The Compliance of Methods of Implementation of IFRS: Study of the Impact on the Relevance of Accounting Information
Bibliographic record
Abstract
During the last two decades, many countries have chosen to implement the IFRS for at least one category of firms. According to Zeef and Nobes (2010), the implementation of IFRS can be classified into four methods. Thus, countries as Israel and South Africa have adopted the method “ implementation process ”, others as Canada, Australia and the European Union have opted for the method called “ Standard by Standard ” while that Switzerland applies the “ optional ” method; China has chosen the “ Not Fully converged ” method. The analysis of these methods of implementation of IFRS demonstrates that these latter differ in terms of degree of compliance with the IFRS as issued by the IASB. This difference of compliance with the IFRS led us to wonder if it affects the quality of accounting information through its qualitative characteristic the “relevance”. To answer this question, we use an empirical model that we apply to a sample of listed companies from six countries opting for different methods of implementation of IFRS. The significant results found demonstrates that the compliance of methods of implementation of IFRS influences positively the relevance of accounting information and that this relevance is better for the listed companies of countries which have chosen a compliant method of implementation with the IFRS as issued by the IASB. These results complement the previous studies on the relevance of accounting information following the transition to IFRS and give new significant proves on the impact of IFRS on the relevance of accounting information.
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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 | 0.028 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".