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Peer effects on passion levels, passion trajectories, and outcomes for individuals and teams

2024· article· en· W4396631449 on OpenAlexaff
Simon Taggar, Anne Domurath, Nicole Coviello

Bibliographic record

VenueJournal of Business Venturing · 2024
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsPassionPsychologySocial psychologyCognitive psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.359
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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