A Warning Approach to Mitigating Bandwagon Bias in Online Ratings: Theoretical Analysis and Experimental Investigations
Authors: Wu, Ding; Guo, Xunhua; Wang, Yuejun; Chen, Guoqing
Journal: Journal of the Association for Information Systems (2023)
DOI: 10.17705/1jais.00817
<jats:p>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’ init…