The Impact of Audit Committee Performance and Composition on Financial Reporting Quality in Jordan
Bibliographic record
Abstract
The corporate governance literature indicates efforts to investigate the role of the audit committee (AC) in improving the financial reporting quality (FRQ) after the emergence of financial scandals in many countries in the world, inclusive Jordan. To date, empirical findings are inconclusive enough to address all audit committee characteristics regarding its competency and responsibilities by employing a questionnaire to collect data about this relationship. Thus, this study measures the correlation between AC (performance and composition) and FRQ of manufacturing corporations registered on the Amman Stock Exchange (ASE). To test this impact empirically, the target population was financial managers, audit committee members, and internal audit managers who are working in manufacturing corporations listed on the (ASE). According to the coefficient (β), the independent variables (Audit Committee Performance and Audit Committee Composition influence the dependent variable FRQ. This research recommends that firms enhance the audit committee work performance and composition to ensure audit committee members effectively enhance the FRQ audit committee is a vital mechanism of the firm's corporate governance system.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".