AI Advertising and Seller Innovation: The Asymmetric Role of Prior Experience

This research investigates how AI advertising adoption influences seller innovation on digital platforms. Leveraging the platform-wide launch of an AI-powered advertising system, we employ a difference-in-differences design with Coarsened Exact Matching on panel data from 4,856 sellers over 35 weeks. Drawing on the Attention-Based View framework, we theorize that AI advertising constitutes an attention-redirecting mechanism enabling reallocation of freed cognitive resources toward strategic innovation. We examine exploitation (category expansion) and exploration (new product introduction) as innovation outcomes. Results confirm that AI advertising promotes both outcomes, yet effects are asymmetrically conditioned by prior experience structure. Experience Frequency attenuates innovation benefits as routinized attention structures constrain strategic reallocation, whereas Experience Intensity leaves effects intact. Repetitive accumulation of advertising experience, rather than channel diversity, constitutes the operative boundary condition shaping AI advertising's strategic value.