Intervention Strategy for Online Reviews in Shared Accommodation Platforms: An Agent-Based Simulation Model

Although previous studies have underscored the significance of management interventions for online reviews on sharing platforms, limited research has comprehensively examined the dynamics of review interventions within competitive contexts. This study addresses this gap by developing a data-driven agent-based model that can simulate complex stakeholder interactions on shared accommodation platforms. This model examines the coevolution of direct interventions (i.e., positive review incentives) and indirect interventions (i.e., managerial responses and overstatement) alongside pricing and service strategies. The market is categorized into egoistic, altruistic, and hybrid types, thereby showing the influences of market composition on the effectiveness and marginal effects of review interventions. Findings reveal that positive review incentives and overstatement have a complementary relationship: the increase in one aspect results in a rise in the other aspect. However, prohibiting positive review incentives has unintended consequences, such as increased overstatement. Meanwhile, a substitutive relationship exists between positive review incentives and managerial responses. These interventions exert different impacts on providers’ profits: positive review incentives have an inverted U-shaped effect on providers’ profits, whereas other interventions have a positive correlation with profits. Furthermore, the shifts between market types change the marginal effects of these strategies, which can mitigate some of their negative consequences. This research advances the understanding of review intervention dynamics across different market settings and provides guidance for platforms in formulating effective operation policies.