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

Summary

What question does this paper answer?

How do manufacturing supply chain decision-makers perceive regulatory-volatility risks — labor, environmental, customs, ownership, military-logistics, and distribution — and do those perceptions predict whether a supply chain engagement succeeds or fails?

What did the study find?

Analysing 1,988 anonymized decision-maker records from a multinational logistics firm, the study finds that the six risks do not form a single "regulatory volatility" construct, and that the sustainability-motivated risks (labor and environmental) were rated lowest of all — a median labor severity of 0 and environmental severity of 1 on a 0–5 scale — even during a period of record forced-labor enforcement. A logistic regression separated success from failure perfectly in-sample, but only because two near-duplicate items leaked the outcome.

Why does it matter?

The perception gap exposes firms to "sustainability leakage": sudden enforcement pushes them to exit suppliers rather than remediate. For anyone building AI decision support from organizational records, the perfect-accuracy result is a concrete warning about label leakage. The authors recommend multidimensional (not composite) risk dashboards and provenance-aware data governance for supply chain analytics.

Study at a glance

Abstract

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.

Abstract as published in Sustainability under a Creative Commons licence.

Key terms

Regulatory volatility

Rapid, hard-to-predict change in the rules that govern trade and operations — tariffs, customs regimes, labor and environmental (ESG) regulation — that makes supply chains less predictable.

Sustainability leakage

As used in this study: when surprise ESG enforcement leads firms to exit suppliers or move sourcing elsewhere instead of remediating conditions in the scrutinized region.

Label leakage

When a predictor variable effectively encodes the outcome being predicted, producing unrealistically perfect model accuracy that will not hold in real use.

Limitations

• Perfect in-sample separation means no individual predictor effect can be estimated; the two driving items are near-duplicate measures.
• Data come from one multinational logistics firm’s repository, analysed retrospectively.

How to cite

Strang, K. D., & Vajjhala, N. R. (2026). Regulatory Volatility in Digital Supply Chains: An Information Systems Analytics Study of Decision-Maker Risk Perceptions and Sustainability-Related Outcomes. Sustainability, 18(18), 9359. https://doi.org/10.3390/su18189359

@article{strang2026regulatory,
  title = {Regulatory Volatility in Digital Supply Chains: An Information Systems Analytics Study of Decision-Maker Risk Perceptions and Sustainability-Related Outcomes},
  author = {Strang, Kenneth David and Vajjhala, Narasimha Rao},
  journal = {Sustainability},
  volume = {18},
  number = {18},
  pages = {9359},
  year = {2026},
  publisher = {MDPI},
  doi = {10.3390/su18189359},
  url = {https://doi.org/10.3390/su18189359}
}