ECWAC 2011: Advanced Research on Electronic Commerce, Web Application, and Communication pp 359-364 | Cite as
The Impact of Individual Differences on E-Learning System Behavioral Intention
Abstract
This study investigated the impact of contingent variables on the relationship between four predictors and employees’ behavioral intention with e-learning. Seven hundred and twenty-two employees in online training and education were asked to answer questionnaires about their learning styles, perceptions of the quality of the proposed predictors and behavioral intention with e-learning systems. The results of analysis showed that three contingent variables, gender, job title and industry, significantly influenced the perceptions of predictors and employees’ behavioral intention with the e-learning system. This study also found a statistically significant moderating effect of two contingent variables, gender, job title and industry, on the relationship between predictors and e-learning system behavioral intention. The results suggest that a serious consideration of contingent variables is crucial for improving e-learning system behavioral intention. The implications of these results for the management of e-learning systems are discussed.
Keywords
e-learning system behavioral intention quality perceptionsPreview
Unable to display preview. Download preview PDF.
References
- 1.Lu, H.P., Chiou, M.J.: The impact of individual differences on e-learning system satisfaction: A contingency approach. British Journal of Educational Technology 41(2), 307–323 (2010)CrossRefGoogle Scholar
- 2.DeLone, W.D., McLean, E.R.: Information systems success: the quest for the dependent variable. Information Systems Research 3, 60–95 (1992)CrossRefGoogle Scholar
- 3.DeLone, W.D., McLean, E.R.: The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems 19, 9–30 (2003)Google Scholar
- 4.Wang, Y.S.: Assessment of learner satisfaction with asynchronous electronic learning systems. Information & Management 41, 75–86 (2003)CrossRefGoogle Scholar
- 5.Garland, D., Martin, B.N.: Do gender and learning style play a role in how online courses should be designed? Journal of Interactive Online Learning 4, 67–81 (2005)Google Scholar
- 6.Ong, C.S., Lai, J.Y.: Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior 22, 816–829 (2006)CrossRefGoogle Scholar
- 7.Lee, Y.K.: Understanding e-learning consumers: the moderating effects of gender and learner diversity. The Journal of American Academy of Business 11, 223–230 (2007)Google Scholar
- 8.Bagozzi, R.P., Yi, Y.: On the evaluation of structural equation models. Journal of the Academy of Marking Science 16, 74–94 (1988)CrossRefGoogle Scholar
- 9.McDonald, R.P., Ho, M.R.: Principles and practice in reporting structural equation analysis. Psychological Methods 7, 64–82 (2002)CrossRefGoogle Scholar