Team leader’s conflict management styles and innovation performance in entrepreneurial teams
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate how a team leader’s conflict management style (CMS) affects team innovation performance (TIP) in entrepreneurial teams using a team emotion perspective. Design/methodology/approach It is proposed in this study that team passion mediates the impact of team leader’s CMSs on team performance, which is further moderated by team emotional intelligence (TEI). Then this study collected paired data from 105 teams including 105 team leaders and 411 team members to test the proposed model. Findings The results show that a team leader’s cooperative CMS has a significant positive impact on TIP and team passion further mediates the relationship between the team leader’s CMSs and TIP. The results also show that TEI moderates the relationship between the leader’s CMSs and team passion. Originality/value This study helps enriches the literature of conflict management by exploring the mechanisms through which a team leader’s CMSs affect team performance in entrepreneurial activities, and the findings of this study highlight the important role of team passion in this process. In addition, this study integrates the research on conflict management and the research on team passion in entrepreneurial teams to provide a new perspective to explore the dynamic process of entrepreneurial activities, which sheds light on the investigation of the important implications of effective conflict management in the entrepreneurship.
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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 | 0.005 | 0.022 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".