The Influence of Board Attributes towards Tax Avoidance: Evidence from Malaysian Public Listed Companies
Bibliographic record
Abstract
The objective of the research is to examine the relationship between board attributes and TA among publicly listed companies. This study utilised a quantitative approach using secondary data from companies listed on the Bursa Malaysia Top 100 Index. The final sample consists of 79 firms. The annual reports from 2018 to 2022 were used for data collection, with 395 firm-year observations. The dependent variable was TA, which was proxied by the effective tax rate, and the independent variables were board size, board independence, board gender diversity, and CEO duality. This study shows a negative and significant relationship between board size and TA. Meanwhile, there is no association between board independence, board gender diversity, and CEO duality, and TA. This research focused on one facet of CG, namely the board attributes, and only four variables are included in this study. Hence, it provides a narrow view of the relationship between CG and TA. Besides that, this research was conducted in the Malaysian context and thus may differ from research conducted in other countries. The outcome of this study could offer useful insights to investors and other stakeholders in their decision-making process. Besides that, regulators can reference this study to strengthen the CG codes to protect stakeholder interests.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".