Abstract:

Existing credit evaluation system of e-commerce is difficult to accurately measure and compare the differences and the changes in multi-dimension credit indexes, because of the characteristics of single dimension In order to promote the revealing function of current credit rating system in C2C market, online shops’ behavioral patterns and its dynamic mode from multi-dimensional index is analyzed through clustering methods by collecting integrity indicators data of Taobao in seven industries of different credit rating sellers Static mode analysis shows that online shops have six types of characteristics combining dynamic rating, credit rating and positive ratio, and indicates that credit score is weaker than dynamic rating and positive ratio when individually revealing credit performance of online shops Dynamics mode analysis shows that there are three types of combined changes Finally, suggestions are proposed to make improvements to the existing evaluation system, so that it can show the real integrity level of different types of sellers

Key words: C2C e-commerce, integrity behavior, dynamics mode, static mode