“Inverse” Founding and Alternative Paths of Entrepreneurial Organizing
Bibliographic record
Abstract
A central concern in entrepreneurship research is how founders recognize opportunities and organize to capture them. Prior work highlights the role of information asymmetries derived from prior experience and the identification of opportunities as key inputs in the subsequent venture formation process. Yet, is this the only way in which entrepreneurs launch ventures? To address this question, we conducted a field study of 72 de novo entrepreneurial organizations founded in the U.S. in the wake of the COVID-19 pandemic. Surprisingly, less than half of our sample aligns with extant depictions of the entrepreneurial process. By tracing the sequence of steps undertaken by each venture, we identify three distinct “pathways” by which founders recognize opportunities and organize to capture them. Two align with prior research, the third is a novel sequence we term the inverse founding path. We first trace how these three paths unfold over time. We then unpack how the differences between them shape key strategic outcomes such as organizational structure, growth patterns, strategic drift, and longevity. Overall, this study contributes to research on entrepreneurial processes and user innovation, and it carries important implications for research on entrepreneurship and communities.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".