The Influence of Performance Expectancy, Effort Expectancy, and Social Influence on Artificial Intelligence Adoption Behaviour: A Case Study from a Malaysian University

This paper explores the variables that affect the use of Artificial Intelligence among students at the ABC university at Malaysia, based on major constructs of Unified Theory of Acceptance and Use of Technology (UTAUT). The study concentrates on three independent variables, which include the performance expectancy (PE), effort expectancy (EE), and social influence (SE), on Artificial Intelligence (AI) adoption behavior. Data was collected from a sample of 211 students at ABC University via an online questionnaire. The descriptive statistics, reliability test, Spearman correlation were used to analyze the data. Results indicate high levels of internal consistency of all the constructs and high positive associations between each variable and AI adoption behavior. The strongest predictor was effort expectancy, which demonstrates the significance of AI systems that are intuitive and easy to use. The social influence and performance expectancy were also found to play significant roles. Meaning that students are both social-validation- and perceived-academic-benefit-motivated.

Artificial Intelligence (AI) Adoption, Unified Theory of Acceptance and Use of Technology

1. Alessandro Rovetta, Bortolotti, A., & Palumbo, R. (2025). Integrating Team And Organizational Identity: A Systematic Literature Analysis. Frontiers In Organizational Psychology, 2. Https://Doi.Org/10.3389/Forgp.2024.1439269 [Google Scholar] [Crossref]

2. Andrade, C. (2021). The Inconvenient Truth About Convenience And Purposive Samples. Indian Journal Of Psychological Medicine, 43(1), 86–88. Https://Doi.Org/10.1177/0253717620977000 [Google Scholar] [Crossref]

3. Black, R. W., & Tomlinson, B. (2025). University Students Describe How They Adopt AI For Writing And Research In A General Education Course. Scientific Reports, 15(1), 1–10. Https://Doi.Org/10.1038/S41598-025-92937-2 [Google Scholar] [Crossref]

4. Bloomfield, R., & Rushby, J. (2024). Assurance Of AI Systems From A Dependability Perspective. Arxiv (Cornell University). Https://Doi.Org/10.48550/Arxiv.2407.13948 [Google Scholar] [Crossref]

5. Cheng, M., Li, X., & Xu, J. (2022). Promoting Healthcare Workers’ Adoption Intention Of Artificial-Intelligence-Assisted Diagnosis And Treatment: The Chain Mediation Of Social Influence And Human–Computer Trust. International Journal Of Environmental Research And Public Health, 19(20), 13311. Https://Doi.Org/10.3390/Ijerph192013311 [Google Scholar] [Crossref]

6. Chong, L., Zhang, G., Goucher-Lambert, K., Kotovsky, K., & Cagan, J. (2022). Human Confidence In Artificial Intelligence And In Themselves: The Evolution And Impact Of Confidence On Adoption Of AI Advice. Computers In Human Behavior, 127. Https://Doi.Org/10.1016/J.Chb.2021.107018 [Google Scholar] [Crossref]

7. Cornell University. (2023, August 16). AI & Academic Integrity | Center For Teaching Innovation. Teaching.Cornell.Edu. Https://Teaching.Cornell.Edu/Generative-Artificial-Intelligence/Ai-Academic-Integrity [Google Scholar] [Crossref]

8. Cronbach, L. J. (1951). Coefficient Alpha And The Internal Structure Of Tests. Psychometrika, 16(3), 297–334. Https://Doi.Org/10.1007/Bf02310555 [Google Scholar] [Crossref]

9. Cunningham, M.S. (1967) The Major Dimensions Of Perceived Risk: Risk Taking And Information Handling In Consumer Behavior, Graduate School Of Business Administration, Harvard University, Boston. [Google Scholar] [Crossref]

10. 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]

11. Du, L., & Beibei Lv. (2024). Factors Influencing Students’ Acceptance And Use Generative Artificial Intelligence In Elementary Education: An Expansion Of The UTAUT Model. Education And Information Technologies. Https://Doi.Org/10.1007/S10639-024-12835-4 [Google Scholar] [Crossref]

12. Faraon, M., Rönkkö, K., Milrad, M., & Tsui, E. (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]

13. Granić, A. (2023). Technology Acceptance And Adoption In Education. 183–197. Https://Doi.Org/10.1007/978-981-19-2080-6_11 [Google Scholar] [Crossref]

14. Guassi Moreira, J. F., Tashjian, S. M., Galván, A., & Silvers, J. A. (2021). Computational And Motivational Mechanisms Of Human Social Decision Making Involving Close Others. Journal Of Experimental Social Psychology, 93, 104086. Https://Doi.Org/10.1016/J.Jesp.2020.104086 [Google Scholar] [Crossref]

15. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th Ed.). Cengage Learning [Google Scholar] [Crossref]

16. Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. In Classroom Companion: Business. Springer International Publishing. Https://Doi.Org/10.1007/978-3-030-80519-7 [Google Scholar] [Crossref]

17. Hansaram, S.K., Munap, R. (2025). Intersectionality at work: How disability, employer biases, and SME constraints shape employment outcomes for persons with disabilities. Environment and Social Psychology, 10(9), 4013 [Google Scholar] [Crossref]

18. He, C., Shi, L., Yu, M., Jiang, Y., & Liao, C. (2024). Research On Influencing Factors Of Information Adoption Behavior Of College Students In Cloud Class. 601–606. Https://Doi.Org/10.1145/3700297.3700401 [Google Scholar] [Crossref]

19. Ho Ngoc Hai. (2023). Chatgpt: The Evolution Of Natural Language Processing. Authorea (Authorea). Https://Doi.Org/10.22541/Au.167935454.46075854/V1 [Google Scholar] [Crossref]

20. Hoo, W. C., Ching, K. Y. P., Cheng, A. Y., Saeed, K., & Shaznie, A. (2023). An examination on the factors that influence the intention to use chatbots in Malaysia. International Journal of Management and Sustainability, 12(3), 380–390. https://doi.org/10.18488/11.v12i3.3457 [Google Scholar] [Crossref]

21. Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2021). Ethics Of AI In Education: Towards A Community-Wide Framework. International Journal Of Artificial Intelligence In Education, 32(1), 504–526. Https://Doi.Org/10.1007/S40593-021-00239-1 [Google Scholar] [Crossref]

22. Holzmann, P., Gregori, P., & Schwarz, E. J. (2025). Students’ Little Helper: Investigating Continuous-Use Determinants Of Generative AI And Ethical Judgment. Education And Information Technologies. Https://Doi.Org/10.1007/S10639-025-13708-0 [Google Scholar] [Crossref]

23. Hunziker, S., & Blankenagel, M. (2024). Cross-Sectional Research Design. Springer Ebooks, 187–199. Https://Doi.Org/10.1007/978-3-658-42739-9_10 [Google Scholar] [Crossref]

24. Izham, H. I. B., Peng, K. P. Y., Cheng, A. Y., Loy, C. K., & Hansaram, S. K. The Impact of Buy Now, Pay Later Services on the Impulsive Buying Behavior of Generation Z in Shah Alam, Malaysia. [Google Scholar] [Crossref]

25. Jain, R., Garg, N., & Khera, S. N. (2022). Adoption Of AI-Enabled Tools In Social Development Organizations In India: An Extension Of UTAUT Model. Frontiers In Psychology, 13. Https://Doi.Org/10.3389/Fpsyg.2022.893691 [Google Scholar] [Crossref]

26. Kiat, L. S., Hoo, W. C., Cheng, A. Y., Prompanyo, M., & Hossain, S. F. A. (2025). Factors Influencing Intention to use 5G Mobile Technology and Adoption Onwards in Malaysia. [Google Scholar] [Crossref]

27. Kraatz, K., & Xie, S. (2023). Why AI Art Is Not Art – A Heideggerian Critique. Synthesis Philosophica, 38(2), 235–253. Https://Doi.Org/10.21464/Sp38201 [Google Scholar] [Crossref]

28. Li, X., Zhao, Y., & Chen, Z. (2023). Perceived Ease Of Use And AI Adoption: Evidence From Higher Education. Computers & Education, 180, 104567. Https://Doi.Org/10.1016/J.Compedu.2022.104567 [Google Scholar] [Crossref]

29. Mat Roni, S., & Djajadikerta, H. G. (2021). Data Analysis With SPSS For Survey-Based Research. Springer Singapore. Https://Doi.Org/10.1007/978-981-16-0193-4 [Google Scholar] [Crossref]

30. Memon, M. A., Ting, H., Cheah, J.-H., Thurasamy, R., Chuah, F., & Cham, T. H. (2020). Sample Size For Survey Research: Review And Recommendations. Journal Of Applied Structural Equation Modeling, 4(2), 1–20. Https://Doi.Org/10.47263/JASEM.4(2)01 [Google Scholar] [Crossref]

31. Merz, A., Moser, I., & Bergamin, P. B. (2025). Performance Expectancy And Social Influence Drive The Acceptance Of Immersive Virtual Reality For Professional Collaboration. Virtual Reality, 29(3). Https://Doi.Org/10.1007/S10055-025-01182-5 [Google Scholar] [Crossref]

32. Millum, J., & Bromwich, D. (2021). Informed Consent: What Must Be Disclosed And What Must Be Understood? The American Journal Of Bioethics, 21(5), 1–19. [Google Scholar] [Crossref]

33. Https://Doi.Org/10.1080/15265161.2020.1863511 [Google Scholar] [Crossref]

34. Mohd Rokeman, N. R. (2024). Likert Measurement Scale In Education And Social Sciences: Explored And Explained. EDUCATUM Journal Of Social Sciences, 10(1), 77–88. Https://Doi.Org/10.37134/Ejoss.Vol10.1.7.2024 [Google Scholar] [Crossref]

35. Moradi, H. (2025). Integrating AI In Higher Education: Factors Influencing Chatgpt Acceptance Among Chinese University EFL Students. International Journal Of Educational Technology In Higher Education, 22(1). Https://Doi.Org/10.1186/S41239-025-00530-4 [Google Scholar] [Crossref]

36. Mumford, M. D., Higgs, C., & Gujar, Y. (2021). Ethics In Coercive Environments: Ensuring Voluntary Participation In Research. Handbook Of Research Ethics In Psychological Science., 113–123. Https://Doi.Org/10.1037/0000258-008 [Google Scholar] [Crossref]

37. Mustafa, A. S., & Garcia, M. B. (2021). Theories Integrated With Technology Acceptance Model (TAM) In Online Learning Acceptance And Continuance Intention: A Systematic Review. 2021 1st Conference On Online Teaching For Mobile Education (OT4ME). Https://Doi.Org/10.1109/Ot4me53559.2021.9638934 [Google Scholar] [Crossref]

38. Nazari, N., Shabbir, M. S., & Setiawan, R. (2021). Application Of Artificial Intelligence Powered Digital Writing Assistant In Higher Education: Randomized Controlled Trial. Heliyon, 7(5), E07014. Https://Doi.Org/10.1016/J.Heliyon.2021.E07014 [Google Scholar] [Crossref]

39. Nhu, T., Nam Van Lai, & Quyet Thi Nguyen. (2024). Artificial Intelligence (AI) In Education: A Case Study On Chatgpt’s Influence On Student Learning Behaviors. Educational Process: International Journal, 13(2). Https://Doi.Org/10.22521/Edupij.2024.132.7 [Google Scholar] [Crossref]

40. Openai. (2024). Openai Charter. Openai. Https://Openai.Com/Charter/ [Google Scholar] [Crossref]

41. Paek, S., & Kim, N. (2021). Analysis Of Worldwide Research Trends On The Impact Of Artificial Intelligence In Education. Sustainability, 13(14), 7941. Https://Doi.Org/10.3390/Su13147941 [Google Scholar] [Crossref]

42. Pallant, J. (2020). SPSS Survival Manual: A Step By Step Guide To Data Analysis Using IBM SPSS (7th Ed.). Routledge. Https://Doi.Org/10.4324/9781003117452 [Google Scholar] [Crossref]

43. Păvăloaia, V.-D., & Necula, S.-C. (2023). Artificial Intelligence As A Disruptive Technology—A Systematic Literature Review. Electronics, 12(5). Mdpi. Https://Doi.Org/10.3390/Electronics12051102 [Google Scholar] [Crossref]

44. Rahman, M. (2023). Sample Size Determination For Survey Research And Non-Probability Sampling Techniques: A Review And Set Of Recommendations | Journal Of Entrepreneurship, Business And Economics. Www.Scientificia.Com. Https://Www.Scientificia.Com/Index.Php/JEBE/Article/View/201 [Google Scholar] [Crossref]

45. Ruano-Borbalan, J.-C. (2025). The Transformative Impact Of Artificial Intelligence On Higher Education: A Critical Reflection On Current Trends And Futures Directions. International Journal Of Chinese Education, 14(1). Https://Doi.Org/10.1177/2212585x251319364 [Google Scholar] [Crossref]

46. Ruslan, W. N. S. W. (2024). Exploring Drivers Influencing E-Commerce AI Adoption Among Social Media Natives. Pakistan Journal Of Life And Social Sciences (PJLSS), 22(2). Https://Doi.Org/10.57239/Pjlss-2024-22.2.001069 [Google Scholar] [Crossref]

47. Russell, S., Norvig, P., Fabrice Popineau, Laurent Miclet, & Cadet, C. (2021). Intelligence Artificielle : Une Approche Moderne (4e Édition). Hal.Science. Https://Hal.Science/Hal-04245057 [Google Scholar] [Crossref]

48. Safdar, M., Siddique, N., Gulzar, A., Yasin, H., & Khan, A. (2024). Does Chatgpt Generate Fake Results? Challenges In Retrieving Content Through Chatgpt. Digital Library Perspectives. Https://Doi.Org/10.1108/Dlp-01-2024-0006 [Google Scholar] [Crossref]

49. Sánchez-Prieto, J. C., Cruz-Benito, J., Therón, R., & García-Peñalvo, F. (2020). Assessed By Machines: Development Of A TAM-Based Tool To Measure AI-Based Assessment Acceptance Among Students. International Journal Of Interactive Multimedia And Artificial Intelligence, 6(4), 80. [Google Scholar] [Crossref]

50. Sewandono, R. E., Thoyib, A., Hadiwidjojo, D., & Rofiq, A. (2022). Performance Expectancy Of E-Learning On Higher Institutions Of Education Under Uncertain Conditions: Indonesia Context. Education And Information Technologies, 28(4). Https://Doi.Org/10.1007/S10639-022-11074-9 [Google Scholar] [Crossref]

51. Shapiro, S. S., & Wilk, M. B. (1965). An Analysis Of Variance Test For Normality (Complete Samples). Biometrika, 52(3-4), 591–611. Https://Doi.Org/10.1093/Biomet/52.3-4.591 [Google Scholar] [Crossref]

52. Spears, R. (2021). Social Influence And Group Identity. Annual Review Of Psychology, 72(1), 367–390. Https://Doi.Org/10.1146/Annurev-Psych-070620-111818 [Google Scholar] [Crossref]

53. Strzelecki, A. (2023). Students’ Acceptance Of Chatgpt In Higher Education: An Extended Unified Theory Of Acceptance And Use Of Technology. Innovative Higher Education, 49, 223–245. Https://Doi.Org/10.1007/S10755-023-09686-1 [Google Scholar] [Crossref]

54. 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). Https://Doi.Org/10.1038/S41598-025-01897-0 [Google Scholar] [Crossref]

55. Tajfel, H., & Turner, J. C. (1979). An Integrative Theory Of Intergroup Conflict In Austin WG & Worchel S.(Eds.), The Social Psychology Of Intergroup Relations (Pp. 33–47). Monterey, CA: Brooks/Cole. [Google Scholar]. [Google Scholar] [Crossref]

56. Tran, V. D. (2020). The Relationship Among Product Risk, Perceived Satisfaction And Purchase Intentions For Online Shopping. The Journal Of Asian Finance, Economics And Business, 7(6), 221–231. Https://Doi.Org/10.13106/Jafeb.2020.Vol7.No6.221 [Google Scholar] [Crossref]

57. Uzun, L. (2023). Chatgpt And Academic Integrity Concerns: Detecting Artificial Intelligence Generated Content. 3(1), 45–54. Https://Www.Researchgate.Net/Publication/370299956 Chatgpt And Academic Integrity Concerns [Google Scholar] [Crossref]

58. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478. [Google Scholar] [Crossref]

59. 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]

60. Yakubu, M. N., David, N., & Abubakar, N. H. (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]

61. Wen, E. C. Y., Hoo, W. C., Lee, A., & Cheng, A. Y. (2023). Mobile Banking Application (App) Adoption Behaviour Amongst Malaysian Consumers. WSEAS Transactions on Business and Economics, 20, 759–769. https://doi.org/10.37394/23207.2023.20.70. [Google Scholar] [Crossref]

62. Zhang, L., Shao, Z., Chen, B., & Benitez, J. (2024). Unraveling Generative AI Adoption In Enterprise Digital Platforms: The Moderating Role Of Internal And External Environments. IEEE Transactions On Engineering Management, 1–15. Https://Doi.Org/10.1109/Tem.2024.3513773 [Google Scholar] [Crossref]

• The Indirect Effect of Liquidity and Activity on Company Value with Profitability as an Intervening Variable
• Effect of Financial Skills, Knowledge, and Attitude on The Financial Behaviour of Clergy
• A Decade of Review: Trends in Budget Execution and Financial Performance of Development Projects in Tanzania (2014/15-2023/24)
• The Influence of Pre-Project Planning on the Budget Absorption Rate of Public Funded Infrastructure Projects in Kenya a Comparative Case Study of Narok, Migori, and Kisii County Government Projects
• Assessment of Factors Influencing Digital Transformation in Hotels’ Facility Management in Abuja Metropolis, Nigeria