Motivations for Implementing Cleaner Production Principles: A Case Study of Saudi Plastic Manufacturers
Bibliographic record
Abstract
The rapid economic, cultural and industrial development, and population growth in Saudi Arabia has created environmental challenges, threat of energy, resource crisis, and climate change. One of the contributing factors is the increasing number of manufacturers and lack of green practices. Therefore, this study aims to investigate the aspects that influence implementation of Cleaner Production Principles by Saudi Plastic Manufacturers. A conceptual model that was based on technological, environmental, and organizational theory was used to present the primary motivating factors of manufacturers in Jeddah for pollution reduction and also to the achievement of sustainable development goals according to Saudi Vision 2030. The research model provides motivations for the environmental and economic consequence of Cleaner Production Principles Implementation. This work is grounded in hypothesis; such as, regulatory and customer pressures having a positive influence on the implementation of CP principles in the Saudi plastic manufacturer industries. A questionnaire on the implementation of CP principles was provided to the manufacturers. 127 samples were analyzed through SPSS to investigate the relationship between the independent (CP Principles implementation) and dependent variables (motivational factors for implementation). The results have indicated that Regulatory Pressure and Customer Pressure has significant positive influences on the implementation of CP Principles for Saudi manufacturers. The quality of HR, SM support, governmental support, and environmental uncertainty have significant negative influences on the implementation of CP principles. The lack of previous research about CP implementation in KSA will make the results of this research beneficial for future researchers and practitioners.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".