| Summary: | 博士 === 國立臺灣大學 === 國際企業學研究所 === 95 === Conjoint analysis, designed to estimate individual preference and the relative competition among brands, has become one of the most widely-used quantitative methods in Marketing Research. In addition, hierarchical Bayes inference is one of the most favored approaches because of its superior in recovering individual part-worths. However, current application of hierarchical Bayes model is not without drawbacks, because consumer heterogeneity is assumed to follow a multivariate normal distribution. The normal distribution has its own characteristic such as unimodal, symmetric and inverted U shape, which might lead to bias or limitation in part-worth density inference. Alternatively, the mixture of normal distributions is a more flexible and general approach in modeling consumer heterogeneity. It is especially suitable for the heterogeneity density inference, such as the unknown consumer heterogeneity distribution. It is flexible in modeling any symmetric or asymmetric distributions with either multi-modes or heavy tails. Furthermore, the normal distribution is just a special case of mixture of normal distributions. In this study, we develop a Bayesian Inference of multivariate mixture of normal distributions. Then, the model is applied in different hierarchical Bayes models as an assumption to modeling consumer heterogeneity. Two approaches in recent hierarchical Bayes conjoint analysis will be studied. They are continuous response conjoint analysis and discrete choice conjoint analysis. As a final point, the mixture of normal assumption in modeling consumer heterogeneity is also favored by marketing society, because it ideally corresponds to the strategic implication of market segmentation. However, a recent argument regarding segmentation encourages us to focus on extreme rather then the homogeneous segments. Therefore, the author will further investigate these different arguments, and explain why the modeling framework proposed in this study is so flexible providing mixed information in targeting the advantages of either extreme or cluster based approach. As expected, it will provide new insights and strategic implications for market segmentation.
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