The impact of family ownership concentration on the relationship between the characteristics of board of directors and earnings management
Bibliographic record
Abstract
This study aimed to investigate the impact of the characteristics of the board of directors represented by size of board of directors, financial experience, and board of directors’ meetings on earnings management measured by Jones' modified model. The study also aimed to find out the impact of ownership concentration on the relationship between the board of directors’ characteristics combined and the earnings management in Jordanian industrial companies listed on Amman Stock Exchange. In order to achieve the objectives of the study, a quantita-tive analytical method was applied. The study was applied to a sample of 41 industrial compa-nies where their data were available during the period of study from 2013 to 2017. The findings indicated that the characteristics of the board of directors combined influenced on the earnings management in Jordanian industrial companies listed on Amman Stock Exchange. Moreover, the size of the board of directors and the financial experience had some effects on earnings management in Jordanian industrial companies listed on Amman Stock Exchange. However, the board of directors’ meetings had no meaningful effect on earnings management in Jordanian industrial companies listed on Amman Stock Exchange. It was also found that family ownership concentration had an impact on the relationship between the characteristics of the board of directors combined and earnings management in Jordanian industrial companies listed on Amman Stock Exchange. The study concluded a number of recommendations, the most important of which is to encourage the competent bodies and the boards of directors in the public shareholding companies to pay more attention to the development of more legis-lation that focus on earnings quality by reducing earnings management practices. In addition, it is necessary to identify penalties for cases of manipulation and distortion in the financial statements which reduce the use of illegal techniques and attract investors.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".