The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Examining the integrated influence of fairness and quality on learners’ satisfaction and Web‐based learning continuance intention
Abstract
Abstract. As with any other information system (IS), the success of Web‐based learning depends largely on user satisfaction and other factors that eventually increase users’ intentions to continue using the service (continuance intention). This research integrates the IS success model and fairness theory to construct a model for investigating the motivations behind learners’ intentions to continue using Web‐based learning. Our model theorizes that the three dimensions of quality (i.e. information, system and service) and the three dimensions of fairness (i.e. distributive, procedural and interactional) affect learners’ satisfaction. We also argue that satisfaction and the three dimensions of fairness will influence learners’ intention to continue using Web‐based learning. The hypothesized model is validated empirically using data collected from 289 learners of a Web‐based learning service. The results show that information quality, system quality, system use, distributive fairness and interactional fairness exhibit significant positive effects on satisfaction. Also, procedural fairness and satisfaction play significant roles in shaping learners’ intention to continue using Web‐based learning.
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Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions
Oliver, R. L. (1980) · Journal of Marketing Research · doi:10.1177/002224378001700405 · Cited by 14,494
The DeLone and McLean Model of Information Systems Success: A Ten-Year Update
(2003) · Journal of Management Information Systems · doi:10.1080/07421222.2003.11045748 · Cited by 11,791
Adams, J. S. (1965) · Advances in Experimental Social Psychology · doi:10.1016/s0065-2601(08)60108-2 · Cited by 10,818
Information Systems Success: The Quest for the Dependent Variable
DeLone, W. H., & McLean, E. R. (1992) · Information Systems Research · doi:10.1287/isre.3.1.60 · Cited by 10,697
Understanding Information Systems Continuance: An Expectation-Confirmation Model1
Bhattacherjee, A. (2001) · MIS Quarterly · doi:10.2307/3250921 · Cited by 8,767
The Social Psychology of Procedural Justice
Lind, E. A., & Tyler, T. R. (1988) · Critical Issues in Social Justice · doi:10.1007/978-1-4899-2115-4 · Cited by 6,585
Justice at the millennium: A meta-analytic review of 25 years of organizational justice research.
Colquitt, J. A., Conlon, D. E., Wesson, M. J., Porter, C. O. L. H., & Ng, K. Y. (2001) · Journal of Applied Psychology · doi:10.1037/0021-9010.86.3.425 · Cited by 5,942
Mathieson, K. (1991) · Information Systems Research · doi:10.1287/isre.2.3.173 · Cited by 4,216
Motivational determinants of risk-taking behavior.
Atkinson, J. W. (1957) · Psychological Review · doi:10.1037/h0043445 · Cited by 3,706
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Evaluating E-learning systems success: An empirical study
Al-Fraihat, D., Joy, M., Masa'deh, R., & Sinclair, J. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2019.08.004 · Cited by 1,350
What drives social commerce: The role of social support and relationship quality
Liang, T.-P., Ho, Y.-T., Li, Y.-W., & Turban, E. (2011) · International Journal of Electronic Commerce · doi:10.2753/jec1086-4415160204 · Cited by 1,254
The push, pull and mooring effects in virtual migration for social networking sites
Chang, I., Liu, C., & Chen, K. (2014) · Information Systems Journal · doi:10.1111/isj.12030 · Cited by 271
Can AI chatbots help retain customers? Impact of AI service quality on customer loyalty
Chen, Q., Lu, Y., Gong, Y., & Xiong, J. (2023) · Internet Research · doi:10.1108/intr-09-2021-0686 · Cited by 260
Teacher Training Institute, M., Cheok, M. L., Wong, S. L., & Faculty of Educational Studies, U. P. M. (2015) · International Journal of Instruction · doi:10.12973/iji.2015.816a · Cited by 127
Determinants of the management learning performance in ERP context
Costa, C. J., Aparicio, M., & Raposo, J. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e03689 · Cited by 55
Yi, Y. J., You, S., & Bae, B. J. (2016) · Library Hi Tech · doi:10.1108/lht-04-2016-0038 · Cited by 50
Metadata
- Authors
- Chiu, C., Chiu, C., & Chang, H.
- Venue
- Information Systems Journal
- Publisher
- Wiley
- Published
- 2007-06-06
- Volume
- 17
- Issue
- 3
- Pages
- 271-287
- ISSN
- 1350-1917 (ISSN-L)
- 1365-2575
- OpenAlex
- W2121538731
- Cited by
- 367
- References
- 15
- FWCI
- 19.58
- Type
- journal article
- Language
- en
- Open access
- closed
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