Analysis of women’s social entrepreneurship in underdeveloped, emerging and developed economies: a multicultural exploratory study
Bibliographic record
Abstract
Purpose The aim of this study is to understand the entrepreneurial journey of women social entrepreneurs in different countries in underdeveloped, emerging and developed economies. It presents multicultural and unique analysis of women social entrepreneurs’ experiences in various countries. Design/methodology/approach This research is based on a multiple case study design using qualitative analysis of semi-structured interviews from 12 women social entrepreneurs. Considering the focus of this study on underdeveloped, emerging and developed countries, respondents were discreetly selected from different countries, namely, Afghanistan, Canada, France, India, Ukraine and the USA. Findings Results of women social entrepreneurs in Afghanistan, Canada, France, India, Ukraine and the USA explore their motivations, challenges and risks, sources of funding and success in their entrepreneurial journey. Research limitations/implications This study is limited to 12 cases of women social entrepreneurs across Afghanistan, Canada, France, India, Ukraine and the USA. However, the saturation of the interviews carried out does not call into question the validity of our results. Higher number of respondents may increase generalizability. Practical implications This study provides an in-depth analysis of women social entrepreneurs’ journey including their challenges. Challenges, if addressed, will lead to an increased number of women social entrepreneurs around the world. Originality/value This study is valuable as it focuses on multiple cases of women social entrepreneurs from underdeveloped, developing and developed economies. It explores the distinctive entrepreneurial journey of women social entrepreneurs related to their motivations, challenges and risks, sources of funding and success of their social ventures.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".