Hierarchical Bayesian conjoint models incorporating measurement uncertainty
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Hierarchical Bayesian conjoint models incorporating measurement uncertainty John C. Liechty & Duncan K. H. Fong & Eelko K. R. E. Huizingh & Arnaud De Bruyn
Published online: 12 January 2008 # Springer Science + Business Media, LLC 2007
Abstract The authors explore situations where consumers supplement their judgments with a measurement of uncertainty about their own preferences, either implicitly or explicitly, and develop two sets of hierarchical Bayesian conjoint models incorporating such measurements. The first set of models uses the relative location of a rating to determine the importance or weight given to the rating, in a regression setting. The second set uses interval judgment as a dependent variable in a regression setting. After specifying the models, the authors perform a theoretical comparison with a basic Bayesian regression model. They show that, under different conditions, the proposed models will yield more precise individual-level partworth estimates. Two simulated data examples and data from a conjoint study are used to illustrate the gains that could be obtained from modeling uncertainty. In the empirical application, the authors show that model fit improves when ratings for items that respondents do not like are given more weight compared to ratings for items that they do like. Keywords Conjoint analysis . Weighted regression models . Measurement uncertainty . Confidence . Interval data . Hierarchical Bayesian models
Electronic Supplementary Material The online version of this article (doi:10.1007/s11002-007-9026-x) contains supplementary material, which is available to authorized users.
J. C. Liechty (*) : D. K. H. Fong The Pennsylvania State University, 409 BB, University Park, PA 16803, USA e-mail: [email protected] E. K. R. E. Huizingh University of Groningen, Groningen, The Netherlands A. De Bruyn ESSEC Business School, Cergy-Pontoise, France
NO9026; No of Pages
142
Market Lett (2008) 19:141–155
Conjoint analysis is probably the most widely used marketing research method to measure consumer trade-offs between multiattribute products and services. (An extensive review of the method is provided by Green et al. 2001.) No matter how data is collected (ratings, rankings, or choice), conjoint analysis assumes that consumers are capable of assessing and expressing their preferences. In addition we assume that it is possible for consumers to have different degrees of uncertainty about their preference statements. In this paper we explore whether more precise utility estimates can be made by including uncertainty in a hierarchical Bayesian model. The introduction of hierarchical Bayesian models by Allenby et al. (1995) and Lenk et al. (1996) demonstrated the value of Bayesian models with respect to conjoint problems. We follow in the spirit of their approach and propose a Bayesian approach, which can incorporate uncertainty in both measurements and utilities into the analysis.
1 Basic and proposed Bayesian models When consumers are asked to give a preference judgment for a multiattribute object
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