Is Negative Attention Better Than No Attention? The Comparative Effects of Ostracism and Harassment at Work
Bibliographic record
Abstract
Ostracism has been recognized as conceptually and empirically distinct from harassment. Drawing from theory and research that suggests that employees have a strong need to belong in their organizations, we examine the comparative frequency and impact of ostracism and harassment in organizations across three field studies. Study 1 finds that a wide range of employees perceive ostracism, compared with harassment, to be more socially acceptable, less psychologically harmful, and less likely to be prohibited in their organization. Study 2 surveyed employees from a variety of organizations to test our theory that ostracism is actually a more harmful workplace experience than harassment. Supporting our predictions, compared with harassment, ostracism was more strongly and negatively related to a sense of belonging and to various measures of employee well-being and work-related attitudes. We also found that the effects of ostracism on well-being and work-related attitudes were at least partially mediated by a sense of belonging. Study 3 replicated the results of Study 2 with data collected from employees of a large organization and also investigated the comparative impact of ostracism and harassment on employee turnover. Ostracism, but not harassment, significantly predicted actual turnover three years after ostracism and harassment were assessed, and this was mediated by a sense of belonging (albeit at p < 0.10). Implications for theory, research, and practice are discussed.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".