| Recent ten years, electronic commerce has been developing rapidly around theglobe and also in China. At the same time, the volume of online transactions is alsoincreasingly expanded. With online shopping becoming more and more popularamong customers, building word-of–mouth or online reputation systems has becomean urgent task facing online transaction platforms. The text feedback plays a veryimportant role in the online reputation systems for the online transaction platforms likeTaobao.com. The textual reviews will significantly impact consumer purchase andstimulate the occurrence of new word-of-mouth.This paper conducts an empirical analysis of the consumers’ behavior in providingproduct reviews, using transaction data and seller/buyer reputation data randomlyselected from Taobao.com. In addition, we introduce the text mining technique intothis study in order to measure the value of the text feedback. We find severalinteresting results: firstly, some consumers’ features will significantly affect whetherthe consumer leave a product text feedback. An anonymous consumer has a higherprobability to leave a product review; however a more experienced consumer who hasa higher online reputation tends to not leave a product review. Secondly, we find thatwhether a consumer leaves text feedbacks for a transaction is highly influenced by hisor her previous behavior. If a buyer leaves a text feedback in the last transaction, it issignificantly more likely that he or she will continue to do so. Further studies on thereview length and review value show similar results.Based on the findings from this study, we give some suggestions to onlinetransaction platforms and online sellers on how to motivate consumers to leave morevaluable product reviews. |
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