The Impact of Organizational Culture on the Effectiveness of Corporate Governance to Control Earnings Management
Bibliographic record
Abstract
The relationship between culture, earnings management and corporate governance has been studied in different ways, but the influence that culture has over the actual effectiveness of corporate governance to control earnings management has not, even though it should be a determinant factor to define successful governance schemes. Using Hofstede four organizational models as a framework, in this paper, we analyze a sample of companies listed in 16 different stock markets in terms of organizational culture, assessing their governance standards and performance in relation to earnings management, and measuring their actual effectiveness. The results confirm that earnings management is conditioned by organizational culture and that corporate governance acts as a brake on earnings management, regardless of the cultural field in which it is analyzed. However, its effectiveness depends on organizational culture, mostly on the uncertainty avoidance and the power distance. Therefore, modelling a country based on its organizational culture does limit the success of corporate governance policies and standards. This study brings in a new perspective for policy makers and practitioners to design and enforce their corporate governance policies targeting earnings management, according to the prevailing culture. The previous literature on the subject is complemented and enriched by this significant contribution, through which limitations in terms of the number of countries studied could be overcome by further studies addressing specific regions or sectors.
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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 | 0.005 | 0.016 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 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".