Preliminary Study to Understand the Gen Z Student’s Behaviour towards Adoption AI Chatbots with the Moderating Role of Gender Using Modified UTAUT2

The prompt progression of Artificial Intelligence (AI) spread to all sectors including the education sectors. The development of AI chatbots as advanced instruments can enhance the teaching and learning in higher learning institutions. Regardless of powerful chatbots, the effective implementation use of AI chatbots among students in higher learning institutions depends on a composite relationship among technological, behavioural, and demographic circumstances. Furthermore, this study focuses on the Generation Z (Gen Z) students that are known as digital citizens born between the middle 1990s and early 2010s. The main objective of this preliminary study is to understand the Gen Z students’ behaviour towards adoption of AI chatbots by using Unified Theory of Acceptance and Use of Technology (UTAUT2). There are two variables added to this study. The population for this study is the students from public and private higher learning institutions in Terengganu. Hence, this study is using the convenience sampling technique to get the respondents. However, the sample size for this study is 118 students based on the G-Power. The instruments for this study were conducted online and as a result, 205 respondents have completed and returned the questionnaires. The data collected is analysed using SPSS 28.00 and PLS 4.1. There are nine behavioural intentions factors and 12 hypotheses that were constructed for this study. Nevertheless, only four were supported and the rest eight were rejected. As a result, the gender as a moderating effect between behavioural intention and adoption use of AI chatbots was rejected. This study is suggested to apply in other higher learning institutions to see the comparison between them. Furthermore, it is recommended for future research to use new variables as mediating effects or new variables as moderating effects.

Adoption AI chatbots, UTAUT2, Students’ behaviour

1. Priporas, C. V., Stylos, N., & Fotiadis, A. K. (2017). Generation Z consumers' expectations of interactions in smart retailing: A future agenda. Computers in human behavior, 77, 374-381. [Google Scholar] [Crossref]

2. AlFarraj, O., Alalwan, A. A., Obeidat, Z. M., Baabdullah, A., Aldmour, R., & Al-Haddad, S. (2021). Examining the impact of influencers’ credibility dimensions: attractiveness, trustworthiness and expertise on the purchase intention in the aesthetic dermatology industry. Review of International Business and Strategy, 31(3), 355-374. [Google Scholar] [Crossref]

3. Gunasinghe, A., & Nanayakkara, S. (2021). Role of technology anxiety within UTAUT in understanding non-user adoption intentions to virtual learning environments: the state university lecturers' perspective. International Journal of Technology Enhanced Learning, 13(3), 284-308. [Google Scholar] [Crossref]

4. Gunasinghe, A., & Nanayakkara, S. (2021). Role of technology anxiety within UTAUT in understanding non-user adoption intentions to virtual learning environments: the state university lecturers' perspective. International Journal of Technology Enhanced Learning, 13(3), 284-308. [Google Scholar] [Crossref]

5. Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the association for Information Systems, 17(5), 328-376. [Google Scholar] [Crossref]

6. Dimock, M. (2019). Defining generations: Where Millennials end and Generation Z begins. Pew research center, 17(1), 1-7. [Google Scholar] [Crossref]

7. Turner, A. (2015). Generation Z: Technology and social interest. The journal of individual Psychology, 71(2), 103-113. [Google Scholar] [Crossref]

8. Camilleri, M. A., & Camilleri, A. C. (2024, July). The acceptance and usage of ChatGPT: An Information Adoption Model perspective. In 2024 8th International Conference on Communications and Future Internet (ICCFI) (pp. 61-66). IEEE. [Google Scholar] [Crossref]

9. Miličević, A., Despotović-Zrakić, M., Stojanović, D., Suvajžić, M., & Labus, A. (2024). Academic performance indicators for the hackathon learning approach–The case of the blockchain hackathon. Journal of Innovation & Knowledge, 9(3), 100501. [Google Scholar] [Crossref]

10. Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the association for Information Systems, 17(5), 328-376. [Google Scholar] [Crossref]

11. Al-Maroof, R. S., Alhumaid, K., Akour, I., & Salloum, S. (2021). Factors that affect e-learning platforms after the spread of covid-19: Post acceptance study. Data, 6(5), 49. [Google Scholar] [Crossref]

12. Alalwan, A. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28-44. [Google Scholar] [Crossref]

13. Al-Okaily, M., Al-Fraihat, D., Al-Debei, M. M., & Al-Okaily, A. (2022). Factors influencing the decision to utilize eTax systems during the COVID-19 pandemic: the moderating role of anxiety of COVID-19 infection. International Journal of Electronic Government Research (IJEGR), 18(1), 1-24. [Google Scholar] [Crossref]

14. Huang, J., Pinmanee, S., & Chaveesuk, S. (2024, November). Literature Review on Behavior to Use Digital Education Platform Based on UTAUT2 in China. In The Global Conference on Entrepreneurship and the Economy in an Era of Uncertainty (pp. 1507-1521). Singapore: Springer Nature Singapore. [Google Scholar] [Crossref]

15. Haddad, C. R., Nakić, V., Bergek, A., & Hellsmark, H. (2022). Transformative innovation policy: A systematic review. Environmental Innovation and Societal Transitions, 43, 14-40. [Google Scholar] [Crossref]

16. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS quarterly, 51-90. [Google Scholar] [Crossref]

17. Abdullah, O., Shaharuddin, A., Wahid, M. A., & Harun, M. S. (2024). AI applications for fiqh rulings in Islamic Banks: Shariah committee acceptance. ISRA international journal of Islamic finance, 16(1), 111-126. [Google Scholar] [Crossref]

18. Cheng, X., Zhang, X., Cohen, J., & Mou, J. (2022). Human vs. AI: Understanding the impact of anthropomorphism on consumer response to chatbots from the perspective of trust and relationship norms. Information Processing & Management, 59(3), 102940. [Google Scholar] [Crossref]

19. Meuter, M. L., Ostrom, A. L., Bitner, M. J., & Roundtree, R. (2003). The influence of technology anxiety on consumer use and experiences with self-service technologies. Journal of business research, 56(11), 899-906. [Google Scholar] [Crossref]

20. Hwang, G. J., & Chien, S. Y. (2024). Broad sense and narrow sense perspectives on the metaverse in education: Roles of virtual reality, augmented reality, artificial intelligence and pedagogical theories. International Journal of Mobile Learning and Organisation, 18(1), 1-14. [Google Scholar] [Crossref]

21. Venkatesh, V., & Morris, M. G. (2000). Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS quarterly, 115-139. [Google Scholar] [Crossref]

22. Gefen, D., & Straub, D. W. (1997). Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS quarterly, 389-400. [Google Scholar] [Crossref]

23. Liébana-Cabanillas, F. J., Higueras-Castillo, E., Alonso-Palomo, R., & Japutra, A. (2025). Exploring the determinants of continued use of virtual voice assistants: a UTAUT2 and privacy calculus approach. Academia Revista Latinoamericana de Administración, 38(1), 156-182. [Google Scholar] [Crossref]

24. Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2021). Social isolation and acceptance of the learning management system (LMS) in the time of COVID-19 pandemic: an expansion of the UTAUT model. Journal of Educational Computing Research, 59(2), 183-208. [Google Scholar] [Crossref]

25. Iqbal, M. A., & Su, J. (2024, January). Apparel Professionals’ Readiness Toward Sustainable Technology: A Conceptual Model. In International Textile and Apparel Association Annual Conference Proceedings (Vol. 80, No. 1). Iowa State University Digital Press. [Google Scholar] [Crossref]

26. Kang, H. (2021). Sample size determination and power analysis using the G* Power software. [Google Scholar] [Crossref]

27. Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications. [Google Scholar] [Crossref]

28. Henseler, J., & Chin, W. W. (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural equation modeling, 17(1), 82-109. [Google Scholar] [Crossref]

29. Henseler, J., & Chin, W. W. (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural equation modeling, 17(1), 82-109. [Google Scholar] [Crossref]

30. Cohen, J. (1988). Statistical Power Analysis for the Behavioural Sciences. Hillsdle. [Google Scholar] [Crossref]

31. Mohammed, N. H., Yusof, Y., Salleh, S. M., Mardiah, S. F., & Hamzah, N. H. M. G. (2024). Mediating Effect of Emotional Intelligence on the Relationships between Academician Power Base and Student’ s Performance in Higher Learning Institution. International Journal of Research and Innovation in Social Science, 8(11), 2832-2841. [Google Scholar] [Crossref]

32. Yusof, Y. M. H. M., & Mohammed, N. H. (2024). Exploring Mediating Effect of Technology Readiness between Community of Inquiry and Student Digital Competence among Students. Environment-Behaviour Proceedings Journal, 9(SI21), 71-81. [Google Scholar] [Crossref]

33. Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications. [Google Scholar] [Crossref]

34. Plohl, N., & Babič, N. Č. (2024). Using the UTAUT2 components and trust to predict consumer acceptance of smart home technology: A systematic review. Human Technology, 20(1), 93-113. [Google Scholar] [Crossref]

35. Amnas, M. B., Selvam, M., Raja, M., Santhoshkumar, S., & Parayitam, S. (2023). Understanding the determinants of FinTech adoption: Integrating UTAUT2 with trust theoretic model. Journal of risk and financial management, 16(12), 505. [Google Scholar] [Crossref]

36. Yuliani, P. N., Suprapti, N. W. S., & Piartrini, P. S. (2024). The Literature Review on UTAUT 2: Understanding Behavioral Intention and Use Behavior of Technology in the Digital Era. International Journal of Social Science and Business, 8(2), 208-222. [Google Scholar] [Crossref]

37. Tamilmani, K., Rana, N. P., Wamba, S. F., & Dwivedi, R. (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. [Google Scholar] [Crossref]

38. Gunasinghe, A., & Nanayakkara, S. (2021). Role of technology anxiety within UTAUT in understanding non-user adoption intentions to virtual learning environments: the state university lecturers' perspective. International Journal of Technology Enhanced Learning, 13(3), 284-308. [Google Scholar] [Crossref]

• Smart Iot Device for Weather And Health
• Merlarchive: A Web-Based Academic Hub for UDM With AI-Powered Natural Language Processing
• Enhanced Social Network Security System: Integrating Biometric Authentication for Improved User Verification and Privacy Protection
• Smart Budget Allocation in Public Policy: A Data-Driven Approach for Equitable Resource Distribution
• Decision Support System for Faculty Selection, Promotion, and Reclassification Using Predictive Analytics