Managing Reputation Risk in Supply Chains: The Role of Risk Sharing Under Limited Liability
Bibliographic record
Abstract
When a supplier fails to comply with social and environmental standards, the buyer’s reputation suffers. Reputation costs can typically be very high for the buyer, whereas the supplier’s liability is often limited. Conventional procurement strategies such as dual sourcing mitigate the buyer’s operational risk, but they often do so at the expense of increasing its reputation risk and sourcing costs. In this paper, we propose a risk-sharing contract for managing the buyer’s reputation concerns. We find that by sharing some of the supplier’s operational loss, the buyer may (in some conditions) decrease its reputational risk, but this has to be balanced against an increase in the operational risk. Risk sharing also reduces sourcing costs because the buyer takes on some of the worst-case loss of a wealth-constrained supplier. These results suggest that risk sharing can be superior, as a procurement strategy, to conventional approaches such as dual sourcing or penalty contracts. This is true when reputation and sourcing costs are a significant concern and operational costs are not that high. Under some conditions, the buyer may choose risk sharing even if it increases reputation risk in order to reduce procurement costs. This paper was accepted by Victor Martínez-de-Albéniz, operations management.
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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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".