Improving a supplier's social responsibility: the effect of competition and incomplete visibility
Bibliographic record
Abstract
Firms face increasing pressure from consumers, governments, and non-governmental organisations (NGOs) to implement better social responsibility (SR) practices in their supply chains. However, a lack of visibility into suppliers' operations and competition can hinder a firm's efforts to improve SR in its supply chain. In this study, we consider two competing supply chains, each consisting of a single firm and a single supplier, and explore the case where the firms can invest in their suppliers' SR capabilities. We assume the firms do not have full visibility into the suppliers' SR practices. Moreover, the suppliers are under scrutiny from a third party that may disclose the suppliers' compliance levels to consumers. Our results show that increasing the intensity of price competition leads firms to invest more in their suppliers and improves suppliers' SR. We further show that a firm benefits from having full visibility into its supplier's SR practices only when the suppliers' unit investment costs are low. We extend our results to consider the case where consumers respond to the supplier's actual compliance level (rather than just the news of their noncompliance) and demonstrate the robustness of our findings.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".