Perceived Intelligence and Perceived Anthropomorphism as Additional Predictors of ChatGPT Usage Behaviour
DOI:
https://doi.org/10.32890/jict2026.25.3.3Keywords:
ChatGPT, generative artificial intelligence, perceived anthropomorphism, perceived intelligence, Unified Theory of Acceptance and Use of Technology (UTAUT)Abstract
The rapid advancement of generative artificial intelligence (AI) tools like ChatGPT is transforming various sectors, including education. Although many are adopting ChatGPT for learning, not all students are embracing it. This is because there are contrasting views regarding its usefulness, ease of use, reliability, and ethical implications. ChatGPT acceptance and usage behaviour have been frequently studied through the Unified Theory of Acceptance and Use of Technology (UTAUT) model. However, it may not completely describe the features of AI. Hence, this paper extends the UTAUT model by incorporating two constructs specifically on AI, namely, Perceived Intelligence and Perceived Anthropomorphism as predictors of the ChatGPT usage behaviour. The study followed a quantitative research design. Purposive sampling was used to collect data from 384 students at a public university in Northern Malaysia. Data was analyzed through Partial Least Squares Structural Equation Modeling. The results showed that all six predictors have significant effects on the Behavioural Intention with 48.3% of variance. Behavioural Intention predicts the actual Use Behaviour with 16.5% of variance. The results highlight the robustness of the extended UTAUT model, where the two constructs become predictors of behavioural intention to use. This indicated students’ dependency on the perceived reasoning of ChatGPT. Based on these predictors, the study has constructed a behavioural model to determine the ChatGPT usage among university students. The findings contribute by demonstrating the validity of the extended UTAUT model. Additionally, practical recommendations are provided to universities, educators, and AI developers for integrating ChatGPT in academic settings.
References
Airenti, G. (2015). The cognitive bases of anthropomorphism: From relatedness to empathy. International Journal of Social Robotics, 7(1), 117-127. https://doi.org/10.1007/ s12369-014-0263-x
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Al-Afnan, M. A., Dishari, S., Jovic, M., & Lomidze, K. (2023). ChatGPT as an educational tool: Opportunities, challenges, and recommendations for communication, business writing, and composition courses. Journal of Artificial Intelligence and Technology, 3(2), 60-68. https://doi.org/10.37965/jait.2023.0184
Balakrishnan, J., & Dwivedi, Y. K. (2024). Conversational commerce: Entering the next stage of AI-powered digital assistants. Annals of Operations Research, 333(2), 653-687. https://doi.org/10. 1007/s10479-021-04049-5
Balakrishnan, J., Abed, S. S., & Jones, P. (2022). The role of meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services?. Technological Forecasting and Social Change, 180, 121692. https://doi.org/10. 1016%2Fj.techfore.2022.121692
Bartneck, C., Kulić, D., Croft, E., & Zoghbi, S. (2009). Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. International Journal of Social Robotics, 1(1), 71-81. https://doi.org/10.1007/s12369-008-0001-3
Bougie, R., & Sekaran, U. (2025). Research methods for business. John Wiley & Sons.
Budhathoki, T., Zirar, A., Njoya, E. T., & Timsina, A. (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. https://doi.org/10.1080/03075079. 2024.2333937
Bujang, M. A. (2023). An elaboration on sample size planning for performing a one-sample sensitivity and specificity analysis based on calculations for a specified 95% confidence interval width. Diagnostics, 13(8), 1390. https://doi.org/10.3390/diagnostics13081390
Chaves, A. P., & Gerosa, M. A. (2021). How should my chatbot interact? A survey on social characteristics in human–chatbot interaction design. International Journal of Human– Computer Interaction, 37(8), 729–758. https://doi.org/10.1080/10447318.2020.1841438
Chinmulgund, A., Khatwani, R., Tapas, P., Shah, P., & Sekhar, R. (2023). Anthropomorphism of AI-based chatbots by users during communication. 2023 3rd International Conference on Intelligent Technologies (CONIT), 1-6.
Cohen, J. (2023). Statistical power analysis for the behavioral sciences. 2nd Edition, Routledge.
Collins, C., Dennehy, D., Conboy, K., & Mikalef, P. (2021). Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management, 60, 102383. https://doi.org/10.1016/j.ijinfomgt.2021.102383
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, M. A., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, A. M., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D.,...
Wright, R. (2023). Opinion paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10. 1016/j.ijinfomgt.2023.102642
Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: A three-factor theory of anthropomorphism. Psychological Review, 114(4), 864. https://doi.org/10.1037/0033-295X. 114.4.864
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley, Reading, MA.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 57(1), 39–50. https://doi.org/10.1177/ 002224378101800104
Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case And Application Research, 25(3), 277-304.
Gansser, O. A., & Reich, C. S. (2021). A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application. Technology in Society, 65, 101535. https://doi.org/10.1016/j.techsoc.2021.101535
García-López, I. M., González, C. S. G., Ramírez-Montoya, M. S., & Molina-Espinosa, J. M. (2025). Challenges of implementing ChatGPT on education: Systematic literature review. International Journal of Educational Research Open, 8, 100401.
Hadi, M. A., Abdulredha, M. N., & Hasan, E. (2023). Introduction to ChatGPT: A new revolution of artificial intelligence with machine learning algorithms and cybersecurity. Science Archives, 4(04), 276-285. https://doi.org%2F10.47587%2FSA.2023.4406
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). SAGE Publications.
Hariri, W. (2023). Unlocking the potential of ChatGPT: A comprehensive exploration of its applications, advantages, limitations, and future directions in natural language processing. arXiv preprint arXiv:2304.02017.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of The Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
Holtom, B. C., Baruch, Y., Aguinis, H., & Ballinger, G. A. (2022). Survey response rates: Trends and a validity assessment framework. Human Relations, 75(8), 1560–1584. https://doi.org/10.1177/ 00187267211070769
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org%2F10.1080%2F10705519909540118
Hussein, H., Gordon, M., Hodgkinson, C., Foreman, R., & Wagad, S. (2025). ChatGPT’s impact across sectors: A systematic review of key themes and challenges. Big Data and Cognitive Computing, 9(3), 56. https://doi.org/10.3390/bdcc9030056
Islam, A. Y. M. A. (2011). Viability of the extended technology acceptance model: An empirical study. Journal of Information and Communication Technology, 10, 85-98. https://doi.org/10.32890/jict.10.2011.8110
Jin, S. V., & Youn, S. (2022). Social presence and imagery processing as predictors of chatbot continuance intention in human-AI-interaction. International Journal of Human–Computer Interaction, 39(9), 1874–1886. https://doi.org/10.1080/10447318.2022.2129277
Ko, J., & Chang, B. H. (2023). Effects of AI speaker's cognitive and emotional decision-making on AI trust. International Journal of Contents, 19(2). https://doi.org/10.5392/IJoC.2023.19.2.028
Lal, V., Kumbhar, V., & Varaprasad, G. (2024). Novel extension of the UTAUT Model to assess e-learning adoption in higher education institutes: The role of study-life quality. Knowledge Management & E-Learning, 16(1), 42-64. https://doi.org%2F10.34105%2Fj.kmel.2024.16.002
Lee, Y., & Kim, S. H. (2025). Exploring dimensions of perceived anthropomorphism in conversational AI: Implications for human identity threat and dehumanization. Computers in Human Behavior: Artificial Humans, 100192. https://doi.org%2F10.1016%2Fj.chbah.2025.100192
Lim, X. J., Cheah, J. H., Ng, S. I., Basha, N. K., & Soutar, G. (2021). The effects of anthropomorphism presence and the marketing mix have on retail app continuance use intention. Technological Forecasting and Social Change, 168, 120763.
Long, D., & Magerko, B. (2020, April). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference On Human Factors In Computing Systems (pp. 1-16).
Madsen, M., & Gregor, S. (2000). Measuring human-computer trust. Proceedings of the 11th Australasian Conference on Information Systems (pp. 6–8). Queensland University of Technology.
Mai, W., & Khairani, A. Z. (2026). Exploring UTAUT Research in Higher Education: A Bibliometric Analysis and Future Directions in the Era of AI and ChatGPT. International Journal of Instruction, 19(2), 59-86. https://doi.org/10.29333/iji.2026.1924a
Martin, B. A., Jin, H. S., Wang, D., Nguyen, H., Zhan, K., & Wang, Y. X. (2020). The influence of consumer anthropomorphism on attitudes towards artificial intelligence trip advisors. Journal of Hospitality and Tourism Management, 44, 108-111. https://doi.org/10.1016/j.jhtm.2020. 06.004.
Murad, I. A., Surameery, N. M. S., & Shakor, M. Y. (2023). Adopting ChatGPT to enhance educational experiences. International Journal of Information Technology & Computer Engineering, 3(05), 20-25. https://doi.org/10.55529/ijitc.35.20.25
Namatovu, A., & Kyambade, M. (2025). Leveraging AI in academia: University students’ adoption of ChatGPT for writing coursework (take home) assignments through the lens of UTAUT2. Cogent Education, 12(1), 2485522.
Owan, V. J., Mohammed, I. A., Bello, A., & Shittu, T. A. (2025). Higher education students’ ChatGPT use behavior: Structural equation modelling of contributing factors through a modified UTAUT model. Contemporary Educational Technology, 17(4), ep592. https://doi.org/10.30935/ cedtech/17243
Polyportis, A., & Pahos, N. (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. https://doi.org/10.1080/ 0144929x.2024.2317364
Rezvani, A., Dong, L., & Khosravi, P. (2017). Promoting the continuing usage of strategic information systems: The role of supervisory leadership in the successful implementation of enterprise systems. International Journal of Information Management, 37(5), 417-430. https://doi.org/10.1016/j.ijinfomgt.2017.04.008
Salloum, S. A., Al-Emran, M., Shaalan, K., & Tarhini, A. (2019). Factors affecting the E-learning acceptance: A case study from UAE. Education and Information Technologies, 24(1), 509-530. https://doi.org/10.1007/s10639-018-9786-3
Saxena, N., Gera, N., & Taneja, M. (2023). Factors influencing mobile banking adoption in India: The role of government support as a mediator. The Electronic Journal of Information Systems in Developing Countries, 89(6), e12287. https://doi.org/10.1002/isd2.12287
Sheehan, B., Jin, H. S., & Gottlieb, U. (2020). Customer service chatbots: Anthropomorphism and adoption. Journal of Business Research, 115, 14-24. https://doi.org/10.1016/j.jbusres.2020. 04.030
Shittu, T. A., & Taiwo, Y. H. (2023). Acceptance of WhatsApp social media platform for learning in Nigeria: A test of unified theory of acceptance and use of technology. Journal of Digital Educational Technology, 3(2), Article ep2309. https://doi.org/10.30935/jdet/13460
Siddiqui, T. M., Arifmiboy, N. S., Shuhidan, S. M., & Lokman, A. M. (2025). Factors driving ChatGPT adoption in higher education: A UTAUT-based analysis of student behavioural intentions. Asian Journal of University Education (AJUE), 21(2).
Soliman, M., Ali, R. A., Mahmud, I., & Noipom, T. (2025). Unlocking AI-powered tools adoption among university students: A fuzzy-set approach. Journal of Information and Communication Technology, 24(1), 1-28. https://doi.org/10.32890/jict2025.24.1.1
Strzelecki, A. (2024). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 32(9), 5142-5155. https://doi.org/10.1080/10494820.2023.2209881
Teo, L. (2025, December 25). UUM remains the top choice with over 4500 students enrolling for 2025/2026 academic session. https://uumtoday.com/featured-news/uum-remains-the-top-choice-with-over-4500-students-enrolling-for-2025-2026-academic-session/.
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. https://doi.org/10.2307/30036540
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, 157-178. https://doi.org/10.2307/41410412
Wang, T., Wang, D., Li, B., Ma, J., Pang, X. S., & Wang, P. (2023). The impact of anthropomorphism on ChatGPT actual use: Roles of interactivity, perceived enjoyment, and extraversion (SSRN Working Paper No. 4547430). SSRN. https://doi.org/10.2139/ssrn.4547430
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