Analytics-based modeling of organizational resistance and resilience in Saudi healthcare supply chains

This study develops an integrated analytical framework that combines partial least squares structural equation modeling (PLS-SEM) and agent-based simulation to examine and address resistance to blockchain-based digital transformation in healthcare supply chains. Data were collected from 619 healthcare supply chain professionals working in public and private organizations in Saudi Arabia. The study investigates technological, organizational, and environmental factors, with competitive intensity as a moderating variable and AI-enabled Physical Internet (AI-PI) readiness as a mitigating strategy. The structural model explains 66.5% of the variance in organizational resistance, with key drivers including technological complexity, system immaturity, high implementation costs, and limited knowledge. Simulation results indicate that AI-PI coordination can achieve up to 25% reduction in operational costs, 20% decrease in emissions, and improved disruption recovery performance. These findings provide empirical support for decision-making in the digital transformation of Saudi healthcare supply chains within the Vision 2030 framework.