Curtailing the Impact of Abusive Supervision on Counter-productive Work Behaviors: Using Conservation of Resource Lens to Analyze the Moderating Role of Work Engagement
Bibliographic record
Abstract
Destructive behaviors of leaders have the potential to cause severe damage to the organization by endangering the wellbeing of its internal stakeholders. Abusive supervision is one the most common type of destructive leadership that prevails within organizations and creates a high possibility for subordinates to respond negatively by demonstrating counter-productive work (CWB) behaviors. However, whether their high work engagement motivates them to lower their counterproductive work behaviors when they value their work, is overlooked in the literature. To answer this question, this study approached 304 junior doctors working in tertiary public hospitals, located in the provincial and federal capitals of Pakistan and analyzed the data using Structural Equation Modelling (SEM) with Statistical Package for Social Sciences (SPSS) and Analysis of Moment Structure (AMOS). The results supported the proposed hypotheses and suggested that interventions must be made to increase junior doctors' work engagement to reduce their CWB in the presence of abusive supervision. Moreover, the administration of the hospital must restrain the destructive behaviors of supervisors through strict monitoring and the creation of a grievance cell to protect the junior doctors from verbal abuse and exploitation from their seniors and enhance their engagement with work.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".