Abstract
The linkage of customer satisfaction, customer retention, and firm profitability has been well established in the marketing literature, and provides ample justification as to why customer satisfaction measurement (CSM) has been a focal point in marketing decision making. Although aggregate market level research on understanding the determinants of customer satisfaction is abundant, CSM decisions at segment level are possible only if the individual or market segment differences in the formation of overall satisfaction judgments and subsequent heterogeneity in the role these various determinants play are understood. Based on expectancy-disconfirmation theory in customer satisfaction, we propose a maximum likelihood based latent structure factor analytic methodology which visually depicts customer heterogeneity regarding the various major determinants of customer satisfaction judgments involving multiple attributes, and provides directions for segment-specific CSM decisions. We first describe the proposed model framework including the technical aspects of the model structure and subsequent maximum likelihood estimation. In an application to a consumer trade show, we then demonstrate how our proposed methodology can be gainfully employed to uncover the nature of such heterogeneity. We also empirically demonstrate the superiority of the proposed model over a number of different model specifications in this application. Finally, limitations and directions for future research are discussed.
Similar content being viewed by others
References
Anderson, E.W., & Sullivan, M. (1993). The Antecedents and Consequences of Customer Satisfaction for Forms. Marketing Science, 12(Spring), 125–143.
Borg, I., & Borg, P. (1997). Modern Multidimensional Scaling: Theory and Applications. New York: Springer Verlag.
Churchill, A.G., Jr., & Surprenant, C. (1982). An Investigation Into the Determinants of Customer Satisfaction. Journal of Marketing Research, 19, 491–504.
Cronin, J.J., & Taylor, S.A. (1994). SERVPERF Versus SERVQUAL: Reconciling Performance-Based and Perceptions-Minus-Expectations measurement of Service Quality. Journal of Marketing, 58, 125–131.
DeSarbo, S.W., Manrai, A.K., & Manrai, L. A. (1994). Latent Class Multidimensional Scaling: A Review of Recent Developments in the Marketing and Psychometric Literature. In R. P. Bagozzi (ed.), Advanced Methods of Marketing Research. Oxford: Blackwell.
DeSarbo, S.W., & Cron, W.L. (1988). A Conditional Mixture Maximum Likelihood Methodology for Clusterwise Linear Regression. Journal of Classification, 5, 249–289.
DeSarbo, S.W., Degeratu, A.M., Wedel, M., & Saxton, M.K. (2001). The Spatial Representation of Market Information. Marketing Science, 20(4), 426–441.
DeSarbo, S.W., & Wu, J. (2001). The Joint Spatial Representation of Multiple Data Sets Collected in Marketing Research. Journal of Marketing Research, XXXVIII, 244–253.
Fornell, C. (1992). A National Customer Satisfaction Barometer: The Swedish Experience. Journal of Marketing, 56, 6–21.
Horan, C. B. (1969). Multidimensional Scaling: Combining Observations When Individuals Have Different Perceptual Structures. Psychometrika, 34, 139–165.
Jedidi, K., Jagpal, H.S., & DeSarbo, W.S. (1997). Finite Mixture Structural Equation Models for Response-Based Segmentation and Unobserved Heterogeneity. Marketing Science, 16, 39–59.
Kopalle, P.K., & Lehmann, D.R. (2001). Strategic Management of Expectations: The Role of Disconfirmation Sensitivity and Perfectionism. Journal of Marketing Research, XXXVIII, 386–394.
Krishnan, M.S., Ramaswamy, V., Meyer, M.C., & Damien, P. (1999). Customer Satisfaction for Financial Services: The Role of Products, Services, and Information Technology. Management Science, 45, 1194–1209.
Martilla, J.A., & James, J.C. (1977). Importance Performance Analysis. Journal of Marketing, 41(1), 77–85.
McLachlan, G., & Peel, D. (2000). Finite Mixture Models. Wiley: New York.
Mittal, V., & Kamakura, W.A. (2001). Satisfaction, Repurchase Intent, and Repurchase Behavior: Investigating the Moderating Effect of Customer Characteristics. Journal of Marketing Research, XXXVIII, 131–142.
Mittal, V., Ross, W.T., Jr., & Baldasare, P.M. (1998). The Asymmetric Impact of Negative and Positive Attribute-Level Performance on Overall Satisfaction and Repurchase Intentions. Journal of Marketing, 62, 33–47.
Múthen, L.K., & Múthen, B.O. (1998–2001). Mplus User’s Guide (Second Edition). Los Angles, CA: Múthen & Múthen.
Oliver, R. (1997). Satisfaction: A Behavioral Perspective on the Consumer. Irwin-McGraw Hill: Boston, MA.
Oliver, R., & DeSarbo, W.S. (1988). Response Determinants in Satisfaction Judgements. Journal of Consumer Research, 14, 495–507.
Rust, T.R., Inman, J.J., Jianmin, J., & Zahorik, A. (1999). What you Don’t Know About Customer-Perceived Quality: The Role of Customer Expectation Distributions. Marketing Science, 18(1), 77–92.
Yi, Y. (1990). A Critical Review of Consumer Satisfaction. In Valarie A. Zeithaml (ed.), Review of Marketing 1990. Chicago IL: American Marketing Association, 68–123.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wu, J., DeSarbo, W.S., Chen, PJ. et al. A latent structure factor analytic approach for customer satisfaction measurement. Market Lett 17, 221–238 (2006). https://doi.org/10.1007/s11002-006-7638-1
Issue Date:
DOI: https://doi.org/10.1007/s11002-006-7638-1