Regulatory Volatility in Digital Supply Chains: An Information Systems Analytics Study of Decision-Maker Risk Perceptions and Sustainability-Related Outcomes

Geopolitical conflict, tariff shocks, and rapidly evolving environmental, social, and governance (ESG) regulation have made global supply chains markedly less predictable. This study examined how manufacturing supply chain decision-makers perceive six regulatory-volatility risks, including labor, environmental, customs, ownership, military-logistics, and distribution, and whether those perceptions predict recorded engagement outcomes. Adopting an information systems perspective, we retrospectively analyzed 1988 fully anonymized decision-maker records from the digital lessons-learned repository of a multinational supply chain logistics firm, applying descriptive statistics, exploratory factor analysis with parallel analysis, and binary logistic regression with classification diagnostics. The perceived risks did not converge on a unified regulatory-volatility construct: sampling adequacy was weak (KMO = 0.470), and parallel analysis retained three single-indicator factors (distribution, environmental, and ownership risk) that were close to orthogonal apart from one moderate environment-distribution association. Critically, the sustainability-motivated risks were rated lowest, with a median labor severity of 0 and an environmental severity of 1 on 0–5 scales, during a period of record forced-labor enforcement; this perception gap invites sustainability leakage, whereby surprise enforcement provokes supplier exit and sourcing flight rather than remediation within scrutinized regions. The logistic regression separated success from failure perfectly in-sample (accuracy = AUC = 1.000), driven jointly by the near-collinear distribution and supply items—a complete-separation pattern warning of label leakage when digitized organizational records are mined for artificial intelligence-based decision support. The two items are near-duplicate measures (r = 0.915), so no individual predictor effect is identified. The findings favor multidimensional rather than composite digital risk dashboards, provenance-aware data governance for supply chain analytics pipelines, and digitally enabled ESG compliance under volatile regulation, contributing an empirically grounded information systems lens to sustainable supply chain management.