Perceived Benefits and Challeges of IFRS Adoption in Ghana: Views of Members of Institute of Chartered Accountants, Ghana (ICAG)
Bibliographic record
Abstract
This paper provides an empirical evidence regarding the perceived benefits and challenges of International Financial Reporting Standard (IFRS) adoption in Ghana. It draws on rich body of knowledge in IFRS from both developed and developing countries to develop a conceptual framework for the perceived benefits and challenges that come with IFRS adoption. It used data from a cross-section of 762 members of the Institute of Charted Accountants, Ghana. This study found that a number of perceived benefits and challenges with the adoption of IFRS in Ghana, notable among the benefits was the ease of comparability of financial data across borders, and the top-most challenge was the continuous amendments to IFRS. There were few differences in evaluation between old and young accountants among the respondents. The theoretical and managerial implications are discussed. This study contributes to the limited empirical research regarding the perceived benefits and challenges of IFRS adoption in Sub-Saharan African in general and Ghana in particular.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 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".