Modeling AI adoption intention in manufacturing supply chains

This study examines the influence of technology compatibility, top management support, competitive pressure, and employee capability on AI adoption intention within Malaysian manufacturing supply chains, as well as the moderating role of organizational size. A predictive association research design was employed using a self-administered survey via purposive sampling of supply chain and manufacturing professionals. Data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that top management support, competitive pressure, and employee capability significantly influence AI adoption intention within Malaysian manufacturing supply chains. In contrast, technology compatibility and the moderating role of organizational size were found to be statistically non-significant. The study contributes theoretically by demonstrating that top management support, competitive pressure, and employee capability exert stronger explanatory influence on AI adoption intention than technology compatibility within Malaysian manufacturing supply chains. This challenges the dominant assumption within conventional technology adoption literature that technological conditions represent the primary drivers of organizational adoption decisions. Practically, the findings suggest that manufacturing managers should prioritize leadership commitment and employee upskilling to strengthen AI implementation. Policymakers should strengthen workforce development initiatives, AI-related skills training, and supportive industrial ecosystems that encourage organizational readiness and responsiveness to emerging competitive demands, thereby facilitating AI adoption within manufacturing supply chains.