Conjoint analysis and discrete choice experiments for quality improvement

William Li, Christopher J. Nachtsheim, Ke Wang, Robert Reul, Mark Albrecht

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Conjoint analysis and discrete choice experiments, which were developed in fields such as marketing and economics, are useful for understanding the voice of the customer to guide quality-improvement e?orts. Unfortunately, these methods have received relatively little attention in the quality area. In this article, we provide some guidelines for the use of conjoint analysis and discrete choice experiments. We discuss what they are, why they are useful methodologies for quality improvement, and how a discrete choice experiment can be carried out. We demonstrate the methodology by discussing a real case study in quality improvement in detail. We then introduce a new class of designs for discrete choice experiments that are robust for a class of possible models. We provide several examples in which an optimal design based on the main-effects only models is shown to have limited capability for estimation of two-factor interactions, whereas the proposed robust designs perform well in the presence of two-factor interactions. We conclude with a summary of key points and directions for future research.

Original languageEnglish (US)
Pages (from-to)74-99
Number of pages26
JournalJournal of Quality Technology
Volume45
Issue number1
DOIs
StatePublished - Jan 2013

Keywords

  • Bayesian design
  • Market segmentation
  • Model robust design
  • Multinomial logit
  • Nonlinear design
  • Quality function deployment

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