Abstract
Conjoint analysis is a popular tool for analysing consumer preferences in market research which has undergone rapid development throughout history. It is now generally agreed that choice-based conjoint (CBC) has a stronger theoretical background than traditional conjoint methods and that it mimics the real decision-making process of consumers more closely. When hierarchical Bayesian models allowed robust estimation of consumer preferences from sparse data available from choice-based conjoint tasks, formerly popular self-explicated or hybrid approaches lost their popularity. In this article, it is shown that hybrid approaches can be a useful alternative to pure CBC design. A hybrid approach to CBC that combines self-explicated questions on attribute levels with individualised choice tasks is suggested and illustrated on a real example and its efficiency is compared to traditional CBC and adaptive CBC. The results of the study support the hypothesis that this approach can be beneficial under certain circumstances and yield higher model fit while keeping the questionnaire length and respondent fatigue at an acceptable level.
Highlights
Throughout more than forty years since its beginnings, progress in conjoint analysis has been driven by the effort to satisfy two contradictory requirements of practitioners in market research
Since the use of hierarchical Bayesian models was agreed as a standard in choice-based conjoint analysis, it has become apparent that users of choice-based conjoint face other issues that cannot be solved by introduction of more sophisticated data analysis methods since these are associated with the data collection process
In the case of the hybrid approach to choice-based conjoint (HCBC) group, the estimates were based on direct questions and choice tasks
Summary
Throughout more than forty years since its beginnings, progress in conjoint analysis has been driven by the effort to satisfy two contradictory requirements of practitioners in market research. To obtain useful models of consumer preferences on the market of our interest, we need to account for heterogeneous preferences in the population by modelling preferences of each individual respondent. Adaptive choice-based conjoint (ACBC) is a successful method of conjoint analysis that introduces the use of different types of questions to keep respondent’s motivation to answer responsibly high and increases the efficiency of the data collection process by avoiding the use of choice tasks not relevant for the respondent. A new hybrid approach to choice-based conjoint (HCBC) is presented. This approach combines direct questions with traditional choice tasks, which are made more efficient by using information acquired directly. Under certain conditions, this method can yield more accurate models of choice-behaviour while keeping the length of the questionnaire and fatigue of respondents reasonably low
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