Effect of nascent entrepreneurs' training on their stress: the role of gender and participants' interaction
Bibliographic record
Abstract
Purpose A career in entrepreneurship is stressful, especially during the start-up phase. Training programs for these nascent entrepreneurs are designed to improve entrepreneurial competencies and, more generally to generate learnings. Although learning outcomes can reduce stress, the conditions under which this can happen are not fully understood. The study looks particularly at the effect of learning, interaction with other participants and gender. Design/methodology/approach A six-month three-wave longitudinal study of 120 nascent entrepreneurs has been conducted to investigate the before-and-after effects of training on stress reduction. The training is specially designed to develop competencies, share knowledge about business creation and support the development of the project, not to reduce per se stress. Findings The training has no direct effect on stress levels. However, results indicate that interacting with others has a positive moderating effect on training as stress reduction, just as gender has. Specifically, women reduce their stress through training while men see theirs increasing. The authors conclude that breaking isolation through training is a relevant way to reduce entrepreneurial stress for nascent entrepreneurs. Research limitations/implications Training programs offer different ways to deliver the learning content (online, in cohorts, in the continuous entrance, etc.). The findings of this study suggest ensuring that the participants will have opportunities to interact with others as it reduces the stress on nascent entrepreneurs. Nevertheless, the authors cannot demonstrate that this has a long-term effect as our timeframe is limited to six months. Originality/value This research investigates the stress-reduction effect of training, which is not a common outcome studied related to training. This highlights the importance of looking at other more distal outcomes as nascent entrepreneurs may seek other peripheral outcomes from training, like seeking a sense of belonging or wanting to break isolation.
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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 | 0.004 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".