Can pride be a vice <i>and</i> virtue at work? Associations between authentic and hubristic pride and leadership behaviors
Bibliographic record
Abstract
Summary Pride, a discrete emotion that drives the pursuits of achievement and status, is crucial to consider in leadership contexts. Across three studies, we explored how leaders' experiences of authentic and hubristic pride were associated with their leadership behaviors. In Study 1, a field study of leader–follower dyads, leader trait authentic pride was associated with the use of more effective (i.e., consideration and initiating structure) and fewer ineffective (i.e., abusive supervision) leadership behaviors, and hubristic pride was associated with more abusive behaviors. In Study 2, a daily diary study, on days when leaders experienced more authentic pride than usual, they used more effective leadership behaviors than usual, whereas on days when leaders experienced more hubristic pride than typical, they were more likely to engage in abusive supervision than typical. In Study 3, a scenario‐based experiment, leaders who experienced more authentic pride in response to our experimental manipulation were more likely to intend to use effective leadership behaviors. In contrast, those who experienced more hubristic pride were less likely to use these behaviors and more likely to intend to be abusive. Overall, this work highlights the importance of pride for leadership processes and the utility of examining discrete and self‐conscious emotions within organizations.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".