A Model for Sustainable Success in Herbal Business: Educational Strategies for Entrepreneurial Growth
Bibliographic record
Abstract
This study explored the key factors influencing the sustainable success of herbal businesses in Sakon Nakhon Province, Thailand, amid the growing global demand for herbal products. The research focused on the economic, social, environmental, and managerial aspects affecting sustainability and competitiveness. We employed a quantitative research approach, including Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM), with a sample of 342 herbal business entrepreneurs. It was found that cultural factors, local knowledge, and social capital had the most direct effect on business success (1.271, p < .01). These were followed by external factors like government support and consumer demand (0.486, p < .05) and finally, internal factors like business strategy and management (0.126, p < .05). The study confirms that these variables are essential for developing effective business models to enhance growth, sustainability, and regional economic development. Additionally, the research identifies strategies to optimize management practices, innovation, and marketing tailored to the local herbal industry. These insights serve as valuable resources for stakeholders aiming to strengthen the sector and promote long-term success.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".