Accountants’ Perception on the Factors Affecting the Adoption of International Financial Reporting Standards in Yemen
Bibliographic record
Abstract
This study examines accountants’ perception whether or not the following factors ــgovernment policy, capital market, economic growth, external environment/international exposure, professional bodies, education level of accountants, company size, initial cost of International Financial Reporting Standards (IFRSs) adoption and culture affect the adoption of IFRSs in Yemen. It also examines the differences of opinion among academicians and practitioners with regard to these factors. A questionnaire survey involving 41 Yemeni accounting postgraduate students in Malaysia’s public Universities was conducted. The results indicate that the majority of respondents believe that the lack of government policy, absence of capital market, lack of economic growth, lack of professional bodies, weakness in the education level of accountants, the small size of the companies and initial cost of IFRSs adoption affect the adoption of IFRSs in Yemen. The study also shows that the international environment has a weak effect on IFRSs adoption while the Yemeni culture does not affect IFRSs adoption. The finding may help both policy-makers and the Yemeni Association of Certified Public Accountants (YACPA) to consider these factors and make more precise decisions regarding IFRSs adoption.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".