Title

A Latent Structure Factor Analytic Approach For Customer Satisfaction Measurement

Keywords

Customer satisfaction measurement (CSM); Factor analysis; Finite mixture models; Latent structure analysis; Market segmentation

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. © Springer Science + Business Media, LLC 2006.

Publication Date

7-1-2006

Publication Title

Marketing Letters

Volume

17

Issue

3

Number of Pages

221-238

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/s11002-006-7638-1

Socpus ID

33744932398 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33744932398

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