Critical Incidents Typically Emerging during the Post-Formation Phase of a New Venture: Perspectives for Entrepreneurship Education and Start-Up Counselling
Bibliographic record
Abstract
During the post-formation phase, young ventures are often in danger of sliding into bankruptcy. The entrepreneur has to deal with a multitude of complex problems, decisions have to be made under time pressure or uncertainty, and upcoming crises have to be perceived in time. This paper presents seven critical incidents that are (1) realistic, typical, and likely to emerge during the first years of a start-up’s existence, (2) assumed to cause severe financial crises for the new venture, but (3) possible to be overcome by the entrepreneur if he makes appropriate decisions. Seven incidents were developed on a theoretical basis and then empirically validated by questionnaires presented to (future) entrepreneurs and start-up counsellors (n = 627) as well as to a sample of students who are at least interested or even engaged in the field of entrepreneurship (n = 367). The incidents reveal likely challenges for entrepreneurs in the post-formation phase. This discovery opens new perspectives for preparing entrepreneurs to deal with the typical risks of the post-formation phase. For example, these lessons provide opportunity for an application within case-oriented courses of entrepreneurship in higher and vocational education and opportunity for reflection on probable emerging crises in start-up counselling.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| 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".