Mitigation, Avoidance, or Acceptance? Managing Supplier Sustainability Risk
Bibliographic record
Abstract
This study takes a conceptual theory building approach to develop a framework for managing supplier sustainability risk—the adverse impact on a buying organization from a supplier's social or environmental misconduct. Using anecdotal evidence and the literature, we present four distinct risk management strategies that supply managers adopt: risk avoidance, monitoring‐based risk mitigation, collaboration‐based risk mitigation, and risk acceptance. Drawing on agency and resource dependence theories, we study how the interactions of two key risk management predictors—that is, the supply managers’ perceived risk and the buyer–supplier dependence structure—affect supply managers’ strategy choice. Specifically, we propose that a collaborative‐based mitigation strategy, involving direct interaction and solution development with the suppliers, is selected by supply managers in a high perceived risk‐buyer dominant context. In a low perceived risk‐buyer dominant context, however, a monitoring‐based mitigation strategy is preferred. When the buyer and the supplier are not dependent on each other and there is a low perceived risk, the supply managers accept the risk by taking no actions, whereas in a high perceived risk‐independent context the supply managers would avoid the risk by terminating the relationship with the supplier. We conclude the study by describing the theoretical contributions and managerial implications of the study as well as the avenues for future research.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".