Measuring Satisfaction and Behavioral Intention to Use Online Learning Among Junior Students in a Public University in Yunnan, China
DOI:
https://doi.org/10.14456/shserj.2025.5Keywords:
Online Learning, Task-Technology Fit, Content Quality, Satisfaction, Behavioral IntentionAbstract
Purpose: This study investigates factors impacting third-year student satisfaction and behavioral intention in a public university in Yunnan, China. The research model is built upon service quality, teacher quality, task-technology fit, learning material quality, perceived usefulness, satisfaction, and behavioral intention all have causal linkages. Research Design, Data, and Methods: The researchers sent questionnaires to junior students at four colleges of Yuxi Normal University using a quantitative approach (n=500). The researcher utilized judgmental, quota and convenience sampling to collect the data. Before data collection, the Item Objective Congruence (IOC) and Cronbach's alpha were utilized to guarantee reliability and validity. The data were analyzed using confirmatory factor analysis and structural equation modeling, which included model fit, reliability, and validity assessments. Results: It has been demonstrated that seven hypotheses satisfy the study's goals. Task-technology fit, teacher quality, and service quality all greatly impact perceived usefulness. The educational content's quality, perceived value, and satisfaction highly influence student behavioral intention. Conclusion: To raise students’ happiness and behavioral intention about online learning, school administrators and instructors should maintain a pleasant online learning environment, improve academic achievement, increase instructional care, and develop a favorable image of the school.
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Anderson, R. M., Heesterbeek, H., Klinkerberg, D., & Hollingsworth, T. D. (2020). How will country-based mitigation measures influence the course of the COVID-19 epidemic? The Lancet, 395(10228), 931-934. https://doi.org/10.1016/s0140-6736(20)30567-5
Arbaugh, J. B. (2000). Virtual classroom characteristics and student satisfaction with internet-based MBA. Journal of Management Education, 24(1), 32-54.
https://doi.org/10.1177/105256290002400104
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246. https://doi.org/10.1037/0033-2909.107.2.238
Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation confirmation model. MIS Quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921
Bokhari, R. H. (2001). User participation and user satisfaction in information systems development [Unpublished Doctoral Dissertation]. Brunel University, School of Information Systems, Computing and Mathematics.
Burns, J., Clift, J., & Duncan, J. (1990). Understanding of understanding: implications for learning and teaching. British Journal of Educational Psychology, 61, 276-289.
https://doi.org/10.1111/j.2044-8279.1991.tb00985.x
Cao, M., Zhang, Q., & Seydel, J. (2005). B2C e-commerce web site quality: An empirical examination. Industrial Management & Data Systems, 105(5), 645-661. https://doi.org/10.1108/02635570510600000
Cheng, Y.-M. (2012). Effects of quality antecedents on e-learning acceptance. Internet Research, 22(3), 361-390. https://doi.org/10.1108/10662241211235699
Cheng, Y.-M. (2014). Extending the expectation confirmation model with quality and flow to explore nurses continued blended e-learning intention. Information Technology & People, 27(3), 230-258. https://doi.org/10.1108/itp-01-2013-0024
Cheng, Y.-M. (2020). Students’ satisfaction and continuance intention of the cloud-based e-learning system: roles of interactivity and course quality factors. Education + Training, 62(9), 1037-1059. https://doi.org/10.1108/et-10-2019-0245
Cheng, Y.-M. (2021). Can tasks and learning be balanced? A dual-pathway model of cloud-based e-learning continuance intention and performance outcomes. Kybernetes, 51(1), 210-240. https://doi.org/10.1108/K-07-2020-0440
Comrey, A. L., & Lee, H. B. (2013). A first course in factor analysis (2nd ed.). Lawrence Erlbaum Associates.
Daultani, Y., Goswami, M., Kumar, A., & Pratap, S. (2021). Perceived outcomes of e-learning: identifying key attributes affecting user satisfaction in higher education institutes. measuring business excellence, 25(2), 216-229. https://doi.org/10.1108/mbe-07-2020-0110
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
Ellyana Dwi, D., Redy, A., & Hamzah, A. (2009). Variabel Anteseden dan Konsekuensi Pemanfaatan Sistem Informasi (Studi Kasus pada Pemerintahan Kabupaten Madura). Jurnal Akuntansi dan Keuangan Indonesia, 6(1), 71-88. https://doi.org/10.21002/jaki.2009.04
Feng, D., Xiang, C., Vongurai, R., & Pibulcharoensit, S. (2022). Investigation on Satisfaction and Performance of Online Education Among Fine Arts Major Undergraduates in Chengdu Public Universities. AU-GSB E-JOURNAL, 15(2), 169-177. https://doi.org/10.14456/augsbejr.2022.82
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
Fu, J., Shang, R.-A., Anand, J., Sun, Y., & Hu, F. (2020). Interaction between task characteristics and technology affordances Task-technology fit and enterprise social media usage. Journal of Enterprise Information Management, 33(1), 1-22. https://doi.org/10.1108/jeim-04-2019-0105
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236. https://doi.org/10.2307/249689
Gorla, N., Somers, T. M., & Wong, B. (2010). Organizational impact of system quality, information quality, and service quality. The Journal of Strategic Information Systems, 19(3), 207-228. https://doi.org/10.1016/j.jsis.2010.05.001
Hair, J. F., Babin, A., Money, A., & Samouel, P. (2003). Essentials of business research methods (4th ed.). John Wiley & Sons.
Hopwood, C. J., & Donnellan, M. B. (2010). How should the internal structure of personality inventories be evaluated? Personality and Social Psychology Review, 14(3), 332-346. https://doi.org/10.1177/1088868310361240
Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023). New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution. Sustainability, 15(16), 12451.
https://doi.org/10.3390/su151612451
Kao, R.-H., & Lin, C.-T. (2018). The usage intention of e-learning for police education and training. Policing: An International Journal, 41(1), 98-112. https://doi.org/10.1108/pijpsm-10-2016-0157
Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: an integrative view of four mechanisms underlying postadoption phenomena. Management Science, 51(5), 741-755.
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/10.1287/mnsc.1040.0326
Lee, Y.-C. (2006). An empirical investigation into factors influencing the adoption of an e-learning system. Online Information Review, 30(5), 517-541. https://doi.org/10.1108/14684520610706406
Lin, W. S., & Wang, C. H. (2012). Antecedence 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 and Education, 58(1), 88-99.
https://doi.org/10.1016/j.compedu.2011.07.008
Nunnally, J. C., & Bernstein, I. H. (1994). The Assessment of Reliability. Psychometric Theory, 3, 248-292.
Ozkan, S., & Koseler, R. (2009). Multi-dimensional students’ evaluation of e-learning systems in the higher education context: an empirical investigation. Computers & Education, 53(4), 1285-1296. https://doi.org/10.1016/j.compedu.2009.06.011
Özüdoğru, G. (2021). Problems faced in distance education during Covid-19 Pandemic. Participatory Educational Research (PER), 8(4), 321-333
Parasuraman, A., Zeithaml, V., & Berry, L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49(4), 41-50. https://doi.org/10.1177/002224298504900403
Rughoobur-Seetah, S. (2021). An evaluation of the impact of confinement on the quality of e-learning in higher education institutions Quality Assurance in Education (1st ed.). Emerald Publishing Limited.
Salimon, M., Mokhtar, M., Aliyu, A., & Perumal, S. (2021). Solving e-learning adoption intention puzzles among private universities in Nigeria: an empirical approach. Journal of Applied Research in Higher Education, 15(3), 613-631. https://doi.org/10.1108/jarhe-11-2020-0410
Samsudeen, S., & Mohamed, R. (2019). University students' intention to use e-learning systems: A study of higher educational institutions in Sri Lanka. Interactive Technology and Smart Education, 16(3), 219-238.
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.2005.02.010
Shneiderman, B. (2010). Designing the User Interface: strategies for Effective Human-computer Interaction (1st ed.). Pearson Education India.
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.
Straub, D. W. (1989). Validating instruments in MIS research. MIS Quarterly, 13(2), 147-169. https://doi.org/10.2307/248922
Tadesse, S., & Muluye, W. (2020). The Impact of COVID-19 Pandemic on Education System in Developing Countries: A Review. Open Journal of Social Sciences, 8, 159-170.
https://doi.org/10.4236/jss.2020.810011
Theresiawati, T., Seta, H. B., Hidayanto, A. N., & Abidin, Z. (2020). Variables affecting e-learning services quality in Indonesian higher education: Students’ perspectives. Journal of Information Technology Education, Research, 19, 259-286. https://doi.org/10.28945/4489.
Udo, G. J., Bagchi, K. K., & Kirs, P. J. (2011). Using SERVQUAL to assess the quality of e-learning experience. Computers in Human Behavior, 27(3), 1272-1283. https://doi.org/10.1016/j.chb.2011.01.009
Uppal, M. A., Ali, S., & Gulliver, S. R. (2018). Factors determining e‐learning service quality. British Journal of Educational Technology, 49(3), 412-426. https://doi.org/10.1111/bjet.12552
Wang, C., Xie, A., Wang, W., & Wu, H. (2020). Association between medical students’ prior experiences and perceptions of formal online education developed in response to COVID- 19: A cross-sectional study in China. BMJ Journals, 10(10), 041886. https://doi.org/10.1136/bmjopen-2020-041886
Wang, D., & Zhou, Y. (2022). The popularity of contradictory information about COVID-19 vaccine on social media in China. Computer Human Behavior, 134, 107320.
https://doi.org/10.1016/j.chb.2022.107320.
Wheaton, B., Muthen, B., Alwin, D. F., & Summers, G. (1997). Assessing reliability and stability in panel models. Sociological Methodology, 8(1), 84-136. https://doi.org/10.2307/270754.
Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information and Management, 43(6), 728-739. https://doi.org/10.1016/j.im.2006.05.002
Xu, F., Tian, M., & Xu, A. (2017). Understanding Chinese users’ switching behavior of cloud storage services. The Electronic Library, 35(2), 214-232. https://doi.org/10.1108/el-04-2016-0080
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