The Impact of Corporate Governance Factors and the COVID-19 Pandemic on the Publishing Date of Annual Reports of UK Listed Companies
Bibliographic record
Abstract
The aim of the current study is to examine the role of corporate governance structure and the COVID-19 pandemic on the issuing date of annual reporting of UK non-financial institutions. The corporate governance factors that were examined are: audit committee; board characteristics; ownership structure. To achieve the study objective, the sample’s data was collected from the financial reporting of companies listed on the London Stock Exchange during the period 2008 to 2021. To examine the effect of COVID -19, the sample was spilt into two groups: before and after 2019. The data collected was analysed by using the panel regression random effect method; the issuing date of annual reporting was measured by counting the number of days that passed between year-end and the date of the issuing of financial reports. The study’s findings show that there is a significant relationship between board size, independency of board, audit independence, audit experience, and the issuing date of annual reports. Moreover, after splitting the study’s sample, the empirical results supported that the COVID -19 pandemic has a negative effect on the corporate governance mechanisms that enhance the issuing date of annual reports. The study extends prior studies with evidence that demonstrates a relationship between issuing date (timeliness) of annual reports and the strength of corporate governance during the COVID-19 pandemic, and consequently, these findings confirm that corporate governance factors and auditing process enhance annual reporting quality.
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".