Female foundership in startups : a cross-cultural analysis between canadian and german women in new venture creation
Bibliographic record
Abstract
An increased amount of female founders within a country has a positive impact on the local economy. The leading economic nations Canada and Germany have been expanding their support options for female founders in recent years. However, Canada has more female founders of startups in absolute numbers than Germany, even though the country is less populated. This paper aspires to compare both countries and to find out if and to what extent venture creation of female entrepreneurs differs in both countries. A specific focus is set on startups. The theoretical basis for this is the Cognitive Venture Creation Model by Mitchell et al. which is further built on. This approach allows for a holistic analysis as it considers the Affinity of a country towards venture creation as well as the extent to which founders display Arrangement, Willingness and Ability Scripts. To answer the research question, qualitative interviews were conducted with three female founders of startups from Canada and three female founders from Germany. \nIn general, both countries share similarities and differences in all areas. Canada seems to have a higher Affinity for venture creation in terms of inclusivity and institutional help. Nevertheless, venture creation as a career path is considered unattractive in both countries and female founders are still a rarity. The use of Arrangement Scripts shows similarities and differences. The most prominent is the lack of networking motivation in Germany. Regarding Willingness Scripts, it is clear that all female founders resemble each other in terms of unplanned founding, high altruistic aspirations, and emotional connectedness to their business. The triggers that lead to venture creation however slightly differ in both countries. The Ability Scripts show that female founders in Canada are loyal to the industry they come from and show more visionary thinking. German founders seem to be less confident about their own skills. Founders in both countries however align in their ability to learn from past experiences and feedback as well as staying creative. Thus this paper provides insights into the similarities and differences of female foundership in both countries and allows implications for the German female startup ecosystem.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".