Cross-country determinants of IFRS for SMEs adoption
Bibliographic record
Abstract
Purpose The purpose of this study is to identify the influence of environmental and institutional factors on the adoption of the International Financial Reporting Standard for small and medium-sized entities (IFRS for SMEs). This study used the neo-institutional theory and the economic theory of networks to explain why countries choose to adopt IFRS for SMEs. Design/methodology/approach This study is based on logistic regression analysis to investigate 177 countries, including 77 jurisdictions that adopted IFRS for SMEs between 2009 and 2015. Findings The findings confirm that the adoption of IFRS for SMEs is significantly related to law enforcement quality, culture, trading networks and economic growth. At the institutional level, coercive and normative isomorphism was found to be positively associated with IFRS for SMEs adoption. The results show also that the quality of the audit has no significant effect on the adoption of IFRS for SMEs. However, the joint effect of the quality of audit and quality of law enforcement is significantly related to the adoption of IFRS for SMEs. Practical implications The study contributes to a better understanding of the factors influencing the implementation of IFRS for SMEs standard across the globe and could be used to predict a country’s decision to adopt this standard. Originality/value This study contributes to the literature on international accounting harmonization by examining both environmental and institutional factors that influence the adoption of IFRS for unlisted private companies.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".