Mitigating crisis impact: The influence of corporate social responsibility on non-financial firms’ financial performance
Bibliographic record
Abstract
This study investigates the impact of corporate social responsibility (CSR) on the financial performance of firms by analyzing how it affects the relationship between capital structure and financial performance during the COVID-19 pandemic. We focus on non-financial publicly listed firms in the USA, UK, Canada, Japan, and Italy from 2018 to 2021. Our findings reveal that during the pandemic short-term debt significantly negatively impacts return on assets (ROA), specifically a decrease of 35%. Additionally, we provide empirical evidence that CSR positively influences financial performance. In particular, the combined CSR scores, as well as environmental and social activities, positively affect ROA, return on equity (ROE), and Tobin’s Q across all analyses. We observe that a high combined score (ESG) mitigates the adverse effects of all types of debt on ROA and ROE. However, the positive impact on Tobin’s Q is more pronounced regarding long-term and total debt by 0.987 % and 0.937%, respectively. Furthermore, our analysis indicates that high environmental, social, and governance activities have a more substantial positive effect on the relationship between total debt and both ROE and Tobin’s Q. Overall, our study suggests that investing in CSR can be a wise strategy for firms, not only helping to alleviate the adverse effects of debt during market downturns but also switch the adverse effect to positive which ultimately enhancing financial performance.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".