Darkest Before the Dawn: Entrepreneurial Motivation and Sensemaking from Traumatic Experience
Bibliographic record
Abstract
Moving theorization beyond the focus on positive personality traits and environmental factors, we explore how traumatic experiences can catalyse entrepreneurial motivation. We extend the conversation on adversity by showing that entrepreneurship is not only a necessity-based coping mechanism but a potentially transformative force. Leveraging in-depth qualitative data from military veterans turned entrepreneurs, this study identifies a three-phase process in which (1) initial trauma disrupts an individual's sense of reality, (2) a sensemaking process reconciles this disruption, and (3) entrepreneurial motivation emerges as an outcome of the reconciliation. Our findings reveal that the nature of trauma—whether self-related, other-related, or situational—shapes a sensemaking process leading to distinct entrepreneurial motivations. From those, we derive a framework encompassing the seeker entrepreneur, driven by self-related trauma; the helper entrepreneur, emerging from trauma related to others; and the innovator entrepreneur, resulting from situational trauma. We contribute to the study of entrepreneurship in three ways. First, we reveal new entrepreneurial motivations. Second, we clarify the mechanisms underlying their emergence. Third, we reposition entrepreneurship as not only an adaptive but highly proactive outcome.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".