摘要
本文采用贝叶斯学习分析了网上拍卖中卖方声誉非对称现象产生的原因,并利用从淘宝网站收集的书画和书籍类物品的竞价数据,实证检验了卖方获得的好评次数与差评次数对拍卖物品成交概率和成交价格的影响。研究结果表明,买方对卖方的好评(差评)将增加(减少)新的买方对拍卖物品的预期价值,进而增加(减少)物品的成交概率和成交价格。此外,卖方所获差评的影响大于好评的影响,并且这种非对称性效应在容易辨别其质量的物品拍卖中更为明显。
The paper analyzes the asymmetric characteristics of seller＇s reputation in online auction through a Bayesian learning model, and tests the impact of seller＇s positive and negative feedbacks on the transaction probability and final price using the data in the painting ＆ calligraphy and book categories from Taobao website. The result indicates that the positive （negative） feedback will increase （decrease） the buyer＇s expectation value for the listed item, and further give rise to a more （less） transaction probability and higher （lower） final price for the product. Moreover, compared to the negative feedback, the positive one has more impact on the buying behavior, and that asymmetric phenomena manifest more obviously in some items with easily measuring their quality.
出处
《管理工程学报》
CSSCI
北大核心
2010年第1期59-64,58,共7页
Journal of Industrial Engineering and Engineering Management
基金
国家科技支撑计划资助项目(2006BAH02A05)
关键词
网上拍卖
卖者声誉
非对称性
贝叶斯学习
淘宝网
online auction
seller＇s reputation
asymmetric characteristics
Bayesian learning
Taobao website
作者简介
吉吟东（1962-），男，汉，北京市人。电子科技大学经济与管理学院在职博士研究生，研究方向：网上拍卖。
