Peer effects on passion levels, passion trajectories, and outcomes for individuals and teams
Bibliographic record
Abstract
We consider the influence of inter- and intra-individual team dynamics on entrepreneurial passion change and the relevance of passion change to important outcomes. Drawing on person-environment fit theory, we hypothesize first, that in newly formed teams, the entrepreneurial passion levels of individuals are impacted by their peers' passion (the average passion of their teammates). Second, we expect that individuals' trajectories of passion change are influenced by their perception of fit with the team. Third, passion levels and trajectories are expected to impact entrepreneurial outcomes for both individuals and teams. To examine these temporal dynamics, our hypotheses are tested with data from an accelerator program involving 343 team members nested in 79 newly formed teams. The findings reveal that in new teams, individuals' passion for inventing, founding, and developing are positively (negatively) influenced when teammates have higher (lower) passion for these roles and the association between individual's passion and peers' aggregated passion becomes stronger over time. Over time, positive passion trajectories emerge when an individual perceives higher fit with their team, and entrepreneurial intent is predicted by both (a) end-state levels of individual passion and (b) passion trajectories for inventing and founding (but not developing). Finally, we find that team passion trajectories predict team performance. Implications of these multi-level findings are discussed.
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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 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".