EMOTIONAL TRANSFORMATION OF FAILURE: PASSION, RESILIENCE AND ENTREPRENEURIAL SELF-EFFICACY
Bibliographic record
Abstract
New entrepreneurs often face failures that can erode confidence and self-efficacy, thereby obstructing progress. This study considers the effects of failure on entrepreneurial self-efficacy and proposes a model based on entrepreneurial learning of how passion and resilience may mitigate these effects. Using data from 423 entrepreneurs (both successful and unsuccessful) in North America, it tests a model via structural equation modeling, in which entrepreneurial self-efficacy is directly affected by failure, and indirectly affected by passion and resilience. The results indicate the negative direct effects of failure on entrepreneurial self-efficacy may be offset by strongly positive effects of entrepreneurial passion and by resilience. This appears to be the first empirical study to test directly the moderating effects of entrepreneurial passion and resilience on the relationship between failure and entrepreneurial self-efficacy. In the presence of sufficient passion and resilience, failure may be viewed as a positive influence on self-efficacy. The results suggest that entrepreneurial failure may act as a precursor to entrepreneurial passion. They also suggest that the practical, negative effects of setbacks can be mitigated, or even reversed, by focusing on developing entrepreneurial passion and resilience in new entrepreneurs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".