Factors Affecting the Likelihood of Fraudulent in Procurement Process: the Case of Royal Malaysian Air Force
Bibliographic record
Abstract
Procurement encompasses activities and processes of purchasing good products and services in the public or private sector. It has been reported that 60% of fraud and economic crime perpetrators in the public sector were from government officials. Procurement fraud has increased in the public sector since 2012. This study aims to investigate the practice and likelihood of fraud in the Royal Malaysian Air Force (RMAF) procurement process and to recommend the course to improve the effectiveness of implementing the e-procurement system. This study employed a quantitative method to achieve the objectives of the research. This study adopted a questionnaire from previous research instruments used in many studies related to fraud intention as the primary data collection method. The questionnaire was distributed to the selected officers and other ranks within RMAF. The respondents were among all officers and employees involved directly in the procurement process. Using quantitative research for the data collection method allowed the researcher to evaluate and understand this data through statistical analysis. This study's findings revealed that work commitment, capability, and opportunity had a significant association with the likelihood of fraud in the RMAF e-procurement system when Pearson's correlation was used. Results from regression analysis addressed that capability and opportunity significantly influence the possibility of fraud.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".