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An Integrative Model to Predict the Continuance Use of Electronic Learning Systems: Hints for Teaching

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Wu, J., Tsai, R.J., Chen, C.C. & Wu, Y. (2006). An Integrative Model to Predict the Continuance Use of Electronic Learning Systems: Hints for Teaching. International Journal on E-Learning, 5(2),. Chesapeake, VA: AACE.
Retrieved from http://www.editlib.org/p/5781.

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Journal Information

IJEL

International Journal on E-Learning
ISSN 1537-2456
Volume 5, Issue 2, April 2006
Association for the Advancement of Computing in Education (AACE)  Chesapeake, VA

More Information on IJEL

Table of Contents


Authors

Jiinpo Wu, Tamkang University, Taiwan; Ray J. Tsai, St. Cloud State University, USA; Charlie C. Chen, Appalachian State University, USA; Yachen Wu, Tamkang University, Taiwan

Abstract

There is an increased expectation about the usefulness of electronic learning (e-learning) to complement or substitute traditional face-to-face learning. However, the growth rate of the e-learning market has not properly reflected the high expectation. Researchers began to direct their attention to the assessment of e-learning effectiveness in order to solve the issue. However, little has been known about why some users stop adopting e-learning after their initial experience. Our work focuses on investigating the continuance usage problems in the field of information technology. A theoretical framework is proposed to address the continuance issue. This integrative framework makes three major contributions. First, it integrates the frameworks of computer self-efficacy (CSE) and the expectation-confirmation model (ECM). Second, it theorizes the causal relationship between the factors of perceived usefulness, confirmation, satisfaction, and information system (IS) continuance in the e-learning context. Finally, it explains users' online learning behaviors through a field survey. The results indicate that, in the context of learning conceptual knowledge in undergraduate education, there are significant relationships among the CSE of online learners, their perceived usefulness, confirmation, and satisfaction levels.

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