Factors Impacting College Student Satisfaction, Perceived Usefulness, and Continuance Intention with E-learning in Dezhou, China
DOI:
https://doi.org/10.14456/shserj.2024.18Keywords:
E-Learning, System Quality, Information Quality, Satisfaction, Continuance IntentionAbstract
Purpose: The study aims to identify significant factors impacting junior college students’ continuance intentions to use e-learning at a public university in Dezhou, China. The research model is constructed with key constructs: perceived ease of use, perceived usefulness, system quality, information quality, self-efficacy, satisfaction, and continuation intention. Research design, data, and methodology: The researcher applied a quantitative method by distributing an online questionnaire to 495 respondents who are junior college students in four majors at public institutions in Dezhou, China. The sampling techniques were applied in this study, including purposive, quota, and convenience sampling. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were used to determine the significant relationships and hypotheses testing results. Results: The findings demonstrate that satisfaction strongly influenced continuance intention. Information quality, perceived ease of use, system quality, and perceived usefulness significantly impact satisfaction. Perceived ease of use and self-efficacy has a significant impact on perceived usefulness. Conclusions: University administrators and teaching staff should pay attention to developing significant factors that encourage students to continue using e-learning more effectively. Educators should consider reforming future learning according to the findings of this research, which will help students acknowledge and recognize the effectiveness of online education.
References
Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication. MIS Quarterly, 16(2), 227. https://doi.org/10.2307/249577
Agarwal, B. (2021). Livelihoods in COVID times: Gendered perils and new pathways in India. World Development, 139, 105312.
Ahlemann, F. (2009). Towards a conceptual reference model for project management information systems. International Journal of Project Management, 27(1), 19-30. https://doi.org/10.1016/j.ijproman.2008.01.008
Bagozzi, R., & Yi, Y. (1988). On The Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 16(1), 74-94. https://doi.org/10.1007/BF02723327.
Chang, C. (2013). Exploring the determinants of e-learning systems continuance intention in academic libraries. Library Management, 34(1/2), 40-55. https://doi.org/10.1108/01435121311298261
Chen, C. C., & Tsai, J.-L. (2019). Determinants of behavioral intention to use the Personalized Location-based Mobile Tourism Application: An empirical study by integrating TAM with ISSM. Future Generation Computer Systems, 96, 628-638. https://doi.org/10.1016/j.future.2017.02.028
Cheng, Y. (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. (2018). What drives cloud ERP continuance? An integrated view. Journal of Enterprise Information Management, 31(5), 724-750. https://doi.org/10.1108/jeim-02-2018-0043
Cheng, Y. (2019). Quality antecedents and performance outcome of cloud-based hospital information system continuance intention. Journal of Enterprise Information Management, 33(3), 654-683. https://doi.org/10.1108/jeim-04-2019-0107
Cheng, Y. (2020). Understanding cloud ERP continuance intention and individual performance: a TTF-driven perspective. Benchmarking: An International, 27(4), 1591-1614. https://doi.org/10.1108/bij-05-2019-0208
Chiu, C., Wang, E., Shih, F., & Fan, Y. (2011). Understanding knowledge sharing in virtual communities. Online Information Review, 35(1), 134-153. https://doi.org/10.1108/14684521111113623
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
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. https://doi.org/10.1287/mnsc.35.8.982
Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information & Management, 36(1), 9-21. https://doi.org/10.1016/s0378-7206(98)00101-3
Dubey, P., & Sahu, K. K. (2023). Mediation analysis of students’ perceived benefits in predicting their satisfaction to technology-enhanced learning. Journal of Research in Innovative Teaching & Learning Emerald Publishing Limited, 16(1), 82-99. https://doi.org/10.1108/jrit-11-2021-0074
Edmund, S. C., Sammy, K. H., Flora, F. L., & Marina, W. Y. (2020). Self-Efficacy, Work Engagement, and Job Satisfaction Among Teaching Assistants in Hong Kong’s Inclusive Education. SAGE Open, 10(3), 215824402094100.
Eom, S. B. (2012). Effects of LMS, self-efficacy, and self-regulated learning on LMS effectiveness in business education. Journal of International Education in Business, 5(2), 129-144. https://doi.org/10.1108/18363261211281744
Fenech, T. (1998). Using perceived ease of use and perceived usefulness to predict acceptance of the World Wide Web. Computer Networks and ISDN Systems, 30(1-7), 629-630. https://doi.org/10.1016/s0169-7552(98)00028-2
Fokides, E. (2017). Greek Pre-service Teachers' Intentions to Use Computers as In-service Teachers. Contemporary Educational Technology, 8(1), 56-75. https://doi.org/10.30935/cedtech/6187
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Franque, F. B., Oliveira, T., Tam, C., & Santini, F. (2020). 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
Gefen, D., & Straub, D. W. (1997). Gender Differences in the Perception and Use of E-Mail: An Extension to the Technology Acceptance Model. MIS Quarterly, 21(4), 389. https://doi.org/10.2307/249720
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Prentice-Hall.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, L. R. (2006). Multivariant Data Analysis (6th ed.). Pearson International Edition.
Hsim, K. R., & Lin, C. (2017). The usage intention of e-learning for police education and training. Policing: An International Journal, 42(1), 98-112. https://doi.org/10.1108/pijpsm-10-2016-0157
Huang, Y., Pu, Y., Chen, T., & Chiu, P. (2015). Development and evaluation of the mobile library service system success model A case study of Taiwan. The Electronic Library, 33(6), 1174-1191. https://doi.org/10.1108/el-06-2014-0094
Hulland, J. (1999). Use Of Partial Least Squares (PLS) In Strategic Management Research: A Review of Four Recent Studies. Strategic Management Journal, 20(2), 195-204. https://doi.org/10.1002/(sici)10970266(199902)20:2<195::aid-smj13>3.0.co;2-7
Hussein, M. H., Ow, S. H., Ibrahim, I., & Mahmoud, M. A. (2021). Measuring instructors continued intention to reuse Google Classroom in Iraq: a mixed-method study during COVID-19. Interactive Technology and Smart Education,18(3), 380-402.
Igbaria, M. (1993). User acceptance of microcomputer technology: An empirical test. EconPapers, 21(1), 73-90.
Jiang, L., Liao, M., & Ying, R. (2020). The Relationship between Loneliness, Self-Efficacy, and Satisfaction with Life in Left-Behind Middle School Students in China: Taking Binhai County of Jiangsu Province as an Example. Best Evid Chin Edu, 6(2), 803-824. https://doi.org/10.15354/bece.20.or034
Lan, W., & Luo, J. (2022). Current Situation and Problems of Postgraduate Education: An Analysis based on the Survey Data of the Satisfaction with National Postgraduate Education in 2021. Journal of Graduate Education, 68(2), 72-80.
Ma, J. F., & Yang, F. (2016). TBX5 mutations contribute to early-onset atrial fibrillation in Chinese and Caucasians. Cardiovascular research, 109(3), 442-450.
Masrek, M. N., & Gaskin, J. E. (2016). Assessing users’ satisfaction with web digital library: the case of Universiti Teknologi MARA. The International Journal of Information and Learning Technology, 33(1), 36-56.
Moe, A., Pazzaglia, F., & Ronconi, L. (2010). When being able is not enough. The combined value of positive affect and self-efficacy for job satisfaction in teaching. Teaching and Teacher Education, 26(5), 1145-1153.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill, Inc.
Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, 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/10.1590/0101-60830000000081
Pour, M. J., Mesrabadi, J., & Asarian, M. (2021). Meta -analysis of the DeLone and McLean models in e-learning success: the moderating role of user type. Online Information Review, 7(1), 1468-4527.
Rattanaburi, K. (2021). Factors Influencing Actual Usage of Mobile Shopping Applications: Generation X And Y In Thailand. [Doctoral Dissertation]. Assumption University of Thailand.
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.
Tate, J. (2006). Teachers' self-efficacy beliefs as determinants of job satisfaction and students’ academic achievement: A study at the school level. Journal of School Psychology, 44(2006), 473-490. https://doi.org/10.1016/j.jsp.2006.09.001
Turker, Y., & Kahraman, U. (2021). School Climate and Self-Efficacy as Predictor of Job Satisfaction İş Doyumunun Yordayıcısı Olarak Okul İklimi ve Öz-Yeterlik. Journal of Theoretical Educational Science, 14(4), 548-569.
https://doi.org/10.30831/akukeg.901457
Venkatesh, V., & Davis, D. (2000). A Theoretical Extension of The Technology Acceptance Model: four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Vululleh, P. (2018). Determinants Of Students’ E-Learning Acceptance in Developing Countries: An Approach Based on Structural Equation Modeling (SEM). International Journal of Education and Development Using Information and Communication Technology, 14(1), 141- 151.
Zhang, L., & Qi, L. (2005). Integration of theoretical teaching and experiment in e-learning mode. Journal of social science of Jiamusi university, 23(4), 3-28.
Zhong, K., Feng, D., Yang, M., & Jaruwanakul, T. (2022). Determinants of Attitude, Satisfaction and Behavioral Intention of Online Learning Usage Among Students During COVID-19. AU-GSB E-JOURNAL, 15(2), 49-57. https://doi.org/10.14456/augsbejr.2022.71
