The Technology Acceptance Model: Past, Present, and Future
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While the technology acceptance model (TAM), introduced in 1986, continues to be the most widely applied theoretical model in the IS field, few previous efforts examined its accomplishments and limitations. This study traces TAM's history, investigates its findings, and cautiously predicts its future trajectory. One hundred and one articles published by leading IS journals and conferences in the past eighteen years are examined and summarized. An open-ended survey of thirty-two leading IS researchers assisted in critically examining TAM and specifying future directions.
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- Authors
- Lee, Y., Kozar, K. A., & Larsen, K. R. T.
- Affiliations
- University of Leeds (GB)
- ORCID
- Larsen, Kai R.T.
- Venue
- Communications of the Association for Information Systems
- Publisher
- Association for Information Systems
- Published
- 2003
- Volume
- 12
- ISSN
- 1529-3181 (ISSN-L)
- OpenAlex
- W1543266722
- Cited by
- 2,572
- References
- 22
- Citations by year
- 2026: 62 · 2025: 129 · 2024: 144 · 2023: 189 · 2022: 191 · 2021: 231 · 2020: 171 · 2019: 119 · 2018: 119 · 2017: 99 · 2016: 110 · 2015: 136 · 2014: 124 · 2013: 146 · 2012: 130
- FWCI
- 34.27
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the Association for Information Systems (publishedVersion)
- Journal of the Association for Information Systems (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Sociology and Political Science › Information Systems Theories and Implementation
- Social Sciences › Decision Sciences › Management Science and Operations Research › Innovation Diffusion and Forecasting
- Keywords
- technology acceptance model, information systems research, technology adoption
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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