Social Networks in The Development of Rural Malay Herbal Entrepreneurship in Malaysia
Bibliographic record
Abstract
Previous studies found that networking is the key point for the entrepreneurship development.It has been long believed that social networks always influence entrepreneurship growth.The aim of this study is to investigate how rural Malay family-based herbal entrepreneurs are using social networking to develop their herbal entrepreneurship.It is a phenomenological qualitative research with eight rural Malay herbal entrepreneurs in Peninsular Malaysia namely Kelantan, Kedah, Pahang, Perlis and Terengganu.A purposive sampling approach was used to select the entrepreneurs residing in the rural areas and the selection criteria in the study also included those who have run their business with family members for at least the past four years with a minimum of three products.The findings of this study showed that the five states' rural Malay herbal entrepreneurs are using social network among own community peoples, not like other states entrepreneurs'.As a result, herbal entrepreneurship is not developing owning to lack of social network with other state entrepreneurs and customers, suppliers, as well as financial constraints, technical knowledge.Finally, based on the findings of this study, the entrepreneurs need consistent trainings from the government and other related government link companies on how to develop social network with others and the importance of social network for their entrepreneurship development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".