Probabilistic Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis: Theory and Application

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Bibliographische Detailangaben
Beteiligte: Toubia, Olivier, Hauser, John, Garcia, Rosanna
In: Marketing Science, 26, 2007, 5, S. 596-610
veröffentlicht:
Institute for Operations Research and the Management Sciences (INFORMS)
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Umfang: 596-610
ISSN: 0732-2399
1526-548X
veröffentlicht in: Marketing Science
Sprache: Englisch
Kollektion: sid-55-col-jstoras4
sid-55-col-jstorbusiness1archive
sid-55-col-jstorbusiness
JSTOR Arts & Sciences IV Archive
JSTOR Business I Archive
JSTOR Business & Economics
Inhaltsangabe

<p>Polyhedral methods for choice-based conjoint analysis provide a means to adapt choice-based questions at the individual-respondent level and provide an alternative means to estimate partworths when there are relatively few questions per respondent, as in a Web-based questionnaire. However, these methods are deterministic and are susceptible to the propagation of response errors. They also assume, implicitly, a uniform prior on the partworths. In this paper we provide a probabilistic interpretation of polyhedral methods and propose improvements that incorporate response error and/or informative priors into individual-level question selection and estimation. Monte Carlo simulations suggest that response-error modeling and informative priors improve polyhedral question-selection methods in the domains where they were previously weak. A field experiment with over 2,200 leading-edge wine consumers in the United States, Australia, and New Zealand suggests that the new question-selection methods show promise relative to existing methods.</p>