Success predictors of village financial systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it