You are entitled to access the full text of this documentEnhancing sustainable performance through circular economy: The mediating roles of green supply chain and process innovation ,
Bibliographic record
Abstract
This study examines the relationships between circular economy capability, green supply chain, green process innovation, and sustainable performance in the manufacturing sector of Saudi Arabia. As the country transitions toward its Vision 2030 goals, which emphasize sustainability and economic diversification, the manufacturing sector plays a critical role in adopting circular economy principles and green practices to reduce environmental impact and enhance resource efficiency. Using a cross-sectional research design, data were collected from managerial-level employees through a structured questionnaire. Data analysis was conducted using structural equation modeling (SEM) to examine the hypothesized relationships. The findings reveal that circular economy capability significantly drives green supply chain and green process innovation, which in turn enhance sustainable performance. The study also identifies green supply chain and green process innovation as critical mediators in the relationship between circular economy capability and sustainable performance. The results highlight the importance of integrating circular economy principles with green practices to achieve sustainability goals. It provides actionable insights for organizations to enhance their sustainability efforts, such as investing in resource efficiency, adopting green supply chain practices, and fostering process innovation.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.284 | 0.122 |
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".