The Impact of Sense of Power on Consumer Click-Through Intention Under AI Personalized Recommendation
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This study investigates how consumers' sense of power influences their click-through intentions in AI-driven personalized recommendation systems. While prior research has explored personalized marketings impact on consumer behavior, the role of perceived power remains understudied. Through scenario-based experiments, we find that a heightened sense of power enhances engagement with AI recommendations, with self-efficacy as a key mediator. However, this effect weakens when privacy concerns are high, highlighting privacy as a critical moderating factor. Specifically, empowered consumers are more likely to interact with AI recommendations, but only when privacy risks are minimal. These findings deepen our understanding of consumer-AI interactions, emphasizing the need to balance personalization, user empowerment, and privacy protection. By integrating power dynamics into AI marketing, this research offers theoretical contributions and practical insights for designing ethically responsible and effective recommendation systems.
Abstract
This study investigates how consumers' sense of power influences their click-through intentions in AI-driven personalized recommendation systems. While prior research has explored personalized marketings impact on consumer behavior, the role of perceived power remains understudied. Through scenario-based experiments, we find that a heightened sense of power enhances engagement with AI recommendations, with self-efficacy as a key mediator. However, this effect weakens when privacy concerns are high, highlighting privacy as a critical moderating factor. Specifically, empowered consumers are more likely to interact with AI recommendations, but only when privacy risks are minimal. These findings deepen our understanding of consumer-AI interactions, emphasizing the need to balance personalization, user empowerment, and privacy protection. By integrating power dynamics into AI marketing, this research offers theoretical contributions and practical insights for designing ethically responsible and effective recommendation systems.
