Learning Beyond the Classroom: Entrepreneurial Experiences of Thai Student Entrepreneurs
Bibliographic record
Abstract
This qualitative study explores how Thai university student entrepreneurs develop entrepreneurial competencies and identities while simultaneously managing businesses and being fully enrolled in their universities. Data were collected through semi-structured, in-depth interviews with ten student entrepreneurs from different regions and academic disciplines across Thailand. The sample reflected diversity in age, gender, educational background, and business types. Thematic analysis revealed five key themes: (1) Motivations for becoming an entrepreneur, (2) Facilitators and barriers to pursue entrepreneurship, (3) Entrepreneurial mindset and identity, (4) Entrepreneurial learning processes, and (5) Challenging conventional norms – Redefining success. Findings reveal that student entrepreneurship is a transformative learning journey characterized by self-regulated learning, experiential growth, and identity formation. Thai student entrepreneurs were forced to withstand the cultural tension surrounding career prosperity. As a result, these young entrepreneurs demonstrated high self-discipline, self-motivation in learning, and actively sought out opportunities. Student entrepreneurs primarily acquire competencies through informal learning ecosystems, such as peer mentoring from senior entrepreneurs. While universities provide social capital and networking opportunities, participants criticized the disconnect between academic content and entrepreneurial realities. These student entrepreneurs challenge traditional definitions of success by prioritizing autonomy, meaningful work, and lifestyle flexibility over conventional achievement metrics. The study highlights the need for Thai higher education institutions to recognize informal learning pathways, reform pedagogical approaches, and promote inclusive narratives that validate entrepreneurship as a legitimate and empowering student trajectory.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".