Understanding core self-evaluation through social and experiential factors: a study of potential entrepreneurs across varying educational settings
Bibliographic record
Abstract
Purpose Students in higher education (HE) and Technical Vocational Education and Training (TVET) represent a significant source of future entrepreneurs. While pro-entrepreneurial traits like core self-evaluation (CSE) are vital, little research explores the environmental factors shaping CSE in these groups. Our study addresses this void. Design/methodology/approach Data was captured using a cross-sectional survey of 221 HE and TVET students, and hierarchical regression to model CSE based on a number of environmental factors, namely social conditions (role models, parents with HE) and experiential factors: full-time work experience (FTWE), self-employment experience (SEE), and entrepreneurship education (EE), while controlling for age and gender. Findings Parents with HE positively associated with CSE among TVET students but not HE students. Overall, the study found that TVET and HE students do not share a mutual set of experiential factors associated with their CSE; FTWE and SEE were linked to higher CSE among HE students, but this was not the case among TVET students. Conversely, EE was positively associated with CSE among TVET students but not among HE students. There was a mean difference of −0.10 for our TVET sample. Originality/value Our study is among the first to comparatively examine external influences on potential entrepreneurs’ CSE, focusing on HE and TVET students—two distinct entrepreneurial pathways. We extend the literature on CSE and entrepreneurship by shifting beyond individual traits to social and experiential factors, offering new insights with implications for EE and public policy.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".