Investigating the Motivating Factors of Youths-Students Interest to Become Entrepreneurs: A Case of Anambra State, Nigeria
Bibliographic record
Abstract
This study explored the factors that motivates the youth’s interest (intention) to become entrepreneurs by incorporating the personality attributes from the “Theory of planned behavior” and environmental factors, with some selected “control variables” (experience, gender, and age). A multivariate statistical technique was used to test the relationship between the variables using “Structural Equation Modeling” AMOS Package version 23. A sample of 400 students from Chukwuemeka Odumegwu Ojukwu university in Anambra State, Nigeria was used to analyze the data. The results of the study indicated that attitude, self-efficacy, and subjective norms have a statistically significant effects on student’s entrepreneurial interest. The findings also indicated that entrepreneurial education has an impact on students’ attitude but has no obvious effect on intention. Apart from government support policy which was found to have statistical negative effect on intention, other external barriers have no effect on attitude and intention. Understanding these factors is important to make recommendations to the government and other relevant stakeholders to promote youths’ entrepreneurship in Nigeria. The importance of entrepreneurship is not only vital for economic growth but also for long term sustainable development. Through the vital information gained from investigating these motivating factors of youth’s interest to engage in entrepreneurship, the government and other important stakeholders can formulate effective policies to improve macroeconomic conditions to encourage university students to become entrepreneurs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".