Supplier Internal Control Quality and the Duration of Customer-Supplier Relationships
Bibliographic record
Abstract
ABSTRACT Internal controls influence information quality, thus affecting the ability of supply chain partners, who rely on collaborative systems of information sharing, to reliably contract. Using SOX-related internal control assessments as a proxy for internal control quality and U.S. GAAP-mandated major customer disclosures, we find that supplier internal control quality influences supply chain relationship duration. Specifically, our evidence demonstrates that: (1) poor internal control quality increases the likelihood of subsequent customer-supplier relationship termination; (2) timely control weakness remediation attenuates termination likelihood; and (3) weaknesses affecting customer contracting drive the effect of internal control quality on relationship termination. Our results control for supplier operational quality and performance, and are robust to propensity score matching techniques, controls for reverse causality, and alternative proxies for relationship termination and internal control quality. Overall, our findings are consistent with customers viewing strong supplier controls as important, albeit overlooked, contracting elements with significant implications for supply chain relationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".