Beyond Philanthropy: A Systematic Review of CSR as a Strategic Driver of Financial Performance in Commercial Banking
Bibliographic record
Abstract
<ns3:p>Background The global commercial banking sector is under increasing pressure to integrate Corporate Social Responsibility (CSR) into its core strategies. This shift is driven by evolving regulatory frameworks, heightened stakeholder expectations, and a growing recognition of sustainability’s impact on long-term viability. However, empirical evidence on the CSR-financial performance (FP) relationship remains fragmented and often contradictory, creating a critical knowledge gap for practitioners and scholars alike. Methods This systematic literature review, conducted in accordance with PRISMA 2020 guidelines, synthesizes the most recent evidence (2022-2025). A comprehensive search of Scopus-indexed databases identified relevant studies. The review employed a convergent synthesis approach, integrating quantitative and qualitative findings through thematic analysis and effect size calculations. Study quality was appraised using a modified Newcastle-Ottawa Scale, with 85% of the included studies meeting high-quality thresholds. Results The synthesis indicates a predominantly positive, though context-dependent, association between CSR and financial performance. Meta-analytical findings show moderate positive correlations with key profitability indicators, including return on assets (r = 0.34) and return on equity (r = 0.29). A significant finding is CSR’s pronounced role in risk mitigation, evidenced by a negative correlation with non-performing loans (r = -0.26) and enhanced resilience during market volatility. The relationship is strongly moderated by institutional factors: bank size, regulatory environment, and geographic context. Crucially, the depth of strategic integration was a key differentiator, with substantive, authentic CSR implementations yielding significantly greater financial benefits than symbolic approaches. Conclusion This review provides a contemporary synthesis of the CSR-FP nexus in commercial banking, offering three principal contributions: it identifies key moderators that explain contradictory findings in prior literature; it documents risk mitigation as a fundamental mechanistic pathway linking CSR to financial outcomes. The findings offer evidence-based guidance for banking executives and policymakers to navigate the complex landscape of sustainable finance effectively.</ns3:p>
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".