The effect of decision making related rationalization on fraud and the mediating role of psychosocial work environment
Bibliographic record
Abstract
This study aims to examine the influence of the psychosocial work environment on the proclivity for asset misuse in the context of local government management, with a focus on the perceptions of administrators in Central Sulawesi Province. Employing purposive sampling, a total of 39 government units constituted the study's population, with a final sample size of 114 participants, comprising administrators, officers, and managers. WarpPLS software was employed to analyze the data. The findings reveal a positive relationship between rationalization and the inclination toward asset misuse. Additionally, rationalization exhibits a negative impact on the psychosocial work environment. Finally, the psychosocial work environment demonstrates a negative influence on the propensity for asset abuse. These outcomes suggest that the psychosocial work environment plays a pivotal role in mitigating the inclination for asset misuse within the local government of Central Sulawesi Province. This research sheds light on the significance of fostering a positive psychosocial work environment to enhance decision-making processes and reduce the likelihood of fraudulent activities in the management of government assets. Understanding these dynamics is crucial for the development of strategies to promote ethical and responsible asset management in local government entities.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".