Effect of attitudes, subjective norms and behavioral controls on the intention and corrupt behavior in public procurement: Fraud triangle and the planned behavior in management accounting
Bibliographic record
Abstract
This study explores the values that develop in society which are social constructs that are thought to be related to attitudes, norms, and controlling individual behavior in society and in turn can foster intentions and behavior to corrupt. This research was conducted empirically by involving 265 respondents from accountants, stakeholders, civil servants and inspectors in Central Java, Indonesia who were analyzed by Structural Equation Modeling (SEM) with AMOS analysis tools. The theoretical test results confirm the fraud triangle and the theory of planned behavior to study the opportunity and financial process factors and the rationalization factor which emphasizes the moral psychological aspects. In practical terms, these findings underline the need for tiered supervision in the implementation of goods and services procurement projects in the public sector, improve the quality of reporting and accounting systems, and improve individual integrity to carry out work. Also, it is necessary to increase the remuneration of employees and provide competitive pricing for the private sector involved in procurement projects to minimize intentions for corruption and corrupt behavior by improving the quality of life of the individuals involved in supervision, auditing and reporting.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".