Keywords
ChatGPT
TAM
ECM
Technology adoption
Continued use
Disconfirmation
Anticipatory trust
Calibrated trust
Abstract
Research on generative artificial intelligence (GenAI) has tended to split into work on initial adoption and work on continued use after experience. This article brings those streams together by reading the Technology Acceptance Model (TAM) and the Expectation-Confirmation Model (ECM) as complementary, stage-specific lenses. The analysis draws on 25 peer-reviewed empirical studies located through structured source searches, publisher and DOI checks, scholarly indexes, and citation tracing. The corpus is analytical rather than exhaustive. Most studies come from higher education and rely on single-wave self-report data. Across adoption studies, perceived usefulness appears more consistently associated with uptake than perceived ease of use. Post-adoption studies give greater weight to confirmation, satisfaction, calibrated trust, quality, and perceived value. The synthesis distinguishes anticipatory trust from calibrated trust and treats continuance as only one possible outcome alongside selective use, reduced use, switching, discontinuance, re-adoption, habit, and AI dependency. It also proposes performance, value, and ethical/governance disconfirmation as separate concepts for future testing. Taken together, the evidence suggests that TAM is most informative around entry into use, whereas ECM is better suited to experience-based reassessment and continuance.
References
Alderaan, H. S(2026). From acceptance to practice: A mixed-methods study of generative AI integration among Saudi university EFL teachers [Doctoral dissertation, Washington State University].
Baig, M. I., & Yadegaridehkordi, E. (2024). ChatGPT in the higher education: A systematic literature review and research challenges. International Journal of Educational Research, 127, 102411. https://doi.org/10.1016/j.ijer.2024.102411
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
Chu, M., & Liu, M. (2026). From perception to behavior: ChatGPT’s impact on tourists’ motivation and continuance intention in heritage tourism. npj Heritage Science, 14, 418. https://doi.org/10.1038/s40494-026-02622-4
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Duong, C. D. (2024). Modeling the determinants of HEI students’ continuance intention to use ChatGPT for learning: A stimulus–organism–response approach. Journal of Research in Innovative Teaching & Learning, 17(2), 391–407. https://doi.org/10.1108/JRIT-01-2024-0006
Duong, C. D., Vu, T. N., & Ngo, T. V. N. (2023). Applying a modified technology acceptance model to explain higher education students’ usage of ChatGPT: A serial multiple mediation model with knowledge sharing as a moderator. The International Journal of Management Education, 21(3), 100883. https://doi.org/10.1016/j.ijme.2023.100883
Garcia, M. B. (2025). ChatGPT as an academic writing tool: Factors influencing researchers’ intention to write manuscripts using generative artificial intelligence. International Journal of Human–Computer Interaction, 41(24), 15514–15528. https://doi.org/10.1080/10447318.2025.2499158
Hong, Q. N., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., ... & Pluye, P. (2018). The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Education for Information, 34(4), 285–291. https://doi.org/10.3233/EFI-180221
Hong, Q. N., Pluye, P., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., Gagnon, M.-P., Griffiths, F., Nicolau, B., O’Cathain, A., Rousseau, M.-C., & Vedel, I. (2019). Improving the content validity of the Mixed Methods Appraisal Tool: A modified e-Delphi study. Journal of Clinical Epidemiology, 111, 49–59.e1. https://doi.org/10.1016/j.jclinepi.2019.03.008
Hsieh, M.-T. (2026). Examining ChatGPT continuous usage intention: An integration of expectation-confirmation and flow theories [Master’s thesis, National Sun Yat-sen University].
Hu, P. (2026). Psychological predictors of continued ChatGPT use among university students: The roles of AI literacy, trust, and academic self-efficacy. Frontiers in Psychology, 17, 1900190. https://doi.org/10.3389/fpsyg.2026.1900190
Hu, W. X., Gao, C. Y., Qin, Y. X., & Guo, H. Y. (2026). Factors shaping willingness to adopt generative artificial intelligence tools among university students: A TAM-based investigation. Frontiers in Psychology, 17, 1831505. https://doi.org/10.3389/fpsyg.2026.1831505
Kang, S., Choi, Y., & Kim, B. (2024). Impact of motivation factors for using generative AI services on continuous use intention: Mediating trust and acceptance attitude. Social Sciences, 13(9), 475. https://doi.org/10.3390/socsci13090475
Kavitha, K., & Joshith, V. P. (2025). Exploring Gen Z’s satisfaction and continuance intention towards ChatGPT 4o in higher education: PLS-SEM insights using the ECM. Journal of Educational Technology Systems, 54(2), 407–430. https://doi.org/10.1177/00472395251380294
Kelly, S., Kaye, S.-A., White, K. M., & Oviedo-Trespalacios, O. (2025). What factors predict user acceptance of ChatGPT for mental and physical healthcare: An extended technology acceptance model framework. AI & Society, 40, 6257–6275. https://doi.org/10.1007/s00146-025-02334-6
Khan, I.M., Godwin, E. S., Refat, N., Ahmed, W., & Akanbi, L. A. (2026). Beyond ease and usefulness: A TAM perspective on marketing lecturers’ adoption of generative AI. International Journal of Information and Learning Technology, 43(3), 223–240. https://doi.org/10.1108/IJILT-12-2024-0291
Kim, J. S., & Baek, T. H. (2025). Motivational determinants of continuance usage intention for generative AI: An investment model approach for ChatGPT users in the United States. Behaviour & Information Technology, 44(12), 3080–3096. https://doi.org/10.1080/0144929X.2024.2429647
Lai, C. Y., Cheung, K. Y., & Chan, C. S. (2023). Exploring the role of intrinsic motivation in ChatGPT adoption to support active learning: An extension of the technology acceptance model. Computers and Education: Artificial Intelligence, 5, 100178. https://doi.org/10.1016/j.caeai.2023.100178
LaRose, R., Lin, C. A., & Eastin, M. S. (2003). Unregulated Internet usage: Addiction, habit, or deficient self-regulation? Media Psychology, 5(3), 225–253.
https://doi.org/10.1207/S1532785XMEP0503_01
Lee, N.-C. (2026). How social influence affects college students’ continued intention to use ChatGPT: The mediating roles of affective and cognitive engagement [Master’s thesis, I-Shou University].
Lin, H.-L., Liao, L.-L., Wang, Y.-N., & Chang, L.-C. (2025). Attitude and utilization of ChatGPT among registered nurses: A cross-sectional study. International Nursing Review, 72(2), e13012. https://doi.org/10.1111/inr.13012
Nguyen, B. N. (2026). Explaining ChatGPT Plus users’ continuance intention: Comparing TAM, ECM, and extended ECM. The Electronic Library, 1–22. https://doi.org/10.1108/EL-12-2025-0478
Prasad, K. D. V., Singh, S., Vaidya, R., Pathak, A., & Srinivas, V. (2026). Generative AI in higher education: An integrated framework for generative AI adoption and pedagogical transformation with trust as a mediator. Human Behavior and Emerging Technologies, 2026(1), 6611806. https://doi.org/10.1155/hbe2/6611806
Saif, N., Khan, S. U., Shaheen, I., Alotaibi, F. A., Alnfiai, M. M., & Arif, M. (2024). Chat-GPT; validating Technology Acceptance Model (TAM) in education sector via ubiquitous learning mechanism. Computers in Human Behavior, 154, 108097. https://doi.org/10.1016/j.chb.2023.108097
Sakib, M. N., Islam, M., Fahlevi, M., Rahman, M. S., Younus, M., & Rahman, M. M. (2025). Factors influencing users’ intention to adopt ChatGPT based on the extended technology acceptance model. Computers in Human Behavior: Artificial Humans, 6, 100204. https://doi.org/10.1016/j.chbah.2025.100204
Shahzad, M. F., Xu, S., & Javed, I. (2024). ChatGPT awareness, acceptance, and adoption in higher education: The role of trust as a cornerstone. International Journal of Educational Technology in Higher Education, 21(1), 46. https://doi.org/10.1186/s41239-024-00478-x
Strzelecki, A. (2024). Students’ acceptance of ChatGPT in higher education: An extended unified theory of acceptance and use of technology. Innovative Higher Education, 49(2), 223–245. https://doi.org/10.1007/s10755-023-09686-1
Taheri, K. (2025). Determinants of continuous use intention: An empirical study of ChatGPT among students in German higher education [Master’s thesis, Universidade Católica Portuguesa].
Tiwari, C. K., Bhat, M. A., Khan, S. T., Subramaniam, R., & Khan, M. A. I. (2024). What drives students toward ChatGPT? An investigation of the factors influencing adoption and usage of ChatGPT. Interactive Technology and Smart Education, 21(3), 333–355. https://doi.org/10.1108/ITSE-04-2023-0061
Triangga, B., Noermijati, N., Rohman, F., & Sumiati. (2026). Can’t stop, won’t stop: Continuance ChatGPT usage—enhancing performance or creating dependency? Discover Education, 5(1), 100. https://doi.org/10.1007/s44217-026-01114-8
Xiong, L., Zhou, Y., & Hua, Y. (2026). Why do users continue to use GCAI? A mixed-methods study. Technology in Society, 86, 103309. https://doi.org/10.1016/j.techsoc.2026.103309
Yang, F. (2026). ChatGPT acceptance and use in higher education: Evidence from a systematic review using the Technology Acceptance Model. TechTrends, 70(2), 466–483. https://doi.org/10.1007/s11528-026-01166-5
Zhang, D., & Zhao, X. (2024). Understanding adoption intention of virtual medical consultation systems: Perceptions of ChatGPT and satisfaction with doctors. Computers in Human Behavior, 159, 108359. https://doi.org/10.1016/j.chb.2024.108359
Zhou, T., & Ma, X. (2026). Examining generative AI user continuance intention based on the SOR model. Aslib Journal of Information Management, 78(4), 969–987. https://doi.org/10.1108/AJIM-08-2024-0620
Zou, M., & Huang, L. (2023). To use or not to use? Understanding doctoral students’ acceptance of ChatGPT in writing through technology acceptance model. Frontiers in Psychology, 14, 1259531. https://doi.org/10.3389/fpsyg.2023.1259531
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2026 Chen Wang