Dynamics of founding team diversity and venture outcomes: A simulation approach
Bibliographic record
Abstract
Abstract Research summary Entrepreneurship research overlooks the dynamics of changing diversity in founding teams. Our simulations calibrated from existing studies suggest that founding teams that change diversity exhibit greater discounted performance for their ventures due to being less diverse and thus their ventures surviving longer, compared to teams that maintain their diversity. Moreover, discounted performance is higher for teams changing diversity due to other teams' performance than due to their own poor performance. Simulating without membership changes the interdependence between team diversity, venture performance, and team disruption, we find that while team diversity is overall performance‐enhancing, this association differs across contexts and its impact varies as ventures mature. Founding team diversity should thus be seen as a continuum where moderate diversity can best serve teams in turbulent environments. Managerial summary We simulated the behavior of founding teams over time to show that compared to teams that do not change their diversity, those who do experience greater discounted performance for their business ventures. This improvement stems from the increased longevity, and thus greater accumulated performance, for teams that switch since they are more rather than less homogeneous. Our investigation also indicates that ventures led by teams that change diversity because they aspire to outperform other teams, tend to exhibit greater discounted performance than those that change diversity to outperform themselves. When we investigate the interconnectedness of teams' diversity, ventures' performance, and disruption, albeit without allowing for any changes in team diversity, we find that while diversity usually helps, teams moderately diversified tend to perform best in turbulent times.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".