A latent structure factor analytic approach for customer satisfaction measurement

    Authors

    J. N. Wu; W. S. DeSarbo; P. J. Chen;Y. Y. Fu

    Comments

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    Abbreviated Journal Title

    Mark. Lett.

    Keywords

    customer satisfaction measurement (CSM); market segmentation; latent; structure analysis; finite mixture models; factor analysis; PERFORMANCE; DETERMINANTS; EXPECTATIONS; QUALITY; Business

    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.

    Journal Title

    Marketing Letters

    Volume

    17

    Issue/Number

    3

    Publication Date

    1-1-2006

    Document Type

    Article

    Language

    English

    First Page

    221

    Last Page

    238

    WOS Identifier

    WOS:000238024700005

    ISSN

    0923-0645

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