AI Advertising and Sellers' Innovation Decisions

This research examines how AI advertising on digital platforms influences sellers’ strategic innovation decisions. We conceptualize seller innovation along two dimensions: drawing on the Attention-Based View model, 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 Depth attenuates innovation benefits as routinized attention structures constrain strategic reallocation, whereas Experience Breadth leaves effects intact. Repetitive accumulation of advertising experience, rather than channel diversity, constitutes the operative boundary condition shaping AI advertising's strategic value.