Earnings Quality and IFRS Research in Africa: Recent Evidence, Issues and Future Direction
Bibliographic record
Abstract
This paper review the recent empirical research on IFRS and earnings quality among African studies and show mixed conclusions regarding the impact of IFRS on earnings quality and financial reporting quality in the region. Also, some discussions on factors that led to the growth in the earnings quality African literature over the last decade as well as some challenges in the recent literature, are provided. Also, the study makes several observations regarding IFRS and earnings quality research in Africa and suggests potential directions for future research. The need to (i) understand the recent direction of earnings quality research in Africa, (ii) understand the interaction between policy and earnings quality research, if any, in the African region, and (iii) the need to maintain high-level rigour in earnings quality research while ensuring greater interaction between policy and research, makes this study important. Given the paucity of research on earnings quality in developing countries, this study contributes to the broader earnings quality literature by providing a review of the African earnings quality literature; hence, conclusions based on empirical studies in this review are not intended to be generalised to developed countries but only to developing countries. Finally, while insights in this paper may be informative to the reader, the intended objective is to stimulate debates that would improve the outputs of earnings quality research and the overall quality of accounting disclosure among firms in Africa.
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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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".