Factors Affecting CSR Disclosure by Takaful Insurance Companies During the Pandemic Crisis
Bibliographic record
Abstract
This study explores the key factors driving corporate social responsibility disclosure (CSR_DISC) by Takaful insurance companies (TKIs) in Saudi Arabia during and after the COVID-19 pandemic. We use content analysis and follow an unweighted scoring method to score the CSR_DISC index. Based on a sample of 26 Saudi-listed TKIs, for the period 2020–2024, we employ Poisson panel and negative binomial panel models to examine the interdependent relationships between CSR_DISCs and a set of corporate governance factors. We find that Saudi TKIs increased their CSR_DISCs in their financial reporting during and after the COVID-19 crisis. These findings confirm that board and firm size have a significant and negative effect on corporate CSR_DISC. However, the number of independent board members and female directors positively affect the extent of CSR_DISCs. Finally, the size of the audit committee and the Shariah supervisory board, frequency of board meetings, and profitability do not affect CSR_DISCs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".