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.