Bibliographic record
Abstract
Purpose The purpose of this paper is to further the understanding of the determinants of audit report lag, which is the number of days from a company’s fiscal year-end to the date of its auditor’s report, by synthesizing extant literature. Audit report lag has been a variable of interest in many studies due to its use as a proxy for the occurrence of auditor-client management negotiations and audit efficiency and because long audit report lags delay the release of earnings information to the market. Design/methodology/approach The author uses meta-analysis to examine commonly identified predictors of audit report lag to determine if the prior research provides a consistent portrayal of audit report lag drivers. Findings The author finds that a number of variables relating to client profitability and financial condition, client complexity and audit opinion modifications increase audit report lag. In addition, audit report lag decreases with client size, when clients have positive earnings news to report and when the auditor has long tenure and provides non-audit services. Several variables, such as those relating to corporate governance and various auditor characteristics, have been little explored and would benefit from future research. Originality/value These results will be useful to researchers when selecting control variables for future audit report lag studies and provide insights into the key factors that contribute to the delay in audit reporting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.051 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.032 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".