Workplace aggression targets’ vulnerability factor: job engagement
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine how job engagement affects the experience of workplace aggression and the related outcomes. Job engagement is introduced as a context variable for the stressor-strain model to explain differences for targets of workplace aggression. Design/methodology/approach – A survey was conducted with a sample of 492 North American working adults from a large variety of industries and jobs. Findings – Consistent with the hypotheses, fear and anger mediate the relationship between workplace aggression and strain. Job engagement moderated the relationship between workplace aggression and anger, such that aggression related to anger only for those employees who were engaged in their job. These data are consistent with the suggestion that engagement may create vulnerability for employees. Research limitations/implications – In this study, the authors highlight the need to include contextual factors that may explain differences in impact of workplace aggression and employee wellness. Practical implications – While practitioners may seek to increase job engagement, there appears to be a greater cost should there be workplace aggression. Thus, the key implication for practitioners is the importance of prevention of workplace aggression. Originality/value – With this study, the authors illustrate how job engagement may have a “dark side” for individuals. While previous research has shown that job engagement helps protect employee wellness, others show engagement decreases after incidents of workplace aggression. The authors suggest those who are engaged and targeted will experience worse outcomes. Also, the authors examine the role of anger for targets of workplace aggression as it relates to fear and strain in this study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".