Environmental education using SARITHA-Apps to enhance environmentally friendly supply chain efficiency and foster environmental knowledge towards sustainability
Bibliographic record
Abstract
This study aimed to investigate the impact of Environmental Education on sustainability and the mediating role of Environmentally Friendly Supply Chain Efficiency (EFSC) and Fostered Environmental Knowledge (FENK). Employing a quantitative approach, data were collected from supply chain professionals who participated in Environmental Education programs. The research findings indicate a positive relationship between Environmental Education and supply chain sustainability. The study revealed that Environmental Education significantly enhances EFSC and FENK and positively influences sustainability practices within supply chains. The implications of this research are twofold. Firstly, it underscores the importance of incorporating Environmental Education as a fundamental component of supply chain management, contributing to more environmentally responsible practices. Secondly, the study highlights the mediating role of EFSC and FENK, indicating that not only does Environmental Education directly impact sustainability but also through the enhancement of these mediating factors. This research offers a novel perspective by establishing the link between Environmental Education, EFSC, FENK, and supply chain sustainability. However, certain limitations should be acknowledged, such as the potential for response bias and the need for further research to explore other potential mediators. Nevertheless, this study provides valuable insights for practitioners and policymakers seeking to promote sustainability in supply chains by emphasizing the role of Environmental Education and its interconnectedness with EFSC and FENK.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".