Artificial Intelligence Capability and Enterprise Innovation Performance: The Mediating Role of Knowledge Sharing and the Moderating Role of Environmental Dynamism

Abstract

Artificial intelligence (AI) is becoming an enterprise-level capability rather than a stand-alone technology, but firms differ substantially in their ability to convert AI resources into sustained innovation performance. This study proposes a conditional process model in which AI capability enhances enterprise innovation performance through knowledge sharing, while environmental dynamism strengthens the innovation value of shared knowledge. Building on the resource-based view, the knowledge-based view, and contingency theory, AI capability is conceptualized as the organizational ability to mobilize AI-relevant data, technologies, human expertise, coordination routines, and change capacity. Knowledge sharing is expected to transmit the effects of AI capability by increasing the circulation and recombination of data-driven insights across organizational units. Environmental dynamism is positioned as a second-stage moderator because rapidly changing markets and technologies increase the value of timely knowledge recombination for innovation. A two-wave managerial survey is proposed for Chinese high-tech, digital-service, and advanced-manufacturing enterprises. IBM SPSS Statistics and Hayes' PROCESS Macro provide the core analytical environment. The design combines reliability tests, exploratory factor analysis, common-method diagnostics, correlation analysis, hierarchical regression, and PROCESS Model 14 with 5,000 bootstrap samples. The manuscript provides a publication-oriented theoretical argument, an adaptable questionnaire, SPSS syntax, and results tables that can be populated with actual data after collection.