The Impact of Adherence to Sustainable Development, as Defined by the Global Reporting Initiative (GRI-G4), on the Financial Performance Indicators of Banks: A Comparative Study of the UAE and Iraq
Bibliographic record
Abstract
Based on stakeholder theory, disclosing sustainable development information is fundamental to achieving a competitive advantage and improving a company’s financial performance. There has been a notable absence of studies examining the degree of adherence to sustainability based on the latest indicators from the Global Reporting Initiative (GRI-G4) Guidelines and its impact on financial performance, specifically within the banking sector in emerging Arab economies. Consequently, this study explores the correlation between the degree of adherence to sustainability and its dimensions (economic, social, and environmental) as defined by GRI-G4 and financial performance within a sample of banks in Arab nations (the United Arab Emirates “UAE” and Iraq) from 2019 to 2021. The research hypotheses were examined using a multiple linear regression model. The empirical findings reveal that, on average, UAE banks exhibit a sustainability adherence level of 57% according to GRI-G4, while their Iraqi counterparts demonstrate a significantly lower adherence of 17%. Notably, the degree of sustainability adherence substantially impacts the financial performance of banks in both countries. Furthermore, the results also indicated that the economic dimension of sustainability has a positive impact, while the environmental dimension has a negative impact, and in contrast, the social dimension does not significantly affect the financial performance of banks in both countries. This study provides insights for banks and policymakers to enhance their sustainability practices and elevate the level of disclosure, especially within Arab nations. This, in turn, can lead to greater compliance with sustainability standards, improved transparency, and reduced information asymmetry.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".