Factors Affecting Fraud in the Procurement of Government Goods/Services (PBJP): Empirical Study on BPKP in Indonesia
Bibliographic record
Abstract
This research looks at how BPKP auditors feel about the following factors: the PBJP committee's quality, its income, BPJB systems and procedures, the PBJP's ethics, and its environment. All of these factors have a big effect on procurement fraud in government agencies. The population comprises all PBJP assurance auditors, with a research sample of 141 individuals chosen through purposive sampling. Validity assessment, reliability assessment, multicollinearity analysis, heteroscedasticity testing, multiple regression analysis, hypothesis testing, and finding the coefficient of determination are all parts of testing data. The results show that all of these separate factors have a statistically significant effect on fraud. The PBJP environment has a positive effect on fraud, while the quality of PBJP committees, the income of PBJP officials, PBJP systems and procedures, and PBJP ethics all have a negative effect. Due to the limited scope of this study, extrapolating the results to other countries is not feasible. This study provides important strategic guidelines for policymakers to develop frameworks for better fraud prevention development. This study adds to the literature, particularly on the factors influencing fraud. There is still little research on this topic, particularly in Indonesia. The unique feature of this research is the use of a dataset of professional auditors who investigate most procurement issues.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".