Effect of the University’s Environment and Support System on Subjective Social Norms as Precursor of the Entrepreneurial Intention of Students
Bibliographic record
Abstract
In this study, we aim to understand the influence of the university’s environment and support system (ESS) on subjective social norms (SSN) as one of the precursors of the entrepreneurial intention (EI) of university students. For this, we applied a customized mathematical model of EI based on the theory of planned behavior to probe whether the university’s ESS can affect SSN and analyze the paths that this influence may follow to form the EI of students. In other words, this study argues that the university plays a critical dual role in shaping the EI of students. First, it can provide support mechanisms to help students translate their ideas into viable business models that may further translate into successful ventures. Second, it can help students gain the support of their families and friends who influence their SSN, thus affecting their EI through the mediating effects of the other two precursors of intention. We collected the data from students in a public university in Atlantic Canada via a structured non-disguised questionnaire to test the hypotheses formulated in this study. We analyzed them through partial least square-structural equation modeling of a second-order mathematical model of EI. Analysis of the data indicates that the mathematical model is appropriate for evaluating the relations among the five constructs of the mathematical model of EI. Results of this study support the hypothesis that the university’s ESS may influence students’ perceptions of the opinions of important reference people regarding their prospects of becoming entrepreneurs. Furthermore, we determined that the university’s ESS influences the EI of students mediated by the more proximal precursors of intention. The effect of the university’s ESS is such that it may positively impact the EI of students, but its importance in the mathematical model of EI is still low. These findings can help universities assess their initiatives to promote innovation and entrepreneurship on campus.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".