[
  {
    "id": "10.3390/su18189359",
    "type": "article-journal",
    "title": "Regulatory Volatility in Digital Supply Chains: An Information Systems Analytics Study of Decision-Maker Risk Perceptions and Sustainability-Related Outcomes",
    "author": [
      {
        "family": "Strang",
        "given": "Kenneth David"
      },
      {
        "family": "Vajjhala",
        "given": "Narasimha Rao"
      }
    ],
    "container-title": "Sustainability",
    "issued": {
      "date-parts": [
        [
          2026,
          9,
          11
        ]
      ]
    },
    "volume": "18",
    "issue": "18",
    "number": "9359",
    "publisher": "MDPI",
    "ISSN": "2071-1050",
    "DOI": "10.3390/su18189359",
    "URL": "https://www.narasimharao.net/research/regulatory-volatility-digital-supply-chains-esg-risk/",
    "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.",
    "keyword": "supply chain management, regulatory volatility, ESG compliance, sustainable supply chains, risk perception, forced labor enforcement, information systems analytics, logistic regression, label leakage, AI decision support, data governance, manufacturing",
    "language": "en"
  }
]