Asymmetric Information of Product Authenticity on C2C E-Commerce Platforms: How Can Inspection Services Help?

Linqiu Li, X. Fang, Yun Fong Lim
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Abstract

Problem definition: We consider a customer-to-customer (C2C) platform that provides an inspection service. Uncertain about product authenticity, a seller sells a product through the platform. Before purchasing, a buyer obtains a signal of the product authenticity from the product’s price set by the seller. The platform’s inspection service can detect a counterfeit with a probability. If the product passes the inspection, the platform sends it to the buyer and charges the seller a commission fee. Otherwise, the platform returns it to the seller and charges the seller a penalty fee. Methodology/results: We develop a two-stage game-theoretical model. In the first stage, the platform designs a contract specifying the commission and penalty fees. In the second stage, the seller signals the product authenticity by setting a price and the buyer decides whether to purchase it. This results in a contract design problem that governs a signaling game. We find that the effect of inspection is beyond merely detecting counterfeits. The inspection, even an imperfect one, changes the signaling game’s structure and incentivizes the seller whose product is likely authentic to sell through the platform. This can only be achieved by carefully choosing the commission and penalty fees. Moreover, a larger platform’s expected profit does not imply a larger commission fee or price in equilibrium. Under some mild conditions, the optimal commission increases but the optimal penalty decreases as the platform’s inspection capability improves. Managerial implications: The inspection service is not widely available among leading C2C platforms as it is considered imperfect and costly. Our study suggests that its benefit may be underestimated in practice. Moreover, the inspection can eliminate the seller’s information rent and generate more revenue for the platform. This paper provides guidance on how to set commission and penalty fees when the inspection service is provided. Funding: L. Li is supported by the National Natural Science Foundation of China [Grant 72071198] and the Hong Kong Polytechnic University Distinguished Postdoctoral Fellowship Scheme [Grant 1-YWC7]. X. Fang and Y. F. Lim are grateful for the support from the Lee Kong Chian School of Business, Singapore Management University [Maritime and Port Authority Research Fellowship and Retail Centre of Excellence Research Grant]. Y. F. Lim is supported by the Association of South-East Asian Nations Business Research Initiative Grant [Grant G17C20421], the Research Grants Council of Hong Kong [Grants 15501920 and 15501221], and the Key Program of National Natural Science Foundation of China [Grant 71931009]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1186 .
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C2C电子商务平台产品真伪信息不对称:检验服务如何帮助?
问题定义:我们考虑一个提供检验服务的客户对客户(C2C)平台。不确定产品真伪的卖家通过平台销售产品。在购买之前,买家从卖家设定的产品价格中获得产品真实性的信号。该平台的检测服务可以以一定的概率检测出假货。如果产品通过检查,平台将其发送给买家,并向卖家收取佣金。否则,平台会将其退还给卖家,并向卖家收取罚款。方法/结果:我们开发了一个两阶段博弈论模型。在第一阶段,平台设计一份合同,明确佣金和罚款。在第二阶段,卖方通过设定价格来表明产品的真实性,买方决定是否购买。这就导致了控制信号博弈的契约设计问题。我们发现,检查的作用不仅仅是发现假货。这种检查,即使是不完美的检查,也会改变信号游戏的结构,并激励那些产品可能是正品的卖家通过平台销售。这只能通过仔细选择佣金和罚金来实现。此外,较高的平台预期利润并不意味着较高的佣金或均衡价格。在一定条件下,随着平台检测能力的提高,最优调试量增大,最优惩罚量减小。管理意义:检查服务在领先的C2C平台中并不广泛使用,因为它被认为是不完善且昂贵的。我们的研究表明,它的好处在实践中可能被低估了。此外,检查可以消除卖家的信息租金,为平台创造更多的收入。本文就提供检验服务时如何设定佣金和罚款提供指导。基金资助:李磊获国家自然科学基金[基金资助:72071198]及香港理工大学杰出博士后奖学金计划[基金资助:1-YWC7]资助。方晓芳及林艳芳感谢新加坡管理大学李光前商学院[海事及港务管理局研究奖学金及零售卓越研究中心补助金]的支持。林毅峰获东南亚国家联盟商业研究计划基金[资助项目G17C20421]、香港研究资助局[资助项目15501920及15501221]及中国国家自然科学基金重点项目[资助项目71931009]资助。补充材料:在线附录可在https://doi.org/10.1287/msom.2023.1186上获得。
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Introduction to Special Section on Data-Driven Research Challenge Food Donations, Retail Operations, and Retail Pricing The Design of Optimal Pay-as-Bid Procurement Mechanisms Asymmetric Information of Product Authenticity on C2C E-Commerce Platforms: How Can Inspection Services Help? Believing in Analytics: Managers' Adherence to Price Recommendations from a DSS
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