Corporate Governance Characteristics of Private SMEs’ Annual Report Submission Violations
Bibliographic record
Abstract
Managers are, by law, responsible for the timely disclosure of financial information through annual reports, but despite that, it is usual that they are engaged in the unethical behaviour of not meeting the submission deadlines set in law. This paper sheds light on the afore-given issue by aiming to find out how corporate governance characteristics are associated with annual report deadline violations in private micro-, small- and medium-sized enterprises (SMEs). We use the population of SMEs from Estonia, in total 77,212 unique firms, in logistic regression analysis with the delay of presenting an annual report over the legal deadline as the dependent and relevant corporate governance characteristics as the independent variables. Our results indicate that the presence of woman on the board, higher manager’s age, longer tenure and a larger proportion of stock owned by board members lead to less likely violation of the annual report submission deadline, but in turn, the presence of more business ties and existence of a majority owner behave in the opposite way. The likelihood of violation does not depend on board size. We also check the robustness of the obtained results with respect to the severity of delay, firm age and size, which all indicate a varying importance of the explanatory corporate governance characteristics.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".