Efficiency meets accountability: Performance implications of supply chain configuration, control, and capabilities⋆
Bibliographic record
Abstract
Abstract The public increasingly holds firms accountable for social and environmental outcomes, such as product toxicity problems and human rights violations, throughout their global supply chains. How can companies improve the social and environmental performance within their supply chains, particularly as other competitive pressures, such as cost and quality, continue to escalate? Starting from an efficient versus responsive supply chain framework, we develop an integrative model that blends together elements of supply chain configuration, stakeholder management, and capability development. Specifically, we spotlight the dimensions of control and accountability that collectively determine stakeholder exposure, and show how this new construct affects the linkages between supply chain capabilities, configuration, and performance. In particular, this analysis reveals that the nature of stakeholder exposure determines how social/environmental technical and relational capabilities impact social and environmental outcomes. We conclude with implications for research and practice, discussing how current supply chain theories must be extended to incorporate external stakeholders, to clarify strategies and identify potential pitfalls, and to better predict performance outcomes.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.006 |
| 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".