The impact of the implementation of international financial reporting standards no.15 on improving the quality of accounting information
Bibliographic record
Abstract
This study aims at investigating the impact of the IFRS (15) on the quality of accounting information in terms of relevance and faithful representation. To achieve the study objectives, a questionnaire is designed and distributed randomly on the study sample which includes (100) of external auditors of the Big Four audit companies in Jordan using the descriptive analytical approach. The study hypotheses are tested through the Simple Regression Test and the One Sample T-Test. The study results indicate a statistically significant impact of the implementation of the IFRS 15 on improving the quality of accounting information from the perspective of external auditors at the Big Four audit companies in Jordan. Moreover, the study finds that there is a statistically significant impact of the implementation of the IFRS 15 on improving the relevance and faithful representation of accounting information included in the reports. Moreover, the study findings also show that Jordanian companies face statistically significant difficulties for the implementation of standard 15 when preparing their financial statements. The researchers have also reached many recommendations; the most important of which suggest to inform investors and other decision makers of the results of the study to help them rationalize their investment decisions and raise their awareness about IFRS 15.
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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 | 0.030 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".