Immigrant entrepreneurs and exit decisions in new tech ventures: insights from a scenario-based survey study
Bibliographic record
Abstract
Technology startups are key drivers of economic growth, with immigrant founders playing an increasingly prominent role in advanced economies. While archival studies link immigrant presence in founding teams to strategic outcomes, the mechanisms remain unclear. We examine whether traits central to social psychology theories of entrepreneurship mediate the relationship between immigrant status and exit, focusing on financial harvests through acquisitions—the dominant exit route for technology ventures. Using scenario-based surveys conducted in Israel and Canada, we measure perceived innovativeness, risk propensity, locus of control, and entrepreneurial energy—personal entrepreneurial traits unavailable in archival data. We document that immigrant founders are significantly more likely to pursue financial harvest exits and display greater risk aversion. Multivariate models and bootstrap analyses reveal that risk propensity partially mediates the immigrant–exit relationship, while other traits do not. By identifying psychological mechanisms linking immigrant status to exit, our study advances theory on cultural influences in entrepreneurial strategy and overcomes the limitations of archival designs. The findings carry practical implications for entrepreneurs structuring founding teams, investors evaluating high-growth ventures, and policymakers designing frameworks to foster immigrant-led innovation.
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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 | 0.003 | 0.008 |
| 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 | 0.001 | 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".