Determinants of Unethical Financial Reporting: A Study of the Views of Professional and Academic Accountants in Nigeria
Bibliographic record
Abstract
This study ascertained the determinants of unethical financial reporting, exploring the views of professional and academic accountants in Nigeria. The study utilized the survey design with a sample of 212 respondents drawn from a population made of professional and academic accountants resident in Benue State of Nigeria. The postulated hypotheses were tested using multinomial logistic regression, Kruskal-Wallis H and Chi-square tests, and Mann-Whitney U test. The empirical results evidenced, in order of severity, ‘weak corporate governance’, ‘attempts to conceal deteriorating financial position’, and ‘compensation and bonus incentives’ as the main determinants of unethical financial reporting. The results, however, suggested significant differences in the views of respondent groups on the identified determinants of unethical financial reporting with manifest implications on how policies aimed at addressing the phenomenon of interest would be initiated. The major recommendation of the study is the urgent need to incorporate good corporate governance systems in the overall strategies of corporations in order to curtail incidences of unethical financial reporting.
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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 | 0.003 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".