The impact of growth mindset training on entrepreneurial action among necessity entrepreneurs: Evidence from a randomized control trial
Bibliographic record
Abstract
Abstract Research Summary Although entrepreneurship training programs are designed to help necessity entrepreneurs acquire skills and capabilities to take entrepreneurial action, participants in these programs often fail to do so. In partnership with a local government agency, we conducted a randomized field experiment involving 165 entrepreneurs in rural Tanzania where in addition to providing technical‐skills training, approximately half of the participants also received “growth mindset” psychological training. Those who received the growth mindset training displayed more entrepreneurial action in their business than those in the control group. Importantly, higher levels of entrepreneurial self‐efficacy mediated the positive impact on entrepreneurial action displayed by participants who received the growth mindset training. We discuss how complementing traditional technical‐based training with growth mindset training can improve the efficacy of entrepreneurship training programs. Managerial Summary Entrepreneurship training programs often fall short in translating knowledge into action. To address this issue, we conducted an experiment with 165 entrepreneurs in rural Tanzania. All participants received technical‐skills training, but half were also exposed to “growth mindset” training. Those who received the growth mindset training displayed greater initiative in business growth. The newfound confidence and grit they gained empowered them to apply learned principles effectively, ultimately enhancing the effectiveness of entrepreneurship training programs.
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 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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".