博碩士論文 101423054 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:9 、訪客IP:18.204.48.40
姓名 余芷函(Chih-han Yu)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 滾動式RFM基礎的線上再購行為預測模型 ─以台灣Yahoo!奇摩拍賣女裝分類為例
(A Rolling RFM-based Prediction Model of Online Repurchase Behavior: A Case of Women′s Apparel at Yahoo! Taiwan Auction Website)
相關論文
★ 應用結構行動理論探討跨國企業導入供應鏈管理之個案研究-以資訊電子業為例★ 應用調適性結構行動理論探討ERP卅MES系統導入、轉移和整合之個案研究
★ LCD面板製造廠資訊系統商業價值之個案研究★ 應用調適性結構行動理論探討CIM系統的導入 -以TFT-LCD產業為例
★ ERP系統品質Enhancement的實徵研究★ 以資訊處理理論探討出貨管理系統在TFT-LCD產業的導入及影響之個案研究
★ 連接器供應商於中國大陸地區導入出貨管理系統之個案研究★ 以AHP法探討跨國企業評選固網供應商之決策準則
★ 工具機製造業導入協作式接單服務之探討--以沖床製造廠商為例★ 製造業導入先進規劃與排程系統之探討—以筆電領導廠商為例
★ 經銷商管理的再造-台灣知名飲料業的個案研究★ 運用精實六標準差手法改善資料品質─某TFT-LCD業者之個案研究
★ 第三方物流業者之設施規劃與方案評估-以C物流公司為例★ 期望和認知差異對ERP導入專案的影響-以B公司導入SAP為例
★ 使用者主導系統導入時資訊單位的角色-以W公司導入產品資料管理系統為例★ 運用限制理論探討F公司大型資訊服務專案執行之研究
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 隨著網路購物的快速成長,企業對顧客電子商務受到實務界和學者更多的重視。線上賣家有更多機會接觸到線上消費者,同時消費者在網路購物也有更多的選擇。線上賣家必須專注於回流的顧客才能以更具成本效益的方式增加營收。要實現這些潛在的利潤,線上賣家需要一個兼具效率和效益的預測工具來掌握其顧客的購買行為。以Yahoo!奇摩拍賣女裝分類為目標,本研究運用真實交易資料建立了一個兼具效果穩定且結果準確的滾動式線上再購行為預測模型。
本研究的資料蒐集自Yahoo!奇摩拍賣女裝分類中2013年9月30日以前的所有交易資料,總交易筆數約為558萬筆。本研究將所有資料以敘述統計作初步分析以觀察再購顧客的特性,並且利用三至六個預測變數建立了滾動式預測模型,此六個預測變數分別為:上次交易時間間隔、交易次數、累積交易金額、平均交易金額、上次交易評價及過去再購家數,也檢測了不同時間點及時間範圍的模型分類正確率,來驗證此滾動式預測模型不會受到時間點及時間範圍改變的影響。最後,本研究針對預測模型進行模型適配度檢定及羅吉斯迴歸分析,分析結果顯示上次交易時間間隔越長、平均交易金額越多,再購行為發生的機率越低;相對地,交易次數越多、累積交易金額越多、上次交易評價越佳或過去再購家數越多,再購行為發生的機率越高。其中只有再購家數的結果和我們提出的假說不一致。本研究的主要貢獻有三:(1)實務上可以幫助線上賣家進行目標行銷以留住舊顧客;(2)以最後一次評價和再購家數擴充RFM模型可以有效提昇預測的準確率;(3)根據完整交易資料的敘述統計結果可以作為其他線上消費者研究的參照。
摘要(英) Online shopping has grown rapidly so that B2C e-commerce gets more attention by both practitioners and researchers. While the seller has more opportunities to reach more online consumers, the online shopper has more choices as well. By focusing on returning customers, online sellers can increase revenues in a more cost-effective way. To realize the potential profits, online sellers need an efficient and effective prediction tool to capture their customers’ purchase behavior. Targeting on the woman apparel at Yahoo! Taiwan auction website, this study uses the real transaction data to develop a rolling prediction model of the online repurchase behavior, which exhibits both stability and prediction accuracy.
The dataset collected from Yahoo! Taiwan auction website includes all transaction data dated before September 30, 2013 and the total number of transaction records is over 5.58 million. Based on this rich dataset, we applied a comprehensive description statistics to observe characteristics of repeat customers. We also propose a rolling repurchase behavior prediction model with up to six independent variables, including RFM (recency, frequency, total/average monetary), the last rating and the number of repurchased sellers. Classification rates of different time points and time intervals used in prediction were examined to validate the model. Through tests of goodness of model fit and logistic regression analysis, we found that the recency and the average monetary are negatively related to the probability of repurchase, whereas the higher the frequency, the total monetary, the last rating, and the number of repurchased sellers, the repurchase is more likely to occur. Only the result of the number of repurchased sellers is contradictory to our hypothesis. The contribution of this study has three: (1) practically help online sellers with target marketing to retain old customers; (2) augment the RFM model with the last rating and the number of repurchased sellers can enhance prediction accuracy effectively; (3) the description statistics based on all real transactions can be a reference for online shoppers’ behavior research.
關鍵字(中) ★ 再購行為
★ RFM模型
★ 網路購物
★ 滾動式預測
關鍵字(英) ★ Repurchase Behavior
★ RFM Model
★ Online Shopping
★ Rolling Forecast
論文目次 摘要 i
Abstract ii
誌謝 iii
Contents iv
List of Tables v
List of Figures vi
1 Introduction 1
1.1 Research Background and Motivations 1
1.2 Research Purposes and Questions 4
2 Literature Review 6
2.1 Online shopping 6
2.2 Consumer Behavior 8
2.3 Loyalty and Online Repurchase Behavior 10
2.4 RFM Model 11
3 Research Methodology 13
3.1 Research Model and Hypotheses 13
3.2 Research Design 16
3.2.1 Data Collection 16
3.2.2 Data Crawling Process 18
4 Data Analysis and Results 21
4.1 Description Statistics 21
4.2 Logistic Regression Analysis 28
5 Conclusion 37
5.1 Research Conclusion 37
5.2 Contributions 40
5.3 Managerial Implications 40
5.4 Limitations and Future Research 41
References 42
參考文獻 Anderson, R. E., and Srinivasan, S. S., 2003, “E-Satisfaction and E-Loyalty: A Contingency Framework,” Psychology & Marketing, 20(2), 123-138.
Ariely, D., and Norton, M. I., 2008, “How Actions Create–Not Just Reveal–Preferences,” Trends in Cognitive Sciences, 12(1), 13-16.
Ba, S. and Pavlou, P. A., 2002, “Evidence of the Effect of Trust Building Technology in Electronic Markets: Price Premiums and Buyer Behavior,” MIS Quarterly, 26(3), 243-268.
Bakos, Y., 2001, “The Emerging Landscape for Retail E-Commerce,” The Journal of Economic Perspectives, 15(1), 69-80.
Bapna, R., Goes, P. and Gupta, A., 2001, “Insights and Analyses of Online Auctions,” Communications of the ACM, 44(11), 42-50.
Bauer, C.L., 1988, “A Direct Mail Customer Purchase Model,” Journal of Direct Marketing, 2(3), 16-24.
Belch, G.E., 1978, “Belief Systems and the Differential Role of the Self-Concept,” Association for Consumer Research, 05, 320-325.
Brian, F.B., Kimberly, A.N., and Colin, M.V., 2003, “Innovativeness and Variety of Internet Shopping,” Internet Research, 13(3), 156-169.
Brown, G.H., 1953, Brand Loyalty-fact or Fiction. Trademark Rep., 43, 251.
Cao, Z., Zhou, T., and Chen, S., 2010, “The Differences between Multi-loyal and Uni-loyal Customers,” Management Review, 1(8).
Chiu, C.Y., Lin, Z.P., Chen, P.C., and Kuo, I.K., 2009, "Applying RFM Model to Evaluate the E-loyalty for Information-based Website," International Journal of Electronic Business Management, 7(4), 278-285.
Darley, W.K., Blankson, C., and Luethge, D.J., 2010. “Toward an Integrated Framework for Online Consumer Behavior and Decision Making Process: A Review,” Psychology & Marketing, 27(2), 94-116.
Engel, J.F., Kollat, D.T., and Blackwell, R.D., 1982, Consumer Behavior (4th ed.), New York: Holt, Rinehart and Winston.
Gregg, D. G. and Walczak, S., 2008, “Dressing Your Online Auction Business for Success: An Experiment Comparing Two eBay Businesses,” MIS Quarterly, 32(3), 653-670.
Hsu, C. L., Wu, C. C., and Chen, M. C., 2013, “An Empirical Analysis of the Antecedents of E-satisfaction and E-loyalty: Focusing on the Role of Flow and its Antecedents,” Information Systems and e-Business Management, 11(2), 287-311.
Hughes, A.M., 1996, “Boosting Response with RFM,” Marketing Tools, 3(3), 4-10.
Kamakura, W. A., Mittal, V., De Rosa, F., and Mazzon, J. A., 2002, “Assessing the Service-profit Chain,” Marketing Science, 21(3), 294-317.
Karelaia, N., 2009, Predictably Irrational: The Hidden Forces That Shape Our Decisions. The Academy of Management Perspectives, 23(1), 86-88.
Kim, D.J., Ferrin, D.L., and Rao, H.R., 2009, “Trust and Satisfaction, Two Stepping Stones for Successful E-Commerce Relationships: A Longitudinal Exploration,” Information Systems Research, 20(2), 237-257.
Kwon, W., and Lennon, S.J., 2009, “What Induces Online Loyalty? Online versus Offline Brand Images,” Journal of Business Research, 62, 557-564.
Liu, D.R. and Shih, Y.Y., 2005, “Integrating AHP and Data Mining for Product Recommendation based on Customer Lifetime Value,” Information and Management, 42(3), 387-400.
Malhotra, N.K., Kim, S.S., and Agarwal, J., 2004, “Internet Users’ Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model,” Information Systems Research, 15(4), 336-355.
Mittal, V., and Kamakura, W.A., 2001, “Satisfaction, Repurchase Intent, and Repurchase Behavior: Investigating the Moderating Effect of Customer Characteristics,” Journal of Marketing Research, 38(1), 131-142.
Oliver, R. L., 1997. Satisfaction: A Behavioral Perspective on the Consumer, Boston: McGraw-Hill.
Paul, P., Pennell, M.L., and Lemeshow, S., 2013, “Standardizing the power of the Hosmer-Lemeshow goodness of fit test in large data sets,” Statistics in Medicine, 32(1), 67-80.
Pfeifer, P., 2005, “The optimal ratio of acquisition and retention costs,” Journal of Targeting, Measurement and Analysis for Marketing, 13(2), 179-188.
Posselt, T. and Gerstner, E., 2005, “Pre-sale vs. Post-sale e-Satisfaction: Impact on Repurchase Intention and Overall Satisfaction,” Journal of Interactive Marketing, 19(4), 35-47.
Qu, Z., Zhang, H., and Li, H., 2008, “Determinants of Online Merchant Rating: Content Analysis of Consumer Comments about Yahoo Merchants,” Decision Support Systems, 46, 440-449.
Reichheld, F. F., and Schefter, P., 2000, “E-loyalty: Your Secret Weapon on the Web,” Harvard Business Review, 78(4), 105-113.
Sambandam, R., and Lord, K.R., 1995, “Switching Behavior in Automobile Markets: A Consideration-sets Model,” Journal of the Academy of Marketing Science, 23(1), 57-65.
SeeWhy, 2013, The State of Retailing Online 2013.
Solomon, M.R., Polegato, R., and Zaichkowsky, J.L., 2009, Consumer Behavior: Buying, Having, and Being, Upper Saddle River, NJ: Pearson Prentice Hall.
Srinivasan, S.S., Anderson, R., and Ponnavolu, K., 2002, “Customer Loyalty in E-commerce: An Exploration of its Antecedents and Consequences,” Journal of Retailing, 78(1), 41-50.
Strack, F., & Mussweiler, T., 1997, “Explaining the Enigmatic Anchoring Effect: Mechanisms of Selective Accessibility,” Journal of Personality and Social Psychology, 73(3), 437–446.
Teo, T.S.H., and Yeong, Y.D., 2003, “Assessing the Consumer Decision Process in the Digital Marketplace,” The International Journal of Management Science, 31(5), 349-363.
Van Slyke, C., Shim, J.T., Johnson, R. and Jiang, J.J., 2006, “Concern for Information Privacy and Online Consumer Purchasing,” Journal of the Association for Information Systems, 7(6), 415-443.
Yen, C.H., and Lu, H.P., 2008, “Factors Influencing Online Auction Repurchase Intention,” Internet Research, 18(1), 7-25.
Zeithaml, V. A., Berry, L.L., and Parasuraman, A., 1996, “The Behavioral Consequences of Service Quality,” Journal of Marketing, 60, 31-46.
財團法人台灣網路資訊中心(2012),2012年台灣寬頻網路使用調查報告,取自http://www.twnic.net.tw/download/200307/20120709c.pdf。
資策會(2012),2012 ICT產業白皮書(下)-顯示/消費性電子/軟體服務,財團法人資訊工業策進會產業情報研究所MIC出版。
資策會(2012),2012資訊服務產業年鑑,財團法人資訊工業策進會產業情報研究所MIC出版。
資策會(2013),2013台灣網友網路購物行為調查,資策會(MIC) AISP情報顧問服務資料庫。
資策會(2013),台灣網友網路購物消費趨勢分析,資策會(MIC) AISP情報顧問服務資料庫。
何靖遠、陳慧玲、廖致淵(2014),以RFM為基礎的消費者平台再購行為預測模型──以Yahoo!奇摩拍賣為例,數據分析,第九卷,第一期。
何靖遠、賴宜楓(2012),線上消費者再購行為的實徵研究,電子商務學報,第十四卷,第二期。
何靖遠、廖致淵、許文錦(2013),Yahoo!奇摩拍賣女裝上衣消費者再購行為之預測,2013 ERP國際學術暨實務研討會,台北,銘傳大學。
指導教授 何靖遠(Chin-Yuan Ho) 審核日期 2014-7-10
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明