{"title":"缓解网络评分从众偏见的预警方法:理论分析与实验研究","authors":"Dingyu Wu, Xunhua Guo, Yuejun Wang, Guoqing Chen","doi":"10.17705/1jais.00817","DOIUrl":null,"url":null,"abstract":"Current online review systems widely suffer from rating biases. Biased ratings can lead to violations of customer trust and failures of business intelligence. Hence, both practitioners and researchers have directed massive efforts toward curbing rating biases. In this paper, we investigate bandwagon bias, the rating distortion resulting from individuals posting ratings shifted toward the displayed average rating, and propose a bias warning approach to mitigate this bias. Drawing on the flexible correction model, the theory of valuation in behavioral economics, and previous warning research, we design an effective warning strategy in two steps. First, we start with the risk-alert warning strategy, which prior research has widely employed, and rationalize its deficiencies by synthesizing theoretical analysis and extant empirical evidence. Second, considering the deficiencies, we identify a supplementary content design factor—the ranking task—and construct a risk-alert-with-ranking-task warning strategy. We then empirically test the effects of the two warning strategies on individual ratings in cases in which bandwagon bias either occurs or does not occur in individuals’ initial assessments. The results of four controlled experiments indicate that (1) the risk-alert strategy can reduce bandwagon bias in individual ratings but will elicit unwanted rating distortions when bandwagon bias does not occur in individuals’ initial assessments, and (2) the risk-alert-with-ranking-task strategy can mitigate bandwagon bias while avoiding the unwanted rating distortions above and can thus function as an effective warning strategy. Our research contributes to the literature by proposing an effective debiasing solution for bandwagon bias and a bias warning approach for online rating debiasing, which can help increase rating informativeness on online platforms.","PeriodicalId":51101,"journal":{"name":"Journal of the Association for Information Systems","volume":"54 1","pages":"2"},"PeriodicalIF":7.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Warning Approach to Mitigating Bandwagon Bias in Online Ratings: Theoretical Analysis and Experimental Investigations\",\"authors\":\"Dingyu Wu, Xunhua Guo, Yuejun Wang, Guoqing Chen\",\"doi\":\"10.17705/1jais.00817\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Current online review systems widely suffer from rating biases. Biased ratings can lead to violations of customer trust and failures of business intelligence. Hence, both practitioners and researchers have directed massive efforts toward curbing rating biases. In this paper, we investigate bandwagon bias, the rating distortion resulting from individuals posting ratings shifted toward the displayed average rating, and propose a bias warning approach to mitigate this bias. Drawing on the flexible correction model, the theory of valuation in behavioral economics, and previous warning research, we design an effective warning strategy in two steps. First, we start with the risk-alert warning strategy, which prior research has widely employed, and rationalize its deficiencies by synthesizing theoretical analysis and extant empirical evidence. Second, considering the deficiencies, we identify a supplementary content design factor—the ranking task—and construct a risk-alert-with-ranking-task warning strategy. We then empirically test the effects of the two warning strategies on individual ratings in cases in which bandwagon bias either occurs or does not occur in individuals’ initial assessments. The results of four controlled experiments indicate that (1) the risk-alert strategy can reduce bandwagon bias in individual ratings but will elicit unwanted rating distortions when bandwagon bias does not occur in individuals’ initial assessments, and (2) the risk-alert-with-ranking-task strategy can mitigate bandwagon bias while avoiding the unwanted rating distortions above and can thus function as an effective warning strategy. Our research contributes to the literature by proposing an effective debiasing solution for bandwagon bias and a bias warning approach for online rating debiasing, which can help increase rating informativeness on online platforms.\",\"PeriodicalId\":51101,\"journal\":{\"name\":\"Journal of the Association for Information Systems\",\"volume\":\"54 1\",\"pages\":\"2\"},\"PeriodicalIF\":7.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Association for Information Systems\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.17705/1jais.00817\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Association for Information Systems","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.17705/1jais.00817","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
A Warning Approach to Mitigating Bandwagon Bias in Online Ratings: Theoretical Analysis and Experimental Investigations
Current online review systems widely suffer from rating biases. Biased ratings can lead to violations of customer trust and failures of business intelligence. Hence, both practitioners and researchers have directed massive efforts toward curbing rating biases. In this paper, we investigate bandwagon bias, the rating distortion resulting from individuals posting ratings shifted toward the displayed average rating, and propose a bias warning approach to mitigate this bias. Drawing on the flexible correction model, the theory of valuation in behavioral economics, and previous warning research, we design an effective warning strategy in two steps. First, we start with the risk-alert warning strategy, which prior research has widely employed, and rationalize its deficiencies by synthesizing theoretical analysis and extant empirical evidence. Second, considering the deficiencies, we identify a supplementary content design factor—the ranking task—and construct a risk-alert-with-ranking-task warning strategy. We then empirically test the effects of the two warning strategies on individual ratings in cases in which bandwagon bias either occurs or does not occur in individuals’ initial assessments. The results of four controlled experiments indicate that (1) the risk-alert strategy can reduce bandwagon bias in individual ratings but will elicit unwanted rating distortions when bandwagon bias does not occur in individuals’ initial assessments, and (2) the risk-alert-with-ranking-task strategy can mitigate bandwagon bias while avoiding the unwanted rating distortions above and can thus function as an effective warning strategy. Our research contributes to the literature by proposing an effective debiasing solution for bandwagon bias and a bias warning approach for online rating debiasing, which can help increase rating informativeness on online platforms.
期刊介绍:
The Journal of the Association for Information Systems (JAIS), the flagship journal of the Association for Information Systems, publishes the highest quality scholarship in the field of information systems. It is inclusive in topics, level and unit of analysis, theory, method and philosophical and research approach, reflecting all aspects of Information Systems globally. The Journal promotes innovative, interesting and rigorously developed conceptual and empirical contributions and encourages theory based multi- or inter-disciplinary research.