Financial Sector Performance and Environmental Sustainability: Assessing the Moderating Effect of Social Responsibility
Bibliographic record
Abstract
The study explores the effect of FSEP on Environmental Performance (EPER) in Saudi Arabia's financial sector including a focus on the mediating role of FSSR on EPER.To investigate the hypothesized relationships, we collected primary from 512 employees working in the Saudi financial sector in Alkharj governorate, and SEM was used for hypothesis testing to conclude the results.We find that FSEP enhances both EPER and FSSR and also demonstrates that FSSR positively influences EPER.Furthermore, FSSR plays a significant mediating role as well.Which is strengthening the relationship between FSEP and EPER.These findings conclude the importance of integrating environmentally sustainable financial practices to accelerate economic sustainability and EPER in this sector.We suggest to the Saudi financial sector to further adopt green practices to achieve long-term economic and environmental sustainability in the long run.By reducing energy consumption and mitigating pollution from this sector, the Saudi financial sector can actively contribute to the nation's broader environmental sustainability goals.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".