Abstract

Adaptive choice-based conjoint analysis (ACBC) and hybrid individualized two-level choice-based conjoint analysis (HIT-CBC) were developed to improve standard choice-based conjoint analysis through additional interviewing techniques. Both methods have demonstrated their applicability in comparison to standard choice-based conjoint methods. The objective of our study was a direct comparison of the two adaptive hybrid methods ACBC and HIT-CBC. Therefore, we analysed the previous comparative literature on the methods and used the results to conduct both a Monte Carlo simulation study and an empirical study for validity comparisons. The simulation study confirms the vulnerability of HIT-CBC to produce incorrect ratings of respondents in the last part of the questionnaire. The empirical findings reveal an advantage of ACBC in comparison to the current version of HIT-CBC. We conclude that the rating tasks in the last section of HIT-CBC questionnaires reduce the predictive validity of the method and suggest an improvement to HIT-CBC.

Highlights

  • Conjoint analysis is widely used (Hauser et al, 2006)

  • The adaptive choice-based conjoint (ACBC)-related studies identified in section 3.1 compare the validity of ACBC to standard choice-based conjoint analysis (CBC)

  • The empirical study revealed similar results for hybrid individualized two-level choice-based conjoint analysis (HIT-CBC) and CBC. Their simulation study revealed a predominance of HIT-CBCwhen ratings for the intermediate level utilities of HIT-CBC were correct

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Summary

Introduction

Conjoint analysis is widely used (Hauser et al, 2006). there are many different conjoint analysis methods and selecting the best method for a (research) problem is a difficult task for researchers and practitioners. Online surveys have garnered increasing popularity and the rising online processing power allows for adapting questions on the basis of prior responses (Toubia et al, 2004). These advances led to methods thatcreate respondent-specific choice tasks during the survey(e.g., Toubia et al, 2004 and Toubia et al, 2007 suggested (probabilistic) polyhedral methods and Yu et al, 2011 suggested Bayesian methods for the adaptive design generation)

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