How Are Company Size, Financial Performance and Corporate Governance Related to Directors' Remuneration?
Bibliographic record
Abstract
This study examined the relationship between the company-specific characteristics, namely, company size, company performance, and company leverage and the corporate governance attributes of a company which includes CEO duality and remuneration committee independence as the predictor factors that determine directors’ remuneration. A sample of 260 public listed companies on Bursa Malaysia was selected using stratified random sampling for the financial reporting of 2018. All data concerning the company characteristics and corporate governance attributes were obtained from the annual reports of the companies, which can be accessed from the Bursa Malaysia website. Pearson correlation and multi-regression analysis were used to analyse the data to determine the relationship of the predictor variables with director remuneration. On the one hand, the results of the study showed that directors’ remuneration is positively and significantly related to the size of the company. On the other hand, the financial performance of a company is positively but weakly related to directors remuneration. The remaining predictors have no relationship with directors’ remuneration. The finding suggested that the key determinant factor of directors’ remuneration is company size whereas company performance may have a small impact. Lastly, company compliance with the recommendation of the Malaysian Code on Corporate Governance did not guarantee the effectiveness of the monitoring function of the remuneration committee in ensuring that directors’ remuneration is commensurate with company performance. The result of the study provides additional evidence and support that company size and financial performance are linked to director remuneration. Also the finding of the study reconfirmed prior study that board leadership structure (CEO duality) and remuneration committee independent have no impact on directors’ remuneration.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".