | #687 | 10, 11, 14, 15, 6, 16, 16, 20, 15, 7, 12, 8, 3 | 150 |
| autonomous driving safety | 0 | – | 1, 0, 0, 0, 2, 1, 1, 1, 2, 0, 1, 1, 0 | 10 |
| connected and automated vehicles | 1 | #2550 | 0, 0, 0, 1, 1, 3, 1, 2, 2, 1, 2, 1, 1 | 14 |


---

---
title: "Audit market concentration and the price of an audit"
description: "The Big 4 auditors appear on 32 recent papers in the finance and accounting journals, but only 9 of 38 papers on the audit market carry a concentration, competition or fee keyword. Audit fees have risen sharply, and regulators worry about four firms auditing almost every large company. Does concentration raise fees, lower quality or both?"
url: https://scholar4ai.com/ideas/audit-market-concentration
markdown_url: https://scholar4ai.com/ideas/audit-market-concentration.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "audit", "competition"]
keywords: ["Big 4 auditors", "audit market competition"]
---

# Audit market concentration and the price of an audit

The Big 4 auditors appear on 32 recent papers in the finance and accounting journals, but only 9 of 38 papers on the audit market carry a concentration, competition or fee keyword. Audit fees have risen sharply, and regulators worry about four firms auditing almost every large company. Does concentration raise fees, lower quality or both?

## What prompted this

Since October 2025, 38 papers carry a Big 4 or audit-market keyword (18 in finance journals, 13 in accounting); "Big 4 auditors" appears on 32. Only 9 of the 38 also carry a concentration, competition, fee, choice or market-share keyword, and "audit market concentration" on a single paper.

## Questions

- In local audit markets where one Big 4 office dominates, are fees higher and are restatements more or less common?
- When a Big 4 firm exits a market segment or a country, what happens to the fees and the quality of the clients it leaves?
- Do joint audits or shared audits, as tried in France and proposed in the UK, change concentration or only cost?

## Data that could help

Audit fees and auditor identities (Audit Analytics), office-level market shares, restatements and inspection findings.

## Next step

Compute office-level audit market concentration for US metropolitan areas and relate it to fee growth and restatement rates for 2015–2025.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| Big 4 auditors | 2 | #1148 | 3, 4, 1, 3, 2, 3, 1, 4, 3, 3, 2, 1, 2 | 30 |
| audit market competition | 0 | – | 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0 | 2 |


---

---
title: "Auction design where the money is (research idea)"
description: "Auction design, first- and second-price auctions and bidding behaviour appear on 114 recent papers, mostly in economics, and the topic is fading, with 16 papers in the last quarter against about 32 per quarter before. Procurement auctions, which buy public goods and services worth trillions, appear on 2 papers. Which formats get governments the best price, and do bidders collude in the rest?"
url: https://scholar4ai.com/ideas/auction-design-for-procurement
markdown_url: https://scholar4ai.com/ideas/auction-design-for-procurement.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "economics", "public sector"]
keywords: ["auction design", "reverse auctions", "combinatorial auctions"]
---

# Auction design where the money is

Auction design, first- and second-price auctions and bidding behaviour appear on 114 recent papers, mostly in economics, and the topic is fading, with 16 papers in the last quarter against about 32 per quarter before. Procurement auctions, which buy public goods and services worth trillions, appear on 2 papers. Which formats get governments the best price, and do bidders collude in the rest?

## What prompted this

Since October 2025, 114 papers carry an auction keyword (81 in economics journals, 12 in finance, 10 in operations research): "auction design" on 15, "first-price auctions" on 12, "second-price auctions" on 9, "auctions" on 7, "auction revenue" on 6. Thirty-five of the 114 also carry a procurement, combinatorial, reverse-auction, supplier, bidding, public or government keyword, but "procurement auctions" appears on 2 and "reverse auctions" and "combinatorial auctions" on 4 each. Sixteen auction papers were published in July–September 2026 against about 32 per quarter earlier.

## Questions

- In public procurement data, do scoring auctions that weigh quality beat lowest-price auctions on final cost after change orders and delays?
- Can bid patterns in procurement data detect collusion, and do the detected cartels match prosecuted ones?
- Do combinatorial formats, used for spectrum and transport contracts, deliver the efficiency gains claimed, net of their complexity?

## Data that could help

Procurement portal data with bids, awards and contract outcomes, spectrum auction records, and cartel prosecution records.

## Next step

Compare final costs and delays of road contracts awarded by lowest price and by scoring rules in one country's procurement data.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| auction design | 2 | #1148 | 1, 1, 2, 1, 1, 2, 1, 1, 0, 3, 0, 1, 1 | 14 |
| reverse auctions | 0 | – | 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0 | 4 |
| combinatorial auctions | 0 | – | 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0 | 4 |


---

---
title: "Who checks the sustainability reports? (research idea)"
description: "Sustainability reporting appears on 57 recent papers, but assurance of those reports on far fewer: \"ESG assurance\" on 6, \"external assurance\" on 3, and the topic is rising. A global assurance standard took effect in 2026. Does assurance make reported emissions more accurate, and who should provide it?"
url: https://scholar4ai.com/ideas/assurance-of-sustainability-reports
markdown_url: https://scholar4ai.com/ideas/assurance-of-sustainability-reports.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "esg", "audit"]
keywords: ["ESG assurance", "sustainability reporting"]
---

# Who checks the sustainability reports?

Sustainability reporting appears on 57 recent papers, but assurance of those reports on far fewer: "ESG assurance" on 6, "external assurance" on 3, and the topic is rising. A global assurance standard took effect in 2026. Does assurance make reported emissions more accurate, and who should provide it?

## What prompted this

Since October 2025, 34 papers carry an assurance keyword, 12 of which also carry a sustainability, ESG, CSR, non-financial, carbon or climate keyword: "ESG assurance" appears on 6 papers and "external assurance" on 3. The quarter to September 2026 had 12 assurance papers against about 7 per quarter earlier. "Sustainability reporting" appears on 57 papers.

## Questions

- Do firms whose emissions are assured restate them less often, and are their figures closer to independent estimates?
- Does it matter whether the assurer is an audit firm or an engineering consultancy, and whether the engagement is limited or reasonable?
- When assurance becomes mandatory under the EU's reporting directive, do the gaps between firms' claims and their emissions shrink?

## Data that could help

Assurance statements attached to sustainability reports, reported emissions (CDP, Refinitiv ESG), independent emission estimates, and restatements.

## Next step

Compare year-to-year revisions of reported Scope 1 emissions between firms with and without assurance for 2019–2025.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| ESG assurance | 0 | – | 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 0 | 6 |
| sustainability reporting | 4 | #458 | 1, 4, 4, 3, 5, 5, 5, 8, 7, 2, 3, 8, 3 | 55 |


---

---
title: "The annotation labour behind AI (research idea)"
description: "Annotation cost reduction and reinforcement learning from human feedback appear on 44 recent papers in the AI journals, among 71 papers on annotation and human feedback, but only 8 of them carry a worker, labour, wage, platform or ethics keyword. Millions of people label data and rate model outputs, often through platforms in low-income countries. What are they paid, and how does the pay shape the data?"
url: https://scholar4ai.com/ideas/annotation-labour-behind-ai
markdown_url: https://scholar4ai.com/ideas/annotation-labour-behind-ai.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "labor", "ethics"]
keywords: ["annotation cost reduction", "reinforcement learning from human feedback", "data annotation"]
---

# The annotation labour behind AI

Annotation cost reduction and reinforcement learning from human feedback appear on 44 recent papers in the AI journals, among 71 papers on annotation and human feedback, but only 8 of them carry a worker, labour, wage, platform or ethics keyword. Millions of people label data and rate model outputs, often through platforms in low-income countries. What are they paid, and how does the pay shape the data?

## What prompted this

Since October 2025, 71 papers carry an annotation-cost, human-annotation, annotator, crowdwork, human-feedback, RLHF or data-labelling keyword (69 in AI journals): "annotation cost reduction" on 27, "reinforcement learning from human feedback" on 17, "human-in-the-loop annotation" on 7, "expert annotation" on 5, "data annotation" on 6. Only 8 of the 71 also carry a worker, labour, wage, crowdwork, gig, pay, employment, economics, incentive, platform, fairness or ethics keyword; "ghost workers" and "precarious employment" appear on one and two papers.

## Questions

- What do annotators and raters earn per hour on the main platforms by country and task, and how has pay changed as demand for preference ratings grew?
- Does pay level change label quality and the preferences that are encoded in models, for example toward speed over care?
- Where is annotation work moving, and do the new locations' labour laws cover it?

## Data that could help

Platform task postings and pay rates, worker surveys, and experiments that vary pay and measure label quality.

## Next step

Scrape posted pay rates for annotation and rating tasks on the main platforms over a year and relate them to task type and worker location.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| annotation cost reduction | 0 | – | 2, 1, 2, 6, 3, 4, 1, 2, 0, 0, 3, 3, 0 | 27 |
| reinforcement learning from human feedback | 0 | – | 0, 3, 1, 2, 0, 1, 1, 0, 5, 1, 2, 1, 0 | 17 |
| data annotation | 0 | – | 1, 0, 0, 1, 0, 0, 2, 0, 1, 0, 1, 0, 0 | 6 |


---

---
title: "Analyst coverage when AI writes the summaries"
description: "Analyst attention is rising in the finance journals, and AI tools now summarize filings and earnings calls for anyone. Only 13 of 246 recent papers on analysts carry an AI keyword. Do analysts drop the small firms that machines now summarize, or use the time saved to dig deeper?"
url: https://scholar4ai.com/ideas/analyst-coverage-when-ai-writes-summaries
markdown_url: https://scholar4ai.com/ideas/analyst-coverage-when-ai-writes-summaries.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "accounting", "ai"]
keywords: ["analyst coverage", "analyst attention", "analyst forecast accuracy"]
---

# Analyst coverage when AI writes the summaries

Analyst attention is rising in the finance journals, and AI tools now summarize filings and earnings calls for anyone. Only 13 of 246 recent papers on analysts carry an AI keyword. Do analysts drop the small firms that machines now summarize, or use the time saved to dig deeper?

## What prompted this

In the finance journals, "analyst attention" had 12 papers in July–September 2026 against about 7 expected (see the [finance dashboard](/categories/finance)). Since October 2025, 246 papers carry an analyst keyword such as "analyst coverage" (96 papers) or "analyst attention" (46); 160 of them are in finance journals and 41 in accounting. Only 13 carry an AI keyword.

## Questions

- After brokers adopt AI tools, does each analyst cover more firms, and which firms gain or lose coverage?
- Do forecasts become more accurate, or more alike, as analysts start from the same AI summaries?
- Do investors still react to analyst reports when an AI summary of the same filing is free?

## Data that could help

Analyst forecasts and coverage (I/B/E/S), the dates brokers adopted AI tools (from announcements and job postings), report texts and stock price reactions.

## Next step

Track forecast dispersion and coverage per analyst around brokers' announced AI rollouts between 2022 and 2025.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| analyst coverage | 0 | – | 3, 18, 6, 5, 6, 8, 12, 7, 8, 10, 8, 5, 0 | 96 |
| analyst attention | 1 | #2550 | 2, 2, 6, 2, 4, 2, 2, 6, 5, 5, 5, 4, 1 | 45 |
| analyst forecast accuracy | 0 | – | 3, 2, 1, 1, 1, 0, 0, 1, 1, 2, 1, 0, 0 | 13 |


---

---
title: "Ambulance dispatch and the minutes that matter"
description: "Emergency medical services, ambulance dispatch and emergency departments appear on 28 recent papers across the operations research, AI and economics journals, with 8 in the last quarter against about 6 per quarter before. Response times are rising in many countries as hospitals block ambulances at the door. Which dispatch and triage changes save lives, and which only move the queue?"
url: https://scholar4ai.com/ideas/ambulance-dispatch-and-emergency-care
markdown_url: https://scholar4ai.com/ideas/ambulance-dispatch-and-emergency-care.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "healthcare"]
keywords: ["emergency medical services", "ambulance dispatch", "emergency department"]
---

# Ambulance dispatch and the minutes that matter

Emergency medical services, ambulance dispatch and emergency departments appear on 28 recent papers across the operations research, AI and economics journals, with 8 in the last quarter against about 6 per quarter before. Response times are rising in many countries as hospitals block ambulances at the door. Which dispatch and triage changes save lives, and which only move the queue?

## What prompted this

Since October 2025, 28 papers carry an emergency-department, emergency-medical, ambulance or emergency-care keyword (14 in operations research journals, 7 in AI, 6 in economics): "emergency medical services" on 13, "ambulance dispatch" on 6, "emergency department visits" on 3, "emergency department" on 3, "emergency department triage" on 2. The quarter to September 2026 had 8 such papers against about 6 per quarter earlier.

## Questions

- When ambulances wait outside full emergency departments, how much of the lost response time comes from handover delays rather than from dispatch, and does the dispatch optimization literature model it?
- Do AI triage tools reduce waiting for the sickest patients, or shift waiting to those the tool misjudges?
- Does diverting low-acuity calls to telehealth or community paramedics shorten response times for the rest?

## Data that could help

Ambulance dispatch and handover logs, emergency department arrival and waiting time records, and patient outcome data.

## Next step

Decompose ambulance response times in one region into dispatch, travel and handover components for 2019–2025 and relate the handover component to hospital occupancy.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| emergency medical services | 0 | – | 2, 1, 0, 0, 0, 1, 2, 1, 3, 1, 1, 1, 0 | 13 |
| ambulance dispatch | 0 | – | 0, 0, 0, 0, 0, 1, 0, 0, 3, 1, 0, 1, 0 | 6 |
| emergency department | 0 | – | 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0 | 3 |


---

---
title: "Algorithmic management and the people managed by it"
description: "Algorithmic management and algorithmic control appear on 21 recent papers, mostly in management, and the topic is fading, with 2 papers in the last quarter against about 6 per quarter before. Yet software now sets schedules, rates and tasks for warehouse, delivery and call-centre workers. What does it do to effort, health and the manager's job?"
url: https://scholar4ai.com/ideas/algorithmic-management-and-wellbeing
markdown_url: https://scholar4ai.com/ideas/algorithmic-management-and-wellbeing.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "labor", "technology"]
keywords: ["algorithmic management", "algorithmic control"]
---

# Algorithmic management and the people managed by it

Algorithmic management and algorithmic control appear on 21 recent papers, mostly in management, and the topic is fading, with 2 papers in the last quarter against about 6 per quarter before. Yet software now sets schedules, rates and tasks for warehouse, delivery and call-centre workers. What does it do to effort, health and the manager's job?

## What prompted this

Since October 2025, 21 papers carry an algorithmic-management, algorithmic-control, electronic-monitoring, workplace-surveillance or employee-monitoring keyword (17 in management journals): "algorithmic management" on 9, "algorithmic control" on 6. Only 2 of them were published in July–September 2026 against about 6 per quarter earlier.

## Questions

- When algorithms set targets and monitor pace, do injuries, errors and turnover rise, and does output per worker rise by enough to pay for them?
- Do workers game the metrics, and does the algorithm then tighten, in a cycle?
- What do human managers do once scheduling and evaluation are automated, and do workers trust them more or less?

## Data that could help

Worker-level productivity and injury records from warehouses and delivery platforms, scheduling system logs, and surveys of monitored workers.

## Next step

Compare injury and turnover rates at warehouses before and after the roll-out of algorithmic pace management, using firm records or public injury filings.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| algorithmic management | 0 | – | 1, 3, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0 | 9 |
| algorithmic control | 1 | #2550 | 1, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 1, 1 | 5 |


---

---
title: "Hiring by algorithm, audited (research idea)"
description: "Recruitment, job matching and hiring discrimination appear on 109 recent papers across the economics, management and AI journals, with 32 in the last quarter against about 25 per quarter before; 32 of them carry a bias, fairness, algorithm or AI keyword. Screening tools now read millions of applications, and New York requires bias audits of them. What do the audits find, and do they change outcomes?"
url: https://scholar4ai.com/ideas/algorithmic-hiring-bias
markdown_url: https://scholar4ai.com/ideas/algorithmic-hiring-bias.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "labor", "ethics"]
keywords: ["hiring discrimination", "recruitment", "job matching"]
---

# Hiring by algorithm, audited

Recruitment, job matching and hiring discrimination appear on 109 recent papers across the economics, management and AI journals, with 32 in the last quarter against about 25 per quarter before; 32 of them carry a bias, fairness, algorithm or AI keyword. Screening tools now read millions of applications, and New York requires bias audits of them. What do the audits find, and do they change outcomes?

## What prompted this

Since October 2025, 109 papers carry a hiring, recruitment, résumé, applicant, candidate-screening, job-matching or talent-acquisition keyword (54 in economics journals, 24 in management, 13 in finance, 8 in AI): "recruitment" on 8, "job matching" on 8, "hiring discrimination" on 6, "hiring decisions" on 6, "talent acquisition" on 5. Thirty-two of the 109 also carry a bias, fairness, discrimination, algorithm, language-model, AI, automated or machine-learning keyword. The quarter to September 2026 had 32 such papers against about 25 per quarter earlier.

## Questions

- In the bias audits published under New York's law, what disparities were found, and did employers change or drop the tools that showed them?
- When language models rank résumés, do they discriminate by name, gap years or address in correspondence tests, and does prompting the model to be fair fix it?
- Do algorithmic screens change who applies, as applicants learn to write for the machine?

## Data that could help

Published bias audit reports, correspondence experiments with language-model screeners, applicant tracking data from employers, and job posting text over time.

## Next step

Collect the bias audit reports published under the New York law and code the disparities found and the employers' subsequent tool use.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| hiring discrimination | 0 | – | 1, 0, 1, 0, 0, 1, 2, 0, 0, 0, 0, 1, 0 | 6 |
| recruitment | 0 | – | 1, 2, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0 | 8 |
| job matching | 0 | – | 0, 2, 0, 1, 1, 1, 2, 1, 0, 0, 0, 0, 0 | 8 |


---

---
title: "Algorithm aversion in the era of fluent machines"
description: "Trust in AI appears on 76 recent papers, algorithm aversion on 10 and algorithm appreciation on 3, across the AI and management journals, with 28 papers in the last quarter against about 25 per quarter before. People once rejected algorithmic advice after seeing it err. Language models that explain themselves fluently may get too much trust instead. Which way does it go, and for whom?"
url: https://scholar4ai.com/ideas/algorithm-aversion-in-the-llm-era
markdown_url: https://scholar4ai.com/ideas/algorithm-aversion-in-the-llm-era.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "ai", "decision-making"]
keywords: ["algorithm aversion", "trust in AI", "algorithm appreciation"]
---

# Algorithm aversion in the era of fluent machines

Trust in AI appears on 76 recent papers, algorithm aversion on 10 and algorithm appreciation on 3, across the AI and management journals, with 28 papers in the last quarter against about 25 per quarter before. People once rejected algorithmic advice after seeing it err. Language models that explain themselves fluently may get too much trust instead. Which way does it go, and for whom?

## What prompted this

Since October 2025, 106 papers carry a trust-in-AI, algorithm-aversion, algorithm-appreciation or AI-advice keyword (52 in AI journals, 37 in management): "trust in AI" on 76, "algorithm aversion" on 10, "public trust in AI" on 4, "algorithm appreciation" on 3. The quarter to September 2026 had 28 such papers against about 25 per quarter earlier.

## Questions

- In the same forecasting task, do people follow a language model's advice more than a statistical model's, and does the difference survive after both make the same error?
- Does a fluent explanation increase reliance even when it is wrong, and can calibration cues (stated uncertainty) correct that?
- Do professionals (doctors, loan officers, analysts) show aversion or over-reliance in their own domain, compared with laypeople?

## Data that could help

Incentivized experiments with advice from different sources, and field data from professional settings where model recommendations and human decisions are both logged.

## Next step

Run an experiment in which participants receive identical forecasts framed as coming from a statistical model, a language model with explanations, or a human expert, and measure reliance after errors.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| algorithm aversion | 1 | #2550 | 0, 0, 0, 2, 2, 0, 1, 3, 0, 1, 0, 1, 1 | 10 |
| trust in AI | 4 | #458 | 8, 5, 3, 9, 4, 10, 8, 7, 2, 6, 8, 4, 3 | 74 |
| algorithm appreciation | 0 | – | 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0 | 3 |


---

---
title: "Air pollution and the quality of decisions (research idea)"
description: "Air pollution and PM2.5 appear on 213 recent papers, but only 33 of them carry a cognition, productivity, decision-making, education, mental-health or worker keyword, and \"cognitive performance\" on 5. Pollution affects brains as well as lungs. How much do bad-air days cost in exam scores, work errors and bad investment decisions?"
url: https://scholar4ai.com/ideas/air-pollution-and-cognition
markdown_url: https://scholar4ai.com/ideas/air-pollution-and-cognition.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "health", "environment"]
keywords: ["air pollution", "PM2.5", "cognitive performance"]
---

# Air pollution and the quality of decisions

Air pollution and PM2.5 appear on 213 recent papers, but only 33 of them carry a cognition, productivity, decision-making, education, mental-health or worker keyword, and "cognitive performance" on 5. Pollution affects brains as well as lungs. How much do bad-air days cost in exam scores, work errors and bad investment decisions?

## What prompted this

Since October 2025, 213 papers carry an air-pollution, PM2.5, particulate or air-quality keyword (127 in economics journals, 54 in AI): "air pollution" on 85, "PM2.5" on 41, "air quality" on 24. Only 33 of them also carry a cognition, test-score, productivity, decision-making, education, mental-health, crime, worker, student or labour keyword, and "cognitive performance" appears on 5 papers.

## Questions

- On high-pollution days, do students score lower on high-stakes exams, do workers make more errors, and do traders and analysts make worse calls?
- Are the effects larger for tasks that need attention and memory than for routine tasks?
- Does indoor filtration undo the effect, and what would that imply for the value of clean-air policy?

## Data that could help

Daily pollution readings, exam results with dates and locations, call-centre or factory error logs, and trading records matched to the trader's location.

## Next step

Match the dates and locations of a national exam to daily PM2.5 readings and estimate the score loss per unit of pollution.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| air pollution | 3 | #687 | 7, 7, 6, 7, 10, 16, 4, 9, 3, 4, 6, 4, 3 | 83 |
| PM2.5 | 1 | #2550 | 4, 3, 5, 4, 2, 5, 3, 0, 5, 2, 5, 2, 1 | 40 |
| cognitive performance | 0 | – | 0, 0, 2, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0 | 5 |


---

---
title: "AI-written appeals in crowdfunding and charity"
description: "Charitable giving and crowdfunding appear on 168 recent papers, with 44 in the last quarter against about 38 per quarter before, but only 23 of them carry an AI, text, narrative or image keyword. Campaign success depends on the story. If fundraisers now write with language models, do donors give more, and do they give to the right causes?"
url: https://scholar4ai.com/ideas/ai-written-crowdfunding-appeals
markdown_url: https://scholar4ai.com/ideas/ai-written-crowdfunding-appeals.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "ai"]
keywords: ["charitable giving", "crowdfunding", "crowdfunding campaign success"]
---

# AI-written appeals in crowdfunding and charity

Charitable giving and crowdfunding appear on 168 recent papers, with 44 in the last quarter against about 38 per quarter before, but only 23 of them carry an AI, text, narrative or image keyword. Campaign success depends on the story. If fundraisers now write with language models, do donors give more, and do they give to the right causes?

## What prompted this

Since October 2025, 168 papers carry a crowdfunding, charitable-giving, donation, philanthropy or fundraising keyword (69 in economics journals, 50 in management, 32 in finance): "charitable giving" on 45, "crowdfunding" on 44, "equity crowdfunding" on 18, "crowdfunding campaign success" on 14. The quarter to September 2026 had 44 such papers against about 38 per quarter earlier. Only 23 of the 168 also carry an AI, text, image, narrative or language keyword.

## Questions

- Since 2023, has the language of crowdfunding appeals converged on model-like text, and did success rates change for campaigns that use it?
- Do donors give more to polished appeals, and does that shift money from causes with less-skilled writers?
- Do platforms that offer built-in writing assistants see more campaigns and lower average pledges?

## Data that could help

Campaign text and outcomes from crowdfunding platforms with timestamps, AI-text detectors, and platform feature release dates.

## Next step

Score the text of campaigns on one platform for 2021–2025 with an AI-text detector and relate the score to funding success before and after 2023.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| charitable giving | 4 | #458 | 8, 1, 2, 4, 5, 3, 1, 2, 3, 5, 5, 2, 4 | 41 |
| crowdfunding | 2 | #1148 | 6, 7, 3, 3, 2, 3, 7, 1, 2, 3, 1, 4, 2 | 42 |
| crowdfunding campaign success | 0 | – | 2, 5, 0, 1, 0, 1, 2, 1, 0, 1, 1, 0, 0 | 14 |


---

---
title: "AI weather forecasts and their economic value"
description: "Weather forecasting, precipitation forecasting and typhoons appear on 45 recent papers, mostly in AI journals, and only 9 carry an economic, energy, agricultural, insurance or market keyword. Machine learning models now beat the physics-based systems on many measures at a fraction of the cost. Who captures the value, and does it reach the farmers, utilities and insurers who need it?"
url: https://scholar4ai.com/ideas/ai-weather-forecasts-and-their-economic-value
markdown_url: https://scholar4ai.com/ideas/ai-weather-forecasts-and-their-economic-value.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "climate", "economics"]
keywords: ["weather forecasting", "precipitation forecasting", "typhoons"]
---

# AI weather forecasts and their economic value

Weather forecasting, precipitation forecasting and typhoons appear on 45 recent papers, mostly in AI journals, and only 9 carry an economic, energy, agricultural, insurance or market keyword. Machine learning models now beat the physics-based systems on many measures at a fraction of the cost. Who captures the value, and does it reach the farmers, utilities and insurers who need it?

## What prompted this

Since October 2025, 45 papers carry a weather-forecasting, weather-prediction, precipitation-forecasting, numerical-weather or typhoon keyword (35 in AI journals, 7 in economics): "weather forecasting" on 21, "precipitation forecasting" on 8, "typhoons" on 5, "numerical weather prediction" on 3. Only 9 of the 45 also carry an economics, finance, energy, agriculture, insurance, commodity, value, market, cost or decision keyword.

## Questions

- For which decisions (electricity trading, crop planting, flood warnings, airline operations) does the accuracy gain of machine learning forecasts translate into money, and how much?
- If a few firms own the best forecasts, do they sell them, trade on them, or both, and does public forecasting lose funding?
- Do the new models forecast the extremes that matter for insurance and disaster response as well as they forecast the averages?

## Data that could help

Public forecast archives from both physics-based and machine learning systems, decision records from utilities and farms, and market prices sensitive to weather.

## Next step

Compare the value of physics-based and machine learning forecasts for day-ahead electricity trading in one market over a year, using realized prices.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| weather forecasting | 1 | #2550 | 0, 4, 0, 3, 0, 2, 1, 3, 3, 2, 0, 2, 1 | 20 |
| precipitation forecasting | 0 | – | 1, 2, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0 | 8 |
| typhoons | 0 | – | 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 2, 0, 0 | 5 |


---

---
title: "AI washing and stock price crash risk (research idea)"
description: "Artificial intelligence and stock price crash risk are both rising in the finance journals, and \"AI washing\" has appeared as a keyword. When the AI story in disclosures runs ahead of actual AI investment, does the stock carry more crash risk?"
url: https://scholar4ai.com/ideas/ai-washing-and-crash-risk
markdown_url: https://scholar4ai.com/ideas/ai-washing-and-crash-risk.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["finance", "accounting", "ai"]
keywords: ["AI washing", "stock price crash risk", "artificial intelligence"]
---

# AI washing and stock price crash risk

Artificial intelligence and stock price crash risk are both rising in the finance journals, and "AI washing" has appeared as a keyword. When the AI story in disclosures runs ahead of actual AI investment, does the stock carry more crash risk?

## What prompted this

In the finance journals, "artificial intelligence" had 31 papers in July–September 2026 against about 17 expected from its earlier share, and "stock price crash risk" had 27 against 17 (see the [finance dashboard](/categories/finance)). Since October 2025, 119 papers carry a crash-risk keyword, and 10 of them also carry an AI keyword. The keyword "AI washing" appears on 11 papers (8 in finance journals, 3 in management).

## Questions

- Can an AI-washing score be built from AI talk in filings and calls relative to measurable AI inputs, such as AI job postings, AI patents and computing spending?
- Is a high score followed by more crash risk (negative return skewness, down-to-up volatility), as theories of hoarded bad news predict?
- Did the first regulatory actions against AI washing change how firms talk about AI?

## Data that could help

- Filing and call text (the corporate filings category of the [dataset ranking](/rankings/datasets)), online job postings, patents and daily returns.
- Crash-risk measures from firm-specific weekly returns.

## Next step

Count AI mentions in a sample of 10-K filings from 2020 to 2025 and compare them, firm by firm, with AI job postings.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| AI washing | 1 | #2550 | 0, 0, 0, 2, 1, 2, 0, 3, 0, 0, 3, 0, 1 | 11 |
| stock price crash risk | 6 | #263 | 4, 9, 11, 7, 6, 11, 9, 8, 6, 14, 13, 8, 4 | 106 |
| [artificial intelligence](https://scholar4ai.com/keywords/artificial-intelligence.md) | 36 | #6 | 34, 32, 56, 42, 35, 45, 43, 29, 36, 50, 48, 49, 26 | 499 |


---

---
title: "When the shopping assistant chooses (research idea)"
description: "Purchase intention, purchase decisions and consumer choice appear on 178 recent papers, mostly in management and economics, but only 14 of them carry a generative-AI, conversational-AI or recommendation keyword. Shoppers are starting to let AI assistants search, compare and buy. Who gets recommended, and what happens to brands and prices when a model makes the choice?"
url: https://scholar4ai.com/ideas/ai-shopping-assistants
markdown_url: https://scholar4ai.com/ideas/ai-shopping-assistants.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "marketing", "ai"]
keywords: ["purchase intention", "conversational AI", "online shopping"]
---

# When the shopping assistant chooses

Purchase intention, purchase decisions and consumer choice appear on 178 recent papers, mostly in management and economics, but only 14 of them carry a generative-AI, conversational-AI or recommendation keyword. Shoppers are starting to let AI assistants search, compare and buy. Who gets recommended, and what happens to brands and prices when a model makes the choice?

## What prompted this

Since October 2025, 178 papers carry a purchase-intention, purchase-decision, consumer-choice or online-shopping keyword (88 in management journals, 48 in economics, 24 in operations research): "purchase intention" on 48, "purchase decision" on 38, "consumer choice" on 37, "online shopping" on 19. Only 14 of them also carry a generative-AI, ChatGPT, language-model, AI-assistant, conversational or recommendation keyword, while "conversational AI" appears on 20 papers in the archive.

## Questions

- When an assistant picks products, how concentrated are its choices across brands compared with human shoppers, and do the choices favour the assistant's owner or advertisers?
- Do consumers who delegate to assistants pay less, buy more, or buy different things?
- How do sellers optimize for assistants rather than for human eyes, and does that change product descriptions and prices?

## Data that could help

Logs of assistant-mediated shopping sessions, experiments comparing assistant and human choices for the same shopping tasks, and retailer price and description data over time.

## Next step

Ask several shopping assistants to buy the same 100 everyday products and compare brand concentration and prices with what human panels chose.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| purchase intention | 2 | #1148 | 4, 2, 5, 3, 0, 3, 6, 6, 4, 8, 4, 3, 0 | 48 |
| conversational AI | 0 | – | 0, 2, 2, 1, 0, 2, 5, 2, 1, 0, 3, 2, 0 | 20 |
| online shopping | 0 | – | 3, 4, 2, 2, 1, 2, 0, 0, 2, 0, 1, 2, 0 | 19 |


---

---
title: "Are AI patents like other patents? (research idea)"
description: "Patents appear on 377 recent papers, and patent quality on 24, but only 32 of the patent papers carry an AI or software keyword, and \"AI patents\" on 14. AI patent filings have exploded, especially in China, while much AI progress is published as open code. What do AI patents protect, and do they predict anything?"
url: https://scholar4ai.com/ideas/ai-patents
markdown_url: https://scholar4ai.com/ideas/ai-patents.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "innovation", "ai"]
keywords: ["AI patents", "patent quality", "patents"]
---

# Are AI patents like other patents?

Patents appear on 377 recent papers, and patent quality on 24, but only 32 of the patent papers carry an AI or software keyword, and "AI patents" on 14. AI patent filings have exploded, especially in China, while much AI progress is published as open code. What do AI patents protect, and do they predict anything?

## What prompted this

Since October 2025, 377 papers carry a patent keyword (148 in economics journals, 101 in management, 98 in finance): "patent citations" on 33, "patents" on 31, "patent applications" on 29, "patent quality" on 24. Only 32 of the 377 also carry an AI, machine-learning, generative or software keyword, and "AI patents" appears on 14 papers.

## Questions

- Do AI patents get cited, licensed and litigated like other patents, or are they filed for signalling and subsidies?
- Do firms that patent AI release more or fewer open models and papers, and do the two forms of disclosure substitute for each other?
- Do AI patent counts predict firms' AI revenue or productivity any better than AI job postings do?

## Data that could help

Patent records classified by technology (PATSTAT, PatentsView, CNIPA), citations and litigation, open-source releases and job postings.

## Next step

Compare forward citations, renewals and litigation rates of AI patents with those of other software patents from the same firms and years.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| AI patents | 1 | #2550 | 0, 1, 1, 0, 4, 0, 1, 1, 1, 0, 3, 1, 1 | 13 |
| patent quality | 1 | #2550 | 2, 1, 5, 0, 2, 3, 1, 1, 1, 2, 2, 3, 1 | 23 |
| patents | 0 | – | 3, 1, 1, 4, 1, 3, 7, 4, 3, 2, 1, 1, 0 | 31 |


---

---
title: "Who pays when the AI is wrong? (research idea)"
description: "AI regulation, trustworthy AI, responsible AI and the EU AI Act appear on 229 recent papers, mostly in AI and management journals, but only 12 of them carry an insurance, liability, compensation or damages keyword, and AI liability on 2. Rules now assign duties; they say little about who pays for harm. Can AI harms be insured, and would insurance discipline deployment?"
url: https://scholar4ai.com/ideas/ai-liability-and-insurance
markdown_url: https://scholar4ai.com/ideas/ai-liability-and-insurance.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "law", "insurance"]
keywords: ["AI regulation", "EU AI Act", "AI liability", "responsible AI"]
---

# Who pays when the AI is wrong?

AI regulation, trustworthy AI, responsible AI and the EU AI Act appear on 229 recent papers, mostly in AI and management journals, but only 12 of them carry an insurance, liability, compensation or damages keyword, and AI liability on 2. Rules now assign duties; they say little about who pays for harm. Can AI harms be insured, and would insurance discipline deployment?

## What prompted this

Since October 2025, 229 papers carry an AI-regulation, AI-safety, EU AI Act, AI-alignment, responsible-AI or trustworthy-AI keyword (157 in AI journals, 53 in management): "AI regulation" on 53, "trustworthy AI" on 51, "responsible AI" on 46, "AI safety" on 28, "EU AI Act" on 25. Only 12 of the 229 also carry an insurance, liability, tort, compensation, legal-responsibility or damages keyword, and "AI liability" appears on 2 papers.

## Questions

- For harms already litigated (wrong credit decisions, biased hiring tools, chatbot misinformation, autonomous vehicle crashes), who paid, and how long did it take?
- Could insurers price AI liability given correlated failures across customers of the same model, and would premiums vary with testing and monitoring practices?
- Does the withdrawal of the EU's AI liability directive leave victims with workable claims under existing law?

## Data that could help

Court records and settlements involving AI systems, insurance policy wordings and exclusions, and the texts of liability rules across jurisdictions.

## Next step

Compile a database of litigated AI harms since 2018 with the outcome, the party that paid and the time to resolution.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| AI regulation | 4 | #458 | 4, 3, 5, 7, 3, 4, 5, 5, 3, 1, 5, 5, 3 | 50 |
| EU AI Act | 1 | #2550 | 3, 0, 0, 1, 3, 2, 4, 4, 1, 1, 3, 2, 1 | 24 |
| AI liability | 0 | – | 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0 | 2 |
| responsible AI | 2 | #1148 | 6, 2, 2, 4, 1, 6, 5, 3, 6, 4, 3, 3, 2 | 45 |


---

---
title: "AI in the peer review of economics and finance"
description: "Peer review, research productivity and academic writing appear on 56 recent papers across the economics and AI journals, with 17 in the last quarter against about 12 per quarter before, and 17 of them involve language models. Journals report AI-written reviews and AI-written submissions. How much of each is there, and does it change what gets published?"
url: https://scholar4ai.com/ideas/ai-in-peer-review
markdown_url: https://scholar4ai.com/ideas/ai-in-peer-review.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "science", "publishing"]
keywords: ["peer review", "academic writing", "research productivity"]
---

# AI in the peer review of economics and finance

Peer review, research productivity and academic writing appear on 56 recent papers across the economics and AI journals, with 17 in the last quarter against about 12 per quarter before, and 17 of them involve language models. Journals report AI-written reviews and AI-written submissions. How much of each is there, and does it change what gets published?

## What prompted this

Since October 2025, 56 papers carry a peer-review, scientific-writing, research-productivity, academic-writing or scientific-discovery keyword (22 in economics journals, 20 in AI): "peer review" on 19, "research productivity" on 16, "academic writing" on 8, "scientific discovery" on 3. Seventeen of them also carry a language-model, generative-AI or AI keyword. The quarter to September 2026 had 17 such papers against about 12 per quarter earlier.

## Questions

- What share of referee reports at economics and finance journals since 2023 show the statistical signs of language-model drafting, and do such reports reach different recommendations?
- Do submissions written with AI assistance get accepted at different rates, and has submission volume risen faster than acceptance?
- Would AI screening for errors and missing references improve reviews, and would authors and editors accept it?

## Data that could help

Referee report and submission archives from journals that permit research access, AI-text detectors, and editor surveys.

## Next step

Run an AI-text detector on anonymized referee reports from a journal for 2021–2025 and relate flagged reports to their recommendations and length.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| peer review | 3 | #687 | 1, 1, 0, 0, 4, 2, 1, 0, 1, 0, 2, 6, 1 | 18 |
| academic writing | 1 | #2550 | 0, 0, 2, 0, 0, 2, 2, 1, 0, 0, 0, 0, 1 | 7 |
| research productivity | 2 | #1148 | 0, 2, 3, 0, 0, 0, 1, 2, 1, 0, 3, 4, 1 | 16 |


---

---
title: "When AI governance meets corporate governance"
description: "The keyword \"AI governance\" is growing fastest across the tracked journals, while corporate governance keeps rising in economics and management. Is board-level oversight of AI turning into a research stream of its own?"
url: https://scholar4ai.com/ideas/ai-governance-meets-corporate-governance
markdown_url: https://scholar4ai.com/ideas/ai-governance-meets-corporate-governance.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["ai", "management", "governance"]
keywords: ["AI governance", "corporate governance", "board of directors"]
---

# When AI governance meets corporate governance

The keyword "AI governance" is growing fastest across the tracked journals, while corporate governance keeps rising in economics and management. Is board-level oversight of AI turning into a research stream of its own?

## What prompted this

On the [trends page](/trends), "AI governance" gained more papers than any other keyword over the last three months, mostly in AI and management journals. At the same time "corporate governance" is climbing in economics. The two literatures rarely cite each other's mechanisms.

## Questions

- Do firms that put AI oversight on the board agenda take different risks, or get different audit outcomes?
- Which corporate-governance tools (independent directors, committees, disclosure rules) carry over to AI systems, and which break?
- Is AI-governance research led by different institutions and countries than corporate-governance research?

## Data that could help

- Papers that carry both keywords in [papers.json](/data/papers.json), to map the overlap.
- Annual-report disclosures of AI committees, hand-collected or text-mined.

## Next step

Read the ten most recent papers that carry both keywords and note which governance mechanisms they test.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| AI governance | 7 | #210 | 12, 7, 9, 6, 7, 13, 12, 19, 11, 12, 25, 15, 5 | 148 |
| [corporate governance](https://scholar4ai.com/keywords/corporate-governance.md) | 49 | #4 | 82, 99, 81, 74, 70, 88, 71, 55, 70, 64, 62, 71, 39 | 887 |
| board of directors | 4 | #458 | 2, 5, 2, 2, 1, 2, 5, 4, 4, 8, 2, 5, 4 | 42 |


---

---
title: "AI-generated advertising and the consumers who notice"
description: "Advertising appears on 15 recent papers, online advertising on 9 and AI-generated advertising on 3, in the management and economics journals, while AI-generated content appears on 33. Brands now generate images, copy and whole campaigns with models. Does disclosure of AI use change how consumers respond, and does generated advertising work?"
url: https://scholar4ai.com/ideas/ai-generated-advertising
markdown_url: https://scholar4ai.com/ideas/ai-generated-advertising.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "marketing", "ai"]
keywords: ["advertising", "AI-generated content", "online advertising"]
---

# AI-generated advertising and the consumers who notice

Advertising appears on 15 recent papers, online advertising on 9 and AI-generated advertising on 3, in the management and economics journals, while AI-generated content appears on 33. Brands now generate images, copy and whole campaigns with models. Does disclosure of AI use change how consumers respond, and does generated advertising work?

## What prompted this

Since October 2025, 99 papers carry an advertising keyword (40 in management journals, 25 in economics), including "advertising" on 15, "online advertising" on 9, "advertising expenditure" on 10 and "AI-generated advertising" on 3. Only 14 of the 99 also carry an AI-generated, generative, synthetic, AI or algorithm keyword, while "AI-generated content" appears on 33 papers, mostly in AI journals about detection.

## Questions

- In field experiments, do AI-generated advertisements get the same click and conversion rates as agency-made ones, and does the gap depend on product type?
- When platforms label AI-generated advertisements, as some now do, does the label reduce trust and purchases, and for which brands?
- Do cheaper creative costs lead firms to run more variants and more total advertising, and does that change competition for attention?

## Data that could help

Advertising platform experiments with creative-level performance, labelling policy dates, and advertising spending data by firm.

## Next step

Run a field experiment on an advertising platform comparing labelled and unlabelled AI-generated creatives with human-made ones for the same products.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| advertising | 1 | #2550 | 3, 1, 2, 1, 0, 1, 2, 0, 2, 0, 1, 1, 1 | 14 |
| AI-generated content | 0 | – | 4, 2, 6, 4, 0, 2, 4, 1, 3, 3, 2, 2, 0 | 33 |
| online advertising | 0 | – | 2, 1, 0, 2, 0, 0, 1, 0, 1, 0, 2, 0, 0 | 9 |


---

---
title: "AI and the offshoring of services (research idea)"
description: "Outsourcing, offshoring and digital trade appear on 104 recent papers, but only one of them carries an AI, remote-work or online-labour keyword. If language models make call centres, coding and back-office work cheaper to automate than to offshore, the countries that built service exports on cheap labour face a new competitor. Is service offshoring already slowing?"
url: https://scholar4ai.com/ideas/ai-and-the-offshoring-of-services
markdown_url: https://scholar4ai.com/ideas/ai-and-the-offshoring-of-services.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "trade", "ai"]
keywords: ["offshoring", "digital trade", "outsourcing", "trade in services"]
---

# AI and the offshoring of services

Outsourcing, offshoring and digital trade appear on 104 recent papers, but only one of them carries an AI, remote-work or online-labour keyword. If language models make call centres, coding and back-office work cheaper to automate than to offshore, the countries that built service exports on cheap labour face a new competitor. Is service offshoring already slowing?

## What prompted this

Since October 2025, 104 papers carry an outsourcing, offshoring, digital-trade or trade-in-services keyword (44 in economics journals, 23 in finance, 22 in operations research): "outsourcing" on 29, "digital trade" on 18, "offshoring" on 13, "trade in services" on 12. Only one of the 104 also carries an AI, remote-work, telework, freelancing or online-labour keyword.

## Questions

- Have service exports from India and the Philippines slowed in the tasks most exposed to language models (customer support, transcription, basic coding) relative to other tasks?
- Do firms replace offshore contracts with AI, or do offshore providers adopt AI and cut prices?
- Does the shift change who in the exporting countries loses, by skill and gender?

## Data that could help

Service export statistics by category, online freelancing platform data on jobs posted and prices, and firms' outsourcing contracts and job postings.

## Next step

Compare the growth of online freelance work in AI-exposed and non-exposed task categories since 2022 using platform data.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| offshoring | 0 | – | 1, 2, 0, 0, 2, 2, 1, 0, 1, 0, 3, 1, 0 | 13 |
| digital trade | 2 | #1148 | 0, 1, 1, 3, 6, 0, 0, 1, 0, 1, 2, 1, 2 | 16 |
| outsourcing | 1 | #2550 | 2, 4, 2, 1, 2, 3, 3, 2, 2, 1, 2, 5, 0 | 29 |
| trade in services | 1 | #2550 | 4, 0, 2, 1, 2, 0, 1, 0, 1, 0, 0, 0, 1 | 11 |


---

---
title: "Putting AI and data on the balance sheet (research idea)"
description: "Firms spend heavily on data, software and AI models, but much of that spending is expensed and barely shows on balance sheets. Intangible assets are a steady finance topic, yet only one of 57 recent papers on them carries an AI, data or software keyword. How large is the missing AI capital, and does it distort book-based measures?"
url: https://scholar4ai.com/ideas/ai-and-data-on-the-balance-sheet
markdown_url: https://scholar4ai.com/ideas/ai-and-data-on-the-balance-sheet.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "finance", "ai"]
keywords: ["intangible assets", "intangible capital", "intangible investment"]
---

# Putting AI and data on the balance sheet

Firms spend heavily on data, software and AI models, but much of that spending is expensed and barely shows on balance sheets. Intangible assets are a steady finance topic, yet only one of 57 recent papers on them carries an AI, data or software keyword. How large is the missing AI capital, and does it distort book-based measures?

## What prompted this

Since October 2025, 57 papers carry an intangible-assets, intangible-capital or intangible-investment keyword (30 in finance journals, 11 in economics, 10 in management). Only one of them also carries an AI, data-asset, software or digital-asset keyword, while AI keeps rising in almost every category on the [trends page](/trends).

## Questions

- How much do listed firms spend on AI and data each year, and how much would a capitalized "AI capital" add to their assets?
- Does adding it change book-to-market sorts, the value premium or measured returns on capital?
- Where the rules allow capitalization, are firms that capitalize more of it valued differently?

## Data that could help

- R&D, software and overhead expenses (Compustat), and AI job postings as a proxy for AI labour spending.
- China's accounting rules for enterprise data resources, in force since 2024, which let some firms report data as assets.

## Next step

Estimate AI labour spending of listed firms from job postings and salaries, capitalize it with a perpetual-inventory method, and recompute book-to-market.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| intangible assets | 3 | #687 | 2, 1, 4, 3, 4, 4, 1, 1, 1, 3, 3, 1, 3 | 28 |
| intangible capital | 2 | #1148 | 0, 2, 1, 2, 1, 0, 1, 1, 1, 0, 0, 1, 2 | 10 |
| intangible investment | 1 | #2550 | 0, 2, 0, 1, 0, 0, 2, 0, 2, 1, 0, 1, 1 | 9 |


---

---
title: "AI agents and the boundaries of the firm (research idea)"
description: "Transaction costs are back as a growing keyword in the management journals, while agentic AI and language-model agents rise in AI. Coase argued that firms exist because using markets is costly. If AI agents can search, negotiate and monitor contracts cheaply, do firms buy more and make less?"
url: https://scholar4ai.com/ideas/ai-agents-and-the-boundaries-of-the-firm
markdown_url: https://scholar4ai.com/ideas/ai-agents-and-the-boundaries-of-the-firm.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "economics", "ai"]
keywords: ["transaction cost economics", "agentic AI", "LLM agents"]
---

# AI agents and the boundaries of the firm

Transaction costs are back as a growing keyword in the management journals, while agentic AI and language-model agents rise in AI. Coase argued that firms exist because using markets is costly. If AI agents can search, negotiate and monitor contracts cheaply, do firms buy more and make less?

## What prompted this

In the management journals, "transaction costs" had 6 papers in July–September 2026 against about 1 expected (see the [management dashboard](/categories/management)); in the AI journals, "agentic AI" (11 against 5) and "LLM agents" (14 against 8) are growing. Since October 2025, 170 papers carry a transaction-cost keyword (65 in finance journals, 49 in economics, 37 in management), and only 7 of them carry an AI keyword. "Agentic AI" appears on 41 papers, 27 in AI journals and 11 in management.

## Questions

- Which transaction costs do AI agents lower most (search, bargaining or monitoring), and which do they raise (verification, security)?
- Do firms that adopt AI tools for procurement and contracting outsource more and integrate less?
- Does the change favour small firms, which can now buy what they could not organize, or large firms with more data?

## Data that could help

Firm-level purchases of goods and services (procurement records, firm-level input data), AI adoption measures (job postings, software use) and supplier counts.

## Next step

Track the number and type of suppliers per firm before and after adoption of AI procurement software, for firms whose adoption dates vendors have published.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| transaction cost economics | 3 | #687 | 1, 2, 0, 2, 6, 4, 2, 3, 2, 3, 1, 3, 3 | 29 |
| agentic AI | 2 | #1148 | 1, 3, 1, 4, 4, 1, 5, 8, 1, 3, 7, 1, 2 | 39 |
| LLM agents | 4 | #458 | 2, 3, 3, 4, 3, 3, 5, 3, 1, 6, 1, 7, 3 | 41 |


---

---
title: "Ageing populations and asset prices (research idea)"
description: "Overlapping-generations models are a rising tool in the economics journals, and population aging is a large topic, but few papers connect aging to asset prices. Of 239 recent papers with an aging or demographic keyword, 17 carry a keyword about stocks, interest rates or house prices. As Japan, Korea, China and Europe age, will returns and interest rates fall?"
url: https://scholar4ai.com/ideas/ageing-populations-and-asset-prices
markdown_url: https://scholar4ai.com/ideas/ageing-populations-and-asset-prices.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["economics", "finance", "macroeconomics"]
keywords: ["population aging", "overlapping generations model", "pension reform"]
---

# Ageing populations and asset prices

Overlapping-generations models are a rising tool in the economics journals, and population aging is a large topic, but few papers connect aging to asset prices. Of 239 recent papers with an aging or demographic keyword, 17 carry a keyword about stocks, interest rates or house prices. As Japan, Korea, China and Europe age, will returns and interest rates fall?

## What prompted this

In economics, "overlapping generations model" had 14 papers in July–September 2026 against about 9 expected (see the [economics dashboard](/categories/economics)). Since October 2025, "population aging" appears on 114 papers (61 in economics journals, 35 in finance). Of the 239 papers with a population-aging or demographic keyword, only 17 also carry a keyword about stocks, asset pricing, interest rates, the equity premium or house prices.

## Questions

- Do countries whose working-age share falls faster see lower real interest rates and equity valuations, at the same growth rate?
- Do retirees sell their assets as life-cycle models predict, or hold on to them, as many Japanese households do?
- How much do pension reforms that raise saving or the retirement age offset the effect?

## Data that could help

Population projections by age (UN), real interest rates and equity valuations by country, and household portfolio surveys such as the Survey of Consumer Finances, SHARE or the China Household Finance Survey.

## Next step

Regress long-run real interest rates and price-to-earnings ratios on the ratio of middle-aged to old people across OECD countries for 1970–2025.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| population aging | 4 | #458 | 10, 15, 7, 6, 11, 7, 9, 7, 10, 8, 11, 10, 3 | 111 |
| overlapping generations model | 2 | #1148 | 0, 9, 4, 4, 1, 2, 6, 5, 2, 4, 4, 7, 0 | 48 |
| pension reform | 2 | #1148 | 0, 2, 1, 2, 1, 2, 1, 2, 0, 1, 2, 1, 2 | 15 |


---

---
title: "Crypto on the balance sheet at fair value (research idea)"
description: "Cryptocurrency and digital assets appear on 476 recent papers, but only 6 of them carry an accounting, fair-value, reporting, disclosure or audit keyword. Since 2025, US firms report crypto holdings at fair value through earnings, and some firms now hold Bitcoin as their main asset. What does fair value accounting do to earnings, and does it change who holds crypto?"
url: https://scholar4ai.com/ideas/accounting-for-crypto-at-fair-value
markdown_url: https://scholar4ai.com/ideas/accounting-for-crypto-at-fair-value.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["accounting", "crypto", "financial reporting"]
keywords: ["digital assets", "fair value accounting"]
---

# Crypto on the balance sheet at fair value

Cryptocurrency and digital assets appear on 476 recent papers, but only 6 of them carry an accounting, fair-value, reporting, disclosure or audit keyword. Since 2025, US firms report crypto holdings at fair value through earnings, and some firms now hold Bitcoin as their main asset. What does fair value accounting do to earnings, and does it change who holds crypto?

## What prompted this

Since October 2025, 476 papers carry a crypto, digital-asset or Bitcoin keyword (299 in finance journals, 81 in economics, 65 in AI); "digital assets" appears on 32. Only 6 of the 476 also carry an accounting, fair-value, financial-reporting, disclosure or audit keyword, and "fair value accounting" appears on 4 papers.

## Questions

- After the switch from impairment-only to fair value, did firms' earnings become dominated by crypto swings, and did analysts start excluding them?
- Did the rule change encourage more firms to hold crypto, as treasury-strategy firms claim, or did it mostly affect the few that already did?
- How do auditors verify ownership and valuation of crypto held through custodians, and how often do they qualify opinions?

## Data that could help

Financial statements of crypto-holding firms, their footnotes and audit reports, and earnings call transcripts.

## Next step

Identify every US listed firm reporting crypto assets since 2023 and decompose their earnings volatility into crypto and non-crypto components.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| digital assets | 1 | #2550 | 1, 2, 2, 4, 5, 4, 2, 3, 3, 1, 0, 4, 1 | 31 |
| fair value accounting | 0 | – | 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0 | 4 |


---

---
title: "Absorptive capacity and who gains from AI (research idea)"
description: "Absorptive capacity tops the list of growing keywords across the tracked journals, while AI adoption keeps rising. Do firms need prior know-how to profit from AI, so that AI widens the gap between leaders and laggards?"
url: https://scholar4ai.com/ideas/absorptive-capacity-and-ai
markdown_url: https://scholar4ai.com/ideas/absorptive-capacity-and-ai.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["management", "economics", "ai"]
keywords: ["absorptive capacity", "artificial intelligence", "total factor productivity"]
---

# Absorptive capacity and who gains from AI

Absorptive capacity tops the list of growing keywords across the tracked journals, while AI adoption keeps rising. Do firms need prior know-how to profit from AI, so that AI widens the gap between leaders and laggards?

## What prompted this

On the [trends page](/trends), "absorptive capacity" tops the growing keywords across all journals: 44 papers in July–September 2026 against 22 expected from its earlier share, with growth in both management (21 against 9) and economics (17 against 7). In economics, "artificial intelligence" (26 against 17) and "total factor productivity" (33 against 21) are growing too. Since October 2025, 123 papers carry an absorptive-capacity keyword, but only 11 of them also carry an AI keyword.

## Questions

- Do firms with more R&D, data infrastructure or skilled staff get larger productivity gains from adopting AI?
- Is the binding constraint people (hiring and training) or complementary assets (data and processes)?
- Can subsidies or training programmes narrow the gap?

## Data that could help

- AI adoption from online job postings, and total factor productivity estimated from firm accounts (see the [equation ranking](/rankings/equations)).
- R&D spending and patents as measures of absorptive capacity before adoption.

## Next step

Merge AI job postings with firm-level productivity for listed firms and test whether the gain from AI hiring rises with R&D intensity before adoption.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| absorptive capacity | 9 | #129 | 5, 6, 9, 9, 8, 9, 8, 8, 5, 17, 12, 15, 8 | 111 |
| [artificial intelligence](https://scholar4ai.com/keywords/artificial-intelligence.md) | 36 | #6 | 34, 32, 56, 42, 35, 45, 43, 29, 36, 50, 48, 49, 26 | 499 |
| total factor productivity | 15 | #45 | 24, 17, 26, 12, 17, 19, 13, 16, 15, 14, 16, 19, 14 | 208 |


---

---
title: "The cost curve of drone delivery (research idea)"
description: "Drone delivery is one of the fastest-rising topics in the operations research journals. Most papers optimise routes; few ask who pays, and how drones compare with vans as delivery density changes."
url: https://scholar4ai.com/ideas/cost-curve-of-drone-delivery
markdown_url: https://scholar4ai.com/ideas/cost-curve-of-drone-delivery.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-22"
updated: "2026-10-22"
status: "exploring"
tags: ["operations research", "logistics", "economics"]
keywords: ["drone delivery", "last-mile delivery", "vehicle routing problem"]
---

# The cost curve of drone delivery

Drone delivery is one of the fastest-rising topics in the operations research journals. Most papers optimise routes; few ask who pays, and how drones compare with vans as delivery density changes.

## What prompted this

"Drone delivery" climbs among the [operations research keywords](/categories/operations-research) this year, next to "supply chain coordination" and "price competition". The economics journals barely mention it.

## Questions

- At what delivery density does a drone fleet beat vans on cost per parcel, once batteries, staff and regulation are priced in?
- Who captures the savings: platforms, retailers or customers?
- Do the routing models in the literature assume demand that real cities actually have?

## Possible approach

Combine published routing models with public data on parcel volumes per district, and compare cost curves under a few regulatory scenarios.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| drone delivery | 2 | #1148 | 3, 2, 1, 0, 5, 1, 0, 3, 3, 6, 5, 5, 1 | 34 |
| last-mile delivery | 8 | #153 | 6, 6, 6, 4, 4, 5, 5, 7, 7, 11, 7, 5, 6 | 73 |
| vehicle routing problem | 0 | – | 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 | 0 |


---

---
title: "Does AI-assisted trading speed up price discovery?"
description: "In the finance journals both \"artificial intelligence\" and \"price discovery\" gained papers this year, but rarely in the same paper. Do markets with more AI-assisted trading take in news faster, or just louder?"
url: https://scholar4ai.com/ideas/ai-trading-and-price-discovery
markdown_url: https://scholar4ai.com/ideas/ai-trading-and-price-discovery.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-21"
updated: "2026-10-21"
status: "seed"
tags: ["finance", "ai", "market microstructure"]
keywords: ["artificial intelligence", "price discovery", "algorithmic trading"]
---

# Does AI-assisted trading speed up price discovery?

In the finance journals both "artificial intelligence" and "price discovery" gained papers this year, but rarely in the same paper. Do markets with more AI-assisted trading take in news faster, or just louder?

## What prompted this

The [finance dashboard](/categories/finance) shows "artificial intelligence" and "price discovery" among the keywords growing fastest over the year. The papers that study AI in finance mostly look at prediction; the price-discovery papers mostly look at market structure.

## Questions

- Around earnings announcements, do stocks with more AI-driven trading reach their new price faster?
- Does that speed come with more overshooting and reversal?
- Did anything change after large language models became widely used for reading news?

## Data that could help

- Intraday prices around scheduled announcements.
- A proxy for AI-assisted trading, for example fund disclosures or order-flow patterns.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 25 – Oct 26* | Papers, last 12 months |
|---|---:|---:|---|---:|
| [artificial intelligence](https://scholar4ai.com/keywords/artificial-intelligence.md) | 36 | #6 | 34, 32, 56, 42, 35, 45, 43, 29, 36, 50, 48, 49, 26 | 499 |
| price discovery | 7 | #210 | 2, 6, 2, 3, 3, 5, 3, 6, 5, 7, 5, 11, 5 | 58 |
| algorithmic trading | 4 | #458 | 1, 2, 2, 1, 2, 3, 4, 3, 3, 3, 0, 5, 3 | 29 |