Strategizing AI Recommendations: Focusing on the Role of Agency and Assistance Perception in Enhancing Engagement

초록

Artificial intelligence (AI) is central to digital platforms, particularly in personalized recommendation systems to enhance user interactions. This study investigates how AI recommendation strategies-presenting alternatives and providing reasoning-impact user responses, considering the moderating role of agency perception (control over one's actions) and the mediating role of perceived assistance (the sense of being supported by AI). An online experiment manipulated the presence of alternatives and reasoning to measure perceived agency and assistance. Results show that presenting alternatives had a direct negative impact on users with high agency, viewing alternatives as intrusive or misaligned. In contrast, users with low agency experienced positive outcomes, perceiving greater assistance and showing improved user engagement and satisfaction. These findings reveal a dual-pathway mechanism, where the same AI strategy elicits divergent responses based on agency perception, emphasizing the need for personalized recommendation designs.

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