Drivers and Enablers That Foster Environmental Management Capabilities in Small‐ and Medium‐Sized Suppliers in Supply Chains
Bibliographic record
Abstract
The limited capabilities and resources available within many small‐ and medium‐sized enterprises frequently hamper an effective response to environmental pressures, which in turn hurts large buying firms (i.e., customers). Using a case study method with multiple suppliers of two large buying firms, we mapped factors that initiated and improved environmental capabilities in small‐ and medium‐sized enterprises over time. Through several specific mechanisms, buyers' green supply chain management initiated and then enabled the improvement of suppliers' environmental capabilities. Independent of buyers, internal championing of environmental concerns also provided an impetus for small‐ and medium‐sized enterprise suppliers to acquire resources outside the supply chain. Thus, synergistic linkages emerged in supportive buyer‐supplier relationships, resource acquisition, and capability development. When these findings are combined with earlier research on larger suppliers, an integrative framework emerges that provides direction for suppliers, buyers, and public agencies seeking to improve environmental performance.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".