Exploring the Role of Board Characteristics on Enhancing Financial Performance of Jordanian Listed Companies
Bibliographic record
Abstract
Corporate governance considered important topic at the local and international levels, especially after many financial crises and corporate failures and such as Enron and World Com This paper aims to explore the role of board characteristics, (i.e. board size, board composition and board leadership structure) on enhancing firms’ financial performance; this study used the non-financial companies’ annual reports for 6 years (2011-2016) to extract the needed information. The non- financial sector consisted form 167 companies, only 139 companies are included in this study due the lack of data during study’s period. The results revealed that there is a positive role for board composition, board leadership structure, board size, on enhancing financial performance, while there is no significant role for board tenure, on financial performance. These mixed results on the relationship between board characteristics and financial performance have opened up possible research area in the future. For instance, extending the sample to comprise more sectors from Amman Stock Exchange is worthwhile to further support or refute the results of this study.
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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 | 0.003 |
| 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 | 0.000 |
| 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".