Empowering female decision-making in start-ups: the role of entrepreneurial passion in the formal and informal sectors of the Tunisian economy
Bibliographic record
Abstract
Purpose This study explores how and the extent to which entrepreneurial passion is involved in undertaking an entrepreneurial decision to advance women’s entrepreneurial careers amidst the changing socioeconomic context and challenges emerging since the COVID-19 pandemic. Design/methodology/approach This exploratory study applies a qualitative interpretive methodology. The authors conducted in-depth semi-structured interviews with 24 female entrepreneurs in Tunisia to achieve the research objectives. Findings Our study demonstrates that entrepreneurial passion plays a distinct role across different stages of a startup’s lifecycle. It plays a prominent and explicit role during the pre-startup and startup phases, fueling motivation and driving initial entrepreneurial intentions. In contrast, as the company enters the growth stage, the role of passion becomes more implicit. In this case, rational decision-making tends to dominate passion, highlighting a shift toward more reasonable and strategic approaches in sustaining business expansion. The study’s findings also highlight the remarkable transition by female entrepreneurs from the informal to the formal sector during the start-up stage. Originality/value This is the first study to empirically explore entrepreneurial passion among women and its role in driving decision-making processes in Tunisia. The originality of this study also lies in its focus on start-up development phases to explain the importance of passion in women’s decision-making. Drawing on feedback from female entrepreneurs, this study demonstrates that entrepreneurial passion matters in different ways. As such, the study also provides valuable insight into the status and position of female entrepreneurship in the cultural landscape of the Middle East.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".