Bibliographic record
Abstract
Purpose This research aims to extend the “collaborative paradigm” proposed by others in prior research beyond a supply chain's core operations. To date, this paradigm has generated relatively little empirical research on peripheral, non‐core areas such the natural environment. Antecedents (both plant‐level and supply chain characteristics) of green supply chain practices (GSCP) are examined. Among possible antecedents, prior research pointed to supply chain integration – both logistical (tactical level) and technological (strategic level) – as a potentially important determinant of green practices. Design/methodology/approach Green practices are defined along the two dimensions of environmental collaboration and monitoring. The empirical analysis used data from 84 plants in North America surveyed in 2002. Validity and reliability of scales for new and existing constructs were assessed through factor analysis. Hierarchical linear regression was used to test the hypotheses for the antecedents of GSCP. Findings Technological integration with primary suppliers and major customers was positively linked to environmental monitoring and collaboration. For logistical integration, a linkage was found only with environmental monitoring of suppliers. Finally, as the supply base was reduced, the extent of environmental collaboration with primary suppliers increased. Research limitations/implications Greater supply chain integration can benefit environment management in operations, and the collaborative paradigm can be extended to this domain. A limitation is that the empirical analysis focused on one industry representing a single echelon. Originality/value This is one of the few studies that conceptualize and empirically test GSCP, and consider both and separately upstream and downstream interactions in the supply chain.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".