Ownership Structure, Board Composition and Voluntary Disclosure by Non-financial Firms Listed in )ASE)
Bibliographic record
Abstract
This study aims to examine the impact of ownership structure and board composition on the level of voluntary disclosure by non-financial firms listed in the Amman Stock Exchange (ASE). The study uses panel hand-collected data from 443 annual reports for a 5-year period (2012 – 2016) and employs an OLS-regression to test the study predictions. Compatible with the study predictions and most prior related studies’ findings, both higher managerial ownership and the CEO-duality produce low levels of voluntary disclosure, while foreign ownership is positively associated with the level of voluntary disclosure. Findings also indicate that larger firms deemed to provide higher levels of voluntary disclosures than smaller firms. Besides, companies audited by big4 firms disclose more voluntary information than those audited by others. The study findings have implications for policymakers and regulators. Policymakers and regulators may encourage, emphasize and enforce, if necessary, the regulation that enhances the quality of financial disclosures including the separation between the Chairman of the board of directors and CEO roles to improve the level of control and supervision and enhance the transparency of financial reporting by Jordanian firms.
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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 | 0.005 |
| 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 | 0.000 |
| 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".