The Effect of Board of Directors and Audit Committee Characteristics on Company Performance in Jordan
Bibliographic record
Abstract
A comparative study was conducted to evaluate the effects of audit committee and directorates on the performance characteristics of selected companies in Jordan. The approach of panel data was adopted between 2015 to 2019 (4 years), with listed samples of 140 non-financial industries under the ASE. These firms stand for about 60% of listed firms in Jordan. Considering the audit committee and directorates effects of board characteristics on company’s performance, a total number of seven (7) variables of directorates and that of auditing committee were identified: an independent board of director, meetings of the board, size of the board, structure of the leadership, size of auditing committee, independent auditing committee together with proficiency of audit committee. The performance characteristics of companies were evaluated by means of (ROA) measure of accounting based performance. It was indicated from the results that, the following variables (independent board of directors, expert in auditing committee positively had impact on the performance ability of the selected firms. It was also revealed that, independent auditing committee together with board size of smaller capacity could enhance the performance potential of the firms. In addition to this, No significant difference was revealed on the performance of firms in term of frequency of board meetings and structure of leadership. The present research however adds more contribution to the literature on how the nature of directorate board and auditing committee could affect performance of a company in Jordan and other developing nations. However, information of great important value could assist academicians, policy makers alongside with concern stakeholders.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".