Open-access journal article · 2026
Regulatory Volatility in Digital Supply Chains: An Information Systems Analytics Study of Decision-Maker Risk Perceptions and Sustainability-Related Outcomes
Sustainability, 18(18), Article 9359 · Published · Special Issue Digital Supply Chains Management and Sustainability
Summary
Strang and Vajjhala (2026) retrospectively analysed 1,988 anonymized decision-maker records from one multinational logistics firm to examine whether six perceived regulatory-volatility risks form a single construct and predict engagement outcomes. The risks did not form a single construct (KMO = 0.470), and labor and environmental risks were rated lowest (medians 0 and 1 on 0–5 scales) during a period of record forced-labor enforcement. A logistic regression separated success from failure perfectly in-sample (AUC = 1.000), an artefact of label leakage from near-duplicate items (r = 0.915) rather than genuine predictive skill.
Practical implication. For supply chain analytics, firms should use multidimensional rather than composite risk dashboards and provenance-aware data governance, a recommendation based on 1,988 decision-maker records from one multinational logistics firm.
- The Problem
- Supply chain decision-makers must manage fast-changing trade, labor and environmental regulation, yet it is unknown whether their perceptions of these regulatory-volatility risks form a coherent construct or predict whether a supply chain engagement succeeds.
- The Methodology
- Quantitative retrospective analysis of archival organisational records (N = 1,988): descriptive statistics, exploratory factor analysis with parallel analysis, and binary logistic regression with classification diagnostics.
- The Core Finding
- Perceived regulatory-volatility risks do not form a single construct (KMO = 0.470), and labor and environmental risks were rated lowest of all (medians 0 and 1 on 0–5 scales) during a period of record forced-labor enforcement. A perfect in-sample classifier (AUC = 1.000) was an artefact of label leakage from near-duplicate items (r = 0.915), not genuine predictive skill.
- The Citation
- 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), Article 9359. https://doi.org/10.3390/su18189359
The summary, key findings, methods, implications and relevance notes on this page are this website’s own description of the paper, written from the published abstract and text. The official abstract and citation details are given further down.
Key findings
- Finding 1. The study analysed 1,988 anonymized decision-maker records from the digital lessons-learned repository of a multinational supply chain logistics firm.
- Finding 2. The six perceived regulatory-volatility risks did not converge on a unified construct: sampling adequacy was weak (KMO = 0.470), and parallel analysis retained three single-indicator factors — distribution, environmental, and ownership risk.
- Finding 3. Sustainability-motivated risks were rated lowest: median labor-risk severity was 0 and environmental-risk severity was 1 on 0–5 scales, during a period of record forced-labor enforcement.
- Finding 4. This perception gap invites "sustainability leakage" — surprise enforcement provokes supplier exit and sourcing flight rather than remediation in scrutinized regions.
- Finding 5. Binary logistic regression separated success from failure perfectly in-sample (accuracy = AUC = 1.000), driven by the near-collinear distribution and supply items (r = 0.915); no individual predictor effect is identified.
- Finding 6. The complete-separation pattern is a warning of label leakage when digitized organizational records are mined for AI-based decision support.
Source: Strang & Vajjhala (2026), Sustainability, 18(18), Article 9359. DOI: 10.3390/su18189359
The study in detail
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, in detail?
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.
This study is part of the evidence overview Can ESG compliance be measured reliably? Evidence from ESG ratings, project-level instruments, machine learning and supply chain records
Methods and data
| Research question | Do perceived regulatory-volatility risks form one construct, and do they predict supply chain engagement outcomes? |
|---|---|
| Design | Retrospective analysis of archival records from an information systems perspective |
| Data | 1,988 fully anonymized decision-maker records from the digital lessons-learned repository of a multinational supply chain logistics firm |
| Variables | Perceived severity (0–5) of six risks: labor, environmental, customs, ownership, military-logistics, distribution |
| Methods | Descriptive statistics; exploratory factor analysis with parallel analysis; binary logistic regression with classification diagnostics |
| Main result | No unified construct (KMO = 0.470); sustainability risks rated lowest; perfect in-sample classification (AUC = 1.000) traced to near-collinear items (r = 0.915) |
| Implication | Use multidimensional risk dashboards and provenance-aware data governance in supply chain analytics |
| Citation | Strang & Vajjhala (2026) · DOI 10.3390/su18189359 |
Definitions and 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. — as used in Strang & Vajjhala (2026)
- 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. — as used in Strang & Vajjhala (2026)
- Label leakage
- When a predictor variable effectively encodes the outcome being predicted, producing unrealistically perfect model accuracy that will not hold in real use. — as used in Strang & Vajjhala (2026)
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.
Limitations and open questions
- 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.
Open questions
- Does the perception gap around labor and environmental regulation actually lead to supplier exit rather than remediation, which would require supplier-level sourcing data linked to enforcement events?
- Can prospective, time-stamped data capture and penalized estimation separate genuine predictive signal from leakage in organizational ESG and risk records?
Open questions from the evidence overview ESG and sustainability measurement.
Practical implications
- Firms are advised to use multidimensional, not composite, risk dashboards for supply chain analytics, after six perceived regulatory-volatility risks did not converge on a unified construct (KMO = 0.470) in 1,988 decision-maker records.
- For anyone building AI decision support from organizational records, the perfect in-sample classification (accuracy = AUC = 1.000), driven by two near-duplicate items (r = 0.915), is a concrete warning about label leakage.
- In one firm's data, labor and environmental risks were rated lowest of six risks during a period of record forced-labor enforcement; this perception gap exposes firms to sustainability leakage, where surprise enforcement provokes supplier exit rather than remediation.
- For researchers examining risk perception among supply chain decision-makers, no individual predictor effect can be estimated from the logistic regression, because its perfect in-sample separation was driven by two near-duplicate measures.
Drawn from Strang & Vajjhala (2026), Sustainability, 18(18), Article 9359.
When this research may be relevant
This article may be relevant to researchers examining regulatory volatility and geopolitical risk in supply chains, ESG and forced-labour compliance in sourcing decisions, risk perception among supply chain decision-makers, or the reuse of lessons-learned repositories for AI-based decision support. It is also a documented case of label leakage in organisational data mining and of factor analysis applied to perceived-risk items.
Research topics addressed: 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
How to cite
- APA 7
- 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), Article 9359. https://doi.org/10.3390/su18189359
- MLA 9
- Strang, Kenneth David, and Narasimha Rao Vajjhala. “Regulatory Volatility in Digital Supply Chains: An Information Systems Analytics Study of Decision-Maker Risk Perceptions and Sustainability-Related Outcomes.” Sustainability, vol. 18, no. 18, 2026, article 9359. https://doi.org/10.3390/su18189359.
- IEEE
- K. D. Strang and N. R. Vajjhala, “Regulatory Volatility in Digital Supply Chains: An Information Systems Analytics Study of Decision-Maker Risk Perceptions and Sustainability-Related Outcomes,” Sustainability, vol. 18, no. 18, Art. no. 9359, Sept. 2026, doi: 10.3390/su18189359.
BibTeX
@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}
}RIS
TY - JOUR TI - Regulatory Volatility in Digital Supply Chains: An Information Systems Analytics Study of Decision-Maker Risk Perceptions and Sustainability-Related Outcomes AU - Strang, Kenneth David AU - Vajjhala, Narasimha Rao JO - Sustainability PY - 2026 DA - 2026/09/11 VL - 18 IS - 18 SP - 9359 PB - MDPI SN - 2071-1050 DO - 10.3390/su18189359 UR - https://www.narasimharao.net/research/regulatory-volatility-digital-supply-chains-esg-risk/ LA - en ER -
Links and files
- DOI: https://doi.org/10.3390/su18189359
- Publisher’s page: https://www.mdpi.com/2071-1050/18/18/9359
- Full text (PDF): https://www.mdpi.com/2071-1050/18/18/9359/pdf
- This page as text: https://www.narasimharao.net/research/regulatory-volatility-digital-supply-chains-esg-risk/paper.md
- Permanent address of this page: https://www.narasimharao.net/research/regulatory-volatility-digital-supply-chains-esg-risk/
Related work
Related work: the literature this study cites
In supply chains, behavioral research shows that buyers’ perceptions of disruption magnitude and probability drive decisions such as supplier switching[1], and that how risk is communicated inside an organization alters perceived risk[2]. The finding of compartmentalized, low-rated sustainability risks is linked to literature on forced labor and purchasing practices, which the authors use to describe a possible sustainability leakage, where surprise enforcement leads to supplier exit rather than remediation[3][4]. The leakage diagnosis follows the data-mining literature on target leakage[5], which the authors argue has received little attention in supply chain analytics despite enthusiasm for AI-based risk prediction[6]. The study relies on self-reported or single-source ratings, so common method bias, as described by Podsakoff and colleagues, is a concern[7].
- [1] Ellis, S.C.; Henry, R.M.; Shockley, J. Buyer perceptions of supply disruption risk: A behavioral view and empirical assessment. J. Oper. Manag. 2010, 28, 34–46. https://doi.org/10.1016/j.jom.2009.07.002
- [2] DuHadway, S.; Carnovale, S.; Kannan, V.R. Organizational communication and individual behavior: Implications for supply chain risk management. J. Supply Chain Manag. 2018, 54, 3–19. https://doi.org/10.1111/jscm.12182
- [3] LeBaron, G. The role of supply chains in the global business of forced labour. J. Supply Chain Manag. 2021, 57, 29–42. https://doi.org/10.1111/jscm.12258
- [4] Anner, M. Squeezing workers’ rights in global supply chains: Purchasing practices in the Bangladesh garment export sector in comparative perspective. Rev. Int. Polit. Econ. 2020, 27, 320–347. https://doi.org/10.1080/09692290.2019.1625426
- [5] Kaufman, S.; Rosset, S.; Perlich, C.; Stitelman, O. Leakage in data mining: Formulation, detection, and avoidance. ACM Trans. Knowl. Discov. Data 2012, 6, 15.
- [6] Ivanov, D.; Dolgui, A.; Sokolov, B. The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. Int. J. Prod. Res. 2019, 57, 829–846. https://doi.org/10.1080/00207543.2018.1488086
- [7] Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.-Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879–903.
From the evidence overview Can ESG compliance be measured reliably? Evidence from ESG ratings, project-level instruments, machine learning and supply chain records, which cites only works that appear in the studies’ own reference lists. This is a selection, not the full reference list of Strang & Vajjhala (2026).
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