Student satisfaction with online support systems in digital higher education
Published: August 10, 2026
Abstract
This study identifies factors affecting student satisfaction with online support systems in digital higher education. Drawing on the Information Systems Success Model, the Technology Acceptance Model, and the Unified Theory of Acceptance and Use of Technology, the study examines five determinants: system quality, support and management service quality, effort expectancy, performance expectancy, and facilitating conditions. Data from 3,417 students were analyzed using descriptive statistics and PLS-SEM. The results show that performance expectancy has the strongest positive effect on satisfaction, followed by facilitating conditions, service quality, and system quality. Effort expectancy has no statistically significant independent effect. The model explains 94% of the variance in student satisfaction. The study recommends improving request-handling effectiveness, standardizing support procedures, strengthening service responsiveness, and enhancing the overall system experience.
Keywords
student satisfactiononline support servicesonline learningdigital higher educationPLS-SEM
References
1.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921
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
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
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based SEM. Journal of the Academy of Marketing Science, 43, 115-135. https://doi.org/10.1007/s11747-014-0403-8
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12-40.
8.
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
