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Record W2999328884 · doi:10.5430/afr.v9n1p28

Compliance with Continuing Professional Development (IES7) of Internal Auditor and Quality of Internal Audit Function

2020· article· en· W2999328884 on OpenAlexvenueno aff
Yaser Saleh Al-Frijat

Bibliographic record

VenueAccounting and Finance Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAccountingInternal auditAuditBusinessExternal auditorCompliance (psychology)Joint auditQuality (philosophy)Information technology auditWalk-through testInternal controlPsychology

Abstract

fetched live from OpenAlex

This paper aims to highlight the importance of compliance with continuing professional development (CPD) of internal auditors and its relationship to the quality of internal audit. The study utilised a Partial Least Squares Structural Equation Modelling (PLS-SEM), which emphasise on the analyses of regression and variance by distributing a questionnaire to the study sample of internal auditors in four countries (Jordan, Lebanon, Qatar and Kuwait), and the number of samples valid for statistical analysis reached 104. The paper has reached positive results regarding the importance of CPD in improving the quality of the internal audit, focused on the continuous follow-up to changes related to income and sales tax law, international financial reporting standards (IFRS), International Accounting Standards (IAS), and International Auditing Standards (IAS). Besides, modern Computer and Technological Applications in Internal audit practices and audit ethics. The results also indicated that CPD represents in professional skills is the development of skills to detect and combat theft and fraud, critical thinking skills of analyzed data. Original/Value, the CPD is the impulse by which internal auditors continue to learn new technical knowledge and skills that support them in professional work, to make them more qualified and developed in terms of knowledge and the skills needed within the labour markets.

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)0.003
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 score0.195
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.058
GPT teacher head0.313
Teacher spread0.255 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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