Stakeholders’ Perception of the Implementation of International Financial Reporting Standards (IFRS) in Nigeria
Bibliographic record
Abstract
This study examined the stakeholders’ perception of the adoption of IFRS in Nigeria. It sought to establish among others whether the implementation of IFRS in Nigeria has enhanced the value of financial reporting in the country. The data generated from the questionnaire were analysed using Analysis of Variance (ANOVA). In the study, the opinion of users of financial reports was sought through a cross-sectional survey. The study reveals that significant differences exist in the perceptions of stakeholders regarding the effect of the working of IFRS on the value of financial reports; no considerable differences in their perception about the implementation of IFRS with respect to the improvement on quality of investment decisions ; and as a basis for assessing returns on investment and whether comparison of financial reports in Nigeria have been enhanced. Based on these findings, it was recommended that relevant authorities should ensure that organisations comply with established standards when preparing financial statements; and auditors must assert their independence with a view to ensuring that audit reports reflect the actual position of the entity’s financial circumstances. Government was further advised to strengthen the Financial Reporting Council of Nigeria with qualified personnel in order to adequately perform its functions.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".