Optimizing state revenue through government-driven supply chain efficiency and fair corporate taxation practices
Bibliographic record
Abstract
This research tries to analyze the explicit and implicit impact of the smooth supply chain and fairness of the tax system on corporate taxpayer compliance and its implications for state tax revenues. This research uses quantitative methods, using random sampling techniques, and obtained a sample of 100 respondents consisting of various MSMEs registered with the Ministry of Cooperatives and SMEs in 2023 who are taxpayers in South Jakarta City, DKI Jakarta Province, Indonesia. The data obtained from the surveys was utilized in Structural Equation Modelling with Partial Least Squares (SEM-PLS) for additional analysis. Research results and data analysis show that tax system equity has a direct and significant impact on corporate taxpayer compliance; tax system fairness directly has no and minimal impact on state tax revenues; and corporate taxpayer compliance directly and significantly impacts state tax revenues. A smooth supply chain has a direct and substantial impact on corporate taxpayer compliance. Furthermore, a smooth supply chain has a direct and substantial influence on state tax collections. A seamless supply chain and state tax revenues are largely mediated by corporate taxpayer compliance, which has a substantial impact on both. Furthermore, the relationship between the fairness of the tax system and state tax collections in MSMEs in South Jakarta City is totally mediated by corporate taxpayer compliance.
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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 | 0.005 | 0.015 |
| 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 | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".