MétaCan
Menu
Back to cohort
Record W2073461271 · doi:10.5430/afr.v1n1p198

Stewardship and Corporate Governance in the Banking Sector: Evidence from Nigeria

2012· article· en· W2073461271 on OpenAlexvenueno aff
Jimoh Jafaru, F. O. Iyoha

Bibliographic record

VenueAccounting and Finance Research · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceAccountingBusinessShareholderAuditAudit committeeExternal auditorOrder (exchange)Internal auditFinance

Abstract

fetched live from OpenAlex

A healthy corporate governance culture is imperative in the banking sector where the retention of public confidence remains of utmost importance. In this regard, the board of directors are the essential fulcrum upon which the mechanisms of corporate governance and management rest. In Nigeria, however, poor corporate governance has been identified as one of the major factors in virtually all known instances of distress in banks. This is taking place against the backdrop of the existence of Code of Corporate Governance for organizations (including banks) in Nigeria. This contradiction is evaluated in this study which seeks to identify the challenges of corporate governance faced by directors in the Nigerian banking sector. Using the ex-post facto research design, this study draws on the views of executive and non-executive directors of banks in Nigeria, applying simple percentages, averages and rank order as statistical tools for the analysis of data. The study reveals the major challenges of corporate governance as the ineffectiveness of audit committees and lack of shareholder activism. The study recommends, amongst others that, shareholder activism should be legally required and encouraged and the level of such activism should be reported upon by the chairman in his statement and the auditors in their report. Similarly, audit committee should be responsible for hiring, firing and recommending the fees for non-executive directors and external auditors.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.122
GPT teacher head0.303
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueAccounting and Finance ResearchSame topicCorporate Finance and GovernanceFrench-language works237,207