The Factors Affecting Green Supply Chains: Empirical Study of Agricultural Chains in Vietnam
Bibliographic record
Abstract
This paper aims at identifying the factors affecting green supply chain in agriculture in Vietnam currently. The literature indicates 14 factors affecting green supply chains in agriculture including: (i) manager commitment, (ii) IT system, (iii) new technology, (iv) organizational culture, (v) HR quality, (vi) energy & waste management, (vii) market & competition, (viii) political supports, (ix) knowledge & experience, (x) actors’ participation, (xi) costs, (xii) suppliers, (xiii) logistics management, and (xiv) consumer awareness. Our regression model with 14 independent variables was established to determine the factors affecting the success of green supply chains in Vietnam agriculture. The regression results show six factors affecting significantly and positively green supply chain in agriculture in Vietnam, including: (i) manager commitment, (ii) new technology, (iii) HR quality, (iv) knowledge & experience, (v) logistic management, and (vi) consumer awareness. Hence, the paper suggests some recommendations to Vietnam firms and State for improving green supply chain in agriculture.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".