Fueled by a Fearful Leader: When, to What Extent and How Leader Fear of COVID-19 Promotes Employee Performance
Bibliographic record
Abstract
The literature generally surmises that negative affective states of leaders are detrimental to leader effectiveness and work outcomes. Taking the opposite view, this study explores how the negative affective experiences of leaders related to COVID-19 may foster team commitment and employee performance. By integrating personality systems interaction theory, cognitive appraisal theory, and the literature on stress-based emotions, we develop a model that clarifies when, how, and to what extent leader fearful states related to COVID-19 drive employee performance. Using three-wave and multisource data from 579 employees and their leaders from 69 teams, we found that among leaders who exhibited higher levels of positive affectivity, leader fear of COVID-19 indirectly fostered employee performance via the mediating roles of leader promotion of team goals and team commitment. Moreover, these moderated indirect effects were strongest at moderate levels of leader fear of COVID-19. We discuss the theoretical and practical implications of these findings for research on leader affective states.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".