Workplace Mistreatment: A Review and Agenda for Research
Bibliographic record
Abstract
Abstract Workplace mistreatment researchers study negative interpersonal behaviors under a plethora of different labels, including incivility, bullying, harassment, aggression, and violence. While negative interpersonal behaviors differ in their intensity, intent, and frequency, a common denominator of these behaviors is their adverse impact on employees and organizations. Research has identified the nomological network of workplace mistreatment, which illustrates individual and contextual factors associated with mistreatment behaviors. Authors have also highlighted outcomes of mistreatment, showing that mistreatment results in reduced psychological and physical health, worsened job attitudes, and diminished performance for both targets and bystanders. Further, enacted mistreatment is not without consequences for the perpetrators, and these consequences can be both negative and positive. While workplace mistreatment research has been steadily growing, many questions remain unanswered. There are unexplored topics, approaches, and methodologies. First, there is a need to understand the uniqueness and similarities of different mistreatment constructs to provide a more comprehensive approach for studying workplace mistreatment and highlight alternative ways of measuring mistreatment constructs. Novel methodological approaches, such as HotMap and artificial intelligence, could shed light on the dynamics between targets and perpetrators of mistreatment, allowing researchers to capture the dynamic nature of mistreatment behaviors. Second, the interactions among societal, cultural, and interpersonal factors are likely to shape enacted mistreatment. For instance, social networks within organizations and the interrelations between employees are likely to influence not only the individual who becomes targeted, but also the way in which bystanders are to take action against such mistreatment. Third, while the role of bystanders in the dynamics of workplace mistreatment is undoubtedly important, there is a need to critically investigate the role bystanders may play in curtailing or encouraging mistreatment. More specifically, bystander interventions can take both constructive and destructive forms. Finally, targets’ responses to experienced mistreatment are likely to be relevant to the understanding of the dyadic nature of workplace mistreatment, such that an aggressive target response is likely to cause a mistreatment spiraling. However, it remains unclear what type of target response, if any, would be beneficial in helping de-escalate destructive behavior from the perpetrator. Thus, more research is needed to help address the important question of the best ways to deal with experienced mistreatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".