Determinants of Undergraduate Students' Attitude and Intention to Use AI Chatbots in a Private University at Chengdu, China

Authors

  • Xiaoyu Zhao

Keywords:

Perveived usefulness, perveived ease of use, attitude, performance expectancy, social influence, behavioral intention, AI chatbots

Abstract

This research investigates the key determinants affecting undergraduates’ willingness to adopt and continue engaging with artificial intelligence (AI) chatbots used for academic advising, course information, and campus service guidance in China’s private universities. Against the backdrop of accelerated higher education digitalization and national policies such as the Education Informatization 2.0 Plan and the Digital China Strategy, chatbots have gained prominence as tools for academic assistance and campus services. However, despite their growing presence, their diffusion and acceptance within private institutions remain underexplored. A survey was conducted with 500 undergraduate students enrolled at Geely University of China in Chengdu. Structural equation modeling (SEM) was applied to test six proposed paths, and factor analysis verified the stability, reliability, and validity of the measurement model. Findings reveal that perceived usefulness and perceived ease of use strongly influence students’ attitudes, while social influence and trust provide supplementary effects. Moreover, perceived usefulness was identified as the key driver of attitude, which subsequently served as the most influential predictor of students’ behavioral intention to continue adopting chatbots. In addition, attitudes functioned as a mediating mechanism, converting initial perceptions into long-term usage intentions. The findings underscore the necessity of tailoring chatbot functionalities to academic requirements, strengthening trust mechanisms and data governance, and fostering teacher and peer support to enhance engagement. Overall, the study offers practical insights into promoting equitable access to intelligent learning technologies and optimizing the allocation of higher education resources in the digital era.

References

Adamopoulou, E., & Moussiades, L. (2020). An overview of chatbot technology. In I. Maglogiannis, L. Iliadis, & E. Pimenidis (Eds.), IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2020) (pp. 373-383). Springer.

Ajzen, I. (1991). The theory of planned behaviour. Organizational Behavior and Human Decision Processes, 50(2), 179-211.

Allen, N. J., & Meyer, J. P. (1990). The measurement and antecedents of affective, continuance, and normative commitment to the organization. Journal of Occupational Psychology, 63(1), 1-18.

Al-Mamary, Y. H., Shamsuddin, A., & Aziati, N. (2015). Investigating the key factors influencing on management information systems adoption among telecommunication companies in Yemen: The conceptual framework development. International Journal of Energy, Information and Communications, 6(1), 59-68.

Argote, L., & Miron-Spektor, E. (2011). Organizational learning: From experience to knowledge. Organization Science, 22(5), 1123-1137.

Awang, Z. (2012). A handbook on SEM: Structural equation modeling using AMOS Graphics. Universiti Teknologi MARA Press.

Bass, B. M. (1985). Leadership and performance beyond expectations. Free press.

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246.

Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370.

Black, W. C., & Babin, B. J. (2019). Multivariate data analysis: Its approach, evolution, and impact. In M. C. Matthews & A. P. Scherer (Eds.), The great facilitator: Reflections on the contributions of Joseph F. Hair Jr. to marketing and business research (pp. 121-130). Springer.

Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9(2), 233-255.

Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211.

Davenport, T. H., & Prusak, L. (1998). Working knowledge: How organizations manage what they know. Harvard Business Press.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.

DeVellis, R. F., & Thorpe, C. T. (2021). Scale development: Theory and applications. SAGE Publications.

Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., & Krishen, A. S. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168.

Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.

Fuller, C. M., Simmering, M. J., Atinc, G., Atinc, Y., & Babin, B. J. (2016). Common methods variance detection in business research. Journal of Business Research, 69(8), 3192-3198.

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Pearson Prentice Hall.

Hargittai, E. (2005). Survey measures of web-oriented digital literacy. Social Science Computer Review, 23(3), 371-379.

Ho Cheong, J., & Park, M. C. (2005). Mobile internet acceptance in Korea. Internet Research, 15(2), 125-140.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.

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.

King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755.

Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford publications.

Lohr, S. L. (2021). Sampling: Design and Analysis (3rd ed.). Chapman and Hall/CRC.

McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334-359.

Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20-38.

Okonkwo, C. W., & Ade-Ibijola, A. (2021). Chatbots applications in education: A systematic review. Computers and Education: Artificial Intelligence, 2, 100033.

Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533-544.

Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS Quarterly, 30(1), 115-143.

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903.

Polit, D. F., & Beck, C. T. (2006). The content validity index: Are you sure you know what’s being reported? Critique and recommendations. Research in Nursing & Health, 29(5), 489-497.

Raman, A., & Don, Y. (2013). Preservice teachers’ acceptance of learning management software: An application of the UTAUT2 model. International Education Studies, 6(7), 157-164.

Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. Journal of Behavior Therapy and Experimental Psychiatry, 38(4), 309-322.

Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics (5th ed.). Pearson.

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176.

Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125-143.

Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.

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.

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.

Winkler, R., & Söllner, M. (2018). Unleashing the potential of chatbots in education: A state-of-the-art analysis. Academy of Management Proceedings, 2018(1), 15903.

Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85-102.

Wu, J.-H., & Wang, Y.-M. (2006). Measuring KMS success: A respecification of the DeLone and McLean's model. Information & Manag

Downloads

Published

2026-05-09

How to Cite

Zhao, X. (2026). Determinants of Undergraduate Students’ Attitude and Intention to Use AI Chatbots in a Private University at Chengdu, China . ABAC ODI JOURNAL Vision. Action. Outcome, 14(1), 128-146. Retrieved from https://assumptionjournal.au.edu/index.php/odijournal/article/view/9590