第十四卷第二期 2012 年 6 月 (pp.307~328) 1 * RFM Yahoo! T 2011 3 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 2012 307
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 2009 12 Zappos 100 75 2010 Liu 2009 Taobao 24.3% Reichheld and Schefter, 2000 Pfeifer, 2005 Kauffman Wood 2006 308 June 2012
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! 1 1 1 [S 1 B 1 ] 1 2 [S 1 B 2 ] 2 2 [S 2 B 2 ] 1 2 3 4 5 June 2012 309
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 2 1 2. R FM Information cascadesherdchen, 2008 Self-herding behaviorariely and Norton, 2007 2.1 線上再購行為 310 June 2012
Srinivasan et al., 2002 e-loyalty Mittal Kamakura 2001 100,040 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 2012 311
Oliver, 1980; Kim et al., 2009 Kim 2009 1 H1 Recency effectqu et al., 2008; Posselt and Gerstner, 2005 2 H2 RFM R RecencyF Frequency M MonetaryRFM Kim 2009 3 4 H3 H4 2.3 再購行為的外在環境影響因素 312 June 2012
Ba and Pavlou, 2002 Weinberg and Davis, 2005; Yen and Lu, 2008a Gilkeson Reynolds 2003 5 H5 2 1 1 Logarithm 0 Ba Pavlou 2002 Negative ratingsconvex function Concave function June 2012 313
1 * * 1 0 3 2 1 0 Log 1 * 3. 3.1 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! 2010 2009/12/31 2010/01/04! 67.39% 2001 Yahoo! 20.21% 2007 7 ARO Yahoo! 61.8% 2007 2 314 June 2012
2006 10 / 2006 Yahoo! T JAVA Yahoo! MySQL Yahoo! T 2011/03/01 2011/03/31 T JAVA CyberNeko HTML Parser 3 2011/3/31 4 3 Yahoo! 1215392930 June 2012 315
4 Yahoo! 4 Y0376778020 3.2 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
X b p(x) x Agresti, 1996 SPSS 4. 4.1 敘述統計 Yahoo! 2011 3 1 2011 3 31 Yahoo! T 2011 3 31 2011 3 2011 3 2 2 794 66.39% 794 66.39% 141 11.79% 935 78.18% 71 5.94% 1,006 84.11% 51 4.26% 1,057 88.38% 29 2.42% 1,086 90.80% 22 1.84% 1,108 92.64% 8 0.67% 1,116 93.31% 9 0.75% 1,125 94.06% 17 1.42% 1,142 95.48% 11 0.92% 1,153 96.40% 10 0.84% 1,163 97.24% 33 2.76% 1,196 100% June 2012 317
2 66.39% 11.79% 93% 403.35 319.12 5 1 0 3 3,152 37.9% 88 900 800 700 600 500 400 300 200 100 66.4% 141 71 51 29 22 8 9 17 11 10 33 120% 794 100.0% 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% 5 318 June 2012
3 1,956 62.1% 2,044 64.8% 1,196 37.9% 1,108 35.2% 3,152 100% 3,152 100% 4 98.2% 5 65.6% 5 25.5% 6-10 1.1% 6 15 4 1,088 98.2% 17 1.53% 3 0.27% 1,108 100% 5 5 727 65.6% 6-10 283 25.5% 11-15 86 7.8% 15 12 1.1% 1,108 100% 6 250 25.5% 6 1,500 77.2% 1,500 3,000 14.9% June 2012 319
6 250 282 25.5% 251 ~500 173 15.6% 501 ~750 156 14.1% 751 ~1,000 115 10.4% 1,001 ~1,500 129 11.6% 1,501 ~2,000 74 6.7% 2,001 ~3,000 91 8.2% 3,001 ~4,000 39 3.5% 4,001 ~5,000 23 2.1% 5,001 26 2.3% 1,108 100% 7 4,000 85% 70,000 4,000 7 91.8% 4,000 4,000 72.3% 4,000 4,000 8,001 ~12,000 4,000 B2C 8,000 7 (% (% (% 4,000 801 72.3% 1,877 91.8% 2,678 85.0% 4,001 ~8,000 61 5.5% 58 2.8% 119 3.8% 8,001 ~12,000 18 1.6% 24 1.2% 42 1.3% 12,001 ~14,000 28 2.5% 16 0.8% 44 1.4% 14,001 ~18,000 80 7.2% 40 2.0% 120 3.8% 18,001 120 10.8% 29 1.4% 149 4.7% 1,108 100% 2,044 100% 3,152 100% 320 June 2012
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 2 1 8 Omnibus χ 2 p 0.000 0.000 Hosmer-Lemeshowt χ 2 p 1.000 1.000 Cox & Snell R 2 0.708 0.723 Nagelkerke R 2 0.992 0.996 8 Omnibus Hosmer-Lemeshowt Cox & Snell R 2 9 p-value 0.000 B 11.456 1 Exp(B) p-value 0.000 B -0.133 2 June 2012 321
9 B S.E. p Exp(B) 11.456 2.989 0.000*** 94472.582-0.133 0.035 0.000*** 0.876 5.582 2.630 0.034* 265.638-5.475E-4 7.220E-4 0.448 0.999 8.360E-5 7.600E-5 0.271 1.000-12.751 3.228 0.000 0.000-2LLR 36.546 Chi-square 4050.868 *** p-value 0.001, ** p-value 0.01, * p-value 0.05 p-value 0.034 B 5.582Exp(B) 265.638 3 p-value 0.448 4p-value 0.271 5 5. 5.1 研究結論 Pfeifer, 2005 Self-herding behavior RFM Recency, Frequency, Monetary 322 June 2012
Hughes, 1996 Herding 2011/03/01 2011/03/31 Yahoo! T Self-herding behavior RF Kim 2009Gilkeson Reynolds 2003 Hughes 1996 Shaw 2001RFM RFM M Monetary 6 0 ~2,000 500 1,000 8 10 Pearson p-value 0 1 0.498 June 2012 323
10 Pearson 1040.122 0.000*** 0.498 0.000*** *** 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 8,000 1 8,001 ~14,000 2 14,000 310 11 Pearson p-value 0.000 0.254 11 Pearson 217.033 0.000*** 0.254 0.000*** *** p-value 0.001, ** p-value 0.01, * p-value 0.05 5.2 研究貢獻 1 2 RFM 3 Yahoo! Blackwell 2001 324 June 2012
Recency R Frequency F 5.3 研究限制 T 35.2% Liu 2009 24.3% 2011/03/01 2011/03/31 T T 1,196 5.4 管理意涵與實務意涵 20% 150% 40% 50% 5% 85% June 2012 325
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