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Market segmentation for customer satisfaction studies via a new latent structure multidimensional scaling model

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  • Jianan Wu
  • Wayne S. DeSarbo

Abstract

It has been well documented in the marketing literature that customer satisfaction is critical to any businesses' success. However, it is far less clear as on how marketers comprehend customer differences in customer satisfaction evaluations, and leverage such understanding in forming their marketing strategies. Only recently have researchers begun to explore the notion of individual or segment differences in the formation of overall satisfaction judgments. To extend the exploration of unobserved customer heterogeneity in customer satisfaction studies with multiple attributes, we propose a latent structure multidimensional scaling (MDS) model to visually depict unobserved customer heterogeneity with respect to the theoretical components of customer satisfaction judgments. Our model is developed on the basis of the well‐established expectancy–disconfirmation theory of customer satisfaction. We describe the proposed MDS model and discuss the technical aspects of the model structure and maximum likelihood estimation. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • Jianan Wu & Wayne S. DeSarbo, 2005. "Market segmentation for customer satisfaction studies via a new latent structure multidimensional scaling model," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 21(4‐5), pages 303-309, July.
    • Handle: RePEc:wly:apsmbi:v:21:y:2005:i:4-5:p:303-309
    DOI: 10.1002/asmb.554
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    Cited by:

    1. Esposito Vinzi, Vincenzo & Ringle, Christian M. & Squillacciotti, Silvia & Trinchera, Laura, 2007. "Capturing and Treating Unobserved Heterogeneity by Response Based Segmentation in PLS Path Modeling. A Comparison of Alternative Methods by Computational Experiments," ESSEC Working Papers DR 07019, ESSEC Research Center, ESSEC Business School.
    2. Ringle, Christian M., 2006. "Segmentation for path models and unobserved heterogeneity: The finite mixture partial least squares approach," MPRA Paper 10734, University Library of Munich, Germany.
    3. Fonseca, Jaime R.S., 2009. "Customer satisfaction study via a latent segment model," Journal of Retailing and Consumer Services, Elsevier, vol. 16(5), pages 352-359.
    4. Fordellone, Mario & Vichi, Maurizio, 2020. "Finding groups in structural equation modeling through the partial least squares algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 147(C).

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