The influence of supply chain and knowledge-oriented leadership on the performance of village financial system operators and its implications on the level of village welfare
Bibliographic record
Abstract
The objective of this study is to examine the direct and indirect impact of Supply Chain and Knowledge-Oriented Leadership on the Performance of Village Financial System Operators and its consequences for the Level of Village Welfare. This study employed quantitative methodologies, using Saturation sampling techniques, and obtained a sample of 131 respondents who were village financial information system operators, consisting of 131 villages in Pringsewu Regency, Lampung Province, Indonesia. The data obtained from the surveys was further analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS). The findings of the research and data analysis indicate that: Knowledge-Oriented Leadership has a direct and substantial impact on the Performance of Village Financial System Operators. It also has a direct and substantial impact on the Village Welfare Level. The performance of the Village Financial System Operator has a direct and substantial impact on the Village Welfare Level. Additionally, the Supply Chain has a direct and substantial impact on the Performance of Village Financial System Operators and the Village Welfare Level; The performance of the Village Financial System Operator is able to partially mediate Supply Chain and Knowledge-Oriented Leadership on the Level of Village Welfare in Pringsewu Regency, Lampung Province, Indonesia, because the independent variable is able to significantly influence both directly and indirectly the dependent variable.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".