Determinants of Undergraduate Student Satisfaction and Continuance Intention to Use E-learning in a Public University in Dezhou, China
DOI:
https://doi.org/10.14456/abacodijournal.2023.42Keywords:
E-Learning, System Quality, Information Quality, Satisfaction, Continuance IntentionAbstract
This study aims to examine the crucial factors that significantly influence college undergraduate students’ satisfaction and continuance intention to use E-Learning at a public university in Dezhou, China. The conceptual framework was developed from previous studies and finalized with key constructs: perceived ease of use, perceived usefulness, system quality, information quality, self-efficacy, satisfaction, and continuation intention. The target population is 493 undergraduates in four majors at a public college in Dezhou. The research applied a quantitative method using questionnaires distributed to the target group. The sampling techniques applied in this study include purposive, quota, and convenience sampling. The data were analyzed using confirmatory factor analysis (CFA) and structural equation modeling (SEM). 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. University managers and educators should focus on enhancing student satisfaction and continuance intention to use e-learning more effectively by improving information and system quality in their institutions.
References
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. (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). A hybrid model for exploring the antecedents of cloud ERP continuance. International Journal of Web Information Systems, 15(2), 215-235. https://doi.org/10.1108/ijwis-07-2018-0056
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
Cheng, Y. (2021). Drivers of physicians’ satisfaction and continuance intention toward the cloud-based hospital information system. Kybernetes, 50(2), 413-442. https://doi.org/10.1108/k-09-2019-0628
Chia, P. K., Kuen, Y. L., Hui, M. C., & Yu, T. (2020). Enhancing volunteers’ intention to engage in citizen science: the roles of self-efficacy, satisfaction, and science trust. Journal of Baltic Science Education, 19(2), 234-246.
Cooper, D. R., & Schindler, P. S. (2014). Business Research Methods (12th ed.). McGraw- Hill/Irwin.
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
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
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
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.
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
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.
Liu, J., Li, Q., & Wang, J. (2020). Influencing Factors of Online Office APP Users' Intention Based on UTAUT. Information Science, 38(9), 49-68.
Lu, J. (2022). Analysis of teaching strategy of electrical and electronic course based on Internet. technology of electronics, 51(7), 169-171.
Ma, Y. J., Gam, H. J., & Banning, J. (2017). Perceived ease of use and usefulness of sustainability labels on apparel products: application of the technology acceptance model. Fashion and Textiles, 4(1), 1-10.
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. https://doi.org/10.1108/ijilt-06-2015-0019
Mertens, D. M. (2015). Research and Evaluation in Education and Psychology (4th ed.). SAGE Publications.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
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
Rattanaburi, K. (2021). Factors Influencing Actual Usage of Mobile Shopping Applications: Generation X And Y In Thailand [Doctoral Dissertation]. Assumption University of Thailand.
Salkind, J. (2017). Exploring Research (9th ed.). Pearson Press.
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.
Tam, C., Loureiro, A., & Oliveira, T. (2020). The individual performance outcome behind e-commerce. Internet Research, 30(2), 439-462. https://doi.org/10.1108/intr-06-2018-0262
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, R. (2016). Positive Affect and Self-Efficacy as Mediators Between Personality and Life Satisfaction in Chinese College Freshmen. Journal of Happiness Studies, 17(5), 2007-2021. https://doi.org/10.1007/s10902-015-9682-0
