The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.2307/3151312 · Cited by 109,314
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
The theory of planned behavior
Ajzen, I. (1991) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(91)90020-t · Cited by 90,587
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
Social network sites: definition, history, and scholarship
boyd, D. M., & Ellison, N. B. (2007) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2007.00393.x · Cited by 16,251
Self-efficacy mechanism in human agency
Bandura, A. (1982) · American Psychologist · doi:10.1037/0003-066x.37.2.122 · Cited by 15,975
Moore, G. C., & Benbasat, I. (1991) · Information Systems Research · doi:10.1287/isre.2.3.192 · Cited by 9,150
Understanding information technology usage: a test of competing models
Taylor, S., & Todd, P. A. (1995) · Information Systems Research · doi:10.1287/isre.6.2.144 · Cited by 8,815
Understanding Information Systems Continuance: An Expectation-Confirmation Model1
Bhattacherjee, A. (2001) · MIS Quarterly · doi:10.2307/3250921 · Cited by 8,767
Cited by
Technology acceptance model: a literature review from 1986 to 2013
Marangunić, N., & Granić, A. (2015) · Universal Access in the Information Society · doi:10.1007/s10209-014-0348-1 · Cited by 2,458
Technology acceptance model in educational context: A systematic literature review
Granić, A., & Marangunić, N. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12864 · Cited by 1,204
Abdullah, F., & Ward, R. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2015.11.036 · Cited by 986
Abdullah, F., Ward, R., & Ahmed, E. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.05.014 · Cited by 693
Salloum, S. A., Qasim Mohammad Alhamad, A., Al-Emran, M., Abdel Monem, A., & Shaalan, K. (2019) · IEEE Access · doi:10.1109/access.2019.2939467 · Cited by 651
Tracking e-learning through published papers: A systematic review
Rodrigues, H., Almeida, F., Figueiredo, V., & Lopes, S. L. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.03.007 · Cited by 399
Fabriz, S., Mendzheritskaya, J., & Stehle, S. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.733554 · Cited by 453
A study on the adoption of IoT smart home service: using Value-based Adoption Model
Kim, Y., Park, Y., & Choi, J. (2017) · Total Quality Management & Business Excellence · doi:10.1080/14783363.2017.1310708 · Cited by 328
Scherer, R., Siddiq, F., & Teo, T. (2015) · Computers & Education · doi:10.1016/j.compedu.2015.05.005 · Cited by 220
Gupta, K., & Arora, N. (2019) · South Asian Journal of Business Studies · doi:10.1108/sajbs-03-2019-0037 · Cited by 208
Is it the end of the technology acceptance model in the era of generative artificial intelligence?
Mogaji, E., Viglia, G., Srivastava, P., & Dwivedi, Y. K. (2024) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-08-2023-1271 · Cited by 187
Understanding user perception toward artificial intelligence (AI) enabled e-learning
Kashive, N., Powale, L., & Kashive, K. (2020) · The International Journal of Information and Learning Technology · doi:10.1108/ijilt-05-2020-0090 · Cited by 181
Maican, C. I., Cazan, A.-M., Lixandroiu, R. C., & Dovleac, L. (2019) · Computers & Education · doi:10.1016/j.compedu.2018.09.010 · Cited by 128
Salloum, S. A., & Shaalan, K. (2019) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-319-99010-1_43 · Cited by 128
Kong, S. C., Yang, Y., & Hou, C. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100328 · Cited by 125
Acceptance of 5 G technology: Mediation role of Trust and Concentration
Akbari, M., Rezvani, A., Shahriari, E., Zúñiga, M. Á., & Pouladian, H. (2020) · Journal of Engineering and Technology Management · doi:10.1016/j.jengtecman.2020.101585 · Cited by 72
Zou, B., Wang, C., Yan, Y., Du, X., & Ji, Y. (2025) · International Journal of Applied Linguistics · doi:10.1111/ijal.12658 · Cited by 66
Determinants of the management learning performance in ERP context
Costa, C. J., Aparicio, M., & Raposo, J. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e03689 · Cited by 55
“Thus TRA sees behavioral intention as the main predictor of actual individual behavior (Maranguni (cid:1) c and Grani (cid:1) c, 2015) and claims that two main aspects (people intention and subjective norms) in fl uenced attitudes (Cheung and Vogel, 2013).”
BackgroundAI in essay-based assessment: Student adoption, usage, and performance
Smerdon, D. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100288 · Cited by 43
Sugumar, M., & Chandra, S. (2021) · Journal of International Technology and Information Management · doi:10.58729/1941-6679.1501 · Cited by 34
Lim, J. S., Shin, D., Lee, C., Kim, J., & Zhang, J. (2025) · Journal of Broadcasting & Electronic Media · doi:10.1080/08838151.2025.2487679 · Cited by 32
Kelly, S., Kaye, S.-A., White, K. M., & Oviedo-Trespalacios, O. (2025) · AI & SOCIETY · doi:10.1007/s00146-025-02334-6 · Cited by 17
Metadata
- Authors
- Cheung, R., & Vogel, D.
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2013-04
- Volume
- 63
- Pages
- 160-175
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W2050421060
- Cited by
- 864
- References
- 22
- FWCI
- 51.91
- Type
- journal article
- Language
- en
- Open access
- closed
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
