Impact of Internal Audit Practices on Satisfaction of Administrators: A Case in University of Jaffna, Sri Lanka
Bibliographic record
Abstract
The main purpose of the study is to find out impact of internal audit practices on satisfaction of administrators in University of Jaffna, Sri Lanka. Internal audit play a major role on overall performance of the organization. Primary data was collected from administrators (academic and non-academic administrators) through developed 5 point likert scale questionnaire. Internal audit practices include internal audit practices related with administrative system review, legal compliance, control on assets usage, control on purchase & procurement and control on payment, research grant & allowance. Descriptive, correlation and regression analysis performed in this study. Descriptive analysis reveals that internal audit practices are in the moderate level based on the administrator’s satisfaction however there is below than moderate level internal audit practices related with administrative system review. Correlation analysis confirmed that there is positive significant relationship between internal audit practices and satisfaction of administrators. Further regression analysis confirmed that there is positive significant impact of internal audit practices related with administrative system review, control on purchase & procurement and legal compliance on internal audit quality. According to the findings of the study top management of the university and the government should improve internal audit practices especially they have to improve internal audit practices related with administrative system review, legal compliance and control on purchase & procurement to increase the overall performance of the University.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".