Influencing Factors of Postgraduates' Perceived Usefulness and Continuance Intention Towards Chinese University MOOCs: A Case Study of Chongqing University
Abstract
This study delves into the factors affecting postgraduates’ perceived usefulness (PU) and continuance intention (CI) to use Chinese University MOOCs, focusing on postgraduates from two engineering majors at Chongqing University, China. Rooted in Expectation - Confirmation Theory (ECM), S-O-R model and Technology Acceptance Model (TAM), the research examines seven variables: Perceived Ease of Use (PEU), Learning Engagement (LEN), Perceived Usefulness (PU), Satisfaction (SAT), Cognitive Presence (CP), Continuance Intention (CI) and Teaching Presence (TP). A quantitative approach was adopted, with 500 valid questionnaires collected. CFA (Confirmatory Factor Analysis) results demonstrated that the measurement model boasted high reliability and validity (AVE > 0.5, Cronbach’s Alpha > 0.7). SEM (Structural Equation Modeling) analysis supported all six hypotheses, revealing significant positive relationships. The findings highlight that both system-related factors (PEU, PU) and interaction-related factors (TP, CP, LEN, SAT) are critical to sustaining postgraduates’ MOOC usage.
References
Akyol, Z., & Garrison, D. R. (2008). The development of a community of inquiry over time in an online course: Understanding the progression and integration of social, cognitive and teaching presence. Journal of Asynchronous Learning Networks, 12, 3-22.
Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education, 80, 28-38.
Alshammari, S. H., & Babu, E. (2025). The mediating role of satisfaction in the relationship between perceived usefulness, perceived ease of use and students’ behavioural intention to use ChatGPT. Scientific Reports, 15(1), 7169.
Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351-370.
Blasco-Arcas, L., Buil, I., Hernández-Ortega, B., & Sese, F. J. (2013). Using clickers in class. The role of interactivity, active collaborative learning and engagement in learning performance. Computers & Education, 62, 102-110.
Breslow, L., Pritchard, D. E., DeBoer, J., Stump, G. S., Ho, A. D., & Seaton, D. T. (2013). Studying learning in the worldwide classroom: Research into edX’s first MOOC. Research & Practice in Assessment, 8, 13-25.
Bentler, P. M. (1990). Comparative Fit Indexes in Structural Models. Psychological Bulletin, 107, 238-246.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003.
Downes, S. (2008). Places to go: Connectivism & connective knowledge. Innovate: Journal of Online Education, 5(1), 6.
Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text - based environment: computer conferencing in higher education. The Internet and Higher Education, 2(2-3), 87-105. https://doi.org/10.1016/s1096-7516(00)00016-6
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2013). Multivariate data analysis: Pearson new international edition PDF eBook. Pearson Higher Ed.
Hew, K. F., & Cheung, W. S. (2014). Students’ and instructors’ use of massive open online courses (MOOCs): Motivations and challenges. Educational Research Review, 12, 45-58.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
Huang, H., & Qi, D. (2025). Is MOOC really effective? Exploring the outcomes of MOOC adoption and its influencing factors in a higher educational institution in China. PLOS ONE, 20(2), e0317701.
Jiang, Y., & Peng, J.-E. (2025). Exploring the relationships between learners’ engagement, autonomy, and academic performance in an English language MOOC. Computer Assisted Language Learning, 38(1-2), 71-96.
Joo, Y. J., So, H.-J., & Kim, N. H. (2018). Examination of relationships among students’ self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education, 122, 260-272.
Kanuka, H., & Garrison, D. R. (2004). Cognitive presence in online learning. Journal of Computing in Higher Education, 15(2), 21-39.
Kizilcec, R. F., Piech, C., & Schneider, E. (2017). Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. Proceedings of the Third (2017) ACM Conference on Learning@Scale, 177-186.
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
Law, K. M. Y., Geng, S., & Li, T. (2019). Student enrollment, motivation and learning performance in a blended learning environment: The mediating effects of social, teaching, and cognitive presence. Computers & Education, 136, 1-12.
Ma, L. (2025). Investigation of the impact of cognitive load on EFL learners’ satisfaction with MOOCs: the mediating role of expectation confirmation and perceived usefulness. BMC psychology, 13(1), 1-12.
Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. The MIT Press.
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences ofsatisfaction decisions. Journal of marketing research, 17(4),460-469.
Pedroso, R., Zanetello, L., Guimarães, L., Pettenon, M., Gonçalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry (São Paulo), 43(3), 37-40.
Putra, L. S., Kustandi, C., Wirasti, M. K., Maman, M., & Gunawan, T. B. (2025). Community of Inquiry Approach: The Meaning of Presence for MOOC Learning in Zoonoses Control Training. Edunity Kajian Ilmu Sosial Dan Pendidikan, 4(5), 213-222.
Rappaport, S. D. (2007). Lessons from online practice: new advertising models. Journalof Advertising Research, 47(2), 129-129.
Reich, J. (2020). Failure to disrupt: Why technology alone can’t transform education. Harvard University Press.
Reschly, A. L., & Christenson, S. L. (2022). Jingle-Jangle Revisited: History and Further Evolution of the Student Engagement Construct. In A. L. Reschly & S. L. Christenson (Eds.), Handbook of Research on Student Engagement (pp. 3-24). Springer International Publishing.
Shea, P., Sau Li, C., & Pickett, A. (2006). A study of teaching presence and student sense of learning community in fully online and web-enhanced college courses. The Internet and Higher Education, 9(3), 175-190.
Sica, A., & Ghisi, M. (2007). The analysis of univariate and multivariate non-normality in structural equations models. Computational Statistics & Data Analysis, 51 (4), 2118-232. https://doi.org/10.1016/j.csda.2006.01400
Soper, D. (2006). Calculator: a-priori sample size for structural equation models. Daniel Soper.
Stone, R. W., & Baker-Eveleth, L. (2013). Students’ expectation, confirmation, and continuance intention to use electronic textbooks. Computers in Human Behavior, 29(3), 984-990.
Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232.
