Corporate Social Responsibility as a Driver of Financial Performance: An Exploration of South African Companies
Bibliographic record
Abstract
This study investigates the relationship between corporate social responsibility performance and financial performance among firms listed on the Johannesburg Stock Exchange in South Africa. Utilising a multi-metric approach, the research incorporates corporate social responsibility scores; environmental, social, and governance ratings; and the social pillar score to provide a comprehensive analysis. Data from 104 companies with 624 observations from 2017 to 2022 was analysed. This quantitative study employs a Generalised Least Squares estimation, and the findings reveal a significant positive correlation between corporate social responsibility performance and several key financial metrics, including return on assets, earnings per share, market value added, and Tobin’s Q ratio. The results suggest that companies prioritising corporate social responsibility initiatives are likely to experience improved financial outcomes. Furthermore, the study examines the influence of board characteristics on financial performance, identifying a positive effect of gender diversity and negative impacts from board independence and meeting frequency. Overall, this research contributes to the literature on corporate social responsibility and financial performance by highlighting the importance of corporate social responsibility in driving sustainable business practices and enhancing firm performance within the context of an emerging economy. The findings underscore the need for firms to integrate corporate social responsibility into their strategies to promote long-term success while addressing societal challenges.
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 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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".