The Key Success Female Entrepreneurs Batik Jonegoro in Indonesia
Bibliographic record
Abstract
The success of female entrepreneurs Batik need hard work, self-efficacy, innovation and ambition behavior. The purpose of this research is to find out the role of self-efficacy and innovative behavior towards the success of female entrepreneurs Batik in Bojonegoro. This research uses descriptive and quantitative analysis research design. The population in this research are all female batik entrepreneur in Bojonegoro. The sample collection technique has been carried out by using non-probability sampling in which samples are determined based on certain criteria in accordance with the research purpose. The numbers of samples are 32 respondents. The Data collection techniques has been done by using questionnaires and conducting interviews. The obtained data are analyzed by using Partial Least Square (PLS) which is the equation model of Structural Equation Modeling (SEM) which is based on components or variants. The results of the research show that: self efficacy gives influence to the success of female batik entrepreneurs. Self-efficacy gives influence to the innovative behavior of female batik entrepreneur. Innovative behavior gives influence to the success of female batik entrepreneurs, Self-efficacy gives indirect influence to the success of female entrepreneurs through the mediator i.e. innovative behavior.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".