In the absence of other e ective trust systems, an agent's reputation status becomes a critical factor in online transactions. A higher reputation category may give sellers an advantage in competition on online trading platforms. It is also possible that such reputation bene ts provide su cient incentives for sellers to adjust their pricing behavior. We here propose a simple economic model in which an online seller maximizes the sum of the pro t from current sales and the possible future gain from a targeted higher reputation level. We show that the model can predict a jump in optimal pricing behavior. We adopt a quantile regression threshold model (QRTM) to identify and explore such a pricing pattern as the \goodwill e ect" in this paper. The use of a QRTM also allows us to model the heterogeneous behavior of di erent online sellers. We apply the proposed estimation and testing strategies to a data set obtained from Taobao.com, a leading online trading platform in China. We nd both heterogeneities and jumps in a seller's goodwill pricing strategy in our application.
Heterogeneity, Pricing strategy, Reputation, Structural change, Threshold quantile regression
City or Country
Ju, H.; SU, Liangjun; and XU, P..
Pricing for Goodwill: A Threshold Quantile Regression Approach. (2013). Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1478
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