Dynamic Models Incorporating Individual Heterogeneity: Utility Evolution in Conjoint Analysis

John C. Liechty (), Duncan K. H. Fong () and Wayne S. DeSarbo ()
Additional contact information
John C. Liechty: Marketing Department of Statistics Department, Smeal College of Business, 701 M BAB, Pennsylvania State University, University Park, Pennsylvania 16802
Duncan K. H. Fong: Marketing Department and Statistics Department, Smeal College of Business, 707 G BAB, Pennsylvania State University, University Park, Pennsylvania 16802
Wayne S. DeSarbo: Marketing Department, Smeal College of Business, 701 D BAB, University Park, Pennsylvania 16802

Marketing Science, 2005, vol. 24, issue 2, 285-293

Abstract: It has been shown in the behavioral decision making, marketing research, and psychometric literature that the structure underlying preferences can change during the administration of repeated measurements (e.g., conjoint analysis) and data collection because of effects from learning, fatigue, boredom, and so on. In this research note, we propose a new class of hierarchical dynamic Bayesian models for capturing such dynamic effects in conjoint applications, which extend the standard hierarchical Bayesian random effects and existing dynamic Bayesian models by allowing for individual-level heterogeneity around an aggregate dynamic trend. Using simulated conjoint data, we explore the performance of these new dynamic models, incorporating individual-level heterogeneity across a number of possible types of dynamic effects, and demonstrate the derived benefits versus static models. In addition, we introduce the idea of an unbiased dynamic estimate, and demonstrate that using a counterbalanced design is important from an estimation perspective when parameter dynamics are present.

Keywords: heterogeneity; empirical utility functions; dynamic models; Bayesian analysis; conjoint analysis; unbiased dynamic estimates (search for similar items in EconPapers)
Date: 2005
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (30)

Downloads: (external link)
http://dx.doi.org/10.1287/mksc.1040.0088 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:24:y:2005:i:2:p:285-293

Access Statistics for this article

More articles in Marketing Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().