The Factors Impacting Junior College Students’ Satisfaction and Continuance Intention to Use MOOC Platform in Chengdu, China
Main Article Content
Abstract
Purpose: This article aimed to research factors impacting Junior college students' satisfaction and continuance intention to use Massive Open Online Course platforms in Chengdu, China. The conceptual framework presented cause-and-effect relationships between subjective norms, perceived usefulness, learning engagement, facilitating conditions, hedonic motivation, satisfaction, and continuance intention. Research design, data, and methodology: Descriptive and quantitative methods (n=450) were used to analyze the factors impacting Junior College Students' satisfaction and continuance intention in Chengdu, China. This study selected purposive sampling in the first stage, stratification random sampling, and convenience sampling were used in the second and third stages. Questionnaires are distributed online. Confirmatory factor analysis (CFA) and structural equation model (SEM) were used for data analysis, including model fitting analysis, reliability and validity testing, hypothesis testing, etc. Results: The results showed that subjective norms, Perceived usefulness, learning engagement, facilitating conditions, and hedonic motivation had a significant impact on satisfaction. Satisfaction had a significant impact on continuance intention. Conclusions: The study suggested that to make the National Training Programme more effective, policymakers and programmed operators could increase their investment in the factors that affect teacher performance and loyalty in the NTP and optimize the proportion of investment.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data, or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution License (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
References
Alalwan, A. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28-44. https://doi.org/10.1016/j.ijinfomgt.2019.04.008
Alalwan, A. A., Baabdullah, A. M., Rana, N. P., Tamilmani, K., & Dwivedi, Y. K. (2018). Examining adoption of mobile internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness, and trust. Technology in Society, 55, 100-110. https://doi.org/10.1016/j.techsoc.2018.06.007
Al-Rahmi, W. M., Othman, M. S., & Yusuf, L. M. (2015). Effect of engagement and collaborative learning on satisfaction through the use of social media on Malaysian higher education. Research Journal of Applied Sciences, Engineering and Technology, 9(12), 1132-1142.
Ariffin, S. K., Abd Rahman, M. F. R., Muhammad, A. M., & Zhang, Q. (2021). Understanding the consumer’s intention to use e-wallet services. Spanish Journal of Marketing-ESIC, 25(3), 446-461. https://doi.org/10.1108/sjme-07-2021-0138
Awang, Z. (2012). A Handbook on SEM: Structural Equation Modeling (4th ed). Universiti Teknologi MARA (UiTM) Press.
Bassiouni, D. H., Hackley, C., & Meshreki, H. (2019). The integration of video games in family-life dynamics: An adapted technology acceptance model of family intention to consume video games. Information Technology and People, 32(6), 1376-1396. https://doi.org/10.1108/itp-11-2017-0375
Bataineh, A. Q., Al-Abdallah, G. M., & Alkharabsheh, A. M. (2015). Determinants of continuance intention to use social networking sites (SNSs): Studying the case of Facebook. International Journal of Marketing Studies, 7(4), 121-135.
https://doi.org/10.5539/ijms.v7n4p121
Chan, F. K. Y., Thong, J. Y. L., Venkatesh, V., Brown, S. A., Hu, P. J. H., & Tam, K. Y. (2010). Modeling citizen satisfaction with mandatory adoption of an e-government technology. Journal of the Association for Information Systems, 11(10), 510-549.
Chen, C.-C., Lee, C.-H., & Hsiao, K.-L. (2018). Comparing the determinants of non-MOOC and MOOC continuance intention in Taiwan: Effects of interactivity and openness. Library Hi Tech, 36(4), 705-719. https://doi.org/10.1108/lht-11-2016-0129
Chen, S. C., & Li, S. H. (2010). Consumer adoption of e-service: Integrating technology readiness with the theory of planned behavior. African Journal of Business Management, 4(16), 3556-3563.
Cheng, Y. M. (2022). Which quality determinants cause MOOCs continuance intention? A hybrid extending the expectation-confirmation model with learning engagement and information systems success. Library Hi Tech, 2(3), 1-10.
Cheng, Y. M. (2023). Can media richness and interaction act as stimulants to medical professionals’ learning persistence in MOOCs via fostering learning engagement? Interactive Technology and Smart Education, 4(2), 30-89. https://doi.org/10.1108/ITSE-09-2022-0116
Daneji, A. A., Ayub, A. F. M., & Khambari, M. N. M. (2019). The effects of perceived usefulness, confirmation, and satisfaction on continuance intention in using massive open online courses (MOOCs). Knowledge Management & E-Learning, 11(2), 201-214.
Davis, D., Jivet, I., Kizilcec, R. F., Chen, G., Hauff, C., & Houben, G. J. (2017). Follow the successful crowd: Raising MOOC completion rates through social comparison at scale. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 454-463.
Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results [Doctoral dissertation]. MIT Sloan School of Management.
Fianu, E., Blewett, C., & Ampong, G. O. (2020). Toward the development of a model of student usage of MOOCs. Education + Training, 62(5), 521-541. https://doi.org/10.1108/et-11-2019-0262
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research (1st ed.). Addison-Wesley.
Franque, F. B., Oliveira, T., Tam, C., & Santini, F. D. O. (2021). A meta-analysis of the quantitative studies in continuance intention to use an information system. Internet Research, 31(1), 123-158. https://doi.org/10.1108/intr-03-2019-0103
Hadji, B., & Degoulet, P. (2016). Information system end-user satisfaction and continuance intention: A unified modeling approach. Journal of Biomedical Informatics, 61, 185-193. https://doi.org/10.1016/j.jbi.2016.03.021
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. https://doi.org/10.1016/j.edurev.2014.05.001
Hsu, M. H., & Chiu, C. M. (2004). Predicting electronic service continuance with a decomposed theory of planned behavior. Behaviour & Information Technology, 23(5), 359-373. https://doi.org/10.1080/01449290410001669969
Hu, P. J. H., & Hui, W. (2012). Examining the role of learning engagement in technology-mediated learning and its effects on learning effectiveness and satisfaction. Decision Support Systems, 53, 782-792. https://doi.org/10.1016/j.dss.2012.05.014
Jain, A., Sharma, P., & Meher, J. R. (2023). Effects of online platforms on learner's satisfaction: A serial mediation analysis with instructor presence and student engagement. The International Journal of Information and Learning Technology, 40(5), 453-466. https://doi.org/10.1108/ijilt-02-2023-0017
Jia, N., Li, W., Zhang, L., & Kong, F. (2022). Beneficial effects of hedonic and eudaimonic motivations on subjective well-being in adolescents: A two-wave cross-lagged analysis. The Journal of Positive Psychology, 17(5), 701-707.
https://doi.org/10.1080/17439760.2021.1913641
Jung, Y., & Lee, J. (2018). Learning engagement and persistence in massive open online courses (MOOCs). Computers & Education, 122, 9-22. https://doi.org/10.1016/j.compedu.2018.02.013
Kim, Y. H., Kim, D. J., & Wachter, K. (2013). A study of mobile user engagement (MoEN): Engagement motivations, perceived value, satisfaction, and continued engagement intention. Decision Support Systems, 56, 361-370. https://doi.org/10.1016/j.dss.2013.07.002
Kizilcec, R. F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. Proceedings of the Third International Conference on Learning Analytics and Knowledge, 170-179.
Larsen, T. J., Sørebø, A. M., & Sørebø, Ø. (2009). The role of task-technology fit as users’ motivation to continue information system use. Computers in Human Behavior, 25(3), 778-784. https://doi.org/10.1016/j.chb.2009.02.006
Lee, K. Y., Sheehan, L., Lee, K., & Chang, Y. (2021). The continuation and recommendation intention of artificial intelligence-based voice assistant systems (AIVAS): The influence of personal traits. Internet Research, 31(5), 1899-1939. https://doi.org/10.1108/intr-06-2020-0327
Lu, Y., Wang, B., & Lu, Y. (2019). Understanding key drivers of MOOC satisfaction and continuance intention to use. Journal of Electronic Commerce Research, 20(2), 1-10.
Marandu, E. E., Mathew, I. R., Svotwa, T. D., Machera, R. P., & Jaiyeoba, O. (2023). Predicting students' intention to continue online learning post-COVID-19 pandemic: Extension of the unified theory of acceptance and usage technology. Journal of Applied Research in Higher Education, 15(3), 681-697. https://doi.org/10.1108/jarhe-02-2022-0061
Masrani, S. A., Mohd Amin, M. R., Sivakumaran, V. M., & Piaralal, S. K. (2023). Important factors in measuring learners' satisfaction and continuance intention in open and distance learning (ODL) institutions. Higher Education, Skills and Work-Based Learning, 13(3), 587-608. https://doi.org/10.1108/heswbl-12-2022-0274
Muzammil, M., Sutawijaya, A., & Harsasi, M. (2020). Investigating student satisfaction in online learning: The role of student interaction and engagement in distance learning universities. Turkish Online Journal of Distance Education, 21, 88-96. https://doi.org/10.17718/tojde.770928
Rizvi, S., Rienties, B., & Khoja, S. A. (2019). The role of demographics in online learning: A decision tree-based approach. Computers & Education, 137, 32-47. https://doi.org/10.1016/j.compedu.2019.04.001
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
Shah, J., & Khanna, M. (2023). Determining the post-adoptive intention of millennials for MOOCs: An information systems perspective. Information Discovery and Delivery, 52(2), 243-260. https://doi.org/10.1108/idd-11-2022-0109
Singh, A., & Sharma, A. (2021). Acceptance of MOOCs as an alternative for internship for management students during COVID-19 pandemic: An Indian perspective. International Journal of Educational Management, 35(6), 1231-1244.
https://doi.org/10.1108/ijem-03-2021-0085
Smith, J. R., & McSweeney, A. (2007). Charitable giving: The effectiveness of a revised theory of planned behavior model in predicting donating intentions and behavior. Journal of Community and Applied Social Psychology, 17(5), 363-386. https://doi.org/10.1002/casp.906
Su, J., & Tong, X. (2021). Catching silver consumers in China: An integrated model of Chinese older adults’ use of social networking technology. Asia Pacific Journal of Marketing and Logistics, 33(9), 1903-1917. https://doi.org/10.1108/apjml-05-2020-0352
Sun, Y., Guo, Y., & Zhao, Y. (2020). Understanding the determinants of learner engagement in MOOCs: An adaptive structuration perspective. Computers & Education, 157, 1-37.
Taylor, S., & Todd, P. A. (1995). Assessing IT Usage: The Role of Prior Experience. MIS Quarterly, 19, 561-570. https://doi.org/10.2307/249633
Teo, T. (2009). The impact of subjective norm and facilitating conditions on pre-service teachers’ attitude toward computer use: A structural equation modeling of an extended technology acceptance model. Educational Computing Research, 40(1), 89-109. https://doi.org/10.2190/ec.40.1.d
Teo, T., & Wong, S. L. (2013). Modeling key drivers of e-learning satisfaction among student teachers. Educational Computing Research, 48(1), 71-95. https://doi.org/10.2190/ec.48.1.d
Van de Oudeweetering, K., & Agirdag, O. (2018). MOOCs as accelerators of social mobility? A systematic review. Journal of Educational Technology & Society, 21(1), 1-11.
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
Wang, J., Yang, Y., Li, H., & van Aalst, J. (2021). Continuing to teach in a time of crisis: The Chinese rural educational system’s response and student satisfaction and social and cognitive presence. British Journal of Educational Technology, 52(4), 1494-1512. https://doi.org/10.1111/bjet.13129
Wijaya, F., & Solikhatin, S. A. (2021). Analysis of end-user satisfaction of Zoom application for online lectures. 2021 3rd East Indonesia Conference on Computer and Information Technology, 348-353.
Yang, Q., & Lee, Y. C. (2021). The critical factors of student performance in MOOCs for sustainable education: A case of Chinese universities. Sustainability, 13(14), 8089.https://doi.org/10.3390/su13148089