An Exploratory Study of Entrepreneurial Intention Among Hispanic Entrepreneurs in Toronto
Bibliographic record
Abstract
The Problem Entrepreneurship scholars have claimed that immigrants are more likely to become self-employed due to limited job opportunities, access to social networks, cultural background, necessity, and education and skills. However, the entrepreneurial experience of Hispanic immigrants in Toronto has yet to be examined. This quantitative study examined the attitudes, norms, and perceptions influencing entrepreneurial intention and the factors that encourage self-employment continuation among immigrants in Canada. Method This study used a survey questionnaire of ninety-four Hispanic entrepreneurs in Toronto to comprehend their entrepreneurial intention. It examined their proficiencies, studied their business characteristics, and predicted their behavior toward self employment continuation. The collected data responded to the descriptive variables, the control variables, and the intention variables. The latter variables were related to the TPB’s three dimensions: a) attitude toward entrepreneurship, b) social norm, and c) behavioral control. This dissertation studied the predictive power of the TPB in self employment continuation among Hispanic entrepreneurs. Conclusions Established on the theory of planned behavior, the investigation found that attitudes towards the behavior, subjective norms, and perceived behavioral control were significant predictors of Hispanic’s entrepreneurial intentions to continue in self employment. In addition, the study shows that perceived behavioral control offers the most substantial predictability for self-employment among Hispanics in contrast with attitudes toward the behaviors and social norms within this subculture reported by the research. The study outcomes have supported the theory of planned behavior and provided new insights for immigrant entrepreneurship research.
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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 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".