Competitive strategy development through green supply chain practices
Bibliographic record
Abstract
Nowadays, the topic of preserving the environment and serving local communities is a hot issue. Hence, this study aims to explore how the green supply chain affects Jordan's pharmaceutical manufacturing industry's ability to compete globally. This study's research methodology is quantitative, descriptive, and cause-effect. Data was gathered from a sample of 124 managers selected randomly from a pool of 300 managers working in 10 out of 14 pharmaceutical manufacturing organizations. The study tool underwent evaluations for normality, validity, and reliability before the data were subjected to descriptive analysis and a correlation analysis was performed between variables. Finally, hypothesis testing was conducted through the application of multiple regression analysis. The findings show that green practices affect competitive strategy, where green operations were having the highest effect on competitive strategies, then green purchasing, and green selling, respectively. The study's conclusions show that the adoption of a green supply chain improves the competitiveness of the Jordanian pharmaceutical manufacturing sector. Accordingly, the study recommends that Jordanian pharmaceutical manufacturing companies should include green supply chain practices in their daily supply practices to increase the competitiveness of the organizations.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".