The effects of knowledge sharing, social capital and innovation on marketing performance
Bibliographic record
Abstract
Women entrepreneurs and the informal sector are looking for footholds in the COVID-19 pandemic, which will lead women to develop creative businesses. This study examines the role of sharing knowledge and innovation in addressing gaps in social capital and marketing performance. Purposive sampling is used in the technique sample with 229 samples and Structural Equation Modeling (SEM-PLS) analysis techniques with SmartPLS is used for processing applications. The results show that social capital has a positive effect on the business performance of women entrepreneurs in Bali, Indonesia. The knowledge-sharing variable can be a mediator in the relationship between social capital and performance, and social capital has a significant positive effect on innovation, but innovation does not have a positive effect on marketing performance and knowledge sharing. In the end, women entrepreneurs will use knowledge sharing to create various innovations to meet market demand. However, opportunities for women entrepreneurs are very limited on capital due to the lack of guaranteed capital, and a lack of entrepreneurial skills in the era of technology, market access, bureaucracy, and legal matters. In addition, managerial skills, access to information technology, and the perspective that men should excel in Balinese culture and customs, limit business for women entrepreneurs.
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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 | 0.011 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".