Human Competencies: Amplifying Financial Reporting Quality in Indonesian Local Government
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
This quantitative study examines the determinants of financial reporting quality in Indonesian local governments, focusing on good governance, regional financial accounting systems, internal control systems, organizational commitment, and information technology utilization, with HR competencies as a moderator. Data were collected via surveys from 170 Local Government Work Units (SKPDs) across South Sulawesi Province, Indonesia. Employing Structural Equation Modeling (SEM), the findings indicate that good governance, regional financial accounting systems, internal control systems, organizational commitment, and information technology utilization all positively influence financial reporting quality. Crucially, human resource competencies were found to significantly moderate the relationship between the internal control system and organizational commitment with financial reporting quality. However, this moderating effect was not significant for the relationships involving good governance, regional financial accounting systems, and information technology utilization. These results highlight the essential role of human resource development and systemic enhancements in fostering greater financial accountability and transparency within the public sector. Therefore, policy recommendations should focus not only on enhancing individual competencies but also on synergistically strengthening systems and governance frameworks to achieve transparent and reliable public financial reporting.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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