Government Ownership as a Catalyst: Corporate Governance and Corporate Social Responsibility in Jordan’s Industrial Sector
Bibliographic record
Abstract
This research examines how corporate governance (CG) affects corporate social responsibility (CSR) disclosure with government ownership as a moderation factor by analyzing panel data from 30 industrial firms listed on the Amman Stock Exchange during 2018–2022. The study employed board of directors and audit committee characteristics as independent variables to represent CG while developing a CSR disclosure index. The research controlled for company size and financial leverage in its model. The findings demonstrate that corporate governance dimensions affect CSR disclosure, while government ownership significantly enhances this relationship in a positive direction. Government ownership increases R2 values, which shows that corporate governance merged with government ownership modifies the corporate governance and CSR disclosure relationship by strengthening the impact when government stakes rise. Statistical analysis revealed that board independence, board duality, audit committee size and independence, along with audit committee meeting frequency, all had positive effects on CSR disclosure. The study found no statistically significant effect of board size, frequency of board meetings, or the financial expertise of audit committee members on CSR disclosure. Based on the findings, this study outlines recommendations to strengthen governance practices that support social disclosure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".