营销科学学报 ›› 2013, Vol. 9 ›› Issue (2): 111-125.

• 论文 • 上一篇    下一篇

在线商品评论可信吗——在线商品评论的偏差分析及矫正策略

李雨洁,廖成林,李忆,伏红勇   

  1. 李雨洁,重庆大学经济与工商管理学院博士研究生,E-mail: liyujie87@gmail.com。
    廖成林,重庆大学经济与工商管理学院教授,博士生导师,E-mail: liaocl2005@126.com。
    李忆,重庆邮电大学经济与工商管理学院副教授,E-mail: cquliyi@163.com。
    付红勇,重庆大学经济与工商管理学院博士研究生,E-mail: fuhongyong.cqu@gmail.com。
  • 出版日期:2013-06-01 发布日期:2013-08-09
  • 基金资助:

    非常感谢匿名评审专家提出的宝贵和中肯的修改意见,特此致谢!

Are Online Product Reviews Credible: the Deviation and Correction of Online Product Review’s Distribution

Li Yujie, Liao Chenglin, Li Yi, Fu Hongyong   

  1. Li Yujie, School of Economics and Business Administration, Chongqing University;
    Liao Chenglin, School of Economics and Business Administration, Chongqing University;
    Li Yi, School of Economics and Business Administration, Chongqing University of Posts and Telecommunications;
    Fu Hongyong, School of Economics and Business Administration, Chongqing University.
  • Online:2013-06-01 Published:2013-08-09

摘要:

本文根据中国的商业环境,对淘宝网数据进行实证分析,发现在线商品评论呈近似反“L”型的非对称分布,且评论的均值不是商品质量的无偏估计量。这与现有研究认为在线商品评论呈正态分布且评价的均值是无偏估计量的结论不同,与国外的实证结果在线评论呈双峰分布亦不相同。为了深入研究在线商品评论存在偏差的原因,以及反“L”型分布的特征,本文构建了基于消费者效用的在线商品评论模型,该模型得出商品评论的均值作为无偏估计量的条件,并进一步揭示出中国电子商务网站“默认好评”机制、“退货评价”机制和消费者主动评价偏差是在线商品评论存在偏差的重要原因。

关键词: 在线商品评论, 商品质量, 分布, 偏差, 矫正

Abstract:

Based on the Chinese business environment, the paper firstly conducts an empirical analysis using Taobao’s data. Results are the online product review follows a reversed "L"-type distribution, of which the mean is a biased estimator of the product’s true quality. These findings are different from the existed results that online product reviews follow a normal distribution. They are also distinct from the empirical results of foreign e-business data. In order to study the reasons for the biased online product reviews and the features of the reversed "L"-type distribution, the paper constructs a theoretical model to obtain the conditions for the mean of the online product reviews to be an unbiased estimator. And we further demonstrate that the “default good reviews” mechanism, “return evaluation” mechanism and the deviation of consumer’s initiative evaluation are the main reasons for the biased online product reviews.

Key words: Online product reviews, Product quality, Distribution, Deviation, Correction