A Study on Entrepreneurial Intention of University Students in Bangladesh
Bibliographic record
Abstract
Entrepreneurial intention is the function of motives and barriers encountered by the university students during their studies. As the unemployment rate among the university graduates is the highest in Bangladesh, the research focused on university students with the aim to predict their entrepreneurial behaviors. Previous studies identified different environmental contexts of the countries have different influence on the perception regarding motives and barriers of entrepreneurial intention of the students. Hence, to stimulate the development of entrepreneurship, it is important to uncover university student’s perceptions regarding the motives and barriers to develop entrepreneurship and their influences on entrepreneurial intention of the graduates. To conduct the research, the primary data were collected from 398 business students using simple random sampling method through self-administered questionnaire. Descriptive statistics were used to analyze the demographic profiles of the respondents. A multivariate statistical technique like Factor Analysis was used to identify the factors influencing entrepreneurial intention and Regression Analysis was used to predict the significant impact factors. It is evident in the study that the driving factors like autonomy and market opportunity has significant positive impact whereas barriers like financial and government support, lack of skills has significant negative impact on the student’s entrepreneurial intentions. This paper will assist the policy makers, educational institutions and researchers to develop several implementable strategies like enterprise education, liberal tax system, financial and regulatory support to promote entrepreneurship in a developing country like Bangladesh.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".