Digital Twin Adoption for Mechanical Maintenance Management: A Capability-Based Approach to Enhancing Operational Resilience

This study examines how digital twin in mechanical maintenance management influences operational resilience in mechanical systems, while also investigating the mediating role of absorptive capacity and the moderating effect of technological turbulence. The research aims to provide a comprehensive understanding of how digital transformation and knowledge capabilities jointly enhance resilient performance in technologically dynamic environments. Method: A quantitative, cross-sectional design was adopted, and data were collected from 295 mechanical and maintenance professionals using validated scales from prior research. The dataset was analyzed using ADANCO to assess reliability, validity, and the structural relationships among constructs. Measurement evaluation included confirmatory factor analysis, while the structural model was tested through path analysis, mediation, and moderation procedures. Findings: Results revealed that digital twin in mechanical maintenance management significantly improves operational resilience. Absorptive capacity emerged as a significant mediator, demonstrating that knowledge acquisition, assimilation, transformation, and exploitation are essential pathways through which digital twin enhances resilience. Technological turbulence strengthened the relationship between digital twin and operational resilience, confirming its role as a meaningful boundary condition. Originality/Implications: The study extends dynamic capabilities theory by integrating digital twin, absorptive capacity, and technological turbulence into a unified resilience model. Practically, it highlights the strategic value of digital twin for improving system robustness, particularly when supported by strong learning capabilities and aligned with rapidly evolving technological environments.

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