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Record W4414939270 · doi:10.1080/24725854.2025.2569666

Audit and compliance in supply chains with damage cost sharing under supplier’s responsibility standards

2025· article· en· W4414939270 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIISE Transactions · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAuditSupply chainCompliance (psychology)Financial AuditCost sharingOrder (exchange)

Abstract

fetched live from OpenAlex

Governmental and industry standards enforce responsibility in supply chains. However, supplier violations often impose costs on buyers, leading to misaligned incentives. Buyer audits act as proactive measures to ensure supplier compliance, while reactive measures hold non-compliant suppliers accountable for damages.We examine the interaction between buyer audits and supplier safety compliance when reputable buyers and a supplier share damages from non-compliance. Buyers and the supplier set their audit and safety levels, respectively, with the supplier bearing a fraction of the damage cost for under-compliance.Our findings indicate that stricter audits enhance supplier safety when damage costs are low but not when costs are high. Likewise, joint audits can compromise supplier safety at high damage costs but enhance it at lower costs. However, shared audits can result in better supplier safety than joint audits. Finally, with high damage costs, suppliers profit more from joint audits than independent audits, while buyers achieve maximum profits in audit scheme that has audit level sufficiently higher than the others.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.272
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it