{"title":"The Real Deal: Return Policies against Review Manipulation","authors":"Y. Ho, Sheng-Zhi Mao","doi":"10.2139/ssrn.3897140","DOIUrl":null,"url":null,"abstract":"Review manipulation is pervasive on e-platforms. Opportunistic sellers boost sales by manipulating reviews and inflating perceived product qualities. Such immoral behavior ruins market fairness and efficiency, harming social welfare. Though various technologies have been developed to detect fake reviews, review manipulation remains rampant due to the lack of economic incentives from a platform’s perspective (i.e., costs of fake-review-detection technologies and potential loss of commissions). Unlike extant literature focusing on developing advanced algorithms, we take another route to explore economic incentives via return policies. We craft a game theory-based model, endogenizing a platform’s return policy and sellers’ manipulation efforts and pricing, given heterogeneous consumers. Our results show that a full-refund policy is a powerful device to alter sellers’ misbehaviors. Yet, the policy could be a double-edged sword that either inhibits or enhances review manipulation, depending on the severity of sellers’ competition. We further identify a return-manipulation paradox – the platform is more willing to choose the policy (either full-refund or no-return) that encourages manipulation the most. In other words, the platform would hurt the welfare of sellers and consumers while maximizing its profit. To resolve the paradox, we investigate the autonomous return scheme wherein sellers are delegated to make return policies. The analyses suggest that the alternative scheme effectively lowers overall manipulation in the ecosystem and increases social welfare, compared to the platform’s dictatorial return policy (i.e., the centralized scheme). The analytical results are translated into executable actions to consumers, sellers, and platforms for healthy, sustainable e-markets.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Systems & Economics eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3897140","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Review manipulation is pervasive on e-platforms. Opportunistic sellers boost sales by manipulating reviews and inflating perceived product qualities. Such immoral behavior ruins market fairness and efficiency, harming social welfare. Though various technologies have been developed to detect fake reviews, review manipulation remains rampant due to the lack of economic incentives from a platform’s perspective (i.e., costs of fake-review-detection technologies and potential loss of commissions). Unlike extant literature focusing on developing advanced algorithms, we take another route to explore economic incentives via return policies. We craft a game theory-based model, endogenizing a platform’s return policy and sellers’ manipulation efforts and pricing, given heterogeneous consumers. Our results show that a full-refund policy is a powerful device to alter sellers’ misbehaviors. Yet, the policy could be a double-edged sword that either inhibits or enhances review manipulation, depending on the severity of sellers’ competition. We further identify a return-manipulation paradox – the platform is more willing to choose the policy (either full-refund or no-return) that encourages manipulation the most. In other words, the platform would hurt the welfare of sellers and consumers while maximizing its profit. To resolve the paradox, we investigate the autonomous return scheme wherein sellers are delegated to make return policies. The analyses suggest that the alternative scheme effectively lowers overall manipulation in the ecosystem and increases social welfare, compared to the platform’s dictatorial return policy (i.e., the centralized scheme). The analytical results are translated into executable actions to consumers, sellers, and platforms for healthy, sustainable e-markets.