Predicting Factors Influencing Behavioural Intention and Actual Use Behaviour of Artificial Intelligence (AI): Extending UTAUT2 Model with Personal Innovativeness, Perceived Trust and Perceived Risk

This research examined the factors influencing university students’ behavioural intention to use artificial intelligence (AI) and its actual use behaviour using UTAUT2 model and extending it using personal innovativeness, perceived trust, and perceived risk. A cross-sectional study was employed and targeting diploma and undergraduate students from six prominent public universities in Malaysia. The survey evaluated the predictors of UTAUT2 model including performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit and introduced the variables of personal innovativeness, perceived trust, and perceived risk into the original UTAUT2 structure. Data were gathered from 301 respondents, firstly, assessing the influences of UTAUT2 predictor variables on the behavioural intention to use AI. Secondly, the impacts of UTAUT2 predictor variables along with students’ behavioural intention on AI actual use behaviour were evaluated. Lastly, the mediating effect of behavioural intention to use AI on the relationships between each predictor with AI actual use behaviour was investigated. Using hierarchical multiple regression analysis, the results indicated that performance expectancy, facilitating conditions, hedonic motivation, price value, and perceived trust significantly and positively while perceived risk negatively impacted behavioural intention to use AI, suggesting the robustness of the original UTAUT2 structure. Effort expectancy and social influence did not show a significant impact. Performance expectancy, habit, personal innovativeness, perceived trust, and behavioural intention to use AI are revealed to significantly and positively affecting AI actual use behaviour while price value and perceived risk are evident to have negative influences on the actual use. Effort expectancy, social influence, facilitating conditions, and hedonic motivation are shown insignificant with AI actual use behaviour. Results of mediation analyses suggested that students' belief in greater performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, habit, personal innovativeness, perceived trust, and reduced perceived risk will foster strong behavioural intentions to use AI, which ultimately influences their decision to actually use AI technologies. This study contributes to the understanding of the interplay between various factors in behavioural intention to use AI and AI actual use as well as offers strategic insights for the successful implementation of AI systems in Malaysian universities.

Artificial intelligence, UTAUT2 model, behavioural intention to use AI, actual use of AI, performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation

1. Vieriu, A. M., & Petrea, G. (2025). The Impact of Artificial Intelligence (AI) on Students’ Academic Development. Education Sciences, 15(3), 343. https://doi.org/10.3390/educsci15030343 [Google Scholar] [Crossref]

2. Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: a quantitative analysis using structural equation modelling. Education and Information Technologies, 25(5), 3443–3463. https://doi.org/10.1007/s10639-020-10159-7 [Google Scholar] [Crossref]

3. J.A. Oravec, Artificial intelligence implications for academic cheating: expanding the dimensions of responsible human-AI collaboration with ChatGPT, J. Interact. Learn. Res. 34 (2023) 213–237. [Google Scholar] [Crossref]

4. Wu, W., Zhang, B., Li, S., & Liu, H. (2022). Exploring factors of the willingness to accept AI-Assisted learning environments: an empirical investigation based on the UTAUT model and perceived risk theory. Frontiers in Psychology, 13, 870777. https://doi.org/10.3389/fpsyg.2022.870777 [Google Scholar] [Crossref]

5. 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 [Google Scholar] [Crossref]

6. Venkatesh, V., Thong, J. Y., & 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. [Google Scholar] [Crossref]

7. Abbad, M. M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 26(6), 7205–7224. https:// doi.org/ 10. 1007/ s10639- 021- 10573-5 [Google Scholar] [Crossref]

8. Zacharis, G., and K. Nikolopoulou. 2022. Factors predicting university students’ behavioral intention to use e-learning 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. [Google Scholar] [Crossref]

9. Chao C-M (2019) Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Front. Psychol. 10:1652. doi: 10.3389/fpsyg.2019.01652 [Google Scholar] [Crossref]

10. Ridho Bramulya Ikhsan, Hartiwi Prabowo, and Yuniarty (2021). Drivers of the Mobile-Learning Management System’s Actual Usage: Applying the UTAUT Model. ICIC Express Letters, Part B: Applications, Vol.12, No.11, 2021 [Google Scholar] [Crossref]

11. Nahil Abdallah, Odeh Abdallah, OM Bohra (2021). Factors Affecting Mobile Learning Acceptance in Higher Education: An Empirical Study. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 12, No. 4, 2021 [Google Scholar] [Crossref]

12. Nguyen, T. M., & Teng, H. C. (2022). A Study of Mobile Devices’ Acceptance in Developing EFL Listening Skill among Vietnamese High School Learners. International Journal of TESOL & Education, 2(2), 95- 118. DOI: https://doi.org/10.54855/ijte.22226 [Google Scholar] [Crossref]

13. 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. https://doi.org/10.1177/0735633120960421 [Google Scholar] [Crossref]

14. De Blanes Sebastián, M. G., Guede, J. R. S., & Antonovica, A. (2022). Application and extension of the UTAUT2 model for determining behavioral intention factors in use of the artificial intelligence virtual assistants. Frontiers in Psychology, 13, 993935. https://doi.org/10.3389/fpsyg.2022.993935 [Google Scholar] [Crossref]

15. Kingsley Ofosu-Ampong, Bryan Acheampong, Mark-Oliver Kevor, Fred Amankwah-Sarfo (2023). Acceptance of Artificial Intelligence (ChatGPT) in Education: Trust, Innovativeness and Psychological Need of Students. Information and Knowledge Management www.iiste.org Vol.13, No.4, 2023 ISSN 2224-5758 (Paper) ISSN 2224-896X (Online). [Google Scholar] [Crossref]

16. Emon, M. M. H., Hassan, F., Nahid, M. H., & Rattanawiboonsom, V. (2023). Predicting Adoption Intention of Artificial Intelligence CHATGPT. AIUB Journal of Science and Engineering (AJSE), 22(2), 189–199. https://doi.org/10.53799/ajse.v22i2.797 [Google Scholar] [Crossref]

17. Su, J., Wang, Y., Liu, H., Zhang, Z., Wang, Z., & Li, Z. (2025). Investigating the factors influencing users’ adoption of artificial intelligence health assistants based on an extended UTAUT model. Scientific Reports, 15(1), 18215. https://doi.org/10.1038/s41598-025-01897-0 [Google Scholar] [Crossref]

18. Acosta-Enriquez, B. G., Arbulú Ballesteros, M. A., Huamaní Jordan, O., López Roca, C., & Saavedra Tirado, K. (2024). Analysis of college students’ attitudes toward the use of ChatGPT in their academic activities: Effect of intent to use, verification of information and responsible use. BMC Psychology, 12(1), 255. https:// doi. org/ 10. 1186/ s40359- 024- 01764-z [Google Scholar] [Crossref]

19. Artur Strzelecki (2024). Students’ Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology. Innovative Higher Education (2024) 49:223–245 https://doi.org/10.1007/s10755-023-09686-1 [Google Scholar] [Crossref]

20. Patrick M. Cortez, Ardvin Kester S. Ong, John Francis T. Diaz, Josephine D. German, Singh Jassel Satwant Singh Jagdeep (2024). Analyzing Preceding factors affecting behavioral intention on communicational artificial intelligence as an educational tool. Heliyon 10 (2024) e25896 [Google Scholar] [Crossref]

21. Simone Grassini, Maren Linnea Aasen & Anja Møgelvang (2024) Understanding University Students’ Acceptance of ChatGPT: Insights from the UTAUT2 Model, Applied Artificial Intelligence, 38:1, 2371168, DOI: 10.1080/08839514.2024.2371168 [Google Scholar] [Crossref]

22. Pushp Patil, Kuttimani Tamilmani, Nripendra P. Rana, Vishnupriya Raghavan (2020). Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management 54 (2020) 102144 [Google Scholar] [Crossref]

23. Farhana Hanim Mohsin, Norhayati Md Isa, Khairunnisa Ishak, and Hatijah Mohamed Salleh (2024). Navigating the Adoption of Artificial Intelligence in Higher Education. International Journal of Business and Technopreneurship, Volume 14, No 1, February 2024 [109-120] [Google Scholar] [Crossref]

24. Athanasios Polyportis & Nikolaos Pahos (2025) Understanding students’ adoption of the ChatGPT chatbot in higher education: the role of anthropomorphism, trust, design novelty and institutional policy, Behaviour & Information Technology, 44:2, 315-336, DOI: 10.1080/0144929X.2024.2317364 [Google Scholar] [Crossref]

25. Cao, G., Duan, Y., Edwards, J., and Dwivedi, Y. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation 106:102312. doi: 10.1016/j. technovation.2021.102312 [Google Scholar] [Crossref]

26. 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 [Google Scholar] [Crossref]

27. Jyashree Krishnanraw and Kamisah Ismail (2025). Behavioural Intention to use Artificial Intelligence (AI) among Accounting Students: Evaluating the Effect of Technology Readiness. Management and Accounting Review, Volume 24 No 1, April 2025 [Google Scholar] [Crossref]

28. Räisänen, M, Postareff, L, Mattsson, M & Lindblom-Ylänne, S 2020, ' Study-related exhaustion: First-year students’ use of self-regulation of learning and peer learning and perceived value of peer support', Active Learning in Higher Education, vol. 21, no. 3, pp. 173-188. https://doi.org/10.1177/1469787418798517 [Google Scholar] [Crossref]

29. Raffaghelli, J. E., Rodríguez, M. E., Guerrero-Roldán, A. E., & Baneres, D. (2022). Applying the UTAUT Model to Explain the Students' Acceptance of an Early Warning System in Higher Education. Computers & Education, 182, 104468. https://doi.org/10.1016/j.compedu.2022.104468 [Google Scholar] [Crossref]

30. 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 [Google Scholar] [Crossref]

31. Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404–414 [Google Scholar] [Crossref]

32. Jain R, Garg N and Khera SN (2022) Adoption of AI-Enabled Tools in Social Development Organizations in India: An Extension of UTAUT Model. Front. Psychol. 13:893691. doi: 10.3389/fpsyg.2022.893691 [Google Scholar] [Crossref]

33. Limayem, M., Hirt, S. G., and Cheung, C. M. K. 2007. “How Habit Limits the Predictive Power of Intentions: The Case of IS Continuance”, MIS Quarterly (31:4), pp. 705-737 [Google Scholar] [Crossref]

34. Cabrera-Sánchez, J.-P., Villarejo-Ramos, Á. F., Liébana-Cabanillas, F., & Shaikh, A. A. (2021). Identifying relevant segments of AI applications adopters: Expanding the UTAUT2’s variables. Telematics and Informatics, 58, Article 101529. https://doi.org/10.1016/j.tele.2020.101529 [Google Scholar] [Crossref]

35. Su-Chen (Cecilia) Lin, Mei-Chen Chuang, Chen-Yuan Huang, and Chia-En Liu (2023). Nursing Staff’s Behavior Intention to Use Mobile Technology: An Exploratory Study Employing the UTAUT 2 Model. SAGE Open October-December 2023: 1–14 The Author(s) 2023 DOI: 10.1177/21582440231208483 journals.sagepub.com/home/sgo [Google Scholar] [Crossref]

36. Audrey Saudjhana & Herman (2023). Behavioral Intention and Adoption of Digital Bank Mobile Application Among Housewives Using Mixed Method Approach. Journal of Computer Networks, Architecture and High-Performance Computing Volume 5, Number 1, January 2023 [Google Scholar] [Crossref]

37. Rogers, E. M. (2003), Diffusion of innovations (5th ed.). New York, US: Free Press [Google Scholar] [Crossref]

38. Muhammad Shoaib Farooq, Maimoona Salam, Norizan Jaafar, Alain Fayolle, Kartinah Ayupp, Mirjana Radovic-Markovic, Ali Sajid (2017). "Acceptance and use of lecture capture system (LCS) in executive business studies: extending UTAUT2", Interactive Technology and Smart Education, https://doi.org/10.1108/ITSE-06-2016-0015 [Google Scholar] [Crossref]

39. E. Toreini, M. Aitken, K. Coopamootoo, K. Elliott, C. G. Zelaya, and A. Van Moorsel, “The relationship between trust in AI and trustworthy machine learning technologies,” in Proceedings of the 2020 conference on fairness, accountability, and transparency, 2020, pp. 272–283 [Google Scholar] [Crossref]

40. Featherman, M. S., and Pavlou, P. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. International Journal of Human-Computer Studies 59, 451–474. doi: 10.1016/s1071-5819(03)00111-3 [Google Scholar] [Crossref]

41. Susanto, T. D., & Goodwin, R. (2011). In M. Janssen, H. J. Scholl, M. A. Wimmer, & Y. H. Tan (Eds.), User acceptance of SMS-based e-government services (Vol. 6846, pp. 75–87). EGOV 2011, INCS. [Google Scholar] [Crossref]

42. Tao, W., Gao, S., & Yuan, Y. (2023). Boundary crossing: An experimental study of individual perceptions toward AIGC. Frontiers in Psychology, 14, 1185880. https://doi.org/10.3389/fpsyg.2023.1185880 [Google Scholar] [Crossref]

43. Wu, J., Gan, W., Chen, Z., Wan, S., & Lin, H. (2023). AI-Generated Content (AIGC): A survey. arXiv preprint arXiv:2304.06632 [Google Scholar] [Crossref]

44. Weiyi Li (2025) A Study on Factors Influencing Designers’ Behavioral Intention in Using AI-Generated Content for Assisted Design: Perceived Anxiety, Perceived Risk, and UTAUT, International Journal of Human–Computer Interaction, 41:2, 1064-1077, DOI: 10.1080/10447318.2024.2310354 https://doi.org/10.1080/10447318.2024.2310354 [Google Scholar] [Crossref]

45. Lang, C. (2018). Perceived risks and enjoyment of access-based consumption: Identifying barriers and motivations to fashion renting. Fashion and Textiles, 5(1), 1–18. https://doi.org/10.1186/s40691-018-0139-z [Google Scholar] [Crossref]

46. David Strohmaier, Jianqiu Zeng, Muhammad Hafeez (2019). Trust, distrust, and crowdfunding: A study on perceptions of institutional mechanisms. Telematics and Informatics 43 (2019) 101252 [Google Scholar] [Crossref]

47. Biloš, A., Budimir, B. Understanding the Adoption Dynamics of ChatGPT among Generation Z: Insights from a Modified UTAUT2 Model. J. Theor. Appl. Electron. Commer. Res. 2024, 19, 863–879. https://doi.org/10.3390/jtaer19020045 [Google Scholar] [Crossref]

48. Mohamed Merhi, Kate Hone, Ali Tarhini (2019). A cross-cultural study of the intention to use mobile banking between Lebanese and British consumers: Extending UTAUT2 with security, privacy and trust. Technology in Society 59 (2019) 101151 [Google Scholar] [Crossref]

49. Artur Strzelecki (2025). ChatGPT in higher education: Investigating bachelor and master students’ expectations towards AI tool. Education and Information Technologies (2025) 30:10231–10255 https://doi.org/10.1007/s10639-024-13222-9 [Google Scholar] [Crossref]

50. Montathar Faraon, Kari Rönkkö, Marcelo Milrad, Eric Tsui (2025). International perspectives on artificial intelligence in higher education: An explorative study of students’ intention to use ChatGPT across the Nordic countries and the USA. Education and Information Technologies https://doi.org/10.1007/s10639-025-13492-x [Google Scholar] [Crossref]

51. Hamzeh Moradi (2025). Integrating AI in higher education: Factors influencing ChatGPT acceptance among Chinese university EFL students. International Journal of Educational Technology in Higher Education (2025) 22:30 https://doi.org/10.1186/s41239-025-00530-4 [Google Scholar] [Crossref]

52. Cabero-Almenara, J., Palacios-Rodríguez, A., Rojas Guzmán, H.d.l.Á., Fernández Scagliusi, V. Prediction of the Use of Generative Artificial Intelligence Through ChatGPT Among Costa Rican University Students: A PLS Model Based on UTAUT2. Appl. Sci. 2025, 15, 3363. https://doi.org/ 10.3390/app15063363 [Google Scholar] [Crossref]

53. Romero-Rodríguez, J., Ramírez-Montoya, M., Buenestado-Fernández, M., & Lara-Lara, F. (2023). (Romero-Rodríguez et al., 2023) 12(2), 323-339. doi: 10.7821/naer.2023.7.1458 [Google Scholar] [Crossref]

54. Tribikram Budhathoki, Araz Zirar, Eric Tchouamou Njoya & Achyut Timsina (2024) ChatGPT adoption and anxiety: a cross-country analysis utilising the unified theory of acceptance and use of technology (UTAUT), Studies in Higher Education, 49:5, 831-846, DOI:10.1080/03075079.2024.2333937 [Google Scholar] [Crossref]

55. Rashid, S. (2025). Habit Predicting Higher Education EFL Students’ Intention and Use of AI: A Nexus of UTAUT-2 Model and Metacognition Theory. Education Sciences, 15(6), 756. https://doi.org/10.3390/educsci15060756 [Google Scholar] [Crossref]

56. Benicio Gonzalo Acosta‑Enriquez, Marco Agustín Arbulú Ballesteros, Olger Huamaní Jordan, Carlos López Roca and Karina Saavedra Tirado (2024). Analysis of college students’ attitudes toward the use of ChatGPT in their academic activities: Effect of intent to use, verification of information and responsible use. BMC Psychology (2024) 12:255 https://doi.org/10.1186/s40359‑024‑01764‑z [Google Scholar] [Crossref]

57. Swathi A, Janani S, Satvik Vats, Selvanathan N, Shanmugapriya S and Alagarraja K (2025). [Google Scholar] [Crossref]

58. Emotionally Aware Artificial Intelligence Systems for Strengthening Global Human Connections and Enhancing Psychological Well Being. Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 24), Advances in Computer Science Research 120, https://doi.org/10.2991/978-94-6463-718-2_48 [Google Scholar] [Crossref]

59. Mohammed Nasiru Yakubu, Nakama David, Naima Hafiz Abubakar (2025). Students’ behavioural intention to use content generative AI for learning and research: A UTAUT theoretical perspective. Education and Information Technologies https://doi.org/10.1007/s10639-025-13441-8 [Google Scholar] [Crossref]

60. Abeer S. Almogren, Waleed Mugahed Al-Rahmi, Nisar Ahmed Dahri (2024). Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective. Heliyon 10 (2024) e31887 [Google Scholar] [Crossref]

61. Nurul Aulia Ramadhina, Nurafni Rubiyanti, Rajiv Dharma Mangruwa (2022). The Influence of Performance Expectancy, Effort Expectancy, And Social Influence on Use Behavior with Behavioral Intention as A Mediator (A Case Study of Cash on Delivery (COD) System Users in Marketplaces). International Journal of Social Science and Human Research ISSN (print): 2644-0679, ISSN (online): 2644-0695 Volume 08 Issue 01 January 2025 DOI: 10.47191/ijsshr/v8-i1-24, Impact factor- 7.876 Page No: 215 – 224 [Google Scholar] [Crossref]

62. Kim, Y., Blazquez, V., Oh, T. Determinants of Generative AI System Adoption and Usage Behavior in Korean Companies: Applying the UTAUT Model. Behav. Sci. 2024, 14, 1035. https://doi.org/10.3390/ bs14111035 [Google Scholar] [Crossref]

63. Selvi, M.S., Önem, ¸S. Impact of Variables in the UTAUT 2 Model on the Intention to Use a Fully Electric Car. Sustainability 2025, 17, 3214. https://doi.org/10.3390/su17073214 [Google Scholar] [Crossref]

64. Wang, Y., & Wang, Y. (2019). Development and validation of an artificial intelligence anxiety scale: an initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619–634. https://doi.org/10.1080/10494820.2019.1674887 [Google Scholar] [Crossref]

65. Hayes, Andrew F. 2018. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. Second Edition. New York, NY: The Guilford Press. [Google Scholar] [Crossref]

66. Senali, M. G., Iranmanesh, M., Ismail, F. N., Rahim, N. F. A., Khoshkam, M., & Mirzaei, M. (2023). Determinants of intention to use e-wallet: Personal innovativeness and propensity to trust as moderators. International Journal of Human–Computer Interaction, 39(12), 2361-2373. https://doi.org/10.1080/10447318.2022.2076309 [Google Scholar] [Crossref]

67. Devadas Menon, 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 (2023) e20962 [Google Scholar] [Crossref]

68. Resam Lal Poudel, Chandra Kanta Bastakoti (2024). Impact of Behavioural Intention to Use Generative Artificial Intelligence on Academic Performance of Students in Higher Education Institutions. Prithvi Journal of Research and Innovation [A Peer-Reviewed, Open Access Multidisciplinary Bilingual Journal; Indexed in NepJOL] ISSN 2705-4888 [Print]; ISSN 2705-4896 [Online]; JPPS Star-Rated Journal Volume 6; 15 December 2024; pp. 1-21 [Google Scholar] [Crossref]

69. Sneha Kandoth, Suraj Kushe Shekhar (2022). Social influence and intention to use AI: the role of personal innovativeness and perceived trust using the parallel mediation model. Forum Scientiae Oeconomia Volume 10 (2022) No. 3 DOI: 10.23762/FSO_VOL10_NO3_7 [Google Scholar] [Crossref]

70. Seo, K.H., Lee, J.H. The Emergence of Service Robots at Restaurants: Integrating Trust, Perceived Risk, and Satisfaction. Sustainability 2021, 13, 4431. https://doi.org/10.3390/su13084431 [Google Scholar] [Crossref]

71. Flavián, C., Pérez-Rueda, A., Belanche, D. and Casaló, L.V. (2021). “Intention to use analytical Artificial Intelligence in services. The effect of technology readiness and awareness”, Journal of Service Management. Forthcoming. DOI 10.1108/JOSM-10-2020-0378. [Google Scholar] [Crossref]

72. Li, X., Jiang, M.Y.-c., Jong, M.S.-y., Zhang, X.; Chai, C.-s. Understanding Medical Students’ Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study. Int. J. Environ. Res. Public Health 2022, 19, 8733. https://doi.org/10.3390/ ijerph19148733 [Google Scholar] [Crossref]

73. Qianli Wu, Jinyan Tian, Ziyang Liu (2025). Exploring the usage behavior of generative artificial intelligence: a case study of ChatGPT with insights into the moderating effects of habit and personal innovativeness. Current Psychology (2025) 44:8190–8203 https://doi.org/10.1007/s12144-024-07193-w [Google Scholar] [Crossref]

74. Ojo, AO, Alias, M, Dorasamy, M, Sarwar, A & Ojo, OT 2022, 'Moderating effects of opportunity on the Cognitive and Motivational Factors of Internet engagement: A Study of the digital divide in Malaysia', International Journal of Technology and Human Interaction, vol. 18, no. 1. https://dx.doi.org/10.4018/IJTHI.299353 [Google Scholar] [Crossref]

75. Prensky, M. (2001). Digital natives, digital Immigrants. On the Horizon, 9(5), https://www.marcprensky.com/writing/Prensky%20%20Digital%20Natives,%20Digital%20Immigrants%20-%20Part1.pdf [Google Scholar] [Crossref]

76. Polyportis A (2024) A longitudinal study on artificial intelligence adoption: understandingthe drivers of ChatGPT usage behavior change in higher education. Frontiers in Artificial Intelligence. 6:1324398. doi: 10.3389/frai.2023.1324398 [Google Scholar] [Crossref]

77. Kooli, C. Chatbots in Education and Research: A Critical Examination of Ethical Implications and Solutions. Sustainability 2023, 15, 5614. https://doi.org/10.3390/ su15075614 [Google Scholar] [Crossref]

78. Zahir Osman, Bibi Nabi Ahmad Khan, Mohammad Naim Ismail, Yuzery Yusoff, Noral Hidayah Alwi, Khairul Hamimah Mohamad Jodi (2023). Artificial Intelligence Usage in Higher Education: Academicians' Perspective. International Journal of Academic Research in Accounting, Finance & Management Sciences Vol. 13, No. 4, 2023, E-ISSN: 2225-8329 © 2023 [Google Scholar] [Crossref]

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