Effectuation Dynamics in AI Entrepreneurship: A Multi-case Study

This study investigates the evolution mechanism of effectuation logic in artificial intelligence (AI) startups, with particular attention to the moderating role of dynamic capabilities. Drawing upon Sarasvathy's effectuation theory and the dynamic capabilities framework, we examine how AI-induced environmental uncertainty influences entrepreneurs' decision-making logic and how this logic evolves as ventures mature. Through multiple case studies of six relatively successful AI startups in China, we identify a three-stage evolution pathway: exploration, integration, and optimization. Our findings reveal that dynamic capabilities—specifically sensing, seizing, and reconfiguring capabilities—serve as critical moderators in this evolutionary process, while also demonstrating reciprocal relationships with effectual reasoning. We discuss the limitations inherent in studying successful ventures and explicitly articulate boundary conditions for our theoretical propositions, acknowledging that our findings may characterize successful adaptation strategies rather than typical entrepreneurial behavior.