A meta-analytic comparison of why workplace ostracism relates to discretionary behavior
Bibliographic record
Abstract
It is well established that the victims of workplace ostracism (WO) engage in fewer organizational citizenship behaviors (OCB) and more counterproductive workplace behaviors (CWB). However, significant gaps remain in our knowledge of why these relationships exist. To advance current comprehension of intermediary processes within this domain, we investigate the leading conceptual account, belongingness theory, alongside two theoretically compelling alternatives: social exchange and self-regulatory resources. Drawing on meta-analytic structural equation modeling (MASEM) and data generated from 160 samples, we investigate two possible mediational models: (a) a competitive model that directly pits the three mechanisms against one another, and (b) a sequential integrative model that combines them. Overall, our findings indicate that even though belongingness is a key mediating process, social exchange and resource mechanisms are critical and complement it. Furthermore, our results suggest the nature of the explanatory mechanisms is nuanced and depends not only on the outcome under investigation (i.e., OCB versus CWB), but also the mediational model tested (i.e., competitive mediation versus sequential mediation). Secondarily, in our study, we control for other forms of mistreatment, such as workplace incivility, and demonstrate that the effect of WO is incremental and independent of other forms of mistreatment. Findings are discussed in terms of how they advance WO theory as well as our understanding of why WO relates to CWB and OCB.
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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 | 0.026 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.021 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".