Predicting Joint Choice Using Individual Data

Anocha Aribarg (), Neeraj Arora () and Moon Young Kang ()
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Anocha Aribarg: Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109
Neeraj Arora: Wisconsin School of Business, University of Wisconsin–Madison, Madison, Wisconsin 53706
Moon Young Kang: Wisconsin School of Business, University of Wisconsin–Madison, Madison, Wisconsin 53706

Marketing Science, 2010, vol. 29, issue 1, 139-157

Abstract: Choice decisions in the marketplace are often made by a collection of individuals or a group. Examples include purchase decisions involving families and organizations. A particularly unique aspect of a joint choice is that the group's preference is very likely to diverge from preferences of the individuals that constitute the group. For a marketing researcher, the biggest hurdle in measuring group preference is that it is often infeasible or cost prohibitive to collect data at the group level. Our objective in this research is to propose a novel methodology to estimate joint preference without the need to collect joint data from the group members. Our methodology makes use of both stated and inferred preference measures, and merges experimental design, statistical modeling, and utility aggregation theories to capture the psychological processes of preference revision and concession that lead to the joint preference. Results based on a study involving a cell phone purchase for 214 parent-teen dyads demonstrate predictive validity of our proposed method.

Keywords: joint decision making; preference revision; utility aggregation; Bayesian (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:29:y:2010:i:1:p:139-157

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