Board Structure of Corporate Organizations and Earnings Management: Does Size and Independence of Corporate Boards Matter for Nigerian Firms?
Bibliographic record
Abstract
The relationship subsisting between board structure of corporate organizations and earnings management has attracted several concerns particularly to regulatory agencies, management, accounting practitioners and researchers alike. Therefore, this study, examined the extent to which board independence and size influence the level of earnings management of publicly quoted Nigerian firms. For this purpose, the adoption of the International Financial Reporting Standards (IFRS) and the age of firms were introduced as mediating variables. Secondary data were however pooled from the financial statements of ninety-two (92) firms cutting across ten (10) industrial sectors from 2007–2018 (12 years). The regression analysis amidst other relevant statistical techniques was adopted to analyze the collated pooled data. Evidence from our result indicates that with the introduction of IFRS adoption and firm age as mediating variables, the Fcal obtained was 1.72 (p-value = 0.1424), thus indicating that the size of boards and the presence of independent directors (board independence) in corporate boards could not significantly influence the level of earnings management in Nigerian firms. We therefore recommend that in order to regulate managements’ opportunistic behavior/earnings management, regulators and stakeholders who are charged with the task of performing oversight functions on the activities of management should lay more emphasis on ensuring that preparers of financial statements fully comply with the provisions of IFRS and other regulatory requirements for financial reporting.
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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 | 0.007 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".