Drivers of AI Adoption in Smart Supply Chains: An Empirical Study of Readiness, Trust, and the Insignificance of Perceived Risk.

Abstract

Artificial Intelligence (AI)-enabled Smart Supply Chain Management (SSCM) systems are increasingly implemented within manufacturing organizations to enhance operational efficiency and strategic decision-making. While perceived risk is widely recognized as a deterrent in technology adoption research, its explanatory role in institutionalized business-to-business (B2B) environments remains under-theorized. This study investigates the determinants of AI-enabled SSCM system usage among Malaysian manufacturing firms, focusing on Effort Expectancy, Technology Readiness, Trust, and Perceived Risk. Using survey data from 308 manufacturing professionals, Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to test the proposed relationships. To complement explanatory modelling, machine learning techniques including Extreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANN) were applied to assess predictive capability. The results indicate that Effort Expectancy, Technology Readiness, and Trust significantly influence SSCM usage behaviour, whereas Perceived Risk does not exhibit a significant effect. Drawing on institutional theory and organizational risk absorption perspectives, the findings suggest that governance mechanisms and formal safeguards attenuate the behavioural impact of perceived uncertainty in B2B contexts. This study contributes theoretically by identifying an institutional boundary condition of perceived risk in AI adoption and methodologically by integrating SEM with validated predictive AI modelling.