Opportunism in supply chains: Dynamically building governance mechanisms to address sustainability-related challenges
Bibliographic record
Abstract
Opportunism has long been highlighted as one of the hazards of complex buyer–supplier relationships. This hazard has become more challenging to manage as the pressure from consumers and the public for improved sustainability-related performance is passed along the supply chain between buyers and suppliers. For example, buyer demands for and supplier implementation of sustainability requirements, such as low carbon emissions or fair worker treatment, are often poorly defined; difficult to document; and subject to change as pressure for immediate improvement builds, as scientific understanding deepens and as societal expectations advance. This complex setting generates tempting openings for either a buyer or supplier to act opportunistically. In an effort to advance both theory and managerial practice, we consider three aspects of sustainability-related opportunism. First, in line with prior research, we focus on defining sustainability-related opportunism as jointly considering the codification of expectations and verification of performance (while allowing for false suspicions). Second, our conceptualization stresses the need to move away from an implied static view to embrace more fully the changing nature of stakeholders’ sustainability-related concerns. For example, supply chain relationships evolve based on repeated interactions as firms influence each other’s beliefs, practices and outcomes. Third, in a related sense, a dynamic model can combine several theoretical perspectives to inform how the balance of transactional and relational governance mechanisms might adapt as our understanding of sustainability changes, institutional forces evolve, and dyadic relationships mature.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".