Team Entrepreneurial Passion: Linking Intra- and Inter-personal Influences with Outcomes
Bibliographic record
Abstract
We are beginning to understand the nature and role of passion in entrepreneurship but most studies remain focused on individuals. Only recently have researchers in this area begun to consider passion as it pertains to teams, despite the likelihood that entrepreneurial decisions and actions likely occur at this level. With this study, we examine the antecedents and consequences of three types of team entrepreneurial passion (TEP) in new ventures. We employ a multi-wave, multi-level design to study 79 teams participating in an intensive accelerator programme. The results show that an individual’s entrepreneurial passion for each of innovation, founding and development is a function of a common fundamental assessment of themselves as captured in their core self-evaluation, but past experience and business education also influences certain role-related passion. TEP incorporates an assessment of the team and is a function of perceptions of person-team fit. At the team level, entrepreneurial passion also predicts business feasibility. Our study adds theoretical clarity and empirical support for the occurrence of TEP.
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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 | 0.006 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".