The impact of busy boards on earnings management: A case study of estate companies listed on the Vietnamese stock exchange
Bibliographic record
Abstract
While busy boards have been widely studied in corporate governance, research on this topic in Vietnam is lacking. In the real estate sector, where high leverage and regulatory challenges per-sist, busy boards may impact earnings management (EM). This study explores their influence on EM in listed Vietnamese real estate firms, contributing to corporate governance insights. This research aims to investigate the presence of busy boards and Board of Directors (BOD) character-istics on EM behavior. This research employs the OLS, FEM, REM and Generalized Least Squares (GLS) regression model to analysis. Analysis results show that the number of busy boards has a positive impact on EM behavior. The results of this study extend the composite measure of BOD in Vietnam by adding a new factor, which has not been included in previous studies, namely busy boards. Thereby, it helps to improve corporate governance in controlling the "performance results" of the board of directors. Busy boards influence positively EM and oth-er factors: board size, board independence, board expertise, female on board negatively affect EM. The findings of this study demonstrate a relationship between busy boards and EM, subsequently affecting the quality of financial statements. Therefore, the policy makers are recommended to consider comprehensive reviews and possibly "legislate" the advantages of diversity within corpo-rate boards during the drafting, amending, and supplementing of corporate governance regula-tions and rules in Vietnam.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".