How Institutional Logics Influence Growth: A Field Experiment with Tunisian Women Entrepreneurs
Bibliographic record
Abstract
Entrepreneurial training programmes promoting women’s entrepreneurship in low- and middle-income countries command significant global attention and concomitant resources. Despite this broad investment, many ventures in these contexts fail to grow. Prior research suggests that institutionalized patterns of behaviors, in part dictated by institutional logics, may cause this lack of growth. In the context of Tunisian female entrepreneurship, this research explores the effects of community and market logics on entrepreneurial growth outcomes. Using a field experiment, we demonstrate that institutional logics affect the growth aspirations of entrepreneurs through individual empowerment and emotional energy. This research has theoretical implications for institutional logics, entrepreneurial growth, and emotions literatures. Firstly, institutional logics affect entrepreneurial growth outcomes. Secondly, logics are processed by individuals through both cognitive (empowerment) and social (emotions) constructs and we contribute to knowledge of the integrated macro to micro psychological and social processes of institutional logics. Finally, cultural differences explain why logics do not have the expected effects we think they may in the promotion of Western neoliberal entrepreneurial training programs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".