Purpose This study aims to examine how artificial intelligence (AI) technological dynamics are linked to knowledge innovation performance. It studies a novel dual-path model to examine the roles of knowledge enablement and adaptive competence, integrating the lens of the enabling-constraining theory of technology (ECTT) and the knowledge-based view (KBV) to provide a nuanced understanding of how organizational capabilities both facilitate and hinder innovation. Design/methodology/approach This study used a quantitative research design using structural equation modeling, with a structured questionnaire distributed to IT professionals worldwide. Data from 326 respondents were analyzed using SmartPLS-based partial least squares structural equation modeling to test the proposed hypotheses and the overall research model. Findings The results endorse that AI technological dynamics significantly enhance both knowledge enablement and adaptive competence. However, the path to knowledge innovation performance is primarily driven by adaptive competence, not directly by knowledge enablement. This reveals a critical insight: while AI empowers knowledge processes, the organization’s adaptive capacity is the key mechanism that translates these capabilities into tangible innovation outcomes. Practical implications For managers and policymakers, this study provides a clear mandate: investing in AI technology must be accompanied by deliberate efforts to build the organization’s adaptive capabilities. Leaders should foster agile cultures, flexible governance and employee empowerment to ensure that AI-driven knowledge assets are effectively converted into innovative performance. Originality/value This study contributes a novel dual-path model that integrates the KBV with the enabling-constraining theory of technology (ECTT). It moves beyond a techno-centric view by empirically demonstrating that adaptive competence, rather than knowledge capabilities alone, is the critical conduit through which AI dynamics fuel innovation, offering a more realistic and actionable framework for digital transformation.
Cheng et al. (Thu,) studied this question.