The Effect of Audit Committee Characteristics on the Profitability: Panel Data Evidence
Bibliographic record
Abstract
This study attempts to investigat the relationship between audit committee characteristics (size, independence, meeting and financial expertise) and the profitability of industrial companies listed on the Amman Stock Exchange (ASE) for the years 2013 to 2017. The model of this study is theoretically founded on both the agency theory and the resource dependence theory. To examine the developed model, the data were gathered from the annual reports of 51 listed industrial firms. To analyse the data, this study utilized the panel data methodology on 51companies with 255 observations. Moreover, this study used company size and leverage as control variables. Based on the panel data results, the fixed-effect model was used to examine the effect of the experimental variables on profitability, measured by return on investment (ROI) and return on equity (ROE). The results show that the audit committee characteristics have a significant effect on profitability of the industrial companies listed on the ASE. This study evinces that the RD theory is more significant compared to the agency theory when describing CG practices in Jordan.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".