Why Research in Sustainable Supply Chain Management Should Have no Future
Bibliographic record
Abstract
In the last two decades, the topic of sustainability has moved from the fringes of supply chain management research to the mainstream and is now an area of significant research activity. In this paper, we argue that while this increase in acceptance and activity is welcome and has lead to a greater understanding of sustainability, our present knowledge is not sufficient to create truly sustainable supply chains. We build on this insight to identify five main issues that future research needs to address. We argue that when it comes to the theory of sustainable supply chain management, previous research has focused on the synergistic and familiar while overlooking trade‐offs and radical innovation. These theoretical issues are compounded by measures that do not truly capture a supply chain's impacts and methods that are better at looking backwards than forwards. The paper concludes by proposing a series of recommendations that address these issues to help in the development of truly sustainable supply chains.
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 | 0.090 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.018 | 0.059 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".