Megaprojects face evolving and tightly coupled risks that require continuous monitoring, contextual interpretation, and cross-organizational coordination. This study examines how AI–Digital Twin synergy, defined as the integration of AI-enabled analytics with digital twin-based representation and simulation, supports dynamic risk management through a governance-moderated coordination mechanism. It further investigates the mediating roles of risk contextual intelligence and decision adaptiveness, and the moderating role of inter-organizational governance maturity.
A survey-based empirical study used data from 323 professionals involved in megaproject risk management across multiple project types, regions, and organizational roles. Structural equation modeling (SEM) was used to test the hypothesized relationships among AI–Digital Twin synergy, dynamic risk management performance, risk contextual intelligence, decision adaptiveness, and inter-organizational governance maturity.
AI–Digital Twin synergy significantly improves dynamic risk management performance in megaprojects. This effect operates primarily through a strengthened shared understanding of complex risk contexts. Risk contextual intelligence serves as a key mediator, whereas decision adaptiveness does not emerge as an independent mediator. Inter-organizational governance maturity further moderates these relationships by shaping the extent to which AI–Digital Twin synergy translates into dynamic risk management performance.
This study advances research on megaproject risk governance by developing and empirically examining a governance-moderated coordination mechanism through which AI–Digital Twin synergy supports dynamic risk management. It reveals how digital capabilities translate into coordinated risk management outcomes in complex inter-organizational environments and offers practical implications for improving risk governance in megaprojects.
