臺灣博碩士論文加值系統

聯合分析法的研究可以幫助企業瞭解消費者潛藏在內心的需求，亦可以瞭解品牌間的相對競爭優勢，多年來一直是廣為企業所採用的一種行銷調查方法。在聯合分析法之參數的估計的方法中，層級貝氏的方法因其可以有效的推估每一個消費者不同的偏好結構 (亦稱為成份效用值)，因此在去的幾年來中逐漸嶄露頭角，同時受到學術界與實務界的重視與推廣。然而在目前的應用方面，層級貝氏的方法仍有其限制性，因其通常假設消費者的異質性服從多變量常態的分配。常態分配有其獨特的性質，例如：鐘型、單峰、對稱性，這些特性容易產生分配估計上的限制。相對的，混合常態則假定某一個分配是由許多個不同的常態分配所組成，因此提供了一種更具有彈性且一般化的方式來估計消費者的成份效用值及其分配。貝氏混合常態的優點是可以用來配適具不確定性之參數的機率密度分配，如未知的成份效用分配。再者，此分配具有相當的彈性，可用來配適多峰、不對稱性或長尾等特殊的分配形狀，且常態分配可視為其分配之一個特例。在本研究中，作者將解釋多變量貝氏混合常態的模式，並以此做為消費者異質性成份效用分配之假設，然後將其整合至層級貝氏模型中，接著將這些模型應用於聯合分析法中成份效用值之推論。本研究將多變量混合常態分配應用於層級貝氏聯合分析法，分別為: (1) 連續型聯合分析法; 以及(2) 選擇式聯合分析法。值得一提的是，混合常態的異質性假設將相對的更受到行銷社群的歡迎，因為其概念與市場區隔的概念不謀而合。然而近年關於新產品開發與市場區隔的概念有一新的發展趨勢，其鼓勵研究者將焦點放在極端偏好區隔，而非平均值。在管理意涵的部份，本研究亦針對此議題進行討論並且說明本文所發展的模式如何能彈性的整合兩種市場區隔策略的優點及其策略意涵。

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.

Dissertation Committee i
Acknowledgments ii
Abstract in Chinese iii
Abstract in English iv
Table of Contents v
List of Tables vii
List of Figures ix

Chapter 1 : Introduction 1
1.1 Research Background 1
1.2 Research Purpose 5

Chapter 2: Literature Review 7
2.1 Conjoint Analysis 7
2.2 Hierarchical Bayes Estimation in Conjoint Analysis 11

Chapter 3: The Models 15
3.1 HB Multivariate Mixture of Normals Distribution (MMN) Model 15
3.2 HB Linear Model with MMN Heterogeneity Model 19
3.3 HB Logit Model with MMN Heterogeneity Model 22

Chapter 4 : Estimation Algorithms 25
4.1 HB Multivariate Mixture of Normals Distribution (MMN) 25
4.2 HB Linear Model with MMN Heterogeneity 27
4.3 HB Logit Model with MMN Heterogeneity 35

Chapter 5 : Simulation Experiments 40
5.1 Simulation of HB Multivariate Mixture of Normals Distribution (MMN) 40
5.2 Simulation of HB Linear Model with MMN Heterogeneity 55
5.3 Simulation of HB Logit Model with MMN Heterogeneity 65

Chapter 6: Empirical Applications 78
6.1 Rating Based Conjoint Analysis 78
6.2 Choice Based Conjoint Analysis 99

Chapter 7: Conclusion 126
7.1 Discussion 126
7.2 Implications for Market Segmentation 127
7.3 Conclusion 132

LIST OF REFERENCE 134

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