AI Technology Adoption, Knowledge Sharing, and Manufacturing Firms’ Innovation Performance: The Moderating Effect of Absorptive Capacity

Abstract

As artificial intelligence (AI) technologies reshape manufacturing processes, their impact on innovation through knowledge sharing remains understudied and contested. In this article, we investigate how AI adoption influences innovation performance via two distinct pathways: explicit and tacit knowledge sharing. Drawing on the absorptive capacity theory, the study further examines how a firm's ability to assimilate and apply knowledge moderates these relationships. Based on the survey data from 290 Chinese manufacturing firms and analyzed using structural equation modeling, the findings reveal that AI facilitates both types of knowledge sharing, yet only the link between tacit knowledge sharing and innovation is significantly strengthened by higher absorptive capacity. The study contributes to engineering management literature by unpacking the dual-role mechanism of AI in knowledge-driven innovation and highlighting the critical boundary condition of absorptive capacity. For practitioners, it offers strategic insights into how AI tools and absorptive capacity can be codeveloped to unlock innovation potential. These findings highlight the need for tailored AI adoption and robust knowledge-sharing mechanisms, supported by absorptive capacity, to drive sustained innovation outcomes.