Technology acceptance model (TAM)
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Intention to use a technology depends on its perceived usefulness and ease of use; estimated with SEM.
LaTeX: BI = \beta_1\, PU + \beta_2\, PEOU + \varepsilon,\quad PU = \gamma\, PEOU + \nu
Variables and notation#
BI is behavioral intention, PU perceived usefulness and PEOU perceived ease of use; beta coefficients link predictors to intention and gamma ease to usefulness; epsilon/nu are disturbances.
Worked example#
For PU=4,PEOU=3,beta1=0.6,beta2=0.2 and zero error, predicted BI=2.4+0.6=3.
Related methods and when to use them#
TAM focuses on usefulness and ease; UTAUT adds a broader set of acceptance constructs. Survey scales, measurement validity and omitted factors affect interpretation; fitted associations do not establish adoption causality.
Sources and further reading#
Key findings#
- Technology acceptance model (TAM) was mentioned in 27 papers in the last 30 days (2026-10-07 to 2026-11-05), ranking #56 of the 248 equations and models mentioned.
- By journal category: Higher Education and Research (6), Information Systems (3), Operations Research (3), Tourism and Hospitality (3), Health Economics and Policy (3), Management (3), Finance (2), AI (2), Economics (1) and Marketing (1).
- Most of them appeared in Research in Transportation Business & Management (2), Educational Technology Research and Development (2) and Future Business Journal (2).
- Often mentioned together with Structural equation modeling (SEM) (12), UTAUT (10) and Variance inflation factor (7).
- Datasets named in the same papers: Eurostat (2), Economic Policy Uncertainty index (1) and Twitter / X (1).
- From Nov 2023 to Oct 2026, 1,372 archived papers mentioned Technology acceptance model (TAM) in their title, abstract, keywords or full text, most of them in Mar 2026 (60).
- From 2017 to 2026 (so far), Technology acceptance model (TAM) was mentioned in 1,931 papers in the tracked journals, most of them in higher education and research (533), management (527) and information systems (287).
- 441 of them were published in 2025, against 36 in 2017.
- Since 2023, the institutions with the most of these papers were University of Johannesburg (23), Universidad de Granada (18) and Korea University (14).
- The journals publishing most of them were Education and Information Technologies (199), Future Business Journal (79) and Technological Forecasting and Social Change (52).
- The most-cited of them is “What factors contribute to the acceptance of artificial intelligence? A systematic review” (Telematics and Informatics, 2022), with 1,028 citations.
Mentions over 36 months#
| Trend | Nov 23–Oct 24 | Nov 24–Oct 25 | Nov 25–Oct 26 | 36 months |
|---|---|---|---|---|
| 408 | 446 | 518 | 1,372 |
Papers per year#
20172026 (so far)
| Category | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 (so far) |
|---|---|---|---|---|---|---|---|---|---|---|
| Higher Education and Research | 10 | 15 | 27 | 20 | 28 | 25 | 56 | 120 | 127 | 105 |
| Management | 6 | 9 | 8 | 21 | 32 | 35 | 47 | 124 | 127 | 118 |
| Information Systems | 11 | 19 | 18 | 12 | 12 | 23 | 24 | 47 | 51 | 70 |
| Marketing | 4 | 6 | 5 | 9 | 7 | 7 | 12 | 42 | 24 | 24 |
| Operations Research | 1 | 7 | 5 | 1 | 5 | 11 | 11 | 12 | 26 | 26 |
| Tourism and Hospitality | 1 | 4 | 3 | 3 | 3 | 7 | 7 | 9 | 15 | 25 |
| AI | 1 | 2 | 2 | 5 | 3 | 4 | 4 | 8 | 19 | 19 |
| Finance | 0 | 2 | 0 | 1 | 3 | 0 | 4 | 11 | 15 | 27 |
| Economics | 1 | 0 | 2 | 0 | 1 | 1 | 6 | 11 | 19 | 21 |
| Statistics | 1 | 0 | 1 | 0 | 1 | 2 | 2 | 3 | 8 | 14 |
| Health Economics and Policy | 0 | 1 | 0 | 2 | 0 | 2 | 2 | 8 | 7 | 8 |
| Accounting | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 4 |
| All categories | 36 | 65 | 71 | 74 | 95 | 117 | 176 | 395 | 441 | 461 |
Where the papers come from#
| # | Institution | Papers |
|---|---|---|
| 1 | University of Johannesburg | 23 |
| 2 | Universidad de Granada | 18 |
| 3 | Korea University | 14 |
| 4 | University of Hong Kong | 14 |
| 5 | Hong Kong Polytechnic University | 13 |
| 6 | Education University of Hong Kong | 12 |
| 7 | Sunway University | 12 |
| 8 | Universidade Nova de Lisboa | 12 |
| 9 | University of Malaya | 12 |
| 10 | Monash University | 11 |
| # | Country | Papers |
|---|---|---|
| 1 | China | 203 |
| 2 | United Kingdom | 182 |
| 3 | United States | 156 |
| 4 | Germany | 140 |
| 5 | Spain | 104 |
| 6 | Australia | 95 |
| 7 | India | 85 |
| 8 | Italy | 78 |
| 9 | Malaysia | 74 |
| 10 | Turkey | 69 |
| # | Keyword | Papers |
|---|---|---|
| 1 | technology acceptance model | 496 |
| 2 | perceived usefulness | 332 |
| 3 | perceived ease of use | 288 |
| 4 | technology acceptance | 182 |
| 5 | technology adoption | 178 |
| 6 | behavioral intention | 148 |
| 7 | generative AI | 100 |
| 8 | social influence | 98 |
| 9 | ChatGPT | 90 |
| 10 | university students | 86 |
Most-cited papers#
Cite this page
Scholar4AI. (2026). Technology acceptance model (TAM). Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/equations/technology-acceptance-model-tam
@misc{scholar4ai-equations-technology-acceptance-model-tam,
author = {{Scholar4AI}},
title = {Technology acceptance model (TAM)},
year = {2026},
howpublished = {\url{https://scholar4ai.com/equations/technology-acceptance-model-tam}},
urldate = {2026-11-05},
note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}