Does Mediation Matter in Explaining the Relationship between ESG and Bank Financial Performance? A Scoping Review
Bibliographic record
Abstract
This study identifies and synthesizes patterns and trends in the emerging body of literature of environmental, social, and corporate governance (ESG) endeavors on the financial performance (FP) of the banking firms. It specifically aims to highlight the relationship of ESG–FP. The scoping review analysis is based on 1856 journal articles from two online databases, namely Scopus and Web of Science (WoS) for the period of 2015 to 2023. The analysis reveals inconsistent results regarding the ESG–FP relationship, with some studies reporting positive impacts, others negative, and several showing no significant relationship. Notably, non-linear studies consistently identify an inverted U-shaped relationship, suggesting that there is a threshold level of ESG investment beyond which additional investments do not yield proportional benefits. This indicates that threshold-based policies may be more effective at maximizing ESG benefits. The study also found that numerous studies suggested exploring the indirect effect or mediating variables in the ESG–FP relationship to better explain the FP variance. Thus, the study identifies a need for future research to explore indirect relationships by testing potential moderators or mediators, particularly bank risk-taking, to better understand the ESG–FP dynamics. Policymakers and regulators should adopt non-linear analytical approaches and set threshold-based ESG investment policies, while bank management should strategically invest in ESG activities, integrating ESG considerations into risk management frameworks. Continuous monitoring and evaluation, along with stakeholder engagement, are crucial for optimizing ESG investments. By adopting these strategies, banks can enhance financial performance and contribute to sustainable and responsible banking practices.
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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 | 0.043 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".