The Next Billion in Business: Women Entrepreneurs in Emerging Markets
Bibliographic record
Abstract
Women-owned businesses are not only among the fastest-growing entrepreneurial ventures in the world but also have a significant impact on other women businesses and the economies at large. This paper uses an in-depth multiple-case study design to study twenty-two Women Entrepreneurs (WE) from diverse geographical, social, economic, and industrial sectors in two of the world’s fastest-growing emerging markets, India and the Philippines. The main message of our study is that in emerging markets, WEs ability to (simultaneously) sell products or offer solutions to niche segments and their capabilities to optimize resources by being innovative in identifying sources of funding, despite the institutional voids in emerging markets, enhances the competitive advantage of their businesses. To this extent, we introduce ‘A Framework to Explain the Paths of Building Women-Owned Businesses’ Competitive Advantage’ and identify some ‘propositions’ as anchors for further theory building. Finally, the findings of this study provide guidelines for entrepreneurs, educators, and policymakers that boosting women’s entrepreneurship and economic empowerment requires systemic solutions at scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".