Mitigating collusive manipulation of reviews in e-commerce platforms: Evolutionary game and strategy simulation

Abstract

The growing review manipulation has seriously hampered credit regulation on e-commerce platforms, yet few studies have explored its complex dynamics. Unlike current research centering on merchants creating various management strategies, this study examines the collusion between merchants and consumers. By integrating evolutionary game theory and a system dynamics approach, this study offers meaningful conclusions for platform credit management. First, our findings indicate that merchants can maintain honesty regardless of the regulatory strategy implemented. For positive regulation, platforms can impose higher penalties; for negative regulation, maintaining lower exposure is feasible. Second, our analysis illustrates the necessity of breaking the collusion between merchants and consumers. Under positive regulation, platforms can amplify penalties or enhance the regulatory impact on platform revenues. Conversely, negative regulation allows for reducing the short-term financial impact of reviews or adjusting cashback. Third, we uncover that dynamic punishment strategies are not always optimal. In some cases, static punishment strategies outperform linear dynamic punishment strategies, highlighting the importance of carefully evaluating the effectiveness of different regulatory approaches in various contexts.