DIGITAL TRANSFORMATION IMPACT ON SUPPLY CHAIN RESILIENCE

This study investigates mechanisms through which logistics digital transformation influences supply chain resilience, testing hypotheses that transparency, integration, and capacity serve as parallel mediating pathways. The research addresses the gap in understanding how digital investments translate into resilient outcomes through distinct transmission mechanisms.A cross-sectional survey collected data from 287 supply chain professionals in the manufacturing and distribution sectors. Sample size was justified through power analysis (G*Power: α = 0.05, power = 0.80, f² = 0.15, minimum n = 138). Partial least squares structural equation modeling was conducted using SmartPLS 4.0 with a confirmatory approach. Bootstrap procedure employed 5,000 subsamples for bias-corrected confidence intervals. All constructs used validated multi-item scales measured on a seven-point Likert format.The model explained 58.3% of the variance in supply chain resilience (R² = 0.583) with confirmed predictive relevance (Q² = 0.402). Digital transformation significantly influenced all mediators: transparency (β = 0.412, p < 0.001), integration (β = 0.523, p < 0.001), and capacity (β = 0.467, p < 0.001). Total effect on resilience (β = 0.687, 95% CI [0.593, 0.781]) substantially exceeded direct effect (β = 0.190). Integration demonstrated the strongest mediation (f² = 0.35), followed by transparency (f² = 0.29) and capacity (f² = 0.18). Approximately 72% of the total effect operated through mediated pathways (VAF = 72.3%). All ten hypotheses were supported.Findings reveal that technology adoption alone captures only 27.7% of potential resilience benefits; deliberate capability development across transparency, integration, and capacity dimensions is essential. The mathematical predictive model enables quantitative estimation of resilience improvements from digital investments, supporting evidence-based resource allocation. Future research should employ longitudinal designs to establish temporal ordering and examine boundary conditions.