Determining factors affecting the transition of financial statement preparation from VAS to IFRS in enterprises in Vietnam
Bibliographic record
Abstract
According to the Ministry of Finance's roadmap for applying IFRS in Vietnam, listed enterprises on the stock market and state-owned enterprises holding the dominant power are the first group of enterprises to alter the preparation of financial statements according to Vietnamese Accounting Standards (VAS) to the application of International Financial Reporting Standards (IFRS). The voluntary application period is from 2022 to 2025 and the period after 2025 will be the mandatory one. This study was conducted to determine the factors affecting the transition from preparing financial statements following VAS to IFRS for this group of enterprises. The study involved surveying managers and chief accountants at 120 enterprises belonging to the group of companies listed on the stock market, state-owned enterprises holding the dominant power, with the adoption of a regression analysis method. The research results show that five factors are affecting the transition of financial reporting from VAS to IFRS in this group of enterprises, with the order of influence being ranked from high to low, respectively as (i) Size and operation characteristics of the enterprise; (ii) Competence of accountants; (iii) Viewpoints of the enterprise administrators; (iv) Applied accounting regime, and (v) Enterprise owners. On that basis, the study proposes some recommendations for the transition from preparing financial statements according to VAS to IFRS for enterprises to meet the Ministry of Finance's roadmap for IFRS application.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".