Factors Influencing Auditor Independence among Listed Companies in Nigeria: Generalized Method of Moments (GMM) Approach
Bibliographic record
Abstract
The study examines the factors influencing auditor independence among listed companies in Nigeria. A sample of 65 firms out of the 194 listed on the Nigerian Stock Exchange (NSE) were purposively selected for analysis, these comprise 14 money deposit banks (financial), 1 mortgage bank and 50 non-financial firms. Secondary data were employed for the study and were sourced from the audited financial reports of sampled companies and fact book of the Nigerian Stock Exchange between the periods of 2006 and 2013. Data were analysed using descriptive statistics and Generalised Method of Moments (GMM). Preliminary tests were carried out such as Sargan test, Arellano-Bond Serial Correlation Test among others. The study revealed that Big4, audit tenure, profitability, leverage and inventory with account receivable had negative significant impact, which can impair auditor independence, while size of the firms and loss had positive influence on auditor independence in Nigeria. Also, the square root of the number of subsidiaries was positively related to auditor independence, but not significant and the total number of subsidiaries had positive influence on auditor independence but not significant. These results implied that the two variables can increase the complexity of the audit and, consequently, a rise in audit fees expect in their presence. This will in turn reduce auditor independence. The study therefore recommended that joint audit be adopted and audited tenure be reviewed. The findings of the study would enable management, regulators, investors and other stock market participants to play their unique and important roles in enhancing auditor independence in Nigeria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".