The implication of applying IFRS in Vietnamese enterprises from an expert perspective
Bibliographic record
Abstract
According to the survey results of the International Accounting Standards Board (IASB), currently, 131/143 countries (accounting for 93%) have had official statements on the application of IFRS with different forms.Among countries which have not permitted IFRS application, there is also a tendency to adjust the national accounting standards system to match up to IFRS.Vietnam is preparing a roadmap for the application of IFRS in 2022.This paper conducts a study with 23 in-depth interviews and survey with 92 experts who are university lecturers, researchers from research institutes, experts in the fields of securities, banking, finance in order to collect some ideas on the roadmap, subjects and scope of IFRS application; assess the benefits and challenges when applying IFRS; then give the implications of IFRS application process in Vietnam.The ideas are consistent with the opinion that preparing IFRS financial statements will improve the transparency of financial statements.However, experts think that the biggest challenge when adopting IFRS is associated with high expenses.Therefore, most opinions suggest that businesses need 3 to 5 years to prepare human resources and other necessary conditions for IFRS application.In addition, in the first period, there is a need to apply IFRS to listed companies, public companies, foreign-invested enterprises, encourage large companies to apply voluntarily.Besides, the study suggests some implications about the implementation process for organizations, individuals and the government.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".