Addressing supplier sustainability misconducts: response strategies to nonmarket stakeholder contentions
Bibliographic record
Abstract
Purpose Firms are increasingly accountable for their suppliers' social and environmental practices. Nonmarket stakeholders nowadays do not hesitate to confront buying firms for their suppliers' misconducts by mobilizing demonstrations, social media campaigns and boycotts. This paper aims to develop a typology of response strategies by targeted firms when they face such contentions and to empirically investigate why these strategies vary among those firms. Design/methodology/approach Drawing on social movement and stakeholder salience theories, the authors develop a set of hypotheses linking their typology of four response strategies to three key contextual factors – nonmarket stakeholder salience, nonmarket stakeholder ideology and the target firm reputation – and examine them using a vignette-based experiment methodology. Findings The results suggest that nonmarket stakeholder salience significantly impacts the nature of response (reject or concede), whereas the nonmarket stakeholder ideology is significantly related to the intensity of response (trivial or vigorous). Interestingly, the firms' reputation was found to have no significant effect on their response strategy when they faced stakeholder contentions. Originality/value This paper adds both theoretical and methodological value to the existing literature. Theoretically, the study develops and tests a comprehensive typology of response strategies to nonmarket stakeholder contentions. Methodologically, this study is original in leveraging a vignette-based experiment that allows establishing causal factors of response strategies following a supplier sustainability misconduct.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".