Effect of ESG Financial Materiality on Financial Performance of Firms: Does ESG Transparency Matter?
Bibliographic record
Abstract
Transparency in ESG financial materiality disclosure by corporations is now in doubt due to the inconsistent ESG framework that governs ESG disclosures, particularly in developing nations like South Africa. This is evident in the financial performance of banks and manufacturing firms as a result of the higher rate of susceptibility to ESG issues. Hence, this study empirically investigated the effect of ESG financial materiality disclosure on the financial performance of banks and manufacturing firms in South Africa from 2015 to 2024. Also, the moderating role of ESG transparency on the relationship between ESG financial materiality disclosure and financial performance was investigated. Descriptive analysis, a correlation matrix, and panel regression analysis were employed for analysis purposes. The financial metrics include ROA, ROE, and Tobin’s Q, while ESG financial materiality disclosure and the ESG disclosure score of the firms were the independent variable and moderating variable, respectively. The results show that ESG financial materiality exerts a significant adverse impact on ROA and ROE but an insignificant positive effect on Tobin’s Q in banks. For manufacturing firms, the impact is insignificant and negative on ROA, ROE, and Tobin’s Q. Also, the interactive effect of transparency insignificantly weakens the effect of ESG financial materiality disclosure on financial performance in both banks and manufacturing firms. This concludes that the transparency in ESG financial materiality disclosure is not sufficient to improve financial performance in both sectors but should be integrated in the core business objectives of firms. Also, it suggests that over-disclosure and greenwashing of ESG reports should be avoided.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".