Roadmap for the implementation of IFRS in Vietnam: Benefits and challenges
Bibliographic record
Abstract
Since 2001, the International Accounting Standards Board has issued a set of accounting principles with the name International Financial Reporting Standards (IFRS). Along with the adoption of IFRS in many countries around the world, Vietnam is preparing a roadmap for the implementation of IFRS by 2022. This study was conducted by surveying 119 directors and corporate accountants for the reasons: (1) to collect their opinions about the roadmap and the scope of IFRS implementation; (2) to investigate the benefits to companies, investors, policy makers and government agencies; and (3) to assess the challenges of IFRS implementation. The results show that IFRS implementation increases the comparability and quality of financial information, reduces investment risks, increases market efficiency and attracts foreign direct investment. However, organizations face many difficulties to adopt IFRS as cost, human resources, legal and market issues. Analysis and comparison with the Sample Test show that there is no difference in assessing benefits and challenges by qualifications, gender, position, region nor firm size. ANOVA analysis showed that there is a difference in the benefits for policy makers by age, and for investors by type of business. This study also suggests implications in policies for the implementation 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 | 0.018 | 0.018 |
| 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 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".