Research productivity of International Financial Reporting Standards (IFRS) from 2003 to 2020
Bibliographic record
Abstract
Purpose This study aims to examine the quantitative research productivity of International Financial Reporting Standards (IFRS) globally by using the bibliometric approach. The method was applied to articles indexed in the Scopus database to analyze the publication patterns, trends and research productivity of the selected papers. Design/methodology/approach Bibliometric analysis is applied to analyze research productivity of IFRS from 2003 to 2020. The method was applied to articles indexed in the Scopus database to analyze the publication patterns and research productivity of the selected papers. Findings This study finds that a good number of articles have been published on IFRS, the top five countries are the USA, UK, Australia, Germany and Canada. This clearly shows that developed markets have the highest number of publications on IFRS. This could be as a result of the early adoption of IFRS by those economies and owing to the interest of researchers in those markets. Most of the studies are quantitative in nature; this study indicates that publication on accounting standards is popular as the number of citations is significant; most of the articles have two or more authors and were published in top-ranking journals. Practical implications This study provides up-to-date literature on the global research productivity of IFRS; as a result, it supports the development of policies by the users of this accounting standards. The findings of this study also serve as a reference point for firms and regulators around the world. Given the thoroughness of the methodology of this study, the results make it easier to effectively identify the direction of research on the implementation of IFRS in organizations. Originality/value This study provides a more comprehensive bibliometric analysis on the growth of IFRS literature (2003–2020) in the Scopus database; most of the prior studies have covered relatively few areas of focus as well as a fewer number of high impact factor journals. The relevance of this finding is in uncovering different areas of IFRS research productivity globally.
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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 | 0.052 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.038 | 0.061 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".