Effects of dysfunctional career beliefs and university’s environment and support system on university students’ entrepreneurial intention
Bibliographic record
Abstract
This thesis investigates the effect of dysfunctional career beliefs (DCB) and a university’s environment and support system (ESS) on entrepreneurial intentions (EI) among students at Memorial University of Newfoundland. The analysis is divided into two components: a cross-sectional study investigating how DCB influence EI and their effect on the relationship between ESS and EI; and a longitudinal study assessing ESS’s impact on EI and its antecedents over time. The study is grounded in the Theory of Planned Behavior, a well-established psychological theory that predicts human behavior, and employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data, with longitudinal data spanning 2020 to 2024, capturing changes in ESS influence amid economic fluctuations. Findings reveal that students with high decision-criticality DCB tend to perceive entrepreneurship more favorably, indicating that such beliefs may frame entrepreneurship as a purposeful career choice. DCB in professional help also shapes ESS effectiveness, underscoring mentorship’s role in university support systems. The longitudinal analysis shows that ESS’s influence on EI varies with economic conditions but consistently supports ATB and PBC. This research advances the understanding of how psychological barriers and support systems intersect to shape entrepreneurial paths in higher education. It discusses practical implications for universities, career counselors, and policymakers, such as the need for adaptable ESS frameworks, targeted interventions for dysfunctional beliefs, and expanded mentorship networks. These recommendations are based on the findings of this study and can be directly applied to improve entrepreneurial education. Future research directions include further exploration of DCBs, cross-cultural studies, and developing ESS models resilient to economic fluctuations.
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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 | 0.009 |
| 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 | 0.000 | 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".