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研究生:林彧瑋
研究生(外文):LING, YU-WEI
論文名稱:以多元羅吉斯模型分析消費者品牌決策
論文名稱(外文):Applying Multinomial Logit Model to Analyze Consumer Brand Choice
指導教授:高淩菁高淩菁引用關係
指導教授(外文):KAO, LING-JING
口試委員:邱志洲呂奇傑高淩菁
口試委員(外文):CHIU, CHIH-CHOULU, CHI-JIEKAO, LING-JING
口試日期:2020-07-15
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:經營管理系
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:29
中文關鍵詞:層級貝氏模型多元羅吉斯模型品牌選擇
外文關鍵詞:hierarchical Bayesian modelmultinomial logit modelbrand choice
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面對電子商務的蓬勃發展,讓貨架空間有限且毛利低的實體零售通路必須要更加了解消費者的決策,因此通路業者紛紛透過資料分析希望找出消費需求及偏好,以有效制定行銷策略提高消費者對實體零售通路的忠誠度。在眾多的模型中,多元羅吉斯模型因具有易於瞭解的數學架構及容易估計的優勢廣為實務及學術界使用,因此本研究也採用此模型做分析,並透過蒙地卡羅馬可夫鏈進行參數估計,本研究以美國零售通路啤酒銷售資料進行實證研究。實證結果顯示,「CausalAD」對於消費者有正面影響,意味著消費者可以透過CausalAD獲取更多商品促銷的資訊,進而增加購買。另外,優惠券的使用除了讓消費者享有折扣外還能更進一步將商品的促銷方案傳達給消費者。
Due to the competition from e-commerce, physical stores which have low margin and limited shelf space must grasp consumer decision-making. Therefore, to make effective marketing plan, retailers apply various data analysis techniques to better understand consumer preference and consumer demands. Among marketing models, multinomial logit model is one of the most popular approaches in both industry and academic because of its mathematical simplicity. Therefore, in this research, multinomial logit model was applied to analyze consumer brand choice in the US beer retail market. The empirical result shows that CausalAd and Coupon had positive impact on consumer brand choice.
摘要 i
Abstract ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究流程 3
第二章 文獻探討 4
2.1 選擇模型 4
2.2 多元羅吉斯模型 5
2.3 層級貝氏模型 7
2.3.1 層級貝氏模型之簡介 7
2.3.2 層級貝氏模型相關文獻回顧 8
第三章 研究方法 10
3.1 貝氏統計 10
3.2 蒙地卡羅–馬可夫鏈演算法 11
3.2.1 Gibbs Sampling 12
3.2.2 Random–Walk Metropolis–Hasting Algorithm 13
3.3 多元羅吉斯模型 14
3.4 參數估計 14
3.5 模擬實驗 16
第四章 實證分析 19
4.1 敘述統計 19
4.2 實證結果 22
第五章 結論與限制 25
5.1 研究結論 25
5.2 研究建議 25
參考文獻 26

一、英文文獻
Ainslie, A., & Rossi, P. E. (1998). Similarities in choice behavior across product categories. Marketing Science, 17(2), 91-106.
Allenby, G. M. (1989). A unified approach to identifying, estimating and testing demand structures with aggregate scanner data. Marketing Science, 8(3), 265-280.
Allenby, G. M. (1990a). Cross-validation, the Bayes theorem, and small-sample bias. Journal of Business & Economic Statistics, 8(2), 171-178.
Allenby, G. M. (1990b). Hypothesis testing with scanner data: the advantage of Bayesian methods. Journal of Marketing Research, 27(4), 379-389.
Allenby, G. M., & Ginter, J. L. (1995). Using extremes to design products and segment markets. Journal of Marketing Research, 32(4), 392-403.
Allenby, G. M., & Rossi, P. E. (1991). Quality perceptions and asymmetric switching between brands. Marketing Science, 10(3), 185-204.
Andrews, R. L., Ansari, A., & Currim, I. S. (2002). Hierarchical Bayes versus finite mixture conjoint analysis models: A comparison of fit, prediction, and partworth recovery. Journal of Marketing Research, 39(1), 87-98.
Bucklin, R. E., & Gupta, S. (1992). Brand choice, purchase incidence, and segmentation: An integrated modeling approach. Journal of Marketing Research, 29(2), 201-215.
Chintagunta, P. K. (1993). Investigating purchase incidence, brand choice and purchase quantity decisions of households. Marketing Science, 12(2), 184-208.
Corstjens, M. L., & Gautschi, D. A. (1983). Formal choice models in marketing. Marketing Science, 2(1), 19-56.
Erdem, T. (1996). A dynamic analysis of market structure based on panel data. Marketing Science, 15(4), 359-378.
Erdem, T., & Swait, J. (2004). Brand credibility, brand consideration, and choice. Journal of Consumer Research, 31(1), 191-198.
Ferraro, R., Bettman, J. R., & Chartrand, T. L. (2009). The power of strangers: The effect of incidental consumer brand encounters on brand choice. Journal of Consumer Research, 35(5), 729-741.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis: CRC press.
Guadagni, P. M., & Little, J. D. (1983). A logit model of brand choice calibrated on scanner data. Marketing Science, 2(3), 203-238.
Hanemann, W. M. (1984). Discrete/continuous models of consumer demand. Econometrica: Journal of the Econometric Society, 541-561.
Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications.
Humphrey Jr, W. F., Laverie, D. A., & Rinaldo, S. B. (2017). Brand choice via incidental social media exposure. Journal of Research in Interactive Marketing.
Lattin, J. M., & McAlister, L. (1985). Using a variety-seeking model to identify substitute and complementary relationships among competing products. Journal of Marketing Research, 22(3), 330-339.
Lenk, P. J., & Rao, A. G. (1990). New models from old: Forecasting product adoption by hierarchical Bayes procedures. Marketing Science, 9(1), 42-53.
Müller, P. (1991). A generic approach to posterior integration and Gibbs sampling: Purdue University, Department of Statistics.
Mehta, N. (2007). Investigating consumers’ purchase incidence and brand choice decisions across multiple product categories: A theoretical and empirical analysis. Marketing Science, 26(2), 196-217.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The journal of chemical physics, 21(6), 1087-1092.
Montgomery, A. L., & Rossi, P. E. (1999). Estimating price elasticities with theory-based priors. Journal of Marketing Research, 36(4), 413-423.
Nummelin, E. (2004). General irreducible Markov chains and non-negative operators (Vol. 83): Cambridge University Press.
Robert, C., & Casella, G. (2013). Monte Carlo statistical methods: Springer Science & Business Media.
Rossi, P. E., Allenby, G. M., & McCulloch, R. (2012). Bayesian statistics and marketing: John Wiley & Sons.
Russell, G. J. (2014). Brand choice models. The history of marketing science, 17, 19-46.
Sandor, Z., & Wedel, M. (2001). Designing conjoint choice experiments using managers' prior beliefs. Journal of Marketing Research, 38(4), 430-444.
Talukdar, D., Sudhir, K., & Ainslie, A. (2002). Investigating new product diffusion across products and countries. Marketing Science, 21(1), 97-114.
Thurstone, L. L. (1959). The measurement of values.
Tierney, L. (1994). Markov chains for exploring posterior distributions. the Annals of Statistics, 1701-1728.
Yang, S., Zhao, Y., Erdem, T., & Zhao, Y. (2010). Modeling the intrahousehold behavioral interaction. Journal of Marketing Research, 47(3), 470-484.

二、中文文獻
林士彥、林卓民、李俊彥(2007)。應用多項羅吉特模型分析消費支出與服務品質之研究-以溫泉區旅館爲例。戶外遊憩研究,20(2),39-57。
彭美玲(2010)。台灣廠商技術策略選擇之研究:以多項Logit模型。經濟與管理論叢,6(1),133-155。
廖子毅、張羢琦、鍾振聲、邱志洲(2005)。整合層級貝氏及基因演算法在顧客偏好解讀與市場區隔上之應用。第九屆科際整合管理研討會,103-123。
劉欣芸(2001)。農家已婚婦女勞動參與的多重選擇模型。農業經濟半年刊,(69),31-54。
謝淑芬(2007)。溫泉遊憩區遊客對溫泉旅館選擇行為之研究。旅遊管理研究,7(2),165-168。


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