Interactions of Financial Assistance and Financial Reporting Competency: Evidence From Local Government in Papua and West Papua Indonesia
Bibliographic record
Abstract
Good local government financial statements are financial statements according to the qualitative characteristics of financial statements, which are relevant, reliable, comparable and understandable. However, the phenomenon shows that there are still weaknesses in financial reporting in several local governments in Indonesia, especially in the provinces of Papua and West Papua based on the findings of the Audit Board of the Republic of Indonesia (IHPS II BPK, 2017). The purpose of this study is to obtain empirical evidence of the role of moderating financial assistance and apparatus competency on the quality of government financial reports. Explanation of the relationship between variables was using an institutional theory perspective. The survey was conducted in 2018 on 42 Local Governments in Papua and West Papua. Methods of processing and analyzing data were using SEM-PLS with WarpPLS 6.0 statistical software. The results of the apparatus competency research have a positive effect on the quality of financial statements. A financial resistance positively strengthens the influence of apparatus competency on the quality of local government financial reports. Thus, efforts to overcome the presentation of quality financial statements require competent apparatus through the existence of financial assistance policies. Limitations of the study are the method of collecting data using a questionnaire and that it is very possible for the bias to occur. Therefore, efforts to achieve better results need to be accompanied by an interview method in order to obtain additional information as a comparison of respondents' answers; 2) the determination coefficient value of R- square is 0.41 or 41% indicating that there are still 0.59 or 59% variability in the quality of Local Government Financial Statements (LKPD) which can be explained by other variables outside the research model.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".