To Err Is Human, but Should Leaders Share It?
Bibliographic record
Abstract
Leaders inevitably commit errors at work, but little is known about what happens to followers’ perceptions of leaders when leaders share their errors. Treating error sharing as sensitive information disclosure, we apply the sensitive self-disclosure framework to propose that leader error sharing is positively related to followers’ evaluations of leader ability and integrity, which ultimately enhance followers’ perceptions of leader effectiveness. We also propose that the above positive relationships will be moderated by followers’ leader-oriented perfectionism. Across two studies (i.e., a field study with 98 leaders and 398 members from 98 teams, and a scenario-based experiment with 320 participants), we found support for the view that leader error sharing is positively related to leader effectiveness perceptions via enhanced ability and integrity evaluations. Further, we found that the positive indirect relationship between leader error sharing and leader effectiveness via ability evaluation is mitigated when followers have higher leader-oriented perfectionism. Our study contributes to the leadership and error sharing literature by revealing whether leaders’ error sharing benefits or harms their image in the eyes of their followers.
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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 | 0.010 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".