| dc.description.abstract | Without physical contact with the sellers in Internet shopping, buyers rely on the available information at the platform to select sellers and make purchase decisions. Relative to other platforms such as Yahoo!Auction and eBay, the largest online shopping platform in China, Taobao, provides more information about sellers, such as: “collection popularity,” “product variety,” “percentage of major product lines,” “product authentication evaluation score,” “service attitude evaluation score,” “shipping speed evaluation score,” “average refund processing days,” “percentage of refund,” “percentage of customer complaint,” and “punishment number.” Thus, understanding the relationship between seller’s public information and its number of customers, number of repurchasing customers, and percentage of returning customers, would help the e-commerce businesses to strategize their online operations in order to attract new customers and retain existing ones. Using web content mining technique, we collected real data of seller’s public information from women’s clothing online stores at Taobao from April 2 to May 31 of 2012. Through the PLS analysis, the major findings are: (1) among various public information, seller’s “product variety,” “percentage of refund,” and “punishment number” are significantly related to its number of customers and number of repurchasing customers. “product variety,” “percentage of major product lines,” “percentage of refund,” and “punishment number” are significantly related to its percentage of returning customers; (2) increasing “collection popularity” of seller is a very effective way in raising the number of customers and the number of repurchasing customers; (3) a reasonably good service evaluation score is needed to reflect in terms of the number of customers.
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