Combined effects of abusive supervision, willpower and waypower on employees’ task performance and helping behavior, through quality of work life
Bibliographic record
Abstract
Purpose Extending the efforts of previous scholars, this study examines how abusive supervision undermines employees’ ability to meet performance expectations and propensity to engage in helping behavior. Specifically, we investigate a hitherto unexplored mediating role of quality of work life (QWL) in this relationship. We further suggest that employees’ psychological resources, namely willpower and waypower, act as protective shields against this harmful process. Design/methodology/approach We tested the proposed hypotheses using multisource (self- and supervisor-rated) three-wave time-lagged data (N = 185) collected from employees and their supervisors in eight organizations that operate in the service sector of Pakistan. Findings The findings corroborate our predicted hypotheses. The results indicate that employees' exposure to abusive supervision deteriorates their quality of work life (QWL), hindering their ability to deliver expected performance and tendency to help other colleagues. However, this negative process is less pronounced for employees who possess sufficient psychological resources of willpower and waypower. Practical implications This study provides valuable insights to organizations by explicating the process that undermines employees’ ability to channel their energies into performance-enhancing activities when faced with humiliation from their supervisors. Originality/value This study details three previously unexplored factors that explain how and when abusive behavior steers service sector employees away from meeting performance expectations and assisting colleagues, via thwarting their quality of work life.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".