Miaomiao Liu , Xiaohua Zeng , Cheng Zhang , Yong Liu
{"title":"当平台披露与产品评论相关的购买记录时会发生什么?","authors":"Miaomiao Liu , Xiaohua Zeng , Cheng Zhang , Yong Liu","doi":"10.1016/j.dss.2024.114367","DOIUrl":null,"url":null,"abstract":"<div><div>In striking a balance between attracting more product reviews versus maintaining review quality, online platforms have started to label reviews with whether they are associated with verifiable purchases. This paper examines the impact of such disclosure policy on the strategic behavior of review writers and the helpfulness of verified reviews (VRs) and non-verified reviews (NVRs) for review users. We propose that the introduction of the verified purchase tag induces two competing effects for VRs, increased credibility and concerns for acquisition bias, which in turn influence the behaviors of both writers and users. By exploiting the exogenous shock resulting from a policy change on Amazon, we find that, after the disclosure, NVRs became longer in length and VRs started to contain more unique information. Surprisingly, we find strong evidence that VRs receive fewer helpfulness votes than NVRs. We further explore the underlying mechanism, namely review users' concerns about acquisition bias associated with VRs, and identify conditions under which these unexpected effects can be mitigated. Our findings generate important implications for online platforms seeking to design a more effective review ecosystem and for review writers aiming to produce more helpful content.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"188 ","pages":"Article 114367"},"PeriodicalIF":6.7000,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"What happens when platforms disclose the purchase history associated with product reviews?\",\"authors\":\"Miaomiao Liu , Xiaohua Zeng , Cheng Zhang , Yong Liu\",\"doi\":\"10.1016/j.dss.2024.114367\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In striking a balance between attracting more product reviews versus maintaining review quality, online platforms have started to label reviews with whether they are associated with verifiable purchases. This paper examines the impact of such disclosure policy on the strategic behavior of review writers and the helpfulness of verified reviews (VRs) and non-verified reviews (NVRs) for review users. We propose that the introduction of the verified purchase tag induces two competing effects for VRs, increased credibility and concerns for acquisition bias, which in turn influence the behaviors of both writers and users. By exploiting the exogenous shock resulting from a policy change on Amazon, we find that, after the disclosure, NVRs became longer in length and VRs started to contain more unique information. Surprisingly, we find strong evidence that VRs receive fewer helpfulness votes than NVRs. We further explore the underlying mechanism, namely review users' concerns about acquisition bias associated with VRs, and identify conditions under which these unexpected effects can be mitigated. Our findings generate important implications for online platforms seeking to design a more effective review ecosystem and for review writers aiming to produce more helpful content.</div></div>\",\"PeriodicalId\":55181,\"journal\":{\"name\":\"Decision Support Systems\",\"volume\":\"188 \",\"pages\":\"Article 114367\"},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2024-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Decision Support Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167923624002008\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Decision Support Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167923624002008","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
What happens when platforms disclose the purchase history associated with product reviews?
In striking a balance between attracting more product reviews versus maintaining review quality, online platforms have started to label reviews with whether they are associated with verifiable purchases. This paper examines the impact of such disclosure policy on the strategic behavior of review writers and the helpfulness of verified reviews (VRs) and non-verified reviews (NVRs) for review users. We propose that the introduction of the verified purchase tag induces two competing effects for VRs, increased credibility and concerns for acquisition bias, which in turn influence the behaviors of both writers and users. By exploiting the exogenous shock resulting from a policy change on Amazon, we find that, after the disclosure, NVRs became longer in length and VRs started to contain more unique information. Surprisingly, we find strong evidence that VRs receive fewer helpfulness votes than NVRs. We further explore the underlying mechanism, namely review users' concerns about acquisition bias associated with VRs, and identify conditions under which these unexpected effects can be mitigated. Our findings generate important implications for online platforms seeking to design a more effective review ecosystem and for review writers aiming to produce more helpful content.
期刊介绍:
The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems (DSSs).