Collaborative mechanisms for sustainability-oriented supply chain initiatives: state of the art, role assessment and research opportunities
Bibliographic record
Abstract
Whether from government policies, customer expectations or personal beliefs, there is increasing pressure on firms and their supply chains to adopt sustainable practices. Manufacturing companies are particularly targeted, for example, to reduce CO2 emissions, offer sustainable products, etc. Research in this field has significantly increased in recent years. Most research states the importance of collaboration with upstream and downstream entities as a critical success factor when aiming for a sustainable supply chain and proposes various collaborative mechanisms (CMs) to enable firms in the implementation of a sustainability-oriented initiative. The goal of this paper is to investigate the role of collaboration in these initiatives and explore the proposed CMs via a systematic literature review method. A total of 404 articles were reviewed and the multitude of CMs proposed in the literature were classified into seven categories: relationship management, contractual and economic practices, joint practices, technological and information sharing practices, governance practices, assessment practices, and supply chain design. This systematic mapping of the field provides an in-depth view of the current state of research as well as research gaps. It also intends to help practitioners by highlighting the role played by these mechanisms in four phases of sustainable supply chain deployment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".