A Bayesian Approach to the Spatial Representation of Market Structure from Consumer Choice Data

European Journal of Operational Research, Volume 111, Issue 2, Pages 285–305

Posted: 11 Jun 2016

See all articles by Wayne S. DeSarbo

Wayne S. DeSarbo

Pennsylvania State University

Youngchan Kim

University of Groningen - Department of Marketing & Marketing Research

Michel Wedel

University of Maryland - Robert H. Smith School of Business, Marketing Department; University of Groningen - Faculty of Economics and Business

Duncan K. H. Fong

Pennsylvania State University

Date Written: December 1998

Abstract

This paper is concerned with the spatial representation of market structure calibrated on actual or intended choice data. Previous models developed for that purpose accommodate consumer heterogeneity by estimating parameters for each consumer, typically using the method of maximum likelihood. This approach to heterogeneity avoids assuming any particular distribution of the individual level parameters across the sample, but leads to problems related to the consistency of the estimates, sufficient degrees of freedom, and the validity of asymptotic standard errors and test statistics. Of greater concern is the assumption of independence of the choice observations within the same individual. This assumption is necessary in a maximum likelihood (MLE) framework to make the estimation computationally feasible. However, the marketing and psychology literature (cf. Manrai, 1995; Tversky and Simonson, 1993; Kim et al., forth coming) demonstrates that dependencies among choice alternatives may exist, and negligence to take such covariance into account may lead to inconsistent estimates, reduced predictive validity, and incorrect managerial action. We develop a new multidimensional scaling (MDS) model that estimates spatial market structures from pick-any/J choice data, provides for individual level parameters, and allows for correlations among the choice alternatives across individuals. We provide a Bayesian estimation method that overcomes the traditional problems associated with estimating models with such correlated alternatives. We provide an application to pick-any/J data for various brands of portable telephones. In a comparative analysis, we show that the proposed model outperforms one in which the utilities are assumed to be uncorrelated across choice alternatives.

Keywords: Multidimensional scaling, Context effects, Bayesian analysis, Choice models, Competitive market structure

Suggested Citation

DeSarbo, Wayne S. and Kim, Youngchan and Wedel, Michel and Fong, Duncan K. H., A Bayesian Approach to the Spatial Representation of Market Structure from Consumer Choice Data (December 1998). European Journal of Operational Research, Volume 111, Issue 2, Pages 285–305. Available at SSRN: https://ssrn.com/abstract=2792286

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

Youngchan Kim

University of Groningen - Department of Marketing & Marketing Research ( email )

Netherlands

Michel Wedel

University of Maryland - Robert H. Smith School of Business, Marketing Department ( email )

College Park, MD 20742
United States
301.405.2162 (Phone)
301.405.0146 (Fax)

HOME PAGE: http://www.rhsmith.umd.edu/marketing/faculty/wedel.html

University of Groningen - Faculty of Economics and Business ( email )

Postbus 72
9700 AB Groningen
Netherlands

Duncan K. H. Fong

Pennsylvania State University ( email )

308 armsby
university park, PA 16802
United States

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