Antecedents of Entrepreneurial Intention: Entrepreneurship Education as a Moderator and Entrepreneurial Self-efficacy as a Mediator
Bibliographic record
Abstract
This study investigates the effect of entrepreneurial orientation (EO) on entrepreneurial intention. A conceptual model is developed to examine the impact of EO on entrepreneurial intention mediated by entrepreneurial self-efficacy (ESE) and moderated by entrepreneurial education (EE). Data collected from 390 respondents from two districts in the southern part of India (Tamil Nadu) were analysed to test the hypothesised relationships. First, the psychometric properties of the survey instrument were tested by partial least squares structural equation modelling, and then hypotheses were tested using PROCESS macros. The results indicate that (a) all three dimensions of EO—innovativeness, risk-taking and proactiveness—are significant predictors of ESE and (b) that ESE mediated the relationship between EO and entrepreneurial intention. This study also found that EE moderated the relationship between innovativeness, risk-taking, proactiveness and ESE. This research has several theoretical and practical implications for academics, government and non-government entrepreneurship-supporting organisations. This research provides detailed insights into the antecedents of entrepreneurial intention and guides academics and entrepreneurship training institutions in shaping individuals’ entrepreneurial careers.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".