Estimation of Benefits and Difficulties When Applying IFRS in Vietnam: From Business Perspective
Bibliographic record
Abstract
While many countries around the world have adopted IFRS at different levels, Vietnam is in the process of IFRS adoption by 2022. The study was conducted through the survey of 119 directors and accountants to estimate the benefits and difficulties of applying IFRS in Vietnam. The forecast content include (i) Business benefits; (ii) Benefits for investors; (iii) Benefits to policy makers; (iv) Benefits for state management agencies; (v) Challenges of applying IFRS. By regression analysis, the forecast results showed that all factors have a significantly affect on the IFRS adoption in Vietnam and they explain 54.5% of the reasons for the application. The group of benefits has a positive impact on IFRS application in Vietnam, of which, the strongest impact is the business benefits, while the challenge factor has a negative impact on IFRS adoption in Viet Nam. This result suggests policy implications for the application of IFRS in Vietnam.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".