The Effect of IFRS Adoption on the Business Climate: A Country Perspective
Bibliographic record
Abstract
Based on the ten areas that are measured by the ease of doing business (EDB) and based on the getting credit (GC) indicator, this study seeks to analyze factors that lead to a more favorable business climate in different countries. The methodology of fuzzy-set qualitative comparative analysis (fsQCA) was used to determine the paths taken by configurations or conditions in which variables affect an outcome. The results showed that high EDB and GC scores may be obtained under specified levels of IFRS (International Financial Reporting Standards) adoption degree and user experience requirements. Therefore, the adoption of IFRS could result in a better business climate in a nation since it would increase the comparability of financial statements, which will lower costs for investors, draw in foreign investors, and boost trust. Finally, the findings indicated that, depending on the presence of specific levels of GDP per capita, entrepreneurship, income group, and foreign direct investment (FDI) inflows, low or high values of IFRS adoption and high experience in applying IFRS are necessary to achieve high GC scores.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".