Driven by AI: the influence of multiple factors on employee innovation behavior

Abstract

Artificial intelligence (AI) reshapes work practices and employee innovative behavior. Drawing on Organizational Adaptation Theory (OAT) and the Technology Acceptance Model (TAM), this study proposes the “Cognition, Attitude, Ability and Collaboration” (CAAC) framework to explain employee innovation in AI-enriched environments. Combining necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA), we examine whether individual conditions are necessary for employee innovation and identify configurations sufficient for high and non-high innovative behavior. The analysis considers AI understanding, role clarity, AI trust, AI skills, knowledge sharing, and employee–AI collaboration. Results show that no single condition is sufficient for innovation. Instead, four distinct pathways emerge: one features high AI understanding and role clarity as core conditions, while another emphasizes AI skills, trust, and collaboration. The findings reveal complementarities between AI understanding and role clarity and functional substitution between AI skills and AI trust. This study explains how cognitive, attitudinal, technical, and collaborative factors jointly shape employee innovation, offering managers targeted strategies for fostering innovation during AI implementation.