Workplace Mistreatment: New Insights From the Perspectives of Targets, Perpetrators, and Observers
Bibliographic record
Abstract
Workplace mistreatment is a pervasive and costly phenomenon within organizations. It is an umbrella term that includes a variety of interpersonal harmful behaviors, such as abusive supervision, workplace incivility, and workplace ostracism. Research indicates that, on average, 34% of employees have experienced mistreatment, with 44% having observed it. The estimated annual cost of workplace mistreatment to organizations ranges from $691.70 billion to $1.97 trillion. Given its prevalence and detrimental impact, previous research has extensively examined the antecedents and outcomes of workplace mistreatment from the perspectives of targets, perpetrators, and observers. Despite significant progress in prior studies, the existing literature still grapples with mixed findings and knowledge gaps, leaving many essential questions unanswered. For instance, there remains uncertainty about how supervisors react to covert mistreatment behaviors from their employees, why supervisors may mistreat employees who render favors to them, how being authentic can make one rude toward others, and why observers may respond negatively to those who are mistreated. This symposium aims to address these questions by bringing together four papers. These papers utilize diverse methods, from experience sampling methods to multi-wave surveys to scenario experiments, exploring workplace mistreatment at various levels and over different time spans. Additionally, they draw on novel theoretical perspectives, providing fresh insights into workplace mistreatment from the viewpoints of targets, perpetrators, and observers. Isolated at the Top: Examining Supervisor Mixed Responses to Upward Ostracism Author: Rui Zhong; Penn State Smeal College of Business Author: Lance Ferris; Telfer School of Management, U. of Ottawa Subordinates’ Favor-Rendering Behavior toward the Supervisor Leads to Abusive Supervision Author: Jie Li; Wilfrid Laurier U. Author: Huiwen Lian; Texas A&M U. Author: Daniel J Brass; U. of Kentucky Author: Flora Chiang; China Europe International Business School (CEIBS) Author: Thomas A. Birtch; U. of Exeter When and Why Authenticity Leads to Workplace Incivility Author: Nicholas Andriese; U. of Central Florida Author: Dana Joseph; U. of Central Florida Author: Shannon G. Taylor; U. of Central Florida How Are Targets of Workplace Mistreatment Stigmatized? Author: Zhanna Lyubykh; Beedie School of Business Simon Fraser U. Author: Jennifer E Jennings; U. of Alberta
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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 | 0.028 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.011 |
| 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".