Business Strategy and Auditor Report Lag: Do Board Characteristics Matter? Evidence from an Emerging Market
Bibliographic record
Abstract
This study investigates the association between business strategy and audit report lag (ARL). In addition, it reveals the moderating influence of board characteristics on this relationship. We used data collected from Egyptian firms listed on EGX100 during the period from 2014 to 2019, which were analyzed using ordinary least squares and binary logistic regression models. Our study revealed a decrease in ARL for firms adopting cost leadership or differentiation strategies. In addition, we found that ARL decreased for cost leadership firms with a higher percentage of non-executive director and board meetings. Moreover, ARL decreased for firms adopting a differentiation strategy with a higher percentage of non-executive directors. This study contributes to the literature on the potential factors affecting the link between business strategy and the quality of financial reporting by focusing on ARL, which is rarely examined in the literature, especially in emerging markets such as Egypt. The findings of this study are valuable to investors, auditors, corporate management, and other stakeholders, who should consider particular board attributes to better predict ARL and ensure the effective adoption and implementation of business strategies.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".