Integrating Conjoint and Maximum Difference Scaling Data

YiChun Miriam Liu (), Joachim Büschken (), Bryan Orme () and Greg M. Allenby ()
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YiChun Miriam Liu: Towson University, Towson, Maryland 21252
Joachim Büschken: Catholic University Eichstätt-Ingolstadt, 85049 Ingolstadt, Germany
Bryan Orme: Sawtooth Software, Provo, Utah 84604
Greg M. Allenby: Ohio State University, Columbus, Ohio 43210

Management Science, 2025, vol. 71, issue 11, 9404-9422

Abstract: Customer preferences for product features play an important role in designing successful goods and services. Preferences for features are typically obtained by utilizing a model of choice where the utility for all but one level of an attribute is estimable. That is, the traditional discrete choice model can provide information on the change in utility between attribute-levels, but cannot separately estimate the utility associated with all levels of an attribute. In this paper, we propose a model that integrates conjoint and Maximum Difference scaling data to identify part-worth utilities for all product features, using the outside good as a common reference level, instead of the usual practice of having a reference level for each product attribute. The preference data are also integrated with satisfaction data to identify market opportunities for new and existing products. We illustrate our model with data from a survey measuring customer satisfaction and preferences for large-screen TVs.

Keywords: conjoint analysis; MaxDiff analysis; fixed-point ratings; reference levels; stable preferences (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:71:y:2025:i:11:p:9404-9422

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