Smart or Diverse Start-up Teams? Evidence from a Field Experiment
Bibliographic record
Abstract
This paper explores the relationship between cognitive abilities and team performance in a start-up setting. We argue that performance in this setting hinges on three tasks: opportunity recognition, problem solving, and implementation. We theorize that cognitive ability at the individual level has a positive effect on opportunity recognition and problem solving but no clear effect on implementation. Within teams, a combination of higher and lower cognitive ability levels may be productive insofar as some individuals can be assigned to mundane tasks (that are often involved in implementation), while others can be assigned to tasks that impose a greater cognitive load (problem solving or opportunity recognition). We present the results of a field experiment in which 573 students in 49 teams started up and managed real companies. We ensured exogenous variation in—otherwise random—team composition by assigning students to teams based on their measured cognitive abilities. Each team performed a variety of tasks, often involving complex decision making. The key result of the experiment is that the performance of start-up teams first increases and then decreases with ability dispersion. Strikingly, average team ability is not related to team performance.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".