Cool or poor: Assessing the effectiveness of GenAI-powered review summary feature on the OTA platform

Despite the growing adoption of generative artificial intelligence (GenAI) in online travel agencies (OTAs), its impact on tourists' review behavior remains poorly understood. Within the information overload framework, this study offers a novel dual-path theoretical framework that integrates both generalized reciprocity and suppressed social loafing mechanisms. Using a multi-method design comprising a natural experiment with regression discontinuity design and controlled online experiments across three studies, we investigated how GenAI-powered review summaries shape tourists' review intention. Our findings reveal that exposure to GenAI summaries enhances review intention through generalized reciprocity. By alleviating information overload and reducing perceived processing effort, these summaries motivate tourists to reciprocate by composing their own reviews, thereby ''paying forward'' the benefits received, rather than triggering free-riding that would diminish review intention. These findings advance theoretical knowledge of information processing, GenAI's influence on tourists' decision-making, and tourists' review behavior, while providing actionable insights for OTAs and hospitality management.