Power asymmetries in supply chains and implications for environmental governance: a study of the beef industry
Bibliographic record
Abstract
Purpose Supply chain governance constitutes the rules, structures and institutions that guide supply chains toward various objectives, including environmental sustainability. Previous studies have provided insight into the relationship between governance and sustainability but have overlooked two crucial dimensions: power dynamics and the influence of outside actors. This paper aims to address these two gaps by measuring differential power (i.e. power asymmetries) among actors across the supply chain, including external actors. Design/methodology/approach This paper quantifies power dynamics across the entire chain through a structured survey in which supply chain participants rank their peer’s ability to affect environmental and social outcomes. This paper tests this approach by surveying 200 industry professionals (e.g. feedlot owners, retailers) and external actors (e.g. NGOs) in the US beef sector. Findings Respondents ranked the most powerful actors as follows: feedlot owners; processing plant owners; and regulatory agencies. Results also revealed that trade associations, retailers and cow–calf producers and ranchers perceive a sense of powerlessness. This study reveals multiple power nodes and confirms a shift in the power structure depending on which indicator respondents considered (e.g. environmental impacts vs employee safety). This study concludes that the buyer–producer dichotomy often used to assess supply chain governance fails to capture the complex dynamics among actors within supply chains. Originality/value This study demonstrates a novel approach to measure perceptions of power in supply chains. This method enables researchers to map networks of power across entire supply chains, including internal and external actors, to advance understanding of supply chain governance dynamics. Previous studies have misidentified who governs environmental outcomes in supply chains, and NGOs have overestimated the power of consumers and retailers to influence producers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".