The impact of the enablers of green supplier selection and procurement on supply chain performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The selection of environmentally friendly suppliers, a critical aspect of supply chain performance, is vital for businesses striving to retain their competitive edge as they increasingly outsource tasks. With growing public concern for environmental protection in recent decades, strategies focusing on green supplier selection and procurement have gained traction. Although numerous studies discuss green supplier selection based on economic criteria, the field of environmental research remains nascent. This research offers an in-depth analysis of supply chain performance, green supplier selection, and overall procurement strategies from both economic and ecological viewpoints. Through a literature review and the grey DEMATEL method, we pinpoint the key factors influencing procurement, green supplier selection, and supply chain performance. Our literature assessment identifies the main elements that enhance supply chain performance, and these findings are further confirmed by expert input. By addressing gaps in current models of procurement and green supplier selection, this study advances decision-making theory. Our findings reveal that our proposed model, which accounts for the intricacies of supply chain performance and the uncertainties in expert feedback, offers an effective solution to the challenges of procurement and green supplier selection.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it