Managing supplier sustainability risk: an experimental study
Bibliographic record
Abstract
Purpose Buying firms are increasingly exposed to sustainability risk arising from negative conditions or potential events in their supply base that might provoke adverse stakeholder reactions. Procurement managers at these firms can pursue multiple strategies to address this risk with suppliers, including acceptance, monitoring-based mitigation, avoidance and collaboration-based mitigation. This study aims to investigate how perceived risk, supplier dependence and financial slack resources contribute to the strategic preferences of these managers. Design/methodology/approach A vignette-based experiment with procurement managers is used to examine the factors affecting the managers’ strategic preferences in managing supplier sustainability risk. Findings The empirical results revealed that the procurement managers’ preference for avoidance or collaboration strategies was stronger when they perceived higher risk, but their preference varied based on the degree of supplier dependence. Specifically, when they perceived a high level of risk, procurement managers were more inclined toward a monitoring strategy with dependent suppliers and preferred an avoidance strategy when they dealt with independent ones. Financial slack was also an influential factor: managers with more slack at their disposal preferred to collaborate with suppliers to address the risk; on the other hand, limited slack shifted their preference toward an acceptance strategy, regardless of the level of risk. Originality/value This study helps to develop a more nuanced picture of how procurement managers make challenging and complex trade-offs when responding to supplier sustainability risk.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".