Social Norms, Attitudes, Self-Efficacy, and Entrepreneurial Intentions: Moderating Roles of Education, Risk Tolerance, and Innovation Orientation
Bibliographic record
Abstract
Young people play an increasingly prominent role in economic activity, yet limited research investigates the factors shaping their entrepreneurial intentions, particularly in emerging economies. This study examines the relationships among perceived social norms (PSN), attitudes toward entrepreneurship (ATE), and self-efficacy (SNR) in influencing entrepreneurial intentions (EI) among youth. It also explores the moderating roles of entrepreneurial education (EE), risk tolerance (RT), and innovation orientation (IO) in these relationships. A quantitative research methodology was employed, utilizing an online questionnaire to collect data from 211 Thai university students. Findings reveal that PSN, ATE, and SNR significantly influence EI, with ATE emerging as the most critical factor, demonstrating the role of positive perceptions of entrepreneurship as a viable career path in enhancing intentions. Moderation analyses indicate that entrepreneurial education strengthens the relationship between PSN and EI, and risk tolerance amplifies the impact of SNR on EI. However, the moderating effect of innovation orientation on the relationship between ATE and EI was not supported. These findings highlight the importance of education and individual characteristics, such as risk tolerance, in shaping entrepreneurial intentions while indicating limits to innovation orientation's role in this context. This study provides actionable insights for educators, policymakers, and practitioners seeking to foster entrepreneurial activity among youth in emerging economies, emphasizing the need for targeted educational initiatives and supportive environments to cultivate entrepreneurial skills, confidence, and attitudes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".