Exploring sustainable development goals adoption in supply chain management: A typology of coexisting institutional logics
Bibliographic record
Abstract
Abstract Despite growing interest in the 17 United Nations (UN) sustainable development goals (SDGs), companies still struggle with how to implement them as part of their sustainability policies. Hence, we explore how the SDGs are adopted in supply chain management (SCM) using institutional logic as a theoretical lens. Based on a case study, we analyse 10 interviews conducted with managers and suppliers of a multinational cosmetics company recognised for its sustainability engagement. Our findings show that while the strategic level embraces the SDG framework, supply management processes are exclusively evident at the operational level. This becomes even more salient in the analysis of sustainability and commercial coexisting logics. Although sustainability logic concerns compliance with sustainability‐related regulations and external pressures, commercial logic demonstrates the effective engagement of strategic decisions in favor of profitability. Our results reveal nuances in the existing gap between the SDGs and SCM, illustrated through a typology of coexisting logics at the strategic and operational levels, adding value to the SCM literature. This paper questions the feasibility of implementing the UN SDGs along a supply chain, which opens the door for future research and new practical insights relevant to companies' daily operations.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| 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".