Exploring the Role of Stakeholder Collaboration in Sustainable Supply Chain Management
Bibliographic record
Abstract
Abstract This qualitative study delves into the pivotal role of stakeholder collaboration in sustainable supply chain management (SSCM), aiming to elucidate the mechanisms, challenges, and opportunities inherent in collaborative sustainability efforts within supply chains. Through semi-structured interviews with key stakeholders from diverse industries and sectors, the research explores stakeholders' perspectives, experiences, and practices related to collaboration in SSCM. The findings underscore the critical importance of collaboration in driving sustainability goals within supply chains, facilitating trust, transparency, and mutual understanding among stakeholders. Despite its recognized significance, the study identifies challenges and barriers to effective collaboration, including divergent interests, power imbalances, resource constraints, and communication barriers. Moreover, the study highlights the role of technological advancements and collaborative platforms in enhancing stakeholder collaboration in SSCM, providing tools and resources to enhance transparency, traceability, and accountability within supply chains. Additionally, the study emphasizes the importance of regulatory frameworks and industry standards in shaping collaborative sustainability efforts within supply chains, providing guidelines and incentives for organizations to adopt sustainable practices and collaborate with stakeholders. Overall, the research contributes to a deeper understanding of stakeholder collaboration in SSCM and offers insights for researchers, practitioners, policymakers, and industry stakeholders seeking to harness collaborative approaches to advance sustainability goals and create value for all stakeholders involved.
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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 | 0.029 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".