The Factors That Influence Audit Delay In Manufacturing Companies Listed On The Indonesia Stock Exchange 2016-2017
Bibliographic record
Abstract
Financial report is something that is very important for the sustainability of companies,especially companies that have gone public. One measure of accuracy in the submission of relevant financial statements is audit delay. This Research purposed to analyze and measure firm specific factors that influencing the audit delay, by using five independent variables there are : (1) Company Size, (2) Profitability, (3) Solvability, (4) Public Accountant Size, And (5) Auditor’s Opinion. The data was used in this research is the secondary data gotten directly by visiting the Indonesia Stock Exchange, there are the annual report of manufacturer companies 2016-2017. Sample this research is a number of 148 companies selected using purposive sampling method. Data analysis in this research using multiple linear regression analysis. The result of this research showed that profitability has positive influences on audit delay. Company size, solvability, public accountant size, and auditor’s opinion have negative influences on audit delay. The result of this research showed that these five factors simultaneously influence on Audit Delay. Based on the adjusted R2 value of 7,1% indicates that 7,1% Audit Delay variable explained by company size, profitability, solvability, public accountant size, and audit opinion. While the remaining 92,9% is explained by other variables not examinated in this study.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".