Exploring the Relationship between Corporate Governance, Corporate Social Responsibility and Financial and Non-Financial Reporting: A Study of Large Companies in Greece
Bibliographic record
Abstract
Academics and professionals alike are highly interested in Corporate Social Responsibility (CSR), Corporate Governance (CG), environmental, social, and governance (ESG) and corporate non-financial reporting (CNFR) and how they can improve a brand’s reputation, financial efficiency, and sustainability within businesses and organisations. The main objective of our study was to examine whether the financial data of large companies can be correlated with the data in their non-financial reports and provide information on the level of corporate governance and corporate responsibility and to examine the correlation between them. For this purpose, we conducted research by examining the 100 largest companies in Greece, over a period of 3 years, collecting both financial and non-financial data from their official reports. Using appropriate quantitative tools such as similarity, classification and econometric methods (stepwise method and panel least-squares method), the correlations between the data for CSR, CG and non-financial actions and key financial performance ratios are evaluated. Our research has revealed a strong link between financial performance and ESG actions of large companies and, in particular, we demonstrated the positive correlation of CSR performance with their total assets and whether they are listed on the stock exchange, and of CG with CSR and EBITDA. This study adds to the existing academic discourse on the relationship between financial and non-financial information of corporations in the areas of Corporate Responsibility and Governance and provides a valuable way to assess the decisions of businesses.
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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".