The impact of green productivity strategy on environmental sustainability through measurement of the management support: A field study in industry sector in Jordan
Bibliographic record
Abstract
This study intends to distinguish the green productivity concepts, its strategy and the roadmap of its implementation throughout the adaption of green products, green production, and green innovation process dimensions. Further, it studies the impact of green productivity on environmental sustainability by identifying the mediating role of the management support to these green practices in the Jordanian factories. Methodology population of this study en-tails industry mining firms listed in the Amman Stock Exchange (ASE). A purposive sample was adapted in this study and consisted of specialized employees in terms of operating factories in general and in green production specifically. 100 questionnaires were precisely collected and analyzed via Smart Partial Least Square (PLS) statistics in order to analysis the mediation role. The study results point out a statistically significant impact of the independent variables, namely, the green productivity on environmental sustainability as a dependent variable in the existence of the mediating variable. Further, the results demonstrate the partial mediating effect of the dependent variable. However, there is no mediating effect of the independent to the mediator variable. Accordingly, the study recommends companies to concentrate on the roles played by the top management in the efforts towards green productivity adoption and strategic implementation in the Jordanian environment. The implementation of green productivity is considered at the early stages in Jordan. Thus, the value of the study lies in the search for issues that are eco-friendly practices.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".