Development of a Causal Relationship Model Affecting the Technopreneurship Competencies of Students at Rajamangala University of Technology Srivijaya, Thailand
Bibliographic record
Abstract
This study aimed to develop and validate a causal relationship model of factors affecting students’ technopreneurship competencies at Rajamangala University of Technology Srivijaya in Thailand. A quantitative design was employed, involving the sample of 225 undergraduate students, in accordance with the general rule for Structural Equation Modeling (SEM) to ensure adequate statistical power and model stability. Data were collected through a validated questionnaire and analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). The findings indicated three latent variables: learning management, learner-related, and leadership factors, affecting technopreneurship competencies. The model showed an excellent fit with the empirical data (χ² = 36.60, χ²/df = 1.076, p-value = 0.44, GFI = 0.97, CFI = 1.00, AGFI = 0.94, RMSEA = 0.009, and RMR = 0.004). The learner-related factors exerted the strongest direct influence on technopreneurship competencies (β = 0.59), emphasizing the critical role of motivation, positive attitude, and learning behaviors of learners. The leadership factors showed a moderate effect (β = 0.36), highlighting its importance in supporting competency development and an entrepreneurial environment through various types of leadership. However, the learning management showed a non-significant direct effect (β = −0.05), indicating it serves as a foundation rather than a key factor. This suggests that strengthening learner-related and leadership factors is crucial for promoting technopreneurship competencies. This study provides evidence for the applicability of the causal relationship model in technopreneurship competency development, recommending future research on potential mediating variables, deeper investigation of learning management factor, and multi‑institutional, longitudinal research designs to enhance its explanatory power and generalizability.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".