A New Restricted Bayesian Framework for Deriving The Heterogeneous Drivers of Service Quality Evaluations

Sunghoon Kim, Arizona State University

This paper proposes a series of constrained Bayesian regression models tailored to examine response patterns in service quality evaluations by simultaneously identifying the underlying market segments of consumers (heterogeneity) and the differential significant drivers in their evaluation judgments (variable selection), while enforcing various managerial and theoretical implementation restrictions (constraints) into the model. The authors demonstrate with synthetic data that the new constrained finite mixture Bayesian regression models can be used to identify and represent several constrained heterogeneous response patterns commonly encountered in service practice. In addition, they show that the proposed models are more robust against multicollinearity than traditional methods. Finally, to illustrate the proposed models’ usefulness, the authors apply the proposed constrained models in the context of a service quality (SERVPERF) survey of National Insurance Company’s customers.
Kim, Sunghoon, Simon Blanchard, Wayne S. DeSarbo, and Duncan K. H. Fong (2013), “Implementing Managerial Constraints in Model Based Segmentation: Extensions of Kim, Fong, and DeSarbo (2012) with an Application to Heterogeneous Perceptions of Service Quality”, Journal of Marketing Research, 50 (5), pp. 664-673.