Expectation–Confirmation Theory in Information System Research: A Review and Analysis

Chapter
Part of the Integrated Series in Information Systems book series (ISIS, volume 28)

Abstract

Understanding the antecedents and their effects on satisfaction is crucial, especially in consumer marketing. Most investigations in marketing research have used the Expectation–Confirmation Theory (ECT) which is used by the IS researchers too, with a few modifications and have taken the name Expectation–Confirmation Model (ECM). ECM is broadly applied to examine the continuance intention of IS users rather than just to explain satisfaction. Though the name of the model still contains expectation but practically the pre-consumption expectation is replaced by post-consumption expectations, namely, perceived usefulness which is believed to contribute a more meaningful dimension to theory. In IS research, though the dependent variable, continuance usage intention, is quite consistent but the independent variables, logically, are multi-varied as they are considered from contextual perspectives. Consequently, there is no general agreement concerning the definition, relationship, and measurement methods of the constructs neither in ECT nor in ECM. This chapter, therefore, tries to provide a comprehensive and systematic review of the literature pertaining to “expectation–confirmation” issues in order to observe current trends, ascertain the current “state of play,” and to promising lines of inquiry. Findings of this study suggest that positivist and empirical research is predominantly used with most of the samples being university students. Besides, technology acceptance model (TAM) and theory of planned behavior (TPB) are also integrated with ECT and ECM to have a better understanding of consumer behavior. The trend toward integrating and/or incorporating associated variables and constructs from various theories to ECM has a better fit in related areas of applications. Moreover, active researches are highly concentrated in USA, Hong Kong, and Taiwan. Finally, this study proposes research implications for the future.

Keywords

Expectation Confirmation Performance Satisfaction Continuance intention 

Abbreviations

CS

Consumer satisfaction

DSS

Decision support system

ECM

Expectation–confirmation model

ECT

Expectation–confirmation theory

EDT

Expectation–disconfirmation theory

GPS

Global positioning system

GSS

Group support system

IDT

Innovation diffusion theory

IS

Information system

IT

Information technology

PBC

Perceived behavioral control

RFID

Radio frequency identification

TAM

Technology acceptance model

TPB

Theory of planned behavior

References

  1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.CrossRefGoogle Scholar
  2. Anderson, E. W., & Sullivan, M. W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing Science, 12(2), 125–143.CrossRefGoogle Scholar
  3. Au, Y. A., & Kauffman, R. J. (2003). What do you know? Rational expectations in information technology adoption and investment. Journal of Management Information Systems, 20(2), 49–76.CrossRefGoogle Scholar
  4. Au, N., Ngai, E., et al. (2002). A critical review of end-user information system satisfaction research and a new research framework. Omega: The International Journal of Management Science, 30(6), 451–478.CrossRefGoogle Scholar
  5. Bhattacherjee, A. (2001a). Understanding information systems continuance: An expectation–­confirmation model. MIS Quarterly, 25(3), 351–370.CrossRefGoogle Scholar
  6. Bhattacherjee, A. (2001b). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201–214.CrossRefGoogle Scholar
  7. Bhattacherjee, A., Perlos, J., et al. (2008). Information technology continuance: A theoretical extension and empirical test. The Journal of Computer Information Systems, 49(1), 17–26.Google Scholar
  8. Bhattacherjee, A., & Premkumar, G. (2004). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly, 28(2), 229–254.Google Scholar
  9. Boulding, W., Lee, E., et al. (1994). Mastering the mix: Do advertising, promotion, and sales force activities lead to differentiation?. Journal of Marketing Research, 31(2), 159–172.CrossRefGoogle Scholar
  10. Brady, M. K., & Cronin, J. J. (2001). Some new thoughts on conceptualizing perceived service quality: A hierarchical approach. Journal of Marketing, 65(3), 34–49.CrossRefGoogle Scholar
  11. Brown, S., Venkatesh, V., et al. (2008). Expectation confirmation: An examination of three competing models. Organizational Behavior and Human Decision Processes, 105(1), 52–66.CrossRefGoogle Scholar
  12. Chen, I. Y. L. (2007). The factors influencing members’ continuance intentions in professional virtual communities – A longitudinal study. Journal of Information Science, 33(4), 451–467.CrossRefGoogle Scholar
  13. Cheung, C., Chan, G., et al. (2005). A critical review of online consumer behavior. Journal of Electronic Commerce in Organizations, 3(4), 1–19.CrossRefGoogle Scholar
  14. Chin, W. W., & Lee. M. K. O. (2000). On the formation of end-user computing satisfaction: A proposed model and measurement instrument. In W. Orlikowski, S. Ang, P. Weill, H. C. Krcmar, & J. I. DeGross (Eds.), Proceedings of the Twenty-First International Conference on Information Systems (pp. 553–563). Brisbane: Australia.Google Scholar
  15. Chiu, C., Hsu, M., et al. (2005). Usability, quality, value and e-learning continuance decisions. Computers & Education, 45(4), 399–416.CrossRefGoogle Scholar
  16. Churchill, J. G. A., & Surprenant, C. (1982). An investigation into the determinants of customer satisfaction. Journal of Marketing Research, 19(4), 491–504.CrossRefGoogle Scholar
  17. Dabholkar, P. A., Shepherd, C. D., et al. (2000). A comprehensive framework for service quality: An investigation of critical conceptual and measurement issues through a longitudinal study. Journal of Retailing, 76(2), 139–173.CrossRefGoogle Scholar
  18. Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339.CrossRefGoogle Scholar
  19. Doong, H. S., & Lai, H. (2008). Exploring usage continuance of e-negotiation systems: Expectation and disconfirmation approach. Group Decision and Negotiation, 17(2), 111–126.CrossRefGoogle Scholar
  20. Erevelles, S., & Leavitt, C. (1992). A comparison of current models of consumer satisfaction/­dissatisfaction. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 5(10), 104–114.Google Scholar
  21. Erevelles, S., Srinivasan, S., et al. (2003). Consumer satisfaction for internet service providers: An analysis of underlying processes. Information Technology and Management, 4(1), 69–89.CrossRefGoogle Scholar
  22. Gilly, M. C., & Gelb, B. (1982). Post-purchase consumer processes and the complaining consumer. The Journal of Consumer Research, 9(3), 323–328.CrossRefGoogle Scholar
  23. Ha, H. Y. (2006). An integrative model of consumer satisfaction in the context of e-services. International Journal of Consumer Studies, 30(2), 137–149.CrossRefGoogle Scholar
  24. Hayashi, A., Chen, C., et al. (2004). The role of social presence and moderating role of computer self efficacy in predicting the continuance usage of e-learning systems. Journal of Information Systems Education, 15(2), 139–154.Google Scholar
  25. Hong, S., Thong, J., et al. (2006). Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet. Decision Support Systems, 42(3), 1819–1834.CrossRefGoogle Scholar
  26. Hossain, M., & Quaddus, M. (2010a). The impact of external environmental factors on RFID adoption in Australian livestock industry: An exploratory study. In: Proceedings of 14th Pacific Asia conference on information systems, Taipei, Taiwan.Google Scholar
  27. Hossain, M. A., & Quaddus, M. (2010b). An adoption diffusion model of RFID-based livestock management system in Australia. In J. Pries-Heje, J. J. Venable, D. Bunker, & N. L. Russo (Eds.), Human benefit through the diffusion of information systems design science research (pp. 179–191). Boston: Springer.CrossRefGoogle Scholar
  28. Hsieh, C.-C., Kuo, P.-L., et al. (2010). Assessing blog-user satisfaction using the expectation and disconfirmation approach. Computers in Human Behavior, 26, 1434–1444.CrossRefGoogle Scholar
  29. Hsu, M., Chiu, C., et al. (2004). Determinants of continued use of the WWW: An integration of two theoretical models. Industrial Management and Data Systems, 104(9), 766–775.CrossRefGoogle Scholar
  30. Hsu, M., Yen, C., et al. (2006). A longitudinal investigation of continued online shopping behavior: An extension of the theory of planned behavior. International Journal of Human Computer Studies, 64(9), 889–904.CrossRefGoogle Scholar
  31. Hunt, H. K. (1977). CS/D-overview and future research direction. In H. K. Hunt (Ed.), Conceptualization and measurement of consumer satisfaction and dissatisfaction. Cambridge: Marketing Science Institute.Google Scholar
  32. Irving, P., & Meyer, J. (1999). On using residual difference scores in the measurement of congruence: The case of met expectations research. Personnel Psychology, 52(1), 85–95.CrossRefGoogle Scholar
  33. Jin, X.-L., Cheung, C. M. K., et al. (2008). User information satisfaction with a knowledge-based virtual community: An empirical investigation. Emerging Technologies and Information Systems for the Knowledge Society, 5288/2008, 123–130.CrossRefGoogle Scholar
  34. Johnson, M. D., Anderson, E. W., et al. (1995). Rational and adaptive performance expectations in a customer satisfaction framework. The Journal of Consumer Research, 21(4), 695–707.CrossRefGoogle Scholar
  35. Kang, Y., Hong, S., et al. (2009). Exploring continued online service usage behavior: The roles of self-image congruity and regret. Computers in Human Behavior, 25(1), 111–122.CrossRefGoogle Scholar
  36. Khalifa, M., & Liu, V. (2003). Determinants of satisfaction at different adoption stages of internet-based services. Journal of the Association for Information Systems, 4(5), 206–232.Google Scholar
  37. Khalifa, M., & Liu, V. (2004). The state of research on information system satisfaction. Journal of Information Technology Theory and Application, 5(4), 37–49.Google Scholar
  38. Kim, B. (2010). An empirical investigation of mobile data service continuance: Incorporating the theory of planned behavior into the expectation–confirmation theory. Expert Systems with Applications, 37, 7033–7039.CrossRefGoogle Scholar
  39. Kivela, J., Inbakaran, R., et al. (1999). Consumer research in the restaurant environment, Part 1: A conceptual model of dining satisfaction and return patronage. International Journal of Contemporary Hospitality Management, 11(5), 205–222.CrossRefGoogle Scholar
  40. Lankton, N. K., Wilson, E. V., et al. (2010). Antecedents and determinants of information technology habit. Information Management, 47(5–6), 300–307.CrossRefGoogle Scholar
  41. Liao, C., Chen, J., et al. (2007). Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model. Computers in Human Behavior, 23(6), 2804–2822.CrossRefGoogle Scholar
  42. Liao, C., Palvia, P., et al. (2009). Information technology adoption behavior life cycle: Toward a technology continuance theory (TCT). International Journal of Information Management, 29(4), 309–320.CrossRefGoogle Scholar
  43. Limayem, M., & Cheung, C. (2008). Understanding information systems continuance: The case of internet-based learning technologies. Information Management, 45(4), 227–232.CrossRefGoogle Scholar
  44. Limayem, M., Hirt, S., et al. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705–737.Google Scholar
  45. Lin, C., Wu, S., et al. (2005). Integrating perceived playfulness into expectation–confirmation model for web portal context. Information Management, 42, 683–693.CrossRefGoogle Scholar
  46. Locke, E. (1976). The nature and causes of job satisfaction. In M. D. Dunnette & L. M. Hough (Eds.), Handbook of industrial and organizational psychology (pp. 1297–1349). Palo Alto: Consulting Psychologists Press.Google Scholar
  47. Mahmood, M. A., Burn, J. M., et al. (2000). Variables affecting information technology end-user satisfaction: A meta-analysis of the empirical literature. International Journal of Human Computer Studies, 52(4), 751–771.CrossRefGoogle Scholar
  48. Mathieson, K., Peacock, E., et al. (2001). Extending the technology acceptance model: The influence of perceived user resources. Data base, 32(3), 86.Google Scholar
  49. McKinney, V., Yoon, K., et al. (2002). The measurement of web-customer satisfaction: An expectation and disconfirmation approach. Information Systems Research, 13(3), 296–315.CrossRefGoogle Scholar
  50. Nevo, D., & Wade, M. R. (2007). How to avoid disappointment by design. Communications of the ACM, 50(4), 43–48.CrossRefGoogle Scholar
  51. Oliver, R. L. (1980). A cognitive model for the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17, 460–469.CrossRefGoogle Scholar
  52. Oliver, R. L. (1981). Measurement and evaluation of satisfaction process in retail settings. Journal of Retailing, 57(Fall), 25–48.Google Scholar
  53. Oliver, R. L. (1993). Cognitive, affective, and attribute bases of the satisfaction response. Journal of Consumer Research, 20(3), 418–430.CrossRefGoogle Scholar
  54. Oliver, R. (1999). Whence consumer loyalty? The Journal of Marketing, 63, 33–44.CrossRefGoogle Scholar
  55. Oliver, R., & Burke, R. (1999). Expectation processes in satisfaction formation: A field study. Journal of Service Research, 1(3), 196–214.CrossRefGoogle Scholar
  56. Oliver, R. L., & DeSarbo, W. S. (1988). Response determinants in satisfaction judgments. Journal of Consumer Research, 14(4), 495–507.CrossRefGoogle Scholar
  57. Oliver, R., & Linda, G. (1981). Effect of satisfaction and its antecedents on consumer preference and intention. Advances in Consumer Research, 8, 88–93.Google Scholar
  58. Oliver, R. L., & Winer, R. S. (1987). A framework for the formation and structure of consumer expectations: Review and propositions. Journal of Economic Psychology, 8(4), 469–499.CrossRefGoogle Scholar
  59. Olson, J. C., Phillip, P., et al. (1979). Disconfirmation of consumer expectations through product trial. The Journal of Applied Psychology, 64(2), 179.CrossRefGoogle Scholar
  60. Parthasarathy, M., & Bhattacherjee, A. (1998). Understanding post-adoption behavior in the context of online services. Information Systems Research, 9(4), 362–379.CrossRefGoogle Scholar
  61. Patterson, P. G., & Spreng, R. A. (1997). Modeling the relationship between perceived value, satisfaction and repurchase intentions in a business-to-business service context: An empirical examination. International Journal of Service Industry Management, 8(5), 414–434.CrossRefGoogle Scholar
  62. Petter, S. (2008). Managing user expectations on software projects: Lessons from the trenches. International Journal of Project Management, 26(7), 700–712.CrossRefGoogle Scholar
  63. Premkumar, G., & Bhattacherjee, A. (2008). Explaining information technology usage: A test of competing models. Omega: The International Journal of Management Science, 36(1), 64–75.CrossRefGoogle Scholar
  64. Qin, M. (2007). Consumer behavior towards continued use of online shopping: An extend expectation disconfirmation model (Integration and Innovation Orient to E-Society, Vol. 1, pp. 400–407). Boston: Springer. 251/2007.Google Scholar
  65. Reichheld, F. F. (1993). Loyalty-based management. Harvard Business Review, 71(2), 64–73.Google Scholar
  66. Reisig, M. D., & Chandek, M. S. (2001). The effects of expectancy disconfirmation on outcome satisfaction in police–citizen encounters. Policing: An International Journal of Police Strategies & Management, 24(1), 88–99.CrossRefGoogle Scholar
  67. Roca, J., Chiu, C., et al. (2006). Understanding e-learning continuance intention: An extension of the technology acceptance model. International Journal of Human Computer Studies, 64(8), 683–696.CrossRefGoogle Scholar
  68. Rogers, E. M. (1995). Diffusion of innovation. New York: Free Press.Google Scholar
  69. Rogers, H., Peyton, R., et al. (1992). Measurement and evaluation of satisfaction processes in a dyadic setting. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 5, 12–23.Google Scholar
  70. Ryzin, G. V. (2004). Expectations, performance, and citizen satisfaction with urban services. Journal of Policy Analysis and Management, 23(3), 433–448.CrossRefGoogle Scholar
  71. Shankar, V., Smith, A., et al. (2003). Customer satisfaction and loyalty in online and offline ­environments. International Journal of Research in Marketing, 20(2), 153–175.Google Scholar
  72. Sørebø, Ø., & Eikebrokk, T. (2008). Explaining IS continuance in environments where usage is mandatory. Computers in Human Behavior, 24(5), 2357–2371.CrossRefGoogle Scholar
  73. Spreng, R. A., MacKenzie, S. B., et al. (1996). A reexamination of the determinants of consumer satisfaction. Journal of Marketing, 60, 15–32.CrossRefGoogle Scholar
  74. Stangor, C., & Ford, T. E. (1992). Accuracy and expectancy-confirming processing orientations and the development of stereotypes and prejudice. European Review of Social Psychology, 3(1), 57–89.CrossRefGoogle Scholar
  75. Staples, D., Wong, I., et al. (2002). Having expectations of information systems benefits that match received benefits: Does it really matter? Information Management, 40(2), 115–131.CrossRefGoogle Scholar
  76. Straub, D. W. (1994). The effect of culture on IT diffusion: E-Mail and FAX in Japan and the US. Information Systems Research, 5(1), 23–47.CrossRefGoogle Scholar
  77. Suh, K., Kim, S., et al. (1994). End-user’s disconfirmed expectations and the success of information systems. Information Resources Management Journal, 7(4), 30–39.Google Scholar
  78. Susarla, A., Barua, A., et al. (2003). Understanding the service component of application service provision: An empirical analysis of satisfaction with ASP services. MIS Quarterly, 27(1), 91–123.Google Scholar
  79. Swan, J. E., & Oliver, R. (1991). An applied analysis of buyer equity perceptions and satisfaction with automobile salespeople. The Journal of Personal Selling & Sales Management, 11(2), 15–26.Google Scholar
  80. Swan, J. E., & Trawick, I. F. (1981). Disconfirmation of expectations and satisfaction with a retail service. Journal of Retailing, 57, 49–67.Google Scholar
  81. Szymanski, D. M., & Henard, D. (2001). Customer satisfaction: A meta-analysis of the empirical evidence. Journal of the Academy of Marketing Science, 29(1), 16–35.Google Scholar
  82. Tesch, D., Jiang, J. J., et al. (2003). The impact of information system personnel skill discrepancies on stakeholder satisfaction. Decision Sciences, 34(1), 107–129.CrossRefGoogle Scholar
  83. Thong, J., Hong, S., et al. (2006). The effects of post-adoption beliefs on the expectation–­confirmation model for information technology continuance. International Journal of Human Computer Studies, 64(9), 799–810.CrossRefGoogle Scholar
  84. Tolman, E. (1932). Purposive behavior in animals and men. New York: Appleton Century.Google Scholar
  85. Tse, D. K., & Wilton, P. C. (1988). Models of consumer satisfaction: An extension. Journal of Marketing Research, 25(2), 204–212.CrossRefGoogle Scholar
  86. Tung, L., & Quaddus, M. (2002). Cultural differences explaining the differences in results in GSS: Implications for the next decade. Decision Support Systems, 33(2), 177–199.CrossRefGoogle Scholar
  87. Venkatesh, V., Morris, M., et al. (2003). User acceptance of information technology: Toward a unified view. Information Management, 27(3), 425–478.Google Scholar
  88. Wanous, J., & Lawler, E. (1972). Measurement and meaning of job satisfaction. The Journal of Applied Psychology, 56(2), 95–105.CrossRefGoogle Scholar
  89. Westbrook, R. A., & Oliver, R. L. (1981). Developing better measures of consumer satisfaction: Some preliminary results. Advances in Consumer Research, 8(1), 94–99.Google Scholar
  90. Westbrook, R. A., & Reilly, M. D. (1983). Value-percept disparity: An alternative to the ­disconfirmation of expectations theory of consumer satisfaction. Advances in Consumer Research, 10(1), 256–261.Google Scholar
  91. White, C., & Yu, Y. (2005). Satisfaction emotions and consumer behavioral intentions. The Journal of Services Marketing, 19(6), 411–420.CrossRefGoogle Scholar
  92. Wu, N. C., Nystrom, M. A., et al. (2006). Challenges to global RFID adoption. Technovation, 26, 1317–1323.CrossRefGoogle Scholar
  93. Yen, C., & Lu, H. (2008a). Effects of e-service quality on loyalty intention: An empirical study in online auction. Managing Service Quality, 18(2), 127–146.CrossRefGoogle Scholar
  94. Yen, C. H., & Lu, H.-P. (2008b). Factors influencing online auction repurchase intention. Internet Research, 18(1), 7–25.CrossRefGoogle Scholar
  95. Yi, Y. (1990). A critical review of consumer satisfaction. In A. Valarie Zeithaml (Ed.), Review of Marketing, (pp. 68–123). Chicago: American Marketing Association.Google Scholar
  96. Yüksel, A., & Rimmington, M. (1998). Customer-satisfaction measurement. The Cornell Hotel and Restaurant Administration Quarterly, 39(6), 60–70.Google Scholar
  97. Zeithaml, V. A., & Berry, L. L. (1990). Delivering quality service: Balancing customer perceptions and expectations. New York: Free Press.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Graduate School of Business, Curtin Business SchoolCurtin University of TechnologyPerthAustralia

Personalised recommendations