Sustainable development goals and supply chain practices: a framework based on a meta-synthesis analysis of case studies
Bibliographic record
Abstract
Purpose This study investigates the ways in which supply chain practices are adopted by firms to address the United Nations sustainable development goals (SDGs). By employing a practice-based studies perspective, we explored empirical insights from case studies to advance our knowledge in the sustainable supply chain management (SSCM) field. Design/methodology/approach A meta-synthesis of publications was conducted through the content analysis of 28 primary case studies published between 2015 and 2024. We identified what, where, by whom, why and how multiple practices were explored toward SDGs. Findings Our findings are constituted of four bundles of practices that emerged from the analysis. We noted that supply chain practices rely on their surroundings (e.g. context and sector) and have both direct and indirect relationships with SDGs. Based on our analysis, SDG 12 (responsible consumption and production), SDG 8 (decent work and economic growth), SDG 3 (good health and well-being) and SDG 2 (zero hunger) are the most frequently targeted goals in SSCM. Most cases focused on developing economies and limited to a few sectors (i.e. agri-food related), offering a wide spectrum for further research. Originality/value By exploring a meta-synthesis method, which remains underutilized in our field, this study provides a comprehensive systematization of the ways in which SSCM research has linked SDGs and supply chain practices. We provide an understanding that firms’ supply chain practices can lead to SDG implementation should provide indication of sustainable practices that contribute to the SDGs, instead of assuming implicit connections.
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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 | 0.239 | 0.291 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.074 | 0.043 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| 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".