Factors Influencing the Acceptance of International Public Sector Accounting Standards in Cameroon
Bibliographic record
Abstract
There is a growing consensus that governments should be held financially accountable, and Cameroon like many developing countries faces the challenge of running a sound government accounting system that guarantees accountability and transparency. Governments strive to adopt a new public management philosophy which focuses on the change in management practices of the public sector towards more private sector practices with the aim of rendering the public sector more cost effective and efficient. The transition from cash to accrual based International Public Sector Accounting Standards (IPSAS) in order to overcome the rising unaccountability and opaqueness in the use of public sector finances becomes a daunting task. In this respect, Cameroon tends to accept international accounting standards that can be adapted easily to its local situation and also make its financial reports more reliable, standardised, comparable, and attractive on the international scene. With this backdrop, the paper sought to investigate the factors influencing the acceptance of government accounting reforms in general and IPSAS in particular in Cameroon. A survey was conducted in the Ministry of Finance (MINFI) and the Ordinary Least Squares (OLS) and Ordered Logistics Estimation techniques used. The main findings revealed the determining factors of IPSAS acceptance in Cameroon namely: knowledge and awareness, institutional organisation, staff training and recruitment, management information system, qualification, sex, implementation cost, political support, and age. The paper ends up proposing a careful study of these factors by the government for any successful public sector accounting reform and IPSAS acceptance to take place.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".