Do Board Characteristics Affect Financial Reporting Timeliness? An Empirical Analysis
Bibliographic record
Abstract
This research explores the effect of board characteristics on the timeliness of financial reporting from 2012-2018 for 50 listed financial firms. This research, comprising a survey of 50 companies operating in Nigeria's financial sector, gained insights from the agency theory to investigate the impact of board characteristics on the timeliness of financial reporting. Board characteristics were measured using variables such as board size, board independence, board financial expertise, board diligence and CEO gender. We analysed the data using the logistics regression method. Empirically, the results showed that there is a positive association between the financial experience of the board and the timeliness of financial reporting. The size of the board and the independence of the board indicate a negative relationship to the financial reporting timeliness. While, board diligence revealed a negative and insignificant association with the timeliness of financial reporting. Overall, this indicates that Nigerian financial firms' board characteristics have a bigger effect on the timeliness of financial statements. This study contributes to the literature in emerging economies in the field of corporate governance and 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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".