Corporate Governance Mechanisms and Firm’s Performance: Evidence from Jordan
Bibliographic record
Abstract
The purpose of this study is to investigate the effect of corporate governance mechanisms on the firm’s performance. Corporate governance practices were divided into two groups; board structure and ownership structure. The sample of the study consists of 60 companies from industrial and service sectors that are listed on Amman stock exchange (ASE). Data was gathered manually through the annual financial reports for the period from 2012-2017 results in 366 year-observation. Stata statistical software was used to test the study hypotheses. The results revealed that board meetings frequency and government ownership positively and significantly impact the firm’s performance, these results suggest that board meetings frequency is considered an indicator of the board effectiveness that enhances decision making quality and thus the firm performance, the results suggest that government ownership is providing a helping hand that improves the firm’s performance. The findings also showed that board independence negatively and significantly impact the firm’s performance, this result suggests that independent board members do not guarantee to improve the performance of a firm, and it stays the firm’s responsibility to choose independent board members who are able to exercise effective oversight function for the purpose of enhancing the performance of a firm. This study contributes to the literature by providing empirical evidence from developing countries about the impact of corporate governance measures and practices on firms’ performance.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".