Leader Perfectionism and Team Serendipity: The Role of Team Error Appraisals and Growth Mindset
Bibliographic record
Abstract
Serendipity is crucial for fostering team adaptability and driving innovation in today’s uncertain and dynamic business environments. Errors are a key precursor to serendipity, providing opportunities for exploration and discovery when managed effectively. Leadership plays a critical role, with leader perfectionism introducing a tension between the pursuit of flawless performance and the need to embrace errors as a pathway to achieving optimal outcomes. We propose and test a model that explains how and when leader perfectionism toward the team impacts team serendipity. In a sample of 740 members across 189 R&D teams in three Chinese tech firms, we find that leader perfectionism promotes team serendipity by fostering team appraisals of errors as opportunities and enhancing team error risk-taking. Conversely, leader perfectionism suppresses team serendipity through teams appraising errors as threats and inhibiting team error risk-taking. Furthermore, leader displays of a growth mindset strengthen the positive effects of leader perfectionism on team serendipity by reinforcing the influence of leader perfectionism on team appraisals of errors as opportunities. These findings offer theoretical and practical insights for fostering innovation through leadership in team-based environments.
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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 | 0.009 |
| 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 | 0.001 |
| 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".