Entrepreneurial Characteristics, Development Needs, and a Proposed Development Model for Royal Thai Army Reservists before Discharge: A Mixed-Methods Study
Bibliographic record
Abstract
This study examined the entrepreneurial characteristics, development needs, and proposed a development model for Royal Thai Army reservists before discharge. A mixed-methods design was employed, combining quantitative data from 416 reservists via a structured questionnaire with qualitative insights from interviews with 10 reservists and five commanding officers. Quantitative results indicated high levels across seven entrepreneurial characteristics: motivation (M = 4.12), integrity (M = 3.94), creativity (M = 3.75), reflection (M = 3.75), opportunity recognition (M = 3.47), risk-taking (M = 3.57), and problem-solving (M = 3.53). Development needs were also high in all dimensions, with motivation (M = 4.05) and integrity (M = 4.01) ranking highest. Qualitative findings revealed diverse entrepreneurial aspirations—mainly in food, retail, livestock, and logistics—while highlighting strengths such as integrity, discipline, and perseverance, alongside gaps in creativity, opportunity recognition, and risk management. Reservists favored practical, hands-on training, short theoretical sessions, access to funding, and market opportunities within or near military facilities. Commanding officers emphasized similar strengths but noted constraints, including limited capital, low education, insufficient business knowledge, and a reliance on orders. They recommended integrating mindset training, basic business skills, and innovation into blended workshops with real-market simulations and post-discharge support. The integrated results inform a comprehensive development framework centered on creativity, opportunity identification, and practical operations, supported by institutional facilitation and mentoring to enhance entrepreneurial readiness.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".