Success predictors of village financial systems
Bibliographic record
Abstract
SISKEUDES is a financial system used by the village government aimed at improving accountability, creating common perceptions in the delivery and application of various laws and regulations in the form of village financial management systems and procedures. Implementation of SISKEUDES helps us improve good corporate governance of village government in Indonesia. However, in the Village Assistance Report in Badung Regency in 2018 and 2019, there were input errors from planning, budgeting, administration and financial reporting. Based on this phenomenon, this study tested the predictor of successful application of the system. The measurement of SISKEUDES success is based on DeLone and McLean information system success model. This study examines the direct effect of system quality, information quality, service quality and quality of human resource on use, user satisfaction and net benefits. The sample determination technique uses purposive sampling with criteria of all SISKEUDES users in Badung Regency Village Government with a minimum of 1 year working experience. Data analysis uses Partial Least Square with SmartPLS 3. The results show that only system quality, information quality, and service quality have positive effects on user satisfaction, while the quality of human resources has no effect on user satisfaction. The quality of information, the quality of service, and the quality of human resources have positive effects on the use, while the quality of the system has no effect on the use. The quality of information, service quality, use and user satisfaction have positive effects on net benefits.
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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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".