Are Vietnamese Accounting Academics and Practitioners Ready for International Financial Reporting Standards (IFRS)?
Bibliographic record
Abstract
The purpose of this chapter is twofold. First, it reports the findings of a survey on the perception of Vietnamese accountants regarding the benefits and disadvantages of International Financial Reporting Standards (IFRS) as well as the potential costs and challenges of IFRS implementation. Second, it examines the differences in the perceptions of accounting academics and practitioners pertaining to IFRS adoption in Vietnam. Perceptions of Vietnamese accountants and academics were obtained and analysed from 3,000 mailing survey questionnaires across Vietnam in 2012. A total of 728 usable responses were received producing an effective response rate of 24 per cent.The respondents perceived that credibility and comparability are the most perceived benefits of IFRS reporting. Over-complexity and time-consumption are the most perceived disadvantages of IFRS reporting. Although IFRS reporting was perceived as being costly and challenging, surprisingly, the respondents were optimistic on the long-term benefits and two-thirds of the respondents showed their willingness to adopt IFRS.
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 | 0.006 | 0.022 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".