Designing a Characteristics Effectiveness Model for Internal Audit
Bibliographic record
Abstract
Identifying factors/latent constructs deemed to influence internal audit effectiveness (IAE), through identifying variables used as measures of effectiveness and hypothesising which variables have a statistically significant relationship with IAE was the primary objective. Secondary objectives involved exploring the perceptions and viewpoints of internal auditing and providing general recommendations. To achieve the above objectives, questionnaires were remitted to internal auditors (IA) in various countries, receiving 402 final valid responses. Exploratory factor analysis (EFA) was carried out to identify new latent variables/constructs, with confirmatory factor analysis (CFA) in structural equation modelling (SEM) utilised to confirm these factors. The EFA process identified 7 latent factors, with 5 being confirmed through SEM. These factors, confirmed the positive influence of 8/16 hypotheses with 3/16 having partial confirmation, 4/16 not achieving any statistically significant evidence and 1/16 having negative influence. Risk Management, IA size, competency, management support, External Audit (EA) and Audit Committee (AC) cooperation, follow-up process, and control environment were all deemed to positively influence IA effectiveness. Independence, objectivity, and standard adherence achieved partial confirmation of their positive influence. Audit quality, Big Data, scope limitations and public/private organisations achieved no statistically significant results on their influence, while outsourcing was deemed to negatively influence effectiveness.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".