How Do Founding Teams Form? Toward a Behavioral Theory of Founding Team Formation
Bibliographic record
Abstract
Abstract We advance entrepreneurship research by developing a theoretical model of how founding teams form. Our neo-Carnegie model situates nascent founders in particular network-structural milieus, engaging in aspiration-driven search for and evaluation of prospective co-founders. The formation of co-founding ties between nascent founders can be divided into four theoretical steps, which we label activation, evaluation, approach, and reciprocation. Successful founding team formation is a consequence of mutually favorable evaluations by nascent founders in a multi-sided matching process. Nascent founders with higher and less flexible aspirations are more likely to undertake distant search for co-founders by seeking referrals, forming ties with strangers, and forming new ties to social foci where they might meet potential co-founders. Churn in newly formed founding teams emerges as a consequence of shifting dominant coalition dynamics in the founding team caused by organic venture evolution and intentional changes in strategic direction. Our theoretical model provides new insights on the formation pathways of founding teams, their initial task and relational resource endowments, and initial team dynamics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".