Full Esteem Ahead? Mindset-Oriented Business Training in Ethiopia
Bibliographic record
Abstract
Is there a mindset gap holding women back in business? Can entrepreneurship training instill a set of attitudes, behaviors, and strategies that are thought to underpin success in business such as motivation, perseverance, and self-confidence? This study conducted two randomized controlled trials to evaluate the effect of mindset-oriented business trainings on the performance of women-owned micro and small enterprises in Ethiopia. The trainings were underpinned by psychology with a mission to foster self-esteem and entrepreneurial spirit. Despite a similar approach, however, the quality of delivery seemed to matter as impacts of the trainings on business performance were mixed. A key channel for an impact on profits is if the training can actually effectuate the mindset change, with only one training transferring higher levels of entrepreneurial self-efficacy, personal initiative, and entrepreneurial locus of control to the women, relative to a control group. The study finds suggestive evidence that psychological skills and mindset are better inspired by a trainer who previously owned a business themselves and therefore may have a better understanding of the entrepreneurs'specific challenges. The study concludes that psychological skills are important for women's business success, and these skills can indeed be transferred using training, assuming a shared identity match between trainer and student. Service delivery appears to be critical for inculcating these important skills.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".