Identifying Subject-Specific Relevant Explanatory Variables in Choice-Based Conjoint Studies
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It is customary in conjoint studies to introduce the same set of potential explanatory variables for each subject, so as best to allow any possible trade-offs to be made. However, this presumption can mask the possibility of some subjects’ considering only a subset of the presented attributes. Moreover, such subsets of relevant attributes can vary considerably across the population. This paper presents a model which allows researchers to identify relevant explanatory variables for each subject separately. This is accomplished via a solution to the well-known variable selection problem in the context of discrete choice models; the proposed solution can be widely applied throughout choice studies and in fact to other response types, such as ratings, direct paired comparisons, and ranks, with appropriate changes in likelihood function. When estimated on a choice-based conjoint data for dial-readout scale products, the proposed model is strongly preferred to the traditional random-effect specification for choice-based conjoint. A sizeable group of subjects, approximately 63%, were found to consider proper subsets of all attributes presented. There was a great deal of heterogeneity in attributes deemed relevant across subjects: the proportion of subjects who did not consider a given attribute among the six used in the study ranged from 17.4% to 41.3%. For those who did consider a given attribute, estimated attribute level part-worths were essentially identical for the proposed model and the traditional random-effect conjoint model; but this was not the case for non-considered attributes. In fact, the traditional model was found to suffer from systematic biases in aggregate part-worth magnitudes. Finally,and most important for marketing practice, allowing for the possibility that some subject may not consider particular attributes can lead to substantial design and revenue differences in supposedly ‘optimal’ products, at both the individual- and the aggregate-level.
Abstract
It is customary in conjoint studies to introduce the same set of potential explanatory variables for each subject, so as best to allow any possible trade-offs to be made. However, this presumption can mask the possibility of some subjects’ considering only a subset of the presented attributes. Moreover, such subsets of relevant attributes can vary considerably across the population. This paper presents a model which allows researchers to identify relevant explanatory variables for each subject separately. This is accomplished via a solution to the well-known variable selection problem in the context of discrete choice models; the proposed solution can be widely applied throughout choice studies and in fact to other response types, such as ratings, direct paired comparisons, and ranks, with appropriate changes in likelihood function. When estimated on a choice-based conjoint data for dial-readout scale products, the proposed model is strongly preferred to the traditional random-effect specification for choice-based conjoint. A sizeable group of subjects, approximately 63%, were found to consider proper subsets of all attributes presented. There was a great deal of heterogeneity in attributes deemed relevant across subjects: the proportion of subjects who did not consider a given attribute among the six used in the study ranged from 17.4% to 41.3%. For those who did consider a given attribute, estimated attribute level part-worths were essentially identical for the proposed model and the traditional random-effect conjoint model; but this was not the case for non-considered attributes. In fact, the traditional model was found to suffer from systematic biases in aggregate part-worth magnitudes. Finally,and most important for marketing practice, allowing for the possibility that some subject may not consider particular attributes can lead to substantial design and revenue differences in supposedly ‘optimal’ products, at both the individual- and the aggregate-level.
참고문헌 (20)
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