<scp>Greening the Supply Chain: When Is Customer Pressure Effective?</scp>
Bibliographic record
Abstract
Suppliers face increasing pressure from their customers to improve their environmental performance. When firms downstream in the supply chain seek to achieve such improvements themselves, they frequently request that their suppliers adopt greener practices. This paper investigates the rationale for suppliers to comply with or resist the mandate of their customers to adopt the international environmental management standard ISO 14001 in the North American automotive industry. We argue that the effectiveness of such a mandate will vary according to the characteristics of the relationship between suppliers and customers. We contrast and test hypotheses based on both transaction cost and information theories to suggest that suppliers, whether in a dependent or distant relationship with their customers, have incentives to comply with the requests of their customers but through different mechanisms. Our study analyzed the characteristics of 3,152 automotive suppliers located in the United States, Canada, and Mexico over the 2000–2003 period. Findings indicate that suppliers with highly specialized assets, as well as younger suppliers, suppliers headquartered in Japan, and those reporting to the Toxic Release Inventory, are more likely to respond to their customers' pressures to adopt the certified management standard ISO 14001.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".