MétaCan
Menu
Back to cohort
Record W2992375490 · doi:10.5430/ijfr.v11n1p405

Internal Controls and Performance of Selected Tertiary Institutions in Ekiti State: A Committee of Sponsoring Organisations (COSO) Framework Approach

2019· article· en· W2992375490 on OpenAlexvenueno aff
Gideon Tayo Akinleye, Adebola Daniel Kolawole

Bibliographic record

VenueInternational Journal of Financial Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceState (computer science)Control (management)AccountingBusinessBusiness administrationManagementEconomicsFinanceMathematics

Abstract

fetched live from OpenAlex

This study examined the effect of internal controls on performance of selected tertiary institutions in Ekiti state using a committee of sponsoring organisations (COSO) framework approach. The study employed a survey research design. Primary data were obtained and analysed using multiple regression analysis. Findings from the study showed that the overall influence of COSO components of internal control on performance of selected tertiary institutions in Ekiti state was significantly positive. However, Control activities (CA) (t =2.487, p=0.013 <0.05), Information & communication (IFC) (t=7.195, p=0.000 < 0.05) and Monitoring activities (MA) (t=4.809, p=0.000 < 0.05) had significant and positive influence on organisational performance of the selected tertiary institutions while Control environment (CE) (t =0.569, p=0.570 > 0.05) and Risk assessment (RA) (t=0.446, p=0.656 > 0.05) had positive but insignificant effect on organisational performance of the selected tertiary institutions. The study concluded that internal control had a positive effect on performance and was statistically significant in explaining performance of selected tertiary institutions in Ekiti state. The study thus recommended that those charged with governance in tertiary institutions should strengthen the highlighted components of internal controls.

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)0.002
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.025
GPT teacher head0.297
Teacher spread0.271 · 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.

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

Citations20
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Financial ResearchSame topicAuditing, Earnings Management, GovernanceFrench-language works237,207