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題名 消費者輿情對跨境網購產品銷售量之影響：以淘寶網為例
The Effects of Consumer Comments and Sentiments on Product Sales of Cross-border Shopping Websites: The Taobao Case
作者 呂奕勳
貢獻者 李有仁
呂奕勳
關鍵詞 跨境線上購物行為
線上評論分析
文字探勘
情感分析
Cross-Border Online Shopping Behaviour
Online Review Analytic
Text Mining
Sentiment Analysis
日期 2016
上傳時間 22-Aug-2016 10:45:59 (UTC+8)
摘要 近年來傳統線上購物正面臨著一連串的市場困境，如削價競爭、廉價品競爭等，因此導致銷售量之成長趨緩，反觀跨境線上購物卻出現了蓬勃發展的態勢，因而讓跨境線上購物成為驅動經濟活動與國際貿易的新引擎。另一方面，由於跨境線上購物的情境複雜性遠高於傳統的境內線上購物，業者們欲開發一海外新市場，必須先了解該地消費者行為與其購買決策過程後，才能制定出好的商業策略，並且進一步將產品導向的服務轉化成為以顧客導向的服務，才有機會為傳統線上購物之困境另闢生機。因此，引取並了解消費者所體認的內在價值是經營跨境線上購物最重要的成功因素。
本研究將試圖將傳統境內線上購物研究擴展到跨境線上購物議題，藉由文字探勘(Text Mining)分析、語意情感分析與 k-means 分群演算法，挖掘出消費者對於所購買商品之評論的常見內容型態與所購買商品之類別，並試圖找出跨境網購平台上各項因素及商品評論對於產品銷售量間之關連性，提供未來研究者及跨境網購平台業者決策之依據。
While online shopping websites are facing the difficulties of price and low-quality competition, cross-border online shopping is on a vigorous development trend, showing that cross-border online shopping is an important trend of online shopping field. Due to the complexity of cross-border online shopping is much higher than the traditional domestic online shopping, so understanding the value of cross-border online shopping consumers is the most important success factors. Companies want to develop new markets abroad, must understand the local consumer’s behaviour and their decision-making process in order to make good business strategies.
This study uses text mining analytic technology, semantic analysis techniques, and k-means clustering algorithm to identify characteristics of consumers’ reviews and the common categories of goods they purchased.
After getting the reason why consumers use cross-border online shopping service and what values they got in this service. Researcher can predict and analyse the evolution and development of cross-border online shopping, provide reference for future online shopping academic studies and online shopping industry’s decision-making.
參考文獻 1. 呂雪晴(2015)。我國跨境網絡購物熱潮下的冷思考。經濟縱橫，7，48-51。
2. 林湘昀(2014)。從國際貿易流程架構看臺灣跨境線上購物經營優勢與發展。致理技術學院企業管理系服務業經營管理碩士班碩士論文，未出版，新北市。
3. 陳瑞瑩 (2010)。影響消費者網路代購購買意願之研究。世新大學資訊管理學研究所碩士論文，未出版，台北市。
4. 黃怡萍(2010)。影響網路國際代購持續使用意圖因素之研究。國立高雄應用科技大學資訊管理系碩士在職專班碩士論文，未出版，高雄市。
5. 黃俐茹(2012)。消費者對網路跨國代購態度之研究。中央大學資訊管理學系碩士論文，未出版，桃園市。
6. 楊勝丞(2014)。從物流、金流和資訊流角度探討消費者知覺之跨境線上購物接受意圖之關鍵要素。世新大學資訊管理學研究所碩士論文。未出版，台北市。
7. 資策會(2015)，第七屆「兩岸線上購物產業合作暨交流會議」登場 聚焦「兩岸合作契機」、「網路創新應用」及「行動商務趨勢」 集結兩岸重量級業者 搶攻跨境電商商機，http://www.iii.org.tw/m/News-more.aspx?id=1627 (存取日期2015/12/28)
8. 資策會產業情報研究所 MIC (2009)，2009年台灣線上購物發展趨勢 線上購物發展趨勢-區隔、互動、跨國，http://mic.iii.org.tw/intelligence/pressroom/pop_pressfull.asp?sno=175&type1=2 (存取日期2015/12/28)
9. 臺灣民眾線上購物經驗(2015)，資策會市場情報，http://www.find.org.tw/market_info.aspx?n_ID=8621(存取日期2015/12/28)
10. 蔡宗霖 (2009)。 從美國 PayPal 經驗與歐盟支付服務指令論我國第三方支付服務之現狀與未來. 科技法律透析, 21(10), pp. 47-64.
11. 鄭仁富、翁逸妹(2015)。台灣消費者雙11線上購物行為，http://www.find.org.tw/market_info.aspx?n_ID=8631 (存取日期 2015/12/28)
12. Amblee, N. & Bui, T. X. (2007). The Impact of Electronic-Word-of-Mouth on Digital Microproducts: An Empirical Investigation of Amazon Shorts. In ECIS, pp. 36-47.
13. Banerjee, A. V. (1992). A simple model of herd behavior. The Quarterly Journal of Economics, pp. 797-817.
14. Barkhi, R. & Wallace, L. (2007). The impact of personality type on purchasing decisions in virtual stores. Information Technology and Management, 8 (4), pp. 313-330.
15. Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS quarterly, pp. 351-370.
16. Bone, P.F. (1995). Word-of-mouth effects on short-term and long-term product judgments. Journal of business research, 32(3), pp. 213-223.
17. Buttle, F. A. (1998). Word of mouth: understanding and managing referral marketing. Journal of strategic marketing, 6(3), pp. 241-254.
18. Chakrabarti, R. & Scholnick, B. (2002). International expansion of e‐retailers: Where the Amazon flows. Thunderbird International Business Review, 44(1), pp. 85-104.
19. Chen, K. J. & Bai, M. H. (1998). Unknown word detection for Chinese by a corpus-based learning method. International Journal of Computational Linguistics and Chinese Language Processing, 3(1), pp. 27-44.
20. Davis, A. & Khazanchi, D. (2008). An empirical study of online word of mouth as a predictor for multi‐product category e‐commerce sales. Electronic Markets, 18(2), pp. 130-141.
21. European Commission, 2006. Special Eurobarometer 252 “Consumer protection in the Internal Market”. [pdf] Available at: [Accessed 28 December 2015].
22. European Commission, 2008. Special Eurobarometer 298 “Consumer protection in the internal market”. [pdf] Available at: [Accessed 28 December 2015].
23. European Commission, 2010. Flash Eurobarometer FL 282. “Attitudes towards crossborder sales and consumer protection”. [pdf] Available at: [Accessed 28 December 2015].
24. European Commission, 2011. Flash Eurobarometer FL 299 “Consumer attitudes towards cross-border trade and consumer protection”. [pdf] Available at: [Accessed 28 December 2015].
25. European Commission, 2012. Flash Eurobarometer FL 332 “Consumers’ attitudes towards cross-border trade and consumer protection”. [pdf] Available at: [Accessed 28 December 2015].
26. European Commission, 2013. Flash Eurobarometer FL 358.”Consumer attitudes towards cross-border trade and consumer protection”. [pdf] Brussels: European Commission. Available at: [Accessed 28 December 2015].
27. Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), pp. 82-89.
28. Foucault, B. E. & Scheufele, D. A. (2002). Web vs campus store? Why students buy textbooks online. Journal of Consumer Marketing, 19(5), pp. 409-423.
29. Goldman, E. (2008). Brand Spillovers. Harv. JL & Tech., 22, pp. 381.
30. Gomez-Herrera, E., Martens, B. & Turlea, G. (2014). The Drivers and Impediments for Cross-border e-Commerce in the EU. Information Economics and Policy, 28, pp. 83-96.
31. Han, J., Kamber, M. & Pei, J. (2011). Data mining: concepts and techniques: concepts and techniques. Elsevier.
32. Hennig-Thurau, T., Gwinner, K.P., Walsh, G. and Gremler, D.D. (2004), ‘Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet?’, Journal of interactive marketing, 18(1), pp. 38-52.
33. Henning-Thurau, T. (2003). There’s No Business Like Movie Business. Wirtz, Bernd W.: Handbuch Medien-und Multimediamanagement. Gabler, 1.
34. Hu, M. & Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 168-177. ACM.
35. Huang, T & Oppewal, H. (2006). Why consumers hesitate to shop online? International Journal of Retail and Distribution Management, 34 (4/5), pp. 334-353.
36. Joines, J. L., Scherer, C. W. & Scheufele, D. A. (2003). Exploring motivations for consumer Web use and their implications for e-commerce. Journal of consumer marketing, 20(2), pp. 90-108.
37. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), pp. 1-167.
38. Mayo, E. (1945), The Social Problems of An Industrial Civilization, Routledge, Oxford, UK.
39. Mishra, P. (2015). Motivator of Online Shopping: The Income Factor. Asian Journal of Research in Banking and Finance, 5(11), pp. 34-46.
40. Pang, B. & Lee, L. (2008). Opinion mining and sentiment analysis.Foundations and trends in information retrieval, 2(1-2), pp. 1-135.
41. Ranganathan, C. & Ganapathy, S. (2002). Key dimensions of business-to-consumer web sites. Information & Management, 39(6), pp. 457-465.
42. Schaupp, L. C. & Bélanger, F. (2005). A Conjoint Analysis of Online Consumer Satisfaction1. Journal of Electronic Commerce Research, 6(2), pp. 95.
43. Sebastiani, F. (2002). Machine learning in automated text categorization. ACM computing surveys (CSUR), 34(1), pp. 1-47.
44. Singh, V.K., Piryani, R., Uddin, A. & Waila, P. (2013). Sentiment analysis of movie reviews: A new feature-based heuristic for aspect-level sentiment classification. In Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), 2013 International Multi-Conference on pp. 712-717. IEEE.
45. Sorce, P., Victor, P. & Stanley, W., (2005). "Attitude and age differences in online buying." International Journal of Retail & Distribution Management, 33(2), pp. 122-132.
46. Teo, T.S. (2001). Demographic and motivation variables associated with Internet usage activities. Internet Research, 11(2), pp. 125-137.
47. Vijayasarathy, L.R. (2004). Predicting consumer intentions to use on-line shopping: the case of an augmented technology acceptance model. Information and Management, 41 (6), pp. 747-762.
48. Westbrook, R.A. (1987). Product/consumption-based affective responses and postpurchase processes. Journal of Marketing Research, pp. 258-270.
描述 碩士
國立政治大學
資訊管理學系
103356035
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0103356035
資料類型 thesis
dc.contributor.advisor 李有仁zh_TW
dc.contributor.author (Authors) 呂奕勳zh_TW
dc.creator (作者) 呂奕勳zh_TW
dc.date (日期) 2016en_US
dc.date.accessioned 22-Aug-2016 10:45:59 (UTC+8)-
dc.date.available 22-Aug-2016 10:45:59 (UTC+8)-
dc.date.issued (上傳時間) 22-Aug-2016 10:45:59 (UTC+8)-
dc.identifier (Other Identifiers) G0103356035en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/100464-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 103356035zh_TW
dc.description.abstract (摘要) 近年來傳統線上購物正面臨著一連串的市場困境，如削價競爭、廉價品競爭等，因此導致銷售量之成長趨緩，反觀跨境線上購物卻出現了蓬勃發展的態勢，因而讓跨境線上購物成為驅動經濟活動與國際貿易的新引擎。另一方面，由於跨境線上購物的情境複雜性遠高於傳統的境內線上購物，業者們欲開發一海外新市場，必須先了解該地消費者行為與其購買決策過程後，才能制定出好的商業策略，並且進一步將產品導向的服務轉化成為以顧客導向的服務，才有機會為傳統線上購物之困境另闢生機。因此，引取並了解消費者所體認的內在價值是經營跨境線上購物最重要的成功因素。
本研究將試圖將傳統境內線上購物研究擴展到跨境線上購物議題，藉由文字探勘(Text Mining)分析、語意情感分析與 k-means 分群演算法，挖掘出消費者對於所購買商品之評論的常見內容型態與所購買商品之類別，並試圖找出跨境網購平台上各項因素及商品評論對於產品銷售量間之關連性，提供未來研究者及跨境網購平台業者決策之依據。		zh_TW
dc.description.abstract (摘要) While online shopping websites are facing the difficulties of price and low-quality competition, cross-border online shopping is on a vigorous development trend, showing that cross-border online shopping is an important trend of online shopping field. Due to the complexity of cross-border online shopping is much higher than the traditional domestic online shopping, so understanding the value of cross-border online shopping consumers is the most important success factors. Companies want to develop new markets abroad, must understand the local consumer’s behaviour and their decision-making process in order to make good business strategies.
This study uses text mining analytic technology, semantic analysis techniques, and k-means clustering algorithm to identify characteristics of consumers’ reviews and the common categories of goods they purchased.
After getting the reason why consumers use cross-border online shopping service and what values they got in this service. Researcher can predict and analyse the evolution and development of cross-border online shopping, provide reference for future online shopping academic studies and online shopping industry’s decision-making.		en_US
dc.description.tableofcontents 第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究問題與目的 2
第二章 文獻探討 4
第一節 跨境線上購物之挑戰與相關研究 4
第二節 網路口碑 7
第三節 與論情感分析 9
第四節 文字探勘 10
第三章 研究流程與方法 15
第一節 研究模型 15
第二節 研究方法 19
3.2.1 k-means分群演算法 19
3.2.2 回歸分析檢定 21
第三節 資料蒐集 22
第四節 資料前處理 25
第五節 情感傾向計算 29
第四章 研究結果驗證 30
第一節 商品評論內容分析 30
第二節 研究假說檢定 32
4.2.1 樣本特性分析 32
4.2.2 回歸分析結果 33
第五章 結論 42
第一節 研究結果 42
第二節 研究貢獻 43
5.2.1 學術面貢獻 43
5.2.2 實務面貢獻 43
第三節 研究限制與未來發展 43
第六章 參考文獻 45		zh_TW
dc.format.extent 1591329 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0103356035en_US
dc.subject (關鍵詞) 跨境線上購物行為zh_TW
dc.subject (關鍵詞) 線上評論分析zh_TW
dc.subject (關鍵詞) 文字探勘zh_TW
dc.subject (關鍵詞) 情感分析zh_TW
dc.subject (關鍵詞) Cross-Border Online Shopping Behaviouren_US
dc.subject (關鍵詞) Online Review Analyticen_US
dc.subject (關鍵詞) Text Miningen_US
dc.subject (關鍵詞) Sentiment Analysisen_US
dc.title (題名) 消費者輿情對跨境網購產品銷售量之影響：以淘寶網為例zh_TW
dc.title (題名) The Effects of Consumer Comments and Sentiments on Product Sales of Cross-border Shopping Websites: The Taobao Caseen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 1. 呂雪晴(2015)。我國跨境網絡購物熱潮下的冷思考。經濟縱橫，7，48-51。
2. 林湘昀(2014)。從國際貿易流程架構看臺灣跨境線上購物經營優勢與發展。致理技術學院企業管理系服務業經營管理碩士班碩士論文，未出版，新北市。
3. 陳瑞瑩 (2010)。影響消費者網路代購購買意願之研究。世新大學資訊管理學研究所碩士論文，未出版，台北市。
4. 黃怡萍(2010)。影響網路國際代購持續使用意圖因素之研究。國立高雄應用科技大學資訊管理系碩士在職專班碩士論文，未出版，高雄市。
5. 黃俐茹(2012)。消費者對網路跨國代購態度之研究。中央大學資訊管理學系碩士論文，未出版，桃園市。
6. 楊勝丞(2014)。從物流、金流和資訊流角度探討消費者知覺之跨境線上購物接受意圖之關鍵要素。世新大學資訊管理學研究所碩士論文。未出版，台北市。
7. 資策會(2015)，第七屆「兩岸線上購物產業合作暨交流會議」登場 聚焦「兩岸合作契機」、「網路創新應用」及「行動商務趨勢」 集結兩岸重量級業者 搶攻跨境電商商機，http://www.iii.org.tw/m/News-more.aspx?id=1627 (存取日期2015/12/28)
8. 資策會產業情報研究所 MIC (2009)，2009年台灣線上購物發展趨勢 線上購物發展趨勢-區隔、互動、跨國，http://mic.iii.org.tw/intelligence/pressroom/pop_pressfull.asp?sno=175&type1=2 (存取日期2015/12/28)
9. 臺灣民眾線上購物經驗(2015)，資策會市場情報，http://www.find.org.tw/market_info.aspx?n_ID=8621(存取日期2015/12/28)
10. 蔡宗霖 (2009)。 從美國 PayPal 經驗與歐盟支付服務指令論我國第三方支付服務之現狀與未來. 科技法律透析, 21(10), pp. 47-64.
11. 鄭仁富、翁逸妹(2015)。台灣消費者雙11線上購物行為，http://www.find.org.tw/market_info.aspx?n_ID=8631 (存取日期 2015/12/28)
12. Amblee, N. & Bui, T. X. (2007). The Impact of Electronic-Word-of-Mouth on Digital Microproducts: An Empirical Investigation of Amazon Shorts. In ECIS, pp. 36-47.
13. Banerjee, A. V. (1992). A simple model of herd behavior. The Quarterly Journal of Economics, pp. 797-817.
14. Barkhi, R. & Wallace, L. (2007). The impact of personality type on purchasing decisions in virtual stores. Information Technology and Management, 8 (4), pp. 313-330.
15. Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS quarterly, pp. 351-370.
16. Bone, P.F. (1995). Word-of-mouth effects on short-term and long-term product judgments. Journal of business research, 32(3), pp. 213-223.
17. Buttle, F. A. (1998). Word of mouth: understanding and managing referral marketing. Journal of strategic marketing, 6(3), pp. 241-254.
18. Chakrabarti, R. & Scholnick, B. (2002). International expansion of e‐retailers: Where the Amazon flows. Thunderbird International Business Review, 44(1), pp. 85-104.
19. Chen, K. J. & Bai, M. H. (1998). Unknown word detection for Chinese by a corpus-based learning method. International Journal of Computational Linguistics and Chinese Language Processing, 3(1), pp. 27-44.
20. Davis, A. & Khazanchi, D. (2008). An empirical study of online word of mouth as a predictor for multi‐product category e‐commerce sales. Electronic Markets, 18(2), pp. 130-141.
21. European Commission, 2006. Special Eurobarometer 252 “Consumer protection in the Internal Market”. [pdf] Available at: [Accessed 28 December 2015].
22. European Commission, 2008. Special Eurobarometer 298 “Consumer protection in the internal market”. [pdf] Available at: [Accessed 28 December 2015].
23. European Commission, 2010. Flash Eurobarometer FL 282. “Attitudes towards crossborder sales and consumer protection”. [pdf] Available at: [Accessed 28 December 2015].
24. European Commission, 2011. Flash Eurobarometer FL 299 “Consumer attitudes towards cross-border trade and consumer protection”. [pdf] Available at: [Accessed 28 December 2015].
25. European Commission, 2012. Flash Eurobarometer FL 332 “Consumers’ attitudes towards cross-border trade and consumer protection”. [pdf] Available at: [Accessed 28 December 2015].
26. European Commission, 2013. Flash Eurobarometer FL 358.”Consumer attitudes towards cross-border trade and consumer protection”. [pdf] Brussels: European Commission. Available at: [Accessed 28 December 2015].
27. Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), pp. 82-89.
28. Foucault, B. E. & Scheufele, D. A. (2002). Web vs campus store? Why students buy textbooks online. Journal of Consumer Marketing, 19(5), pp. 409-423.
29. Goldman, E. (2008). Brand Spillovers. Harv. JL & Tech., 22, pp. 381.
30. Gomez-Herrera, E., Martens, B. & Turlea, G. (2014). The Drivers and Impediments for Cross-border e-Commerce in the EU. Information Economics and Policy, 28, pp. 83-96.
31. Han, J., Kamber, M. & Pei, J. (2011). Data mining: concepts and techniques: concepts and techniques. Elsevier.
32. Hennig-Thurau, T., Gwinner, K.P., Walsh, G. and Gremler, D.D. (2004), ‘Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet?’, Journal of interactive marketing, 18(1), pp. 38-52.
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