How contemptuous leaders might harm their organization by putting high-performing followers in their place
Bibliographic record
Abstract
Purpose This study investigates how leaders react when they perceive a threat to their hierarchical position, such as by engaging in abusive supervision in ways that diminish followers’ organizational citizenship behavior. It also tests for a dual harmful role of leaders’ dispositional contempt in this process. Design/methodology/approach Three-wave survey data were collected among 231 leader–follower dyads across different industry sectors. Findings Leaders’ beliefs that their authority is being threatened by high-performing followers can lead followers to halt their voluntary work behaviors, because leaders engage in verbal abuse. The harmful role of leaders’ dispositional contempt in this process is twofold: It enhances abusive supervision directly, and it operates as an indirect catalyst of the mediating role of abusive supervision. Practical implications Organizations would be better placed to decrease the risk that disruptions of the hierarchical order, as perceived by leaders, escalate into diminished work-related voluntarism among employee bases by promoting leadership approaches that consider employees deserving of respect instead of disdain. Originality/value This study details how and when leaders who fear they may lose authority, evoked by the strong performance of their followers, actually discourage followers from doing anything more than their formal job duties.
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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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".