Review Extortion in an Online Market Place
October 15 2016
Status: Reject and resubmit at Management Science.
Coauthors: Kaifu Zhang (Alibaba) and Jack (Xinlei) Chen (SAIF).
Based on a unique dataset from a major e-commerce platform, this paper studies the extortion of online sellers by organized criminals. The extortionists register as normal buyers, purchase from the sellers, demand cash or goodies and threat with negative reviews if their requests are declined. We study how extortionists choose their targets and quantify the short-run and long-run impacts of extortionist attacks on their target. The evidences suggest that the extortionists choose their targets in a systematic way. Furthermore, we find that a single instance of extortion leaves a long-term detriment to a seller’s performance. Extortionist attacks not only decrease the number of orders a seller receives, but also appear to make the victim less willing to deal with innocent buyers who are in fact not extortionists. Moreover, extortion victims seem to overreact to declining sales by cutting prices too deep. As such, extortionists create costs on the marketplace that is far greater than the monetary payments they extract or the physical damage they inflict.
Keywords: Review Extortion, E-Commerce, Consumer Review, Vector Autoregression