职业差评师的产生机理与治理策略研究——以淘宝C2C交易平台为例

Study on the Generation Mechanism of the Occupational Negative Evaluator and Its Control Strategies: Based on Taobao C2C Trading Platform

针对淘宝C2C电子商务交易平台近期出现的职业差评师现象,本文进行了系统的实证分析和理论研究。首先,分析了职业差评师的产生背景、行为特点和作案流程。其次,从淘宝网运营规则和信用评价方法两个角度剖析了职业差评师的产生根源。本文采集1017家网店的信用度、好评率及销售额数据,建立了职业差评师影响机理的数学模型,计算分析了好评率对商家销售额的影响,以及不同商家好评率对差评的敏感度等。在此基础上,设计提出新的好评率计算方法,并分析验证其改进效果。最后提出打击职业差评师的治理建议。

Recently, some occupational negative evaluators were found on Taobao C2C trading platform. The occupational negative evaluators blackmailed money from on-line stores through giving malicious negative evaluations. In this article, the phenomenon was analyzed systematically through empirical analysis and theoretical research. Firstly, the occupational negative evaluator＇s background, behavioral characteristics and blackmail process were analyzed in depth. The root cause of occupational negative evaluator＇s emerging was explored fully through studying on the Taobao management regulation and credit evaluation system. The study found that the evaluation rules tended to protect the interests of buyers, so that the shoppers＇ legitimate rights and interests cannot be insured in some cases. As a consequence, these unreasonable rules provided the opportunity for occupational negative evaluator.Secondly, taking socks as an example, an empirical analysis was conducted. 1017 on- line socks stores＇ sales information on the Taobao C2C trading platform was collected, including shoppers＇ credits, the favorable rates and the sales volume. Based on the data, a mathematical model was constructed for analyzing the impact of the negative evaluation. The model indicated that the on-line stores with high favorable rate had much more potential customers （someone who are possible purchase something from the on-line store） than the stores with low favorable rate which is a key indicator of on-line stores＇ credits. Moreover, the effect of the favorable rate on the socks sales was analyzed quantitatively. Finally, the online stores＇ sensitivity on the negative evaluation was studied. The online stores with higher favorable rate were usually more sensitive on the negative evaluations. The empirical analysis suggested that the existing calculation method of online stores＇ favorable rate had some limitations. So an improved favor- able rate was proposed by integrating customer registration time, customer credit rating and transaction amount. The calculation result indicated that the new proposed favorable rate could make it more difficult for occupational negative evaluators to blackmail money from online stores.