Impact of Corporate Governance on Non-Performing Loans of Nigerian Deposit Money Banks
Bibliographic record
Abstract
This study examines the impact of Corporate Governance (CG) variables of Board Size (BS), Board Composition (BC), Composition of Audit Committee (CAC) and Power Separation (PS) on Non-performing Loans of Nigerian Deposit Money Banks; with a view to finding out whether these CG variable can be useful in curtailing the incidence of non-performing loans that have bedeviled Nigerian Money Deposit Banks. Secondary data was used from fourteen (14) quoted banks on Nigerian Stock Exchange from 2005-2011. Using multivariate regression analysis, the study finds that corporate governance variables of BS, BC, CAC and PS have no significant impact on non-performing loans of Nigerian Deposit Money Banks. Hence, the study concludes that BS, BC, CAC and PS cannot be relied upon to check the rising figure of non-performing loans of Nigerian Deposit Money Banks. Therefore, we recommend that the oversight and monitoring functions of Central Bank of Nigeria should be strengthened to ensure adherence to rules and principles guiding the approval and monitoring of loans and advances.
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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".