Opportunties for implementation of non-cash transactions of supply chain management of village-owned business agencies
Bibliographic record
Abstract
This study aims to analyze the opportunities for implementing Non-Cash Transactions (Non-Cash Applied Transactions) at Village-Owned Enterprises as the application of Enterprise Resources Planning (ERP) Theory in North Sumatra. This type of research is quantitative descriptive research. The test was carried out by using Structural Equation Modeling analysis with the Formative approach. The tool used is SmartPLS. The study population was village-owned enterprises in Simalungun, Batubara and Asahan districts, North Sumatera, Indonesia. The sampling technique was carried out by using purposive sampling method. In addition, an assessment of community perceptions of implementation was also carried out. The research variables used are supervision, provider support, banking support (support of banking facilities), human aspect, regulation, resistance, commitment and training. The training variable at Village Business Entities has a significant partial effect on the Successful Application of Non-Cash Transactions of Village Business Entities in North Sumatra. This shows that the more frequent training and assistance are held at village business entities, the easier it will be to implement non-cash transactions at village enterprises. Meanwhile, the variables of Supervision, Provider Support, Banking Support, Human Resources, Regulation, Resistance and Commitment did not have a significant effect on the success of village non-cash transactions in North Sumatra.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".