Do Board Characteristics Promote Corporate Social Responsibility? An Empirical Analysis for European Companies
Bibliographic record
Abstract
In recent years, businesses have faced increasing pressure from consumers, investors, and regulators to prioritize sustainability, ethics, and transparency. Consequently, Corporate Social Responsibility (CSR) is growing and companies are integrating CSR into their core strategies to enhance long-term value and align with societal expectations. Board characteristics significantly influence CSR outcomes. With boards that actively support and oversee CSR initiatives, sustainable practices would be supported. Hence, studying the relationship between board composition and CSR is very useful. The purpose of this paper is to check whether board characteristics affect CSR for European firms. To achieve this goal, we used a large sample of 1376 listed companies located in 23 European countries from 2014 to 2023. The System Generalized Method of Moment (SGMM) was performed as an econometric approach. Overall, the empirical results show that board-related variables, such as board size, duality, independent investors, gender diversity, culture diversity, and board-specific skills significantly influence ESG performance. In contrast, variables that reflect firm specifics such as firm size and profitability do not have a significant impact on CSR 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.004 | 0.001 |
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".