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Research Article

Understanding University Students’ Acceptance of ChatGPT: Insights from the UTAUT2 Model

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Article: 2371168 | Received 22 Dec 2023, Accepted 14 Jun 2024, Published online: 21 Jun 2024

References

  • Adamopoulou, E., and L. Moussiades. 2020. Chatbots: History, technology, and applications. Machine Learning with Applications 2:100006 %U. doi:10.1016/j.mlwa.2020.100006.
  • Afthanorhan, W. 2013. A comparison of partial least square structural equation modeling (pls-sem) and covariance based structural equation modeling (cb-sem) for confirmatory factor analysis. International Journal of Engineering Science and Innovative Technology 2 (5):198–24.
  • Agarwal, R., and M. Wadhwa. 2020. Review of state-of-the-art design techniques for chatbots. SN Computer Science 1 (5 %U). doi:10.1007/s42979-020-00255-3.
  • Ain, N., K. Kaur, and M. Waheed. 2016. The influence of learning value on learning management system use. Information Development 32 (5):1306–21. doi:10.1177/0266666915597546.
  • Ajzen, I. 1991. The theory of planned behavior. Organizational Behavior and Human Decision Processes 50 (2):179–211. doi:10.1016/0749-5978(91)90020-T.
  • Alotumi, M. 2022. Factors influencing graduate students’ behavioral intention to use google classroom: Case study-mixed methods research. Education and Information Technologies 27 (7):10035–63. doi:10.1007/s10639-022-11051-2.
  • Ameri, A., R. Khajouei, A. Ameri, and Y. Jahani. 2020. Acceptance of a mobile-based educational application (LabSafety) by pharmacy students: An application of the UTAUT2 model. Education and Information Technologies 25 (1):419–35. doi:10.1007/s10639-019-09965-5.
  • Androutsopoulou, A., N. Karacapilidis, E. Loukis, and Y. Charalabidis. 2019. Transforming the communication between citizens and government through ai-guided chatbots. Government Information Quarterly 36 (2):358–67. doi:10.1016/j.giq.2018.10.001.
  • Arain, A. A., Z. Hussain, W. H. Rizvi, and M. S. Vighio. 2019. Extending utaut2 toward acceptance of mobile learning in the context of higher education. Universal Access in the Information Society 18 (3):659–73. doi:10.1007/s10209-019-00685-8.
  • Azizi, S. M., N. Roozbahani, and A. Khatony. 2020. Factors affecting the acceptance of blended learning in medical education: Application of utaut2 model. BMC Medical Education 20 (1):367–67. doi:10.1186/s12909-020-02302-2.
  • Brislin, R. W. 1970. Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology 1 (3):185–216 %U 10.1177/135910457000100301.
  • Buhalis, D., and E. S. Y. Cheng. 2019. Exploring the use of chatbots in hotels: Technology providers’ perspective. In Information and Communication Technologies in Tourism 2020, 231–42. Cham, Switzerland: Springer International Publishing.
  • Camilleri, M. A., and C. Troise. 2023. Live support by chatbots with artificial intelligence: A future research agenda. Service Business 17 (1):61–80. doi:10.1007/s11628-022-00513-9.
  • Casillo, M., F. Colace, L. Fabbri, M. Lombardi, A. Romano, and D. Santaniello. 2020. Chatbot in industry 4.0: An approach for training new employees. 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), 371-76. IEEE.
  • Chávez Herting, D., R. Cladellas Pros, and A. Castelló Tarrida. 2020. Habit and social influence as determinants of PowerPoint use in higher education: A study from a technology acceptance approach. Interactive Learning Environments 31 (1):497–513. doi:10.1080/10494820.2020.1799021.
  • Cotton, D. R. E., P. A. Cotton, and J. R. Shipway. 2023. Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International 61 (2):228–39. doi:10.1080/14703297.2023.2190148.
  • Crawford, J., M. Cowling, and K.-A. Allen. 2023. Leadership is needed for ethical chatgpt: Character, assessment, and learning using artificial intelligence (ai). Journal of University Teaching and Learning Practice 20 (3). doi:10.53761/1.20.3.02.
  • Dajani, D., and A. S. Abu Hegleh. 2019. Behavior intention of animation usage among university students. Heliyon 5 (10):e02536–36. doi:10.1016/j.heliyon.2019.e02536.
  • Davis, F. D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13 (3):319. doi:10.2307/249008.
  • Diamantopoulos, A., M. Sarstedt, C. Fuchs, P. Wilczynski, and S. Kaiser. 2012. Guidelines for choosing between multi-item and single-item scales for construct measurement: A predictive validity perspective. Journal of the Academy of Marketing Science 40 (3):434–49. doi:10.1007/s11747-011-0300-3.
  • Edumadze, J. K. E., K. A. Barfi, V. Arkorful, and N. O. Baffour Jnr. 2022. Undergraduate student’s perception of using video conferencing tools under lockdown amidst COVID-19 pandemic in ghana. Interactive Learning Environments 31 (9):5799–810. doi:10.1080/10494820.2021.2018618.
  • Ehsan, U., Q. V. Liao, M. Muller, M. O. Riedl, and J. D. Weisz. 2021. Expanding explainability: Towards social transparency in ai systems. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama Japan, 1–19.
  • El-Masri, M., and A. Tarhini. 2017. Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (utaut2). Educational Technology Research & Development 65 (3):743–63. doi:10.1007/s11423-016-9508-8.
  • Faqih, K. M. S., and M.-I. R. M. Jaradat. 2021. Integrating ttf and utaut2 theories to investigate the adoption of augmented reality technology in education: Perspective from a developing country. Technology in Society 67:101787. doi:10.1016/j.techsoc.2021.101787.
  • Farooq, M. S., M. Salam, N. Jaafar, A. Fayolle, K. Ayupp, M. Radovic-Markovic, and A. Sajid. 2017. Acceptance and use of lecture capture system (lcs) in executive business studies. Interactive Technology & Smart Education 14 (4):329–48. doi:10.1108/ITSE-06-2016-0015.
  • Gilson, A., C. W. Safranek, T. Huang, V. Socrates, L. Chi, R. A. Taylor, and D. Chartash. 2023. How does chatgpt perform on the United States Medical Licensing Examination? The implications of large language models for medical education and knowledge assessment. JMIR Medical Education 9:e45312–12. doi:10.2196/45312.
  • González-González, C. S., V. Muñoz-Cruz, P. A. Toledo-Delgado, and E. Nacimiento-García. 2023. Personalized gamification for learning: A reactive chatbot architecture proposal. Sensors 23 (1):545. doi:10.3390/s23010545.
  • Grassini, S. 2023a. Development and validation of the ai attitude scale (aias-4): A brief measure of general attitude toward artificial intelligence. Frontiers in Psychology 14:14. doi:10.3389/fpsyg.2023.1191628.
  • Grassini, S. 2023b. Shaping the future of education: Exploring the potential and consequences of ai and chatgpt in educational settings. Education Sciences 13 (7):692. doi:10.3390/educsci13070692.
  • Grassini, S., and M. Koivisto. 2024. Artificial creativity? Evaluating AI against human performance in creative interpretation of visual stimuli. International Journal of Human–Computer Interaction 1–12.
  • Grimaldi, G., B. Ehrler, and Ai. 2023. Machines are about to change scientific publishing forever. ACS Energy Letters 8 (1):878–80 %U doi:10.1021/acsenergylett.2c02828.
  • Guzik, E. E., C. Byrge, and C. Gilde. 2023. The originality of machines: Ai takes the Torrance test. Journal of Creativity 33 (3):100065. doi:10.1016/j.yjoc.2023.100065.
  • Hair Jr, J. F. M. C. Howard, and C. Nitzl. 2020. Assessing measurement model quality in pls-sem using confirmatory composite analysis. Journal of Business Research 109:101–10. doi:10.1016/j.jbusres.2019.11.069.
  • Hair, J., C. Ringle, and M. Sarstedt. 2013. Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning 46 (1–2):1–12. doi:10.1016/j.lrp.2013.01.001.
  • Hair, J. F., C. M. Ringle, and M. Sarstedt. 2011. Pls-sem: Indeed a silver bullet. Journal of Marketing Theory & Practice 19 (2):139–52 %U doi:10.2753/mtp1069-6679190202.
  • Henseler, J., C. M. Ringle, and M. Sarstedt. 2014. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science 43 (1):115–35 %U 10.1007/s11747-014-0403-8.
  • Hoi, V. N. 2020. Understanding higher education learners’ acceptance and use of mobile devices for language learning: A rasch-based path modeling approach. Computers & Education 146:103761. doi:10.1016/j.compedu.2019.103761.
  • Hu, S., K. Laxman, and K. Lee. 2020. Exploring factors affecting academics’ adoption of emerging mobile technologies-an extended utaut perspective. Education and Information Technologies 25 (5):4615–35. doi:10.1007/s10639-020-10171-x.
  • Jakkaew, P., and S. Hemrungrote. 2017. The use of utaut2 model for understanding student perceptions using google classroom: A case study of introduction to information technology course. Paper presentat at the 2017 International Conference on Digital Arts, Chiang Mai, Thailand, Media and Technology (ICDAMT).
  • Kang, M., B. Y. T. Liew, H. Lim, J. Jang, and S. Lee. 2014. Investigating the determinants of mobile learning acceptance in korea using utaut2. Emerging Issues in Smart Learning, 209–16. Berlin Heidelberg: Springer. https://link.springer.com/chapter/10.1007/978-3-662-44188-6_29.
  • Kasthuri, E., and S. Balaji. 2021. A chatbot for changing lifestyle in education. 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), IEEE %U. doi:10.1109/icicv50876.2021.9388633.
  • Kaur, D., S. Uslu, K. J. Rittichier, and A. Durresi. 2022. Trustworthy artificial intelligence: A review. ACM Computing Surveys (CSUR) 55 (2):1–38. doi:10.1145/3491209.
  • Khadija, A., F. F. Zahra, and A. Naceur. 2021. Ai-powered health chatbots: Toward a general architecture. Procedia Computer Science 191:355–60. doi:10.1016/j.procs.2021.07.048.
  • Kim, W., Y. Ryoo, S. Lee, and J. A. Lee. 2023. Chatbot advertising as a double-edged sword: The roles of regulatory focus and privacy concerns. Journal of Advertising 52 (4):504–22. doi:10.1080/00913367.2022.2043795.
  • Kinnunen, J., A. Androniceanu, and I. Georgescu. 2019. Digitalization of EU Countries: A clusterwise analysis. Proceedings of the 13rd International Management Conference: Management Strategies for High Performance, Bucharest, Romania, October, 1–12.
  • Kock, N. 2018. Minimum sample size estimation in PLS-SEM: An application in tourism and hospitality research. In Applying Partial Least Squares in Tourism and Hospitality Research, ed. F. Ali, S. M. Rasoolimanesh, and C. Cobanoglu, 1–16. Leeds, England: Emerald Publishing Limited.
  • Koivisto, M., and S. Grassini. 2023. Best humans still outperform artificial intelligence in a creative divergent thinking task. Scientific Reports 13 (1):13601. doi:10.1038/s41598-023-40858-3.
  • Krishnan, C., A. Gupta, A. Gupta, and G. Singh. 2022. Impact of artificial intelligence-based chatbots on customer engagement and business growth. In Deep learning for social media data analytics, 195–210. Cham: Springer International Publishing.
  • Kumar, J. A., and B. Bervell. 2019. Google classroom for mobile learning in higher education: Modelling the initial perceptions of students. Education and Information Technologies 24 (2):1793–817. doi:10.1007/s10639-018-09858-z.
  • Laitinen, A., and O. Sahlgren. 2021. Ai systems and respect for human autonomy. Frontiers in Artificial Intelligence 4 %U doi:10.3389/frai.2021.705164.
  • Limayem, H., and Cheung. 2007. How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly 31 (4):705. doi:10.2307/25148817.
  • Lim, W. M., A. Gunasekara, J. L. Pallant, J. I. Pallant, and E. Pechenkina. 2023. Generative ai and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education 21 (2):100790. doi:10.1016/j.ijme.2023.100790.
  • Lund, B. D., and T. Wang. 2023. Chatting about chatgpt: How may ai and gpt impact academia and libraries? Library Hi Tech News 40 (3):26–29. doi:10.1108/LHTN-01-2023-0009.
  • Luo, B., R. Y. K. Lau, C. Li, and Y. W. Si. 2021. A critical review of state‐of‐the‐art chatbot designs and applications. WIREs Data Mining and Knowledge Discovery 12 (1). doi:10.1002/widm.1434.
  • Mehta, A., N. P. Morris, B. Swinnerton, and M. Homer. 2019. The influence of values on e-learning adoption. Computers & Education 141:103617. doi:10.1016/j.compedu.2019.103617.
  • Menon, D., and K. Shilpa. 2023. “Chatting with chatgpt”: Analyzing the factors influencing users’ intention to use the open ai’s chatgpt using the utaut model. Heliyon 9 (11):e20962 %U doi:10.1016/j.heliyon.2023.e62.
  • Møgelvang, A., K. Ludvigsen, C. Bjelland, and O. M. Schei. 2023. Hvl-studenters bruk og oppfatninger av ki-chatboter i utdanning. HVL-Rapport.
  • Moore, G. C., and I. Benbasat. 1991. Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research 2 (3):192–222. doi:10.1287/isre.2.3.192.
  • Moriuchi, E., V. M. Landers, D. Colton, and N. Hair. 2021. Engagement with chatbots versus augmented reality interactive technology in e-commerce. Journal of Strategic Marketing 29 (5):375–89. doi:10.1080/0965254X.2020.1740766.
  • Nikolopoulou, K., V. Gialamas, and K. Lavidas. 2020. Acceptance of mobile phone by university students for their studies: An investigation applying utaut2 model. Education and Information Technologies 25 (5):4139–55 %U doi:10.1007/s10639-020-10157-9.
  • Okuda, T., and S. Shoda. 2018. Ai-based chatbot service for financial industry. Fujitsu Scientific & Technical Journal 54 (2):4–8.
  • Osei, H. V., K. O. Kwateng, and K. A. Boateng. 2022. Integration of personality trait, motivation and utaut 2 to understand e-learning adoption in the era of COVID-19 pandemic. Education and Information Technologies 27 (8):10705–30. doi:10.1007/s10639-022-11047-y.
  • Paraschiv, A. M., N. A. Panie, T. M. Nae, and L. Ciobanu. 2021. EU Countries’ Performance in Digitalization. International Conference on Business Excellence, 11–24, Cham: Springer International Publishing, March.
  • Perkins, M. 2023. Academic integrity considerations of ai large language models in the post-pandemic era: Chatgpt and beyond. Journal of University Teaching and Learning Practice 20 (2). doi:10.53761/1.20.02.07.
  • Raman, A., and Y. Don. 2013. Preservice teachers’ acceptance of learning management software: An application of the utaut2 model. International Education Studies 6 (7 %U doi:10.5539/ies.v6n7p157.
  • Raza, S. A., Z. Qazi, W. Qazi, and M. Ahmed. 2021. E-learning in higher education during covid-19: Evidence from blackboard learning system. Journal of Applied Research in Higher Education 14 (4):1603–22. doi:10.1108/JARHE-02-2021-0054.
  • Safdar, N. M., J. D. Banja, and C. C. Meltzer. 2020. Ethical considerations in artificial intelligence. In European Journal of Radiology, vol. 122, 108768. %U. doi:10.1016/j.ejrad.2019.68.
  • Samsudeen, S. N., and R. Mohamed. 2019. University students’ intention to use e-learning systems: A study of higher educational institutions in Sri Lanka. Interactive Technology & Smart Education 16 (3):219–38. doi:10.1108/ITSE-11-2018-0092.
  • Sarstedt, M., C. M. Ringle, and J. F. Hair. 2021. Partial least squares structural equation modeling. In Handbook of Market Research, 587–632. Cham: Springer International Publishing.
  • Schelble, B. G., J. Lopez, C. Textor, R. Zhang, N. J. Mcneese, R. Pak, and G. Freeman. 2022. Towards ethical ai: Empirically investigating dimensions of ai ethics, trust repair, and performance in human-ai teaming. Human Factors 66 (4):1037–55. doi:10.1177/00187208221116952.
  • Strzelecki, A. 2023. To use or not to use chatgpt in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments 1–14. doi:10.1080/10494820.2023.2209881.
  • Suhel, S. F., V. K. Shukla, S. Vyas, and V. P. Mishra. 2020. Conversation to automation in banking through chatbot using artificial machine intelligence language. 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), 611-18: IEEE.
  • Svenningsson, N., and M. Faraon. 2019. Artificial intelligence in conversational agents: A study of factors related to perceived humanness in chatbots. Proceedings of the 2019 2nd Artificial Intelligence and Cloud Computing Conference, ACM %U. doi:10.1145/3375959.3375973.
  • Tamilmani, K., N. P. Rana, and Y. K. Dwivedi. 2018. Use of ‘habit’ is not a habit in understanding individual technology adoption: A review of utaut2 based empirical studies. In IFIP Advances in Information and Communication Technology, 277–94 %@ 978-3-030-04315-5. %U Springer International Publishing. doi:10.1007/978-3-030-15-5_19.
  • Tamilmani, K., N. P. Rana, N. Prakasam, and Y. K. Dwivedi. 2019. The battle of brain vs. Heart: A literature review and meta-analysis of “hedonic motivation” use in utaut2. International Journal of Information Management 46:222–35. doi:10.1016/j.ijinfomgt.2019.01.008.
  • Tamilmani, K., N. P. Rana, S. F. Wamba, and R. Dwivedi. 2021. The extended unified theory of acceptance and use of technology (utaut2): A systematic literature review and theory evaluation. International Journal of Information Management 57:102269 %U doi:10.1016/j.ijinfomgt.2020.102269.
  • Taylor, S., and P. A. Todd. 1995. Understanding information technology usage: A test of competing models. Information Systems Research 6 (2):144–76. doi:10.1287/isre.6.2.144.
  • Twum, K. K., D. Ofori, G. Keney, and B. Korang-Yeboah. 2021. Using the utaut, personal innovativeness and perceived financial cost to examine student’s intention to use e-learning. Journal of Science and Technology Policy Management 13 (3):713–37. doi:10.1108/JSTPM-12-2020-0168.
  • Van Der Heijden, H. 2004. User acceptance of hedonic information systems. MIS Quarterly 28 (4):695. doi:10.2307/25148660.
  • Van Dis, E. A. M., J. Bollen, W. Zuidema, R. Van Rooij, and C. L. Bockting. 2023. Chatgpt: Five priorities for research. Nature 614 (7947):224–26. doi:10.1038/d41586-023-00288-7.
  • Venkatesh, T., and Xu. 2012. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly 36 (1):157 %U. doi:10.2307/41410412.
  • Venkatesh, V., M. G. Morris, G. B. Davis, and F. D. Davis. 2003. User acceptance of information technology: Toward a unified view. MIS Quarterly 27 (3):425. doi:10.2307/30036540.
  • Venkatesh, V., J. Thong, and X. Xu. 2016. Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems 17 (5):328–76. doi:10.17705/1jais.00428.
  • Wong, K.-T., T. Teo, and P. S. C. Goh. 2013. Understanding the intention to use interactive whiteboards: Model development and testing. Interactive Learning Environments 23 (6):731–47. doi:10.1080/10494820.2013.806932.
  • Xu, A., Z. Liu, Y. Guo, V. Sinha, and R. Akkiraju. 2017. A new chatbot for customer service on social media. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, ACM %U. doi:10.1145/3025453.3025496.
  • Yu, C.-W., C.-M. Chao, C.-F. Chang, R.-J. Chen, P.-C. Chen, and Y.-X. Liu. 2021. Exploring behavioral intention to use a mobile health education website: An extension of the utaut 2 model. Sage Open 11 (4):215824402110557. doi:10.1177/21582440211055721.
  • Zacharis, G., and K. Nikolopoulou. 2022. Factors predicting university students’ behavioral intention to use elearning platforms in the post-pandemic normal: An utaut2 approach with ‘learning value’. Education and Information Technologies 27 (9):12065–82. doi:10.1007/s10639-022-11116-2.
  • Zeng, N., Y. Liu, P. Gong, M. Hertogh, and M. König. 2021. Do right pls and do pls right: A critical review of the application of pls-sem in construction management research. Frontiers of Engineering Management 8 (3):356–69 %U doi:10.1007/s42524-021-0153-5.
  • Zwain, A. A. A. 2019. Technological innovativeness and information quality as neoteric predictors of users’ acceptance of learning management system. Interactive Technology & Smart Education 16 (3):239–54. doi:10.1108/ITSE-09-2018-0065.