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A latent structure factor analytic approach for customer satisfaction measurement

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  • Jianan Wu
  • Wayne DeSarbo
  • Pu-Ju Chen
  • Yao-Yi Fu

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. Copyright Springer Science + Business Media, LLC 2006

Suggested Citation

  • Jianan Wu & Wayne DeSarbo & Pu-Ju Chen & Yao-Yi Fu, 2006. "A latent structure factor analytic approach for customer satisfaction measurement," Marketing Letters, Springer, vol. 17(3), pages 221-238, July.
    • Handle: RePEc:kap:mktlet:v:17:y:2006:i:3:p:221-238
    DOI: 10.1007/s11002-006-7638-1
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    References listed on IDEAS

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    1. Roland T. Rust & J. Jeffrey Inman & Jianmin Jia & Anthony Zahorik, 1999. "What You Know About Customer-Perceived Quality: The Role of Customer Expectation Distributions," Marketing Science, INFORMS, vol. 18(1), pages 77-92.
    2. M. S. Krishnan & Venkatram Ramaswamy & Mary C. Meyer & Paul Damien, 1999. "Customer Satisfaction for Financial Services: The Role of Products, Services, and Information Technology," Management Science, INFORMS, vol. 45(9), pages 1194-1209, September.
    3. Kamel Jedidi & Harsharanjeet S. Jagpal & Wayne S. DeSarbo, 1997. "Finite-Mixture Structural Equation Models for Response-Based Segmentation and Unobserved Heterogeneity," Marketing Science, INFORMS, vol. 16(1), pages 39-59.
    4. Wayne DeSarbo & William Cron, 1988. "A maximum likelihood methodology for clusterwise linear regression," Journal of Classification, Springer;The Classification Society, vol. 5(2), pages 249-282, September.
    5. Wayne S. DeSarbo & Alexandru M. Degeratu & Michel Wedel & M. Kim Saxton, 2001. "The Spatial Representation of Market Information," Marketing Science, INFORMS, vol. 20(4), pages 426-441, June.
    6. C. Horan, 1969. "Multidimensional scaling: Combining observations when individuals have different perceptual structures," Psychometrika, Springer;The Psychometric Society, vol. 34(2), pages 139-165, June.
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    3. José Felipe Jiménez-Guerrero & Jerónimo de Burgos-Jiménez & Jorge Tarifa-Fernández, 2020. "Measurement of Service Quality in Trade Fair Organization," Sustainability, MDPI, vol. 12(22), pages 1-16, November.
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    5. Monika Bédiová & Kateřina Ryglová, 2015. "The Main Factors Influencing the Destination Choice, Satisfaction and the Loyalty of Ski Resorts Customers in the Context of Different Research Approaches," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 63(2), pages 499-505.

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