UNDERSTANDING ONLINE LEARNING SATISFACTION AND CONTINUANCE INTENTION AMONG POSTGRADUATE MATERIALS SCIENCE STUDENTS AT PUBLIC UNIVERSITIES IN CHENGDU
Keywords:
Online Courses, Course Satisfaction, Continuance Intention, Postgraduate StudentsAbstract
This study investigated the impact of key factors on satisfaction and continuance intention toward online courses among postgraduate students in Materials Science at three public universities in Chengdu, China. A structured questionnaire was distributed, yielding 481 valid responses. Data were analyzed using Confirmatory Factor Analysis (CFA) to validate the measurement model, followed by Structural Equation Modeling (SEM) to test the hypothesized relationships. Results confirmed that all independent variables significantly influenced satisfaction, which in turn strongly predicted continuance intention. These findings support the robustness of the proposed model and offer actionable insights for improving online course design. The study concludes that enhancing learner satisfaction is essential for sustaining postgraduate engagement in digital learning environments and advancing institutional competitiveness in higher education.
References
Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99–110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002
Ali, W. (2020). Online and remote learning in higher education institutes: A necessity in light of COVID-19 pandemic. Higher Education Studies, 10(3), 16–25. https://doi.org/10.5539/hes.v10n3p16
Alkhawaja, M. I., Abd Halim, M. S., Abumandil, M. S. S., & Al-Adwan, A. S. (2022). System quality and student's acceptance of the e-learning system: The serial mediation of perceived usefulness and intention to use. Contemporary Educational Technology, 14(2), Article ep350. https://doi.org/10.30935/cedtech/11525
Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of the technology acceptance model in context of Yemen. Mediterranean Journal of Social Sciences, 6(4), 88–96.
Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50(2), 248–287. https://doi.org/10.1016/0749-5978(91)90022-L
Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
Bao, W. (2020). COVID-19 and online teaching in higher education: A case study of Peking University. Human Behavior and Emerging Technologies, 2(2), 113–115. https://doi.org/10.1002/hbe2.191
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. https://doi.org/10.2307/3250921
Chen, Y., Chen, N.-S., & Tsai, C.-C. (2018). Investigating MOOC continuance intention: The role of intrinsic motivation. Computers in Human Behavior, 85, 30–39. https://doi.org/10.1016/j.chb.2018.03.028
Chen, Y., & Wang, Y. (2022). Digital competence and online learning satisfaction: Evidence from Chinese postgraduate students. Computers & Education, 185, Article 104516. https://doi.org/10.1016/j.compedu.2022.104516
Cheng, Y.-M. (2019). A hybrid model for exploring the antecedents of cloud ERP continuance: Roles of quality determinants and task-technology fit. International Journal of Web Information Systems, 15(2), 215–235. https://doi.org/10.1108/IJWIS-07-2018-0056
Cheng, Y.-M. (2020). Continuance intention to use MOOCs: Integrating the technology acceptance model and expectation confirmation theory. British Journal of Educational Technology, 51(1), 330–356. https://doi.org/10.1111/bjet.12839
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
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748
Fianu, E., Blewett, C., Ampong, G. O. A., & Ofori, K. S. (2018). Factors affecting MOOC usage by students in selected Ghanaian universities. Education and Information Technologies, 23(2), 815–834. https://doi.org/10.1007/s10639-017-9635-9
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Hafifah, G. N., & Sulistyo, G. H. (2023). Teachers’ ICT literacy and ICT integration in ELT in the Indonesian higher education setting. Turkish Online Journal of Distance Education, 21(3), 186–198. https://doi.org/10.17718/tojde.983077
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). The difference between emergency remote teaching and online learning. Educause Review, 27, 1–12. https://er.educause.edu/articles/2020/3/the-difference-between-emergency-remote-teaching-and-online-learning
Islam, A. K. M. N., Mäntymäki, M., & Bhattacherjee, A. (2018). E-learning system use and its outcomes: Moderating role of perceived compatibility. Telematics and Informatics, 35(1), 342–354. https://doi.org/10.1016/j.tele.2017.12.015
Johnson, N., Veletsianos, G., & Seaman, J. (2021). US faculty and administrators’ experiences and approaches in the early weeks of the COVID-19 pandemic. Online Learning, 25(1), 6–21. https://doi.org/10.24059/olj.v25i1.2462
Liaw, S.-S. (2008). Investigating students' perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864–873. https://doi.org/10.1016/j.compedu.2007.09.005
Lin, W.-S., & Wang, C.-H. (2012). Antecedents to continued intentions of adopting e-learning system in blended learning instruction: A contingency framework based on models of information system success and task-technology fit. Computers & Education, 58(1), 88–99. https://doi.org/10.1016/j.compedu.2011.07.008
Liu, T., De Costa, F., & Yasing, M. A. I. (2024). Perceived usefulness as a mediating variable on the relationship between video quality and audience emotional engagement in social media. Studies in Media and Communication, 13(1), 125–135. https://doi.org/10.11114/smc.v13i1.7234
Liu, X., Zhang, Y., & Wang, R. (2023). Impact of online learning during the COVID-19 pandemic on student satisfaction: Evidence from Hubei Province, China. Frontiers in Education, 8, 1097910. https://doi.org/10.3389/feduc.2023.1097910
Logsdon, M. C., Kondaurova, I., Zhang, N., Das, S., Edwards, B. D., Mitchell, H., & Popa, D. O. (2024). Perceived usefulness of robotic technology for patient fall prevention. Workplace Health & Safety, 72(12), 542–549. https://doi.org/10.1177/21650799231188634
Ministry of Education. (2022). China's education modernization 2035 plan: Strategic priorities and developmental goals. Ministry of Education, People's Republic of China. Retrieved from http://en.moe.gov.cn/documents/reports/2022
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/002224378001700405
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, 43(3), 37–40. https://doi.org/10.1590/0101-60830000000088
Roca, J. C., Chiu, C.-M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies, 64(8), 683–696. https://doi.org/10.1016/j.ijhcs.2006.01.003
Sharma, G. P., Verma, R. C., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282–286. https://doi.org/10.1016/j.jfoodeng.2004.11.036
Shen, D., Cho, M.-H., Tsai, C.-L., & Marra, R. (2013). Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. Internet and Higher Education, 19, 10–17. https://doi.org/10.1016/j.iheduc.2013.04.001
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. In M. A. Lange (Ed.), Leading-edge psychological tests and testing research (pp. 27–50). Nova Science Publishers.
Singh, N., & Sharma, S. K. (2021). Examining the role of psychological factors in influencing smartphone addiction: A mediation model of loneliness and depression. Asian Journal of Psychiatry, 54, 102247. https://doi.org/10.1016/j.ajp.2020.102247
Sørebø, Ø., Halvari, H., Gulli, V. F., & Kristiansen, R. (2009). The role of self-determination theory in explaining teachers' motivation to continue to use e-learning technology. Computers & Education, 53(4), 1177–1187. https://doi.org/10.1016/j.compedu.2009.06.001
Tam, C., & Oliveira, T. (2016). Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective. Computers in Human Behavior, 61, 233–244. https://doi.org/10.1016/j.chb.2016.03.016
Tan, G. W. H., & Kim, S. S. (2015). The moderating role of trust in cloud computing adoption: A meta-analysis. Information & Management, 52(8), 1003–1017. https://doi.org/10.1016/j.im.2015.07.006
Teo, T. (2010). A path analysis of pre-service teachers’ attitudes to computer use: Applying and extending the Technology Acceptance Model in an educational context. Interactive Learning Environments, 18(1), 65–79. https://doi.org/10.1080/10494820802231327
Tseng, M.-L., & Lee, C.-W. (2018). A comprehensive framework for the success of information systems: The case of e-commerce. International Journal of Information Management, 38(1), 112–122. https://doi.org/10.1016/j.ijinfomgt.2017.07.006
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. https://doi.org/10.2307/30036540
Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information & Management, 43(6), 728–739. https://doi.org/10.1016/j.im.2006.05.002
Zhang, Y., Li, H., & Wang, X. (2023). Urban innovation ecosystems in western China: The case of Chengdu’s higher education clusters. Asia Pacific Education Review, 24(1), 51–64. https://doi.org/10.1007/s12564-023-09796-7
Zhou, J., & Li, H. (2024). Self-efficacy in online learning environments during COVID-19: A study of Chinese university students. BMC Medical Education, 24, 5890. https://doi.org/10.1186/s12909-024-05890-5
Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085–1091. https://doi.org/10.1016/j.dss.2012.10.034
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Sarasas Suvarnabhumi Institute of Technology
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The articles published are copyrighted by the Sarasas Journal of Humanities and Social Science. The opinions expressed in each article in this academic journal are those of the individual authors and do not reflect the views of Sarasas Suvarnabhumi Institute of Technology. The authors are solely responsible for all aspects of their respective articles. Any errors or inaccuracies in the articles are the sole responsibility of the authors.
