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研究生:陳逸綺
研究生(外文):Yi-Chi Chen
論文名稱:使用決策樹建立行動通訊服務消費者購買意圖之分類模式
論文名稱(外文):Using Decision Tree to Construct the Classification Model of Consumer Purchasing Intention for Mobile Communication Service
指導教授:何建達何建達引用關係
指導教授(外文):Chien-Ta Ho
口試委員:徐淑媚魏中倫
口試日期:2019-07-24
學位類別:碩士
校院名稱:國立中興大學
系所名稱:科技管理研究所
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:53
中文關鍵詞:決策樹分類模式行動通訊服務購買意圖
外文關鍵詞:Decision treeClassification modelMobile communication servicePurchase intention
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近年台灣行動通訊產業蓬勃發展,競爭相當激烈,行動裝置的使用已成為現代人生活中不可或缺的一環,與之搭配的行動通訊服務也成為消費者的重要考量之一。若要能在競爭激烈的銷售市場中脫穎而出,電信業者必須透過大數據分析了解消費者之購買意圖,此外掌握正確的消費族群也顯得格外重要。本研究以行動通訊服務之相關文獻及國家通訊委員會之統計資料為研究基礎,歸納統整出影響消費者在購買行動通訊服務時的相關因素共計十六項,透過便利抽樣,以使用過行動通訊服務之台灣民眾為調查對象,利用消費者相關數據進行決策樹(Decision Tree)分析,透過CHAID演算法建立行動通訊服務購買意圖之分類模式,目的是提供電信業者了解消費者在進行選擇時可能影響的因素,並針對正確的客群執行有效的行銷策略。本研究所建立之行動通訊服務消費者購買意圖之分類模式,在中華電信用戶續約意願預測分類正確率為92.4%,台灣大哥大用戶續約意願預測分類正確率為84.1%,遠傳電信用戶續約意願預測分類正確率為79.4%,行動通訊服務供應商購買意圖預測分類正確率為51.3%。研究結果顯示,用戶續約預測具有相當之參考價值,而行動通訊服務供應商購買意圖預測未達有效之預測。藉由用戶續約預測,電信業者可掌握自家用戶續約關鍵因素,了解自身的競爭優勢及弱勢加以強化或改善,此外也可依據分類模式,針對不同消費族群斟酌須投入之行銷成本,在吸引消費者購買行動通訊服務的同時,更有效的運用行銷資源。
In recent years, mobile communication industry has developed vigorously in Taiwan, and the competition in the industry is fierce. Mobile devices have become an indispensable part of modern life, and the mobile communication services which accompany mobile devices have become an important consideration for consumers. To stand out from the highly competitive mobile communications services market, telecom carriers have to understand consumer purchasing intentions through big data analytics. Based on the literature review of the mobile communication service and the statistical data of the National Communications Commission, the study summarizes the 16 relevant factors that affect consumers' purchase of mobile communication services. Through convenient sampling, the survey is aimed at Taiwanese people who have used mobile communication services. By using consumer-related data and CHAID algorithm for analysis, constructing a decision tree and establishing the classification model of mobile communication service consumer purchasing intention. The purpose of this study is to provide telecom carries with an understanding of the factors that may influence consumer choice and to implement effective marketing strategies for the target audience. In this study, the accuracy rate of classification of Chunghwa Telecom users' renewal intention is 92.4%, Taiwan Mobile users' renewal intention is 84.1%, and Far EasTone Telecommunications users' renewal intention is 79.4%. However, the accuracy rate of consumers’ classification of purchasing intentions for mobile communication services is only 51.3%. The results of this study show that the forecast of user renewal intention has a certain reference value. The forecast of the purchasing intention of the mobile communication service is not valid. Through the user renewal intention forecast, the telecom carries can grasp the key factors of their own users to renew, understand their own competitive advantages and weaknesses, and then strengthen or improve them. In addition, depending on the classification model, the marketing costs to be invested in different customer groups can be considered. Moreover, while attracting consumers to purchase mobile communication services, telecom carries can more effectively allocate marketing resources.
摘要 i
Abstract ii
目錄 iii
圖目錄 iv
表目錄 v
第一章 緒 論 1
第二章 文獻探討 4
第一節 台灣電信市場現況 4
第二節 電信服務事業說明 12
第三節 購買意圖 17
第四節 決策樹 18
第三章 研究方法 23
第一節 研究流程 23
第二節 研究架構與假設 24
第三節 研究變數之衡量 25
第四節 研究設計 28
第五節 分類模式建立步驟 30
第四章 資料分析 31
第一節 問卷收回 31
第二節 建立決策樹分類模式 37
第五章 結論與建議 45
第一節 結論 45
第二節 研究貢獻 46
第三節 研究限制與建議 47
文獻參考 48
附錄 52
Bühler, S., & Haucap, J. (2004). Mobile number portability. Journal of Industry, Competition Trade, 4(3), 223-238.
Basti, E., Kuzey, C., & Delen, D. (2015). Analyzing initial public offerings' short-term performance using decision trees and SVMs. Decision Support Systems, 73, 15-27.
Berry, M. J., & Linoff, G. S. (2004). Data mining techniques: for marketing, sales, and customer relationship management. United States: John Wiley & Sons.
Blut, M., Frennea, C. M., Mittal, V., & Mothersbaugh, D. L. (2015). How procedural, financial and relational switching costs affect customer satisfaction, repurchase intentions, and repurchase behavior: A meta-analysis. International Journal of Research in Marketing, 32(2), 226-229.
Center, P. R. (2018). List of smart phone penetration rates in various countries. Retrieved from https://www.wikiwand.com/zh/
Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers’ product evaluations. Journal of marketing research, 28(3), 307-319.
Dowling, G. R. (1986). Managing your corporate images. Industrial marketing management, 15(2), 109-115.
Erkan, I., & Evans, C. (2016). The influence of eWOM in social media on consumers’ purchase intentions: An extended approach to information adoption. Computers in Human Behavior, 61, 47-55.
Fishbein, M., & Ajzen, I. (1977). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Philosophy and Rhetoric, 10(2), 130-132.
Frawley, W. J., Piatetsky-Shapiro, G., & Matheus, C. J. (1992). Knowledge discovery in databases: An overview. AI magazine, 13(3), 57-70.
Garbarino, E., & Johnson, M. S. (1999). The different roles of satisfaction, trust, and commitment in customer relationships. Journal of marketing, 63(2), 70-87.
Hallowell, R. (1996). The relationships of customer satisfaction, customer loyalty, and profitability: An empirical study. International Journal of Service Industry Management, 7(4), 27-42. doi:10.1108/09564239610129931
Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques: Elsevier.
Hand, D. J., Mannila, H., & Smyth, P. (2001). Principles of data mining (adaptive computation and machine learning). Cambridge: The MIT Press.
Hartigan, J. A. (1975). Clustering Algorithms (99th ed.). New York: John Wiley & Sons, Inc.
Hellier, P. K., Geursen, G. M., Carr, R. A., & Rickard, J. A. (2003). Customer repurchase intention: A general structural equation model. European journal of marketing, 37(11/12), 1762-1800.
Hsiao, K.-L., & Chen, C.-C. (2016). What drives in-app purchase intention for mobile games? An examination of perceived values and loyalty. Electronic Commerce Research Applications, 16, 18-29.
Hui, S. C., & Jha, G. (2000). Data mining for customer service support. Information Management, 38(1), 1-13.
Kim, D. J., & Hwang, Y. (2012). A study of mobile internet user’s service quality perceptions from a user’s utilitarian and hedonic value tendency perspectives. Information Systems Frontiers, 14(2), 409-421.
Kim, H. S., & Yoon, C. H. (2004). Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market. Telecommunications policy, 28(9-10), 751-765.
Kim, M. K., Park, M. C., & Jeong, D. H. (2004). The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunication services. Telecommunications policy, 28(2), 145-159.
Kotler, P. (1997). Marketing Management: Analysis, Planning, Implementation, and Control. United States: Prentice Hall.
Liang, L. J., Choi, H. C., & Joppe, M. (2018). Understanding repurchase intention of Airbnb consumers: perceived authenticity, electronic word-of-mouth, and price sensitivity. Journal of Travel Tourism Marketing, 35(1), 73-89.
Lyytinen, K. (2001). Mobile Commerce: A New Frontier for E-business. Paper presented at the Proceedings of the 34th Annual Hawaii International Conference on System Sciences.
Morwitz, V. G., & Schmittlein, D. (1992). Using segmentation to improve sales forecasts based on purchase intent: Which “intenders” actually buy? Journal of marketing research, 29(4), 391-405.
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). Servqual: A multiple-item scale for measuring consumer perc. Journal of retailing, 64(1), 12.
Quinlan, J. R. (1979). Discovering rules from large collections of examples: a case study. Paper presented at the Expert systems in the microelectronic age.
Quinlan, J. R. (1986). Induction of Decision Trees. Machine Learning, 1(1), 81-106.
Quinlan, J. R. (2014). C4. 5: programs for machine learning. Amsterdam: Elsevier.
Rohlfs, J. (1974). A Theory of Interdependent Demand for a Communications Service. The Bell Journal of Economics and Management Science, 5(1), 16-37. doi:10.2307/3003090
Spears, N., & Singh, S. N. (2004). Measuring attitude toward the brand and purchase intentions. Journal of current issues research in advertising, 26(2), 53-66.
Stangl, B., Kastner, M., & Prayag, G. (2017). Pay-what-you-want for high-value priced services: Differences between potential, new, and repeat customers. Journal of Business Research, 74, 168-174.
Sugitomo, S., & Minami, S. (2018). Fundamental Factor Models Using Machine Learning. Journal of Mathematical Finance, 8(1), 111-118.
Wang, Z., & Tchernev, J. M. (2012). The “myth” of media multitasking: Reciprocal dynamics of media multitasking, personal needs, and gratifications. Journal of Communication, 62(3), 493-513.
Worcester, R. M., & Downham, J. (1986). Consumer market research handbook. New York: Sole distributors for the USA and Canada, Elsevier Science Pub. Co.
Xu, F., Li, Y., Chen, M., & Chen, S. (2016). Mobile cellular big data: linking cyberspace and the physical world with social ecology. IEEE network, 30(3), 6-12.
Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. Journal of marketing, 52(3), 2-22.
中華電信. (2018). 中華電信emome:加值服務. Retrieved from https://www.emome.net/4g/4g_vas
王慧萍. (2002). 市場分析:歐、美、亞 2.5G / 3G 行動電話服務市場之分析. 拓墣產業研究所研究週報.
台灣大哥大. (2018). 用戶服務. Retrieved from https://www.taiwanmobile.com/csonline/index.html
宋瓊玲. (2003). 資訊加值與圖書館服務. 國立中央大學圖書館通訊, 37.
李少芬. (2017). 4G飆網時代上網速率大調查. 消費者報導雜誌, 57-60.
李宗耀. (1996). 我國行動數據通訊服務的發展機會分析. (碩士學位論文), 國立交通大學, 新竹市.
李建邦, & 林汶鑫. (2012). 評估基於微陣列晶片資料之動態參數基因演算法(GADP)的最適分類器. Journal of Data Analysis, 7(5), 13-31.
施曉娟. (2005). 行動通訊服務產業市場結構與廠商行為之研究. (碩士學位論文), 國立交通大學, 新竹市.
孫善政. (1999). 行動數據通訊之發展趨勢研究. (碩士學位論文), 國立交通大學, 新竹市.
國家通訊傳播委員會. (2019a). 108年度電信統計圖表. Retrieved from https://www.ncc.gov.tw/chinese/news_detail.aspx?site_content_sn=1994&sn_f=41411
國家通訊傳播委員會. (2019b). 108通訊傳播市場調查. Retrieved from https://www.ncc.gov.tw/chinese/files/19031/5081_41115_190314_1.pdf
國家通訊傳播委員會. (2019c). 行動通訊市場統計資訊. Retrieved from https://www.ncc.gov.tw/chinese/news.aspx?site_content_sn=3773&is_history=0
郭文聰. (1996). 無線數據通訊現況與展望. 工業技術研究院電腦與通訊工業研究所. 新竹市.
陳宇信. (2008). 我國行動電話服務產業市場結構、廠商行為與營運績效之研究. (碩士學位論文), 中華大學, 新竹市.
陳美雲, 洪春男, & 李建邦. (2016). 使用決策樹建立汽車消費者購買意圖分類模式. Journal of Data Analysis, 11(2), 47-75.
遠傳電信. (2018). 數位加值服務. Retrieved from https://www.fetnet.net/multimedia/app-list.html
謝品薇. (2015). 4G吃到飽資費演進與相關因素之實證分析. 成功大學, Available from Airiti AiritiLibrary database.
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