第十四卷第二期 2012 年 6 月 (pp.307~328) 1 * RFM Yahoo! T RFM An Empirical Research of Online Consumers Repurchase Behavior Chin-Yuan Ho Yi-Feng Lai Dep

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1 第十四卷第二期 2012 年 6 月 (pp.307~328) 1 * RFM Yahoo! T RFM An Empirical Research of Online Consumers Repurchase Behavior Chin-Yuan Ho Yi-Feng Lai Department of Information Management, National Central University Abstract Acquiring a new customer is five/six times more costly than retaining an existing one has turned into a maxim of interactive marketing. Accordingly, understanding the online consumers repurchase behaviors can facilitate an e-business to identify potential returning customers and increase revenues in a more cost-effective way. Literature of online repurchase intention or loyalty intention can be found, but not for repurchase 1 * 99 chuckho@mgt.ncu.edu.tw June

2 behavior. We established a research model of online consumers repurchase behavior and its antecedents using the RFM model and ratings provided by auction website. Using web content mining technique, we collected real transaction data of T-shirt category from Taiwan s Yahoo! Auction website in March, 2011 and the historical data of those buyerseller pairs found in March transactions. Six-month is used to define valid repurchase cycle and through the logistic regression analysis, we found that the self-herding behavior, including the recency and the frequency of past buyer-seller transactions, and the rating given by the buyer have significant impacts on buyer s repurchase behavior. Finally, we discuss the findings with post-hoc analysis, and provide practical/managerial implications of this study. Key Words: Repurchase Behavior, RFM Model, Seller s Rating, Empirical Research 1. Amazon Zappos Liu 2009 Taobao 24.3% Reichheld and Schefter, 2000 Pfeifer, 2005 Kauffman Wood June 2012

3 Ba and Pavlou, 2002 Melnik and Alm, 2002; Weinberg and Davis, 2005 Kim et al., 2009Hughes 1996 RFM RFM RFM Recency Frequency Monetary valuerfm RFM R FM Expectation disconfirmation theory, EDT Yen and Lu, 2008a; Yen and Lu, 2008b Lee et al., 2011 Yahoo! [S 1 B 1 ] 1 2 [S 1 B 2 ] 2 2 [S 2 B 2 ] June

4 S n B n S 1 B 1 S 1 B 1 S 2 B 2 S 1 B 2 S 1 B 1 S 1 B R FM Information cascadesherdchen, 2008 Self-herding behaviorariely and Norton, 線上再購行為 310 June 2012

5 Srinivasan et al., 2002 e-loyalty Mittal Kamakura ,040 EKB Engel Kollat Blackwell 1982 Liang Lai 2002 EKB EKB Teo Yeong 2003 EKB 2.2 線上消費者自身過去行為的影響因素 e-bay Yahoo! Qu et al., 2008 Yahoo! Yahoo! Ariely Norton 2007 RFM RFM Anderson Srinivasan 2003 e-satisfaction June

6 Oliver, 1980; Kim et al., 2009 Kim H1 Recency effectqu et al., 2008; Posselt and Gerstner, H2 RFM R RecencyF Frequency M MonetaryRFM Kim H3 H4 2.3 再購行為的外在環境影響因素 312 June 2012

7 Ba and Pavlou, 2002 Weinberg and Davis, 2005; Yen and Lu, 2008a Gilkeson Reynolds H Logarithm 0 Ba Pavlou 2002 Negative ratingsconvex function Concave function June

8 1 * * Log 1 * 研究設計與資料收集 Ba and Pavlou, 2002; Kim et al., 2009 Kauffman and Wood, 2006 Yahoo! Yahoo! Yahoo! 1! /12/ /01/04! 67.39% 2001 Yahoo! 20.21% ARO Yahoo! 61.8% June 2012

2006 10 / 2006 Yahoo! T JAVA Yahoo! MySQL Yahoo!

9 / 2006 Yahoo! T JAVA Yahoo! MySQL Yahoo! T 2011/03/ /03/31 T JAVA CyberNeko HTML Parser /3/ Yahoo! June

10 4 Yahoo! 4 Y 資料分析方法 1 0 Hair 2010 Discriminant Logistic regression Y 1 π( x) log it[ π( x)] = log = α+ β1x1+ β2x2+ + β X 1 π( x) k k 316 June 2012

11 X b p(x) x Agresti, 1996 SPSS 敘述統計 Yahoo! Yahoo! T % % % % % 1, % % 1, % % 1, % % 1, % % 1, % % 1, % % 1, % % 1, % % 1, % % 1, % June

12 % 11.79% 93% , % % % % 92.6% 94.1% 96.4% 100% 88.4% 78.2% 90.8% 93.3% 95.5% 97.2% 84.1% 80% 0% 60% 40% 20% June 2012

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14 % 251 ~ % 501 ~ % 751 ~1, % 1,001 ~1, % 1,501 ~2, % 2,001 ~3, % 3,001 ~4, % 4,001 ~5, % 5, % 1, % 7 4,000 85% 70,000 4, % 4,000 4, % 4,000 4,000 8,001 ~12,000 4,000 B2C 8,000 7 (% (% (% 4, % 1, % 2, % 4,001 ~8, % % % 8,001 ~12, % % % 12,001 ~14, % % % 14,001 ~18, % % % 18, % % % 1, % 2, % 3, % 320 June 2012

15 4.2 假說檢定 2 5 Goodness of fit SPSS Omnibus χ 2 Chi-square p0.05 Hosmer-Lemeshowt χ 2 p 0.05 Cox & Snell R 2 R-square Nagelkerke R 2 R Omnibus χ 2 p Hosmer-Lemeshowt χ 2 p Cox & Snell R Nagelkerke R Omnibus Hosmer-Lemeshowt Cox & Snell R 2 9 p-value B Exp(B) p-value B June

16 9 B S.E. p Exp(B) *** *** * E E E E LLR Chi-square *** p-value 0.001, ** p-value 0.01, * p-value 0.05 p-value B 5.582Exp(B) p-value p-value 研究結論 Pfeifer, 2005 Self-herding behavior RFM Recency, Frequency, Monetary 322 June 2012

17 Hughes, 1996 Herding 2011/03/ /03/31 Yahoo! T Self-herding behavior RF Kim 2009Gilkeson Reynolds 2003 Hughes 1996 Shaw 2001RFM RFM M Monetary 6 0 ~2, , Pearson p-value June

18 10 Pearson *** *** *** p-value 0.001, ** p-value 0.01, * p-value 0.05 Gilkeson and Reynolds, 2003; Weinberg and Davis, 2005 Kauffman Wood 2006Kauffman Wood 2006 ebay 8, ,001 ~14, , Pearson p-value Pearson *** *** *** p-value 0.001, ** p-value 0.01, * p-value 研究貢獻 1 2 RFM 3 Yahoo! Blackwell June 2012

19 Recency R Frequency F 5.3 研究限制 T 35.2% Liu % 2011/03/ /03/31 T T 1, 管理意涵與實務意涵 20% 150% 40% 50% 5% 85% June

20 RFM Agresti, A Blackwell, R.D., Miniard, P.W., and Engel, J.F Mar. 2011) 2007Yahoo! Mar. 2011)! Mar. 2011) Anderson, R.E. and Srinivasan, S.S. (2003), E-satisfaction and e-loyalty: A contingency framework, Psychology & Marketing, 20(2), Ariely, D. and Norton, M.I. (2007), How actions create not just reveal preferences, Trends in Cognitive Sciences, 12(1), 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), Chen, Y.F. (2008), Herd behavior in purchasing books online, Computers in Human Behavior, 24(5), Engel, J.F., Kollat, D.T., and Blackwell, R.D. (1982), Consumer Behavior (4th ed.), New York, NY: Holt, Rinehart and Winston. Gilkeson, J.H. and Reynolds, K. (2003), Determinants of internet auction success and closing price: An exploratory study, Psychology & Marketing, 20(6), June 2012

21 Hair, J.F., Black, W.C., Babin, B.J., and Anderson, R.E. (2010), Multivariate Data Analysis: A Global Perspective (7th ed.), Upper Saddle River, NJ: Pearson Prentice Hall. Hughes, A.M. (1996), Boosting response with RFM, Marketing Tools, 3(3), Kauffman, R.J. and Wood, C.A. (2006), Doing their bidding: An empirical examination of factors that affect a buyer s utility in Internet auctions, Information Technology Management, 7(3), 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), Lee, C.H., Eze, U.C., and Ndubisi, N.O. (2011), Analyzing key determinants of online repurchase intentions, Journal of Marketing and Logistics, 23(2), Liang, T.P. and Lai, H.J. (2002), Effect of store design on consumer purchases: An empirical study of on-line bookstores, Information & Management, 39(6), Liu, Y., Yang, L., Wei, K.K., and Chen, H. (2009), Consumers repurchase probability in online marketplace: A belief updating perspective, in Proceedings of the Fifteenth Americas Conference on Information Systems (AMCIS 2009), California, USA, no. 183, Melnik, M.I. and Alm, J. (2002), Does a seller s ecommerce reputation matter evidence from ebay auctions, Journal of Industrial Economics, 50(3), Mittal, V. and Kamakura, W. (2001), Satisfaction, repurchase intent, and repurchase behavior: Investigating the moderating effect of customer characteristics, Journal of Marketing Research, 38(1), Oliver, R.L. (1980), A cognitive model of the antecedents and consequences of satisfaction decisions, Journal of Marketing Research, 17(4), Pfeifer, P. (2005), The optimal ratio of acquisition and retention costs, Journal of Targeting, Measurement and Analysis for Marketing, 13(2), 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), 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(1), Reichheld, F.F. and Schefter, P. (2000), E-loyalty: Your secret weapon on the web, Harvard Business Review, 78(4), June

22 Shaw, M.J., Subramaniam, C., Tan, G.W., and Welge, M.E. (2001), Knowledge management and data mining for marketing, Decision Support Systems, 31(1), 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), Teo, T.S.H. and Yeong, Y.D. (2003), Assessing the consumer decision process in the digital marketplace, Omega, 31(5), Weinberg, B.D. and Davis, L. (2005), Exploring the WOW in online-auction feedback, Journal of Business Research, 58(11), Yen, C.H. and Lu, H.P. (2008a), Factors influencing online auction repurchase intention, Internet Research, 19(1), Yen, C.H. and Lu, H.P. (2008b), Effects of e-service quality on loyalty intention: An empirical study in online auction, Managing Service Quality, 18(2), June 2012

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