RAPID GROWTH, NARROW FOCUS: A BIBLIOMETRIC REVIEW OF TECHNOLOGY ACCEPTANCE RESEARCH ON GENERATIVE ARTIFICIAL INTELLIGENCE IN WEB OF SCIENCE (2023–2026)
DOI:
https://doi.org/10.35631/IJEPC.1164064Keywords:
Bibliometric Analysis, Generative Artificial Intelligence, Higher Education, Technology Acceptance Model, Web of ScienceAbstract
Within three years of the release of ChatGPT, generative artificial intelligence (GenAI) has moved into classrooms, clinics, and counselling rooms, and its acceptance is now a research question in its own right. This literature is now large and scattered, yet few bibliometric studies have systematically mapped the intersection of technology acceptance and GenAI as a distinct research domain. This study aims to map the intellectual, thematic, geographic, and publication structure of research on technology acceptance of GenAI. A topic search of the Web of Science Core Collection on 7 August 2026 returned 644 records; refinement to English-language Articles and Early Access items excluded 72, leaving 572, and eligibility screening removed 2 more, giving 570 articles. Performance analysis indicators covering publication counts, citations, source titles, subject categories, countries, highly cited papers, and term-family frequencies were compiled in Microsoft Excel. Annual output grew from 5 articles in 2023 to 49 in 2024 and 225 in 2025, with 290 carrying a 2026 issue date; the 2026 figure covers records indexed to the search date and is not a full-year total. As of 7 August 2026 the corpus had accumulated 7,113 citations (mean of 12.48 per article; h-index of 40). Education and Information Technologies led the journals (25), Education and Educational Research the categories (224; 39.3%), and China the countries (194; 34.0%), ahead of the United States (75; 13.2%) and South Korea (42; 7.4%); Malaysia ranked seventh (23; 4.0%). The most cited work developed an expectancy value instrument for student perceptions of GenAI (417 citations), indicating an early emphasis on measuring psychological and motivational responses to emerging AI. Survey or questionnaire terminology appears in 54.4% of records, longitudinal terminology in 2.5%, and counselling terminology in 0.5%. The boundaries are a single database and a search defined by acceptance vocabulary.
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Booth, A., Sutton, A., & Papaioannou, D. (2016). Systematic approaches to a successful literature review (2nd ed.). SAGE.
Chan, C. K. Y., & Zhou, W. (2023). An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI. Smart Learning Environments, 10(1), Article 64. https://doi.org/10.1186/s40561-023-00284-4
Christensen, J., Hansen, J. M., & Wilson, P. (2025). Understanding the role and impact of generative artificial intelligence (AI) hallucination within consumers’ tourism decision-making processes. Current Issues in Tourism, 28(4), 545–560. https://doi.org/10.1080/13683500.2023.2300032
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
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Demir, M., Aydoğdu, F., Alabay, M. B., Yaşar, H. K., & Bozkurt, A. (2026). More than an algorithm: Mental health professionals confront the promise and ethical perils of artificial intelligence. Universal Access in the Information Society, 25(1), Article 24. https://doi.org/10.1007/s10209-025-01297-1
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
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), Article 100883. https://doi.org/10.1016/j.ijme.2023.100883
Efron, S. E., & Ravid, R. (2019). Writing the literature review: A practical guide. Guilford Press.
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864
Hart, C. (1998). Doing a literature review: Releasing the social science research imagination. SAGE.
Hempel, S. (2020). Conducting your literature review. American Psychological Association.
Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102(46), 16569–16572. https://doi.org/10.1073/pnas.0507655102
Kubitza, E., & Degen, J. L. (2026). AI as a sparring partner in professional settings: Counselors’ views on ChatGPT’s role and potential in pregnancy termination counseling. Archives of Sexual Behavior. Advance online publication. https://doi.org/10.1007/s10508-026-03437-x
Liu, G. L., Darvin, R., & Ma, C. (2025). Exploring AI-mediated informal digital learning of English (AI-IDLE): A mixed-method investigation of Chinese EFL learners’ AI adoption and experiences. Computer Assisted Language Learning, 38(7), 1632–1660. https://doi.org/10.1080/09588221.2024.2310288
Ma, T. (2025). Systematically visualizing ChatGPT used in higher education: Publication trend, disciplinary domains, research themes, adoption and acceptance. Computers and Education: Artificial Intelligence, 8, Article 100336. https://doi.org/10.1016/j.caeai.2024.100336
Ma, X., & Huo, Y. (2023). Are users willing to embrace ChatGPT? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework. Technology in Society, 75, Article 102362. https://doi.org/10.1016/j.techsoc.2023.102362
Machi, L. A., & McEvoy, B. T. (2022). The literature review: Six steps to success (4th ed.). Corwin.
Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81–95. https://doi.org/10.1007/s10209-014-0348-1
Oo, C. T. L., Wider, W., Pang, N. T. P., Koh, E. B. Y., Vasanthi, R. K., Thet, K. Z. Z., Ramalho, R., Özdemir, B. N., & Mahboob, K. (2026). The benefits and future potential of generative artificial intelligence (GAI) on mental health: A Delphi study. International Journal of Qualitative Studies on Health and Well-being, 21(1), Article 2621802. https://doi.org/10.1080/17482631.2026.2621802
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., . . . Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71
Pritchard, A. (1969). Statistical bibliography or bibliometrics? Journal of Documentation, 25(4), 348–349.
Reddy, S. (2024). Generative AI in healthcare: An implementation science informed translational path on application, integration and governance. Implementation Science, 19(1), Article 27. https://doi.org/10.1186/s13012-024-01357-9
Shahzad, M. F., Xu, S., & Asif, M. (2025). Factors affecting generative artificial intelligence, such as ChatGPT, use in higher education: An application of technology acceptance model. British Educational Research Journal, 51(2), 489–513. https://doi.org/10.1002/berj.4084
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), Article 46. https://doi.org/10.1186/s41239-024-00478-x
Strzelecki, A., & ElArabawy, S. (2024). Investigation of the moderation effect of gender and study level on the acceptance and use of generative AI by higher education students: Comparative evidence from Poland and Egypt. British Journal of Educational Technology, 55(3), 1209–1230. https://doi.org/10.1111/bjet.13425
Strzelecki, A., Cicha, K., Rizun, M., & Rutecka, P. (2024). Acceptance and use of ChatGPT in the academic community. Education and Information Technologies, 29(17), 22943–22968. https://doi.org/10.1007/s10639-024-12765-1
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
Wang, C., Wang, H., Li, Y., Dai, J., Gu, X., & Yu, T. (2025). Factors influencing university students’ behavioral intention to use generative artificial intelligence: Integrating the theory of planned behavior and AI literacy. International Journal of Human–Computer Interaction, 41(11), 6649–6671. https://doi.org/10.1080/10447318.2024.2383033
Yilmaz, F. G. K., Yilmaz, R., & Ceylan, M. (2024). Generative Artificial Intelligence Acceptance Scale: A Validity and Reliability Study. International Journal of Human–Computer Interaction, 40(24), 8703–8715. https://doi.org/10.1080/10447318.2023.2288730
Zhang, Y., Xie, X., & Xu, Q. (2025). ChatGPT in medical education: Bibliometric and visual analysis. JMIR Medical Education, 11, Article e72356. https://doi.org/10.2196/72356
Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629
