Accounting Harmonisation through IAS/IFRS and Internationalisation: Evidence from FDIs and Cross-Border M&A
Bibliographic record
Abstract
The process of accounting harmonisation via International Accounting Standards/International Financial Reporting Standards (IAS/IFRS) adoption is very widespread, and it is still involving a huge number of countries all over the world.Previous academic literature investigated the impact of this process from different perspectives, such as the quality of financial reports, the transparency of financial disclosures, the liquidity of the financial markets and the cost of equity.The aim of this article is to contribute to the research stream exploring the effects of IAS/IFRS adoption, investigating if accounting harmonisation through the IAS/IFRS has had an impact on the internationalisation process. To achieve our objective, two multiple linear regression analyses are presented. The first one focuses on the impact of the adoption of IAS/IFRS on foreign direct investments (FDIs) using data collected from a sample of 34 Organisation for Economic Co-Operation and Development (OECD) member countries. The second statistical analysis investigates the influence of IAS/IFRS adoption on cross-border mergers and acquisitions (M&A), considering operations carried out by European-listed companies towards target companies located in the 34 OECD member countries. The FDIs and cross-border M&A are considered proxies of the internationalisation process.Preliminary results show that the adoption of IAS/IFRS has positively affected FDI flows and increased the value of cross-border M&A.This study has tried to provide theoretical contributions to the literature stream about the effects of the harmonisation process with IAS/IFRS adoption, showing the beneficial impact they may have on the internationalisation process. Moreover, our findings can be useful for policymakers and managers, suggesting that becoming IAS/IFRS adopters can facilitate the internationalisation process of the 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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".