英文摘要 |
‘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 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 airs 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. |