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Expectation-Confirmation Model of Information System Continuance: A Meta-Analysis
Abstract:The expectation-confirmation model (ECM) is one of the most widely used models for evaluating information system continuance, and this model has been extended to other study backgrounds, or expanded with other theoretical perspectives. However, combining ECM with other theories or investigating the background problem may produce some disparities, thus generating inaccurate conclusions. Habit is considered to be an important factor that influences the user’s continuance behavior. This paper thus critically examines seven pairs of relationships from the original ECM and the habit variable. A meta-analysis was used to tackle the development of ECM research over the last 10 years from a range of journals and conference papers published in 2005–2014. Forty-six journal articles and 19 conference papers were selected for analysis. The results confirm our prediction that a high effect size for the seven pairs of relationships was obtained (ranging from r=0.386 to r=0.588). Furthermore, a meta-analytic structural equation modeling was performed to simultaneously test all relationships. The results show that habit had a significant positive effect on continuance intention at p<=0.05 and that the six other pairs of relationships were significant at p<0.10. Based on the findings, we refined our original research model and an alternative model was proposed for understanding and predicting information system continuance. Some theoretical implications are also discussed.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1125535Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1753
 A. Bhattacherjee, “Understanding information systems continuance: An expectation-confirmation model,” MIS Quarterly, vol. 25, no. 3, pp. 351-370, 2001.
 T. C. Lin, S. Wu, J.S. C. Hsu, and Y. C. Chou, “The integration of value-based adoption and expectation–confirmation models: An example of iptv continuance intention,” Decision Support Systems, vol. 54, no. 1, pp. 63-75, 2012.
 C.S. Lin, S. Wu, and R.J. Tsai, “Integrating perceived playfulness into expectation-confirmation model for web portal context,” Information & Management, vol. 42, no. 5, pp. 683-693, 2005.
 T.J. Larsen, A.M. Sørebø, and Ø. Sørebø, “The role of task-technology fit as users’ motivation to continue information system use,” Computers in Human Behavior, vol. 25, no. 3, pp. 778-784, 2009.
 L. Zhang, J. Zhu, and Q. Liu, “A meta-analysis of mobile commerce adoption and the moderating effect of culture,” Computers in Human Behavior, vol. 28, no. 5, pp. 1902-1911, 2012.
 G.V. Glass, “Primary, secondary and meta-analysis of research,” Educational Researcher, vol. 5, no. 10, pp. 3-8, 1976.
 R.L. Oliver, “A cognitive model of the antecedents and consequences of satisfaction decisions,” Journal of Marketing Research, vol. 17, no. 4, pp. 460-469, 1980.
 F.D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly vol. 13, no. 3, pp. 319-340, 1989.
 M. Limayem, S.G. Hirt, and C.M.K. Cheung, “How habit limits the predictive power of intention: The case of information systems continuance,” MIS Quarterly, vol. 31, no. 4, pp. 705-737, 2007.
 L.V. Hedges and I. Olkin, Statistical methods for meta-analysis. Orlando, FL: Academic Press. 1985, pp.
 R. Rosenthal, Meta-analytic procedures for social research. Beverly Hills: Sage Publications. pp.
 J.E. Hunter and F.L. Schmidt, Methods of meta-analysis: Correcting error and bias in research findings. Beverly Hills: Sage Publications. 1990, pp.
 J.F. Hemphill, “Interpreting the magnitudes of correlation coefficients,” American Psychologist, vol. 58, no. 1, pp. 78-79, 2003.
 M. Borenstein, L.V. Hedges, J.P.T. Higgins, and H.R. Rothstein, Introduction to meta-analysis. United Kingdom: John Wiley & Sons Inc. 2009, pp.
 M.W.L. Cheung, Meta-analysis: A structural equation modeling approach. United Kingdom: John Wiley & Sons, Ltd. 2015, pp.
 D.D. Bergh, et al., “Using meta-analytic structural equation modeling to advance strategic management research: Guidelines and an empirical illustration via the strategic leadership-performance relationship,” Strategic Management Journal, vol. 37, no. 3, pp. 477-497, 2016.
 D. Gefen, D. Straub, and M. C. Boudreau, “Structural equation modeling and regression: Guidelines for research practice,” Communications of the Association for Information Systems, vol. 4, no. 1, pp. 1-79, 2000.