Effect of the university on the social entrepreneurial intention of students
Bibliographic record
Abstract
Purpose The study aims to test the applicability of a variant of the model proposed by Hockerts (2017) for assessing the social entrepreneurial intention (SEI) of male and female students. It extends the model by incorporating the university's environment and support system (ESS) as an additional more distal construct. The university's ESS, coupled with the experience with social, cultural and environmental issues can affect SEI by influencing the more proximal precursors of empathy towards others, perceived self-efficacy, perceived community support and social, cultural and environmental responsibility. Design/methodology/approach A structured non-disguised questionnaire was administered to students at a Canadian university. A sample of 485 usable responses was analysed by means of second-order structural equation modelling. Findings The results provide confirmation that the proposed model is a multi-group invariant and appropriate for analysing the SEI of male and female students. They also show that the university's ESS helps predict SEI indirectly through the complete mediation of the more proximal antecedents. Research limitations/implications The questionnaire is limited to universities with social innovation and entrepreneurship initiatives. Practical implications Outcomes of the study can help universities assess the efficacy of their social innovation and entrepreneurship initiatives for instilling a social entrepreneurial mind-set in students. Consequently, universities will be better equipped to raise the perceptions of venture feasibility and desirability, thus increasing students' perceptions of opportunity. Originality/value The study advances the social entrepreneurial knowledge of the university's effect on the precursors of SEI.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".