A hierarchical Bayesian approach for examining heterogeneity in choice decisions

Sunghoon Kim, Wayne S. DeSarbo, Duncan Fong

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

There is a vast behavioral decision theory literature that suggests different individuals may utilize and/or weigh different attributes of an object to form the basis of their opinions, attitudes, choices, and/or evaluations of such stimuli. This heterogeneity of information utilization and importance can be due to several different factors such as differing goals, level of expertise, contextual factors, knowledge accessibility, time pressure, involvement, mood states, task complexity, communication or influence of relevant others, etc. This phenomenon is particularly pertinent to the evaluation of stimuli involving large numbers of underlying attributes or features. We propose a new hierarchical Bayesian multivariate probit mixture model with variable selection accommodating such forms of choice heterogeneity. Based on a Monte Carlo simulation study, we demonstrate that the proposed model can successfully recover true parameters in a robust manner. Next, we provide a consumer psychology application involving consideration to buy choices for intended consumers of large Sports Utility Vehicles. The application illustrates that the proposed model outperforms several comparison benchmark choice models with respect to face validity and choice predictive validation performance.

Original languageEnglish (US)
Pages (from-to)56-72
Number of pages17
JournalJournal of Mathematical Psychology
Volume82
DOIs
StatePublished - Feb 1 2018

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Bayes Theorem
Bayesian Approach
Attribute
Decision Theory
Benchmarking
Probit Model
Mood
Choice Models
Communication Complexity
Evaluation
Variable Selection
Expertise
Accessibility
Mixture Model
Reproducibility of Results
Sports
Monte Carlo Simulation
Communication
Simulation Study
Benchmark

All Science Journal Classification (ASJC) codes

  • Psychology(all)
  • Applied Mathematics

Cite this

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A hierarchical Bayesian approach for examining heterogeneity in choice decisions. / Kim, Sunghoon; DeSarbo, Wayne S.; Fong, Duncan.

In: Journal of Mathematical Psychology, Vol. 82, 01.02.2018, p. 56-72.

Research output: Contribution to journalArticle

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