Online ballot stuffing: Influence of self-boosting manipulation on rating dynamics in online rating systems

Abstract

Vendors on online shopping platforms have incentives to provide misinformation by manipulating consumer ratings. However, few studies have empirically examined whether temporal self-boosting rating manipulation changes the follow-up dynamic pattern of online ratings. This study empirically identifies manipulated ratings and examines how this manipulation influences the evolution of follow-up ratings over time. Rating records of more than 30,000 restaurants are extracted from the server log of a restaurant review website. The results show a statistically nonsignificant difference in the average rating scores between before and after manipulation, indicating that manipulation does not influence follow-up rating dynamics. Moreover, the difference in average ratings between before and after manipulation decreases within a long time window, which indicates that the long-term effect is weaker than the short-term one. The results are further validated by an online field experiment. We then construct a simulation model to reveal the underlying mechanisms of why the follow-up raters could correct the bias of rating manipulation. The manipulated high-rating score of the vendor will increase the diversity of consumers’ individual preferences, which leads to diverse evaluations among users. The aggregation of online ranking by taking the average score of ratings helps form a decentralized climate of opinion and finally helps correct the biased rating. This study challenges the common belief that self-boosting misinformation affects the performance of online commerce. The nonsignificant effect of rating manipulation on rating dynamics echoes the theory of the wisdom of crowds. Furthermore, this study has managerial implications for both online vendors and online rating platforms. We suggested that the system should degrade social influences among users in order to reduce the possible impacts by manipulated ratings.