Hybrid Entrepreneurship: Employees Climbing the Entrepreneurial Ladder
Bibliographic record
Abstract
Recent empirical studies revealed that more than 50% of nascent entrepreneurs start their businesses while still employed. This combination of employment and entrepreneurship has raised the interest of policy makers and researchers who called it "hybrid entrepreneurship". This study focuses on determinants of hybrid entrepreneurship. We examine the influence of socio-demographic variables and of employees' perceptions of resource accessibility and of work and job quality on their hybridation process. More precisely, we try to determine which variables either favor or hinder the transition from one commitment level to the next in the entrepreneurial process. Drawing on the work of Van der Zwan et al. (2010) on the entrepreneurial ladder, we estimate an ordered probit model using a sample of full-time and part-time employees who participated in the 2015 Quebec Entrepreneurial Index Survey (1787 observations). Among others, we find that employees' progress on the entrepreneurial ladder is stimulated by soft support in the form of (perceived) easy access to business advice, and also by a high (perceived) work autonomy in the employee's wage job.\n\nKeywords: hybrid entrepreneurship, entrepreneurial ladder, hybridization process, Quebec
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".