Technology acceptance: a meta-analysis of the TAM: part 2
Abstract
Purpose: This paper is the second of two concerned with a meta‐analysis of the technology acceptance model (TAM). This part aims to present a rigorous and quantitative meta‐analytic review of 569 findings from 95 TAM studies as a basis for identifying gaps and providing guidelines for implementation management and conduct of future research. The paper also seeks to investigate the potential impact of methodological characteristics on the meta‐analytic findings. Design/methodology/approach: The approach consists of meta‐analysis following Hedges and Olkin's procedures, moderator‐analysis using homogeneity Q ‐values, analogue to ANOVA and weighted regression method. Findings: The dominant focus in empirical investigations of the TAM has been on modelling intention for its effect on self‐reported usage behaviour, while the attitudinal construct has been neglected. This raises three questions: whether the exclusion of attitude from the TAM is beneficial for understanding of technology usage behaviour in mandatory settings; whether the revised TAM holds equally for mandatory and voluntary settings; and whether the emphasis on measuring intentions and self‐report use rather than actual usage is warranted. An additional question answered in the meta‐analysis is about the relative importance of PU and PEOU. Originality/value: The paper provides a rigorous meta‐analysis to progress towards a unified view of the TAM.
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The strength of relationships between perceived ease of use (or other variables) and attitudes, behavioral intentions, or actual use depends on factors such as subject type and technology type.
“There is also evidence that how strongly PEOU, or other variables for that matter, is related to attitudes, behavioral intentions, or actual use depends on factors such as subject type and technology type [36].”
From The Technology Acceptance Model: Its past and its future in health care (2010) · cites this paper as [36]
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E-commerce acceptance in the dimension of sustainability
Haryanti, T., & Subriadi, A. P. (2021) · Journal of Modelling in Management · doi:10.1108/jm2-05-2020-0141 · Cited by 60 · see citing works
Unveiling the Value Creation Process of Electronic Human Resource Management
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Predicting Consumer Behavior: An Extension of Technology Acceptance Model
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- Authors
- Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G.
- Affiliations
- Cardiff University (GB)
- Venue
- Journal of Modelling in Management
- Publisher
- Emerald
- Published
- 2007-11-06
- Volume
- 2
- Issue
- 3
- Pages
- 281-304
- ISSN
- 1746-5664 (ISSN-L)
- OpenAlex
- W2915538221
- Cited by
- 257
- References
- 51
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 16 · 2023: 34 · 2022: 19 · 2021: 18 · 2020: 13 · 2019: 14 · 2018: 18 · 2017: 11 · 2016: 14 · 2015: 23 · 2014: 15 · 2013: 11 · 2012: 10
- FWCI
- 9.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Modelling in Management (publishedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Gender Studies › Gender and Technology in Education
- Social Sciences › Sociology and Political Science › Information Systems Theories and Implementation
- Keywords
- technology acceptance model, perceived usefulness, perceived ease of use, user intention, user attitudes
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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