The role of intellectual capital on green supply chain management: Evidence from the Jordanian renewal energy companies
Bibliographic record
Abstract
The study aimed to demonstrate the impact of Green Supply Chain Management (GSCM) with its dimensions (Green IT, Green Manufacturing and Packaging, Green Storing, Green Purchasing, Green Marketing) on the quality of services in renewable energy companies in Jordan. In addition, the study also aimed to measure the impact of intellectual capital on the impact of GSCM on the quality of services in renewable energy companies in Jordan. By adopting the survey/sampling method, data was collected from the study population of (482) companies, and the study sample consisted of (260) managers of renewable energy companies in Jordan using a questionnaire. The study reached several results, the most important of which are: the existence of an impact of green supply chain management on the quality of services in renewable energy companies, that intellectual capital has modified and enhanced the effect, and that renewable energy companies in Jordan seek to use environmentally friendly inputs in their production at a very high rate, several recommendations emerged from these results, most notably: that companies constantly review the production processes followed by suppliers to ensure their compliance with environmental specifications.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".