系統識別號 U0026-2906201517192300
論文名稱(中文) 以線上產品評論探勘法建構高涉入性3C產品特徵價格分析模型之研究
論文名稱(英文) Hedonic Analysis for High-involvement Consumer Electronics Using Online Product Reviews
校院名稱 成功大學
系所名稱(中) 資訊管理研究所
系所名稱(英) Institute of Information Management
學年度 103
學期 2
出版年 104
研究生(中文) 李俊文
研究生(英文) Chun-Wen Li
學號 R76021011
學位類別 碩士
語文別 英文
論文頁數 55頁
口試委員 指導教授-李昇暾
中文關鍵字 特徵價格分析  產品涉入  意見探勘  情感分析  網路口碑 
英文關鍵字 hedonic analysis  product involvement  opinion mining  sentiment analysis  word-of-mouth 
中文摘要 近年來,線上商品評論已被視為能協助消費者制定購買決策的一種極具價值的資訊來源。過去探討線上商品評論影響力的學術文章,多使用製造商無法控制的因素作為其研究中迴歸模型的解釋變數,例如產品評論的數量、評論的平均評分…等。然而,這些因素卻無法提供直接的管理意涵予廠商參考使用,例如管理者無法輕易地增加商品評論的數量以提升其售價或需求。相反地,他們必須追溯為何評論會成長的原因。因此,為了提供製造商更為直覺的建議,本研究採用特徵價格分析法的概念,其將產品需求分解成多個特徵以辨明哪些特徵影響需求最鉅。再者,先前的研究亦發現當消費者購買高涉入性產品時,在確定購買前,會花費較多的時間與精力搜尋商品資訊,同時在整個搜尋過程中會瀏覽多個外部的評論網站。本研究將智慧型手機市場作為研究標的並提出一研究架構以證實上述的研究假設。
英文摘要 In recent years, online product reviews have been considered as a valuable source of information to assist people in making buying decisions. Most of prior studies on the effect of online product reviews have utilized the factors which manufactures cannot control by themselves, such as the number of reviews, the average review rating, as independent variables in their regression models. However, those factors cannot provide direct implications for manufacturers. For example, managers cannot easily increase the number of reviews to rise the product price or demand. In contrast, they have to trace the causes of why the amount of reviews grows. Thus, in order to offer more straightforward suggestions, we adopt the concept of hedonic analysis which decomposes the demand of a commodity into several product features to identify which of them impact its demand mostly. Further, previous surveys find that consumers spend time and effort conducting pre-purchase searches for high-involvement products and visit several external review websites during the search process. In this article, we take smartphone market as our research target and propose a framework to demonstrate these assumptions.
Our framework utilizes opinion mining techniques to extract sentiment words and features from online product reviews, and combines those extracted items with basic characteristics obtained from a specification of each product to form hedonic regressions. In order to examine influences of reviews in more detail, we separate the sources of product reviews into two groups—a retailer-hosted website and third-party hosted websites and take this differential into our models. Thus, we construct three different regression models—(1) considering basic characteristics only; (2) considering the information extracted from a retailer-hosted review source and basic characteristics; (3) considering the information extracted from third-party review sources and basic characteristics, to test which one has the highest value of the coefficient of determination. Finally, we provide managerial implications for firms and help them make proper strategies based on the experiment results.
論文目次 摘 要 I
誌 謝 IV
List of Tables VII
List of Figures VIII
Chapter 1 Introduction 1
1.1 Research Background and Motivations 1
1.2 Research Objectives 3
1.3 Research Process 4
Chapter 2 Literature Review 5
2.1 Hedonic Analysis 5
2.2 Sentiment Analysis 6
2.2.1 Sentiment Lexicon Expansion and Aspect Extraction 9
2.3 The Impact of Online Product Reviews 10
2.3.1 Product Involvement 11
2.3.2 Third-party Product Reviews 14
Chapter 3 Research Methods 16
3.1 Data Collection 17
3.2 Opinion Lexicon Expansion and Feature Extraction 18
3.2.1 Dependency Grammar 19
3.2.2 Data Preprocessing 19
3.2.3 Initial Lexicon 20
3.2.4 Propagation Rules 20
3.2.5 Propagation Algorithm 22
3.2.6 Feature Reduction 25
3.2.7 Polarity Assignment 25
3.3 Hedonic Analysis Method 29
3.3.1 Regression Model 31
Chapter 4 Experiment Results 34
4.1 Data 34
4.2 Variables 35
4.2.1 Original Version 35
4.2.2 Expert Version 36
4.2.3 Customer Version 38
4.3 Results 40
4.3.1 Original Version 40
4.3.2 Expert Version 41
4.3.3 Customer Version 43
4.3.4 Comparison 45
Chapter 5 Conclusion 47
5.1 Managerial Implications 47
5.2 Research Limitations 48
5.3 Future Works 49
參考文獻 Akdeniz, B., Calantone, R. J., & Voorhees, C. M. (2013). Effectiveness of Marketing Cues on Consumer Perceptions of Quality: The Moderating Roles of Brand Reputation and Third-Party Information. Psychology & Marketing, 30(1), 76–89.
ASYMCO. (2013). Updated US Smartphone Saturation Forecast. Retrieved from http://www.asymco.com/2013/12/10/updated-us-smartphone-saturation-forecast/
Awad, N. F., & Zhang, J. (2007). Stay out of My Forum! Evaluating Firm Involvement in Online Ratings Communities. In Proceedings of the 40th Annual Hawaii International Conference on System Sciences (p. 153c–). IEEE Computer Society.
Bagheri, A., Saraee, M., & de Jong, F. (2013). Care More About Customers: Unsupervised Domain-independent Aspect Detection for Sentiment Analysis of Customer Reviews. Knowledge-Based Systems, 52(0), 201–213.
BrightLocal. (2014). Local Consumer Review Survey 2014. Retrieved from http://www.brightlocal.com/wp-content/uploads/2014/07/Local-Consumer-Review-Survey-20141.pdf
Brooks, C. (2008). Introductory Econometrics for Finance (2nd ed.). Cambridge, UK: Cambridge University Press.
Can, A. (1992). Specification and Estimation of Hedonic Housing Price Models. Regional Science and Urban Economics, 22(3), 453–474.
Charlesworth, A. (2009). The Ascent of Smartphone. Engineering & Technology, 4(3), 32–33.
Chen, Y., Liu, Y., & Zhang, J. (2012). When Do Third-Party Product Reviews Affect Firm Value and What Can Firms Do? The Case of Media Critics and Professional Movie Reviews. Journal of Marketing, 76(2), 116–134.
Chevalier, J., & Mayzlin, D. (2006). The Effect of Word of Mouth on Sales: Online Book Reviews. Journal of Marketing Research, 43(3), 345–354.
Chwelos, P. D., Berndt, E. R., & Cockburn, I. M. (2008). Faster, Smaller, Cheaper: An Hedonic Price Analysis of PDAs. Applied Economics, 40(22), 2839–2856.
Combris, P., Lecocq, S., & Visser, M. (1997). Estimation of A Hedonic Price Equation for Bordeaux Wine: Does Quality Matter? The Economic Journal, 107(441), 390–402.
Cui, G., Lui, H.-K., & Guo, X. (2012). The Effect of Online Consumer Reviews on New Product Sales. International Journal of Electronic Commerce, 17(1), 39–58.
Dahlen, M., Lange, F., & Smith, T. (2010). Marketing Communications: A Brand Narrative Approach (1st ed.). Chichester, UK: Wiley-Blackwell.
Dewenter, R., Haucap, J., Luther, R., & Rötzel, P. (2007). Hedonic Prices in The German Market for Mobile Phones. Telecommunications Policy, 31(1), 4–13.
Duan, W., Cao, Q., Yu, Y., & Levy, S. (2013). Mining Online User-Generated Content: Using Sentiment Analysis Technique to Study Hotel Service Quality. In System Sciences (HICSS), 2013 46th Hawaii International Conference on (pp. 3119–3128).
Duggan, M., & Rainie, L. (2012). Cell Phone Activities 2012. Pew Research Center. Washington, D.C. Retrieved from http://www.pewinternet.org/files/old-media/Files/Reports/2012/PIP_CellActivities_11.25.pdf
Esuli, A., Sebastiani, F., & Moruzzi, V. G. (2006). SentiWordNet : A Publicly Available Lexical Resource for Opinion Mining. In Proceedings of LREC (Vol. 6, pp. 417–422).
Feldman, R. (2013). Techniques and Applications for Sentiment Analysis. Communications of the ACM, 56(4), 82.
Garcia-Moya, L., Anaya-Sanchez, H., & Berlanga-Llavori, R. (2013). Retrieving Product Features and Opinions from Customer Reviews. IEEE Intelligent Systems, 28(3), 19–27.
Gu, B., Park, J., & Konana, P. (2011). Research Note—The Impact of External Word-of-Mouth Sources on Retailer Sales of High-Involvement Products. Information Systems Research, 23(1), 182–196.
Harrison, D., & Rubinfeld, D. L. (1978). Hedonic Housing Prices and The Demand for Clean Air. Journal of Environmental Economics and Management, 5(1), 81–102.
Hu, N., Koh, N. S., & Reddy, S. K. (2014). Ratings Lead You to The Product, Reviews Help You Clinch It? The Mediating Role of Online Review Sentiments on Product Sales. Decision Support Systems, 57(0), 42–53.
Hu, N., Liu, L., & Zhang, J. J. (2008). Do Online Reviews Affect Product Sales? The Role of Reviewer Characteristics and Temporal Effects. Information Technology and Management, 9(3), 201–214.
Kapferer, J.-N., & Laurent, G. (1985). Consumers Involvement Profiles: New Empirical Result. Advances in Consumer Research, 12(1), 290–295.
Kapferer, J.-N., & Laurent, G. (1993). Further Evidence on The Consumer Involvement Profile: Five Antecedents of Involvement. Psychology and Marketing, 10(4), 347–355.
Kerris, N., & Brewer, T. (2013, September 23). First Weekend iPhone Sales Top Nine Million, Sets New Record. Apple Inc. Cupertino, California. Retrieved from http://www.apple.com/pr/library/2013/09/23First-Weekend-iPhone-Sales-Top-Nine-Million-Sets-New-Record.html
Koppius, O., & Reijden, P. V.D. (2010). The Value of Online Product Buzz. In Proceedings of the ICIS (p. 171).
Lin, L.-Y., & Chen, C.-S. (2006). The Influence of The Country-of-origin Image, Product Knowledge and Product Involvement on Consumer Purchase Decisions: An Empirical Study of Insurance and Catering Services in Taiwan. Journal of Consumer Marketing, 23(5), 248–265.
Liu, B. (2012). Sentiment Analysis and Opinion Mining. (G. Hirst, Ed.)Synthesis Lectures on Human Language Technologies. San Francisco, CA, USA: Morgan & Claypool.
Liu, B., & Zhang, L. (2012). A Survey of Opinion Mining and Sentiment Analysis. In Mining Text Data (pp. 415–463). Boston, MA: Springer US.
Malpezzi, S. (2002). Hedonic Pricing Models : a Selective and Applied Review. In T. O’Sullivan & K. Gibb (Eds.), Housing Economics and Public Policy (pp. 67–89). Oxford, UK: Blackwell Science Ltd.
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S. J., & McClosky, D. (2014). The Stanford CoreNLP Natural Language Processing Toolkit. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations (pp. 55–60).
Miner, G., Elder, J., Fast, A., Hill, T., Nisbet, R., & Delen, D. (2012). Conceptual Foundations of Text Mining and Preprocessing Steps. In Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications (pp. 43–51). Boston, MA, USA: Academic Press.
Miura, T., & Asami, Y. (2012). Hedonic Analysis for The Estimation of Condominium Rent Utilizing Web Information. Environment and Planning B: Planning and Design, 39(6), 1049–1068.
Mudambi, S. M., & Schuff, D. (2010). What Makes A Helpful Online Review? A Study of Customer Reviews on Amazon.com. MIS Quarterly, 34(1), 185–200.
Nielsen. (2013a). Global Trust in Advertising Report. Retrieved from http://www.nielsen.com/content/dam/corporate/au/en/reports/2013/Nielsen_Global_Trust_in_Advertising_Report_September_2013.pdf
Nielsen. (2013b). The Mobile Consumer: A Global Snapshot. Retrieved from http://www.nielsen.com/content/dam/corporate/uk/en/documents/Mobile-Consumer-Report-2013.pdf
Nielsen. (2014). The Role of Content in the Consumer Decision Making Process. Retrieved from http://www.inpwrd.com/the_role_of_content_inpowered.pdf
Pakes, A. (2003). A Reconsideration of Hedonic Price Indexes with An Application to PC’s. American Economic Review, 93(5), 1578–1596.
Park, J., Gu, B., & Lee, H. (2012). The Relationship Between Retailer-hosted and Third-party Hosted WOM Sources and Their Influence on Retailer Sales. Electronic Commerce Research and Applications, 11(3), 253–261.
Qiu, G., Liu, B., Bu, J., & Chen, C. (2009). Expanding Domain Sentiment Lexicon Through Double Propagation. In Proceedings of the 21st International Jont Conference on Artifical Intelligence (pp. 1199–1204). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
Qiu, G., Liu, B., Bu, J., & Chen, C. (2011). Opinion Word Expansion and Target Extraction through Double Propagation. Computational Linguistics, 37(1), 9–27.
Quester, P., & Lim, A. (2003). Product Involvement/Brand Loyalty:Is There A Link? Journal of Product & Brand Management, 12(1), 22–38.
Synchrony Financial. (2014). Cautious but Climbing: Synchrony Financial’s Third Annual Major Purchase Consumer Study Shows Confidence on the Rise. Retrieved from https://www.synchronyfinancial.com/third-annual-major-purchase.pdf
White, A. G., Abel, J. R., Berndt, E. R., & Monroe, C. W. (2005). Hedonic Price Indexes for Personal Computer Operating Systems and Productivity Suites. Annales d’Economie et de Statistique, (79-80), 787–807.
Zhang, L., Ma, B., & Cartwright, D. K. (2013). The Impact of Online User Reviews on Cameras Sales. European Journal of Marketing, 47(7), 1115–1128.
Zhu, F., & Zhang, X. (2010). Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics. Journal of Marketing, 74(2), 133–148.
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