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Implementing Managerial Constraints in Model-Based Segmentation: Extensions of Kim, Fong, and DeSarbo (2012) with an Application to Heterogeneous Perceptions of Service Quality

Sung‐Hoon Kim¹; Simon J. Blanchard²; Wayne S. DeSarbo¹; Duncan Κ. H. Fong¹

¹ Pennsylvania State University · ² Georgetown University

Kim, Fong, and DeSarbo (2012) recently introduced a finite mixture Bayesian regression model to simultaneously identify market segments of consumers (heterogeneity) and determine how such segments differ with respect to active regression coefficients (variable selection). The current manuscript introduces three extensions of their model to incorporate managerial restrictions (constraints). We demonstrate with synthetic data that the new constrained finite mixture Bayesian regression models can be gainfully employed to identify and represent several constrained heterogeneous response patterns commonly encountered in practice. In addition, we illustrate that the proposed models are more robust against multicollinearity than traditional methods. Finally, to illustrate their usefulness, we apply the proposed constrained models in the context of a service quality (SERVPERF) survey of the National Insurance Company’s customers.

DOI

10.1177/002224371305000512

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