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

Jianan Wu, Wayne S. DeSarbo

Research output: Contribution to journalArticle

9 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)303-309
Number of pages7
JournalApplied Stochastic Models in Business and Industry
Volume21
Issue number4-5
DOIs
StatePublished - Jul 1 2005

Fingerprint

Market Segmentation
Customer Satisfaction
Customer satisfaction
Scaling
Customers
Marketing
Model
Maximum likelihood estimation
Model structures
Maximum Likelihood Estimation
Leverage
Attribute
Market segmentation
Multidimensional scaling
Evaluation
Industry

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Business, Management and Accounting(all)
  • Management Science and Operations Research

Cite this

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Market segmentation for customer satisfaction studies via a new latent structure multidimensional scaling model. / Wu, Jianan; DeSarbo, Wayne S.

In: Applied Stochastic Models in Business and Industry, Vol. 21, No. 4-5, 01.07.2005, p. 303-309.

Research output: Contribution to journalArticle

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