Educational Pathways to Intrapreneurship: A Scoping Review of Empowerment, Leadership, and Innovation in the Context of Thailand’s New S-Curve Industries
Bibliographic record
Abstract
The transition to Thailand 4.0 underscores the critical role of education in preparing a workforce capable of driving innovation in New S-Curve industries such as robotics, digital technologies, smart logistics, and biotechnology. Intrapreneurship—entrepreneurial activity within established organizations—has been identified as a key competency for enhancing empowerment, leadership, and innovation in this context. This scoping review aims to map existing educational strategies that foster intrapreneurial competencies and to identify pathways that align education with Thailand’s industrial transformation. Following Arksey and O’Malley’s framework and PRISMA-ScR guidelines, a systematic search was conducted across Scopus, Web of Science, ERIC, and Google Scholar. A total of 520 records were identified, of which 422 were screened after duplicates were removed. After full-text eligibility assessment (n = 122), 32 studies were included for synthesis. Thematic analysis revealed five interconnected core elements of intrapreneurship education: (1) Preparation through empowerment and digital literacy; (2) Planning via leadership development frameworks; (3) Provision of innovation pedagogy, including project- and problem-based learning; (4) Evaluation of intrapreneurial mindsets and leadership competencies; and (5) Sustainability through institutional support and policy integration. The findings highlight the fragmented nature of intrapreneurship education, while emphasizing the need for integrated educational pathways. For educators, active and experiential pedagogies are recommended; for policymakers, alignment between education and industrial strategies is essential; and for practitioners, leadership and skills development programs can sustain workplace innovation. Collectively, these insights position education as a transformative pathway for advancing intrapreneurship and competitiveness in Thailand’s New S-Curve industries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".