Antecedents of immigrants’ entrepreneurial intention formation process: an empirical study of immigrant entrepreneurs in Canada
Bibliographic record
Abstract
Economic integration of ever-increasing number of immigrants in the host country is a challenge both for the immigrant and their host government. Immigrant entrepreneurship can be one of the solutions to this challenge. However, little is known about how immigrant entrepreneurship intention formation process takes place. Immigrants face various challenging situations that make them psychologically and cognitively distinct. This study models from a holistic perspective, the dimensions of individual and contextual variables as antecedents of Immigrants’ entrepreneurial intention (IEI). The study aims to identify the key factors responsible for developing EI of immigrants with an implementation intent. Cross-sectional data from Canada is examined using a sample of 250 immigrants. The analysis adopts a structural equation modelling approach. In addition to risk perception, bridging social network, and experience, we postulate that the perceived distance of entrepreneurial culture (country of origin versus host country) and entrepreneurial support are crucial factors that influence IEI. Empirical analyses based on survey data partially confirmed our hypotheses. The results show the role of psychological and cognitive factors in determining immigrants’ intention to start a new business. We extend the Theory of Planned Behaviour (TPB) by identifying certain understudied determinants in the literature and presenting a holistic decision-making process in the context of immigration-entrepreneurship nexus. Examining specific factors that appropriately contextualize immigrant entrepreneurship research and relativize the EI through a learning-based approach advances current literature. It offers insights to policymakers and practitioners to contemplate entrepreneurial culture as a shared liability issue (foreignness, host country), and adapt their entrepreneurship guidance accordingly. Thus, this study opens the way to a better understanding of the business behaviour of immigrants. Their impact matters for the entrepreneurial diversity that resilient ecosystems need.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".