Frogs in boiling water: a moderated-mediation model of exploitative leadership, fear of negative evaluation and knowledge hiding behaviors
Bibliographic record
Abstract
Purpose This study aims to utilize the cognitive appraisal theory of stress and coping by conducting a joint investigation of the mediating role of knowledge hiding behaviors in the relationship of exploitative leadership on employee’s work related attitudes (i.e. turnover intentions) and behaviors (e.g. job performance, creativity) and fear of negative evaluation in influencing this mediation. Design/methodology/approach Using the Preacher and Hayes’ (2004) moderated-mediation approach, the authors tested the model by collecting multi-wave and two-source data from employees and fellow peers ( n = 281) working in the service sector of Pakistan. Findings Results of the study demonstrate that exploitative leadership adversely influences one’s performance and turnover intentions through knowledge hiding behaviors. The fear of negative evaluation moderates the indirect effects of exploitative leadership on employee’s outcomes through knowledge hiding behaviors such that these indirect effects are stronger for individuals possessing low levels of fear of negative evaluation. Originality/value The current study contributes to knowledge management and dark leadership literature by suggesting knowledge hiding behaviors as a process through which exploitative leaders unveil their negative effects on employee’s outcomes. This study is also unique in the sense, as it posits that employees might vary because of their dispositional traits (i.e. low fear of negative evaluation) in responding to exploitative leadership with greater knowledge hiding behaviors.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".