Bibliographic record
Abstract
It is important to understand factors that influence audit delay since it directly affects the timeliness of financial reporting which is one of the most important qualitative attributes of financial statements (Ashton et. al., 1987; Carslaw & Kaplan, 1991; and Johnson, 1998). A number of studies have investigated audit market research including the issue surrounding audit delay within the context of developed countries. However, audit market research in the developing countries is very limited despite calls in the literature to expand the scope of market studies to those nations (see for example, Simon et al. 1992; Walker and Johnson 1996; Che-Ahmad and Houghton 1996; Taylor 1997). This study extends previous research by examining the determinants of audit delay in a developing country. Malaysia is one such country that provides a rich setting for audit market research. It seeks to provide empirical evidence concerning audit delay of Malaysian public listed companies. The findings indicated that the mean audit delay of Malaysian companies to be much longer than the delay in Western countries. The multivariate analysis showed that director shareholdings, total assets, number of subsidiaries, type of audit firms, audit opinion and return on equity to be important determinants of audit delay. The regression results for non-banking and finance sectors were very similar. However, only director shareholding variable was found to be strongly significant in banking and finance sub-sample suggesting the importance of ownership structure in influencing audit lag in this sector. The differences in regulatory framework for both sectors could be a significant reason for the differences in the findings and warrant further research.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".