What Happens to Bad Actors in Organizations? A Review of Actor-Centric Outcomes of Negative Behavior
Bibliographic record
Abstract
Negative workplace behavior has received substantial research attention over the past several decades. Although we have learned a lot about the consequences of negative behavior for its victims and third-party observers, a less understood but equally important research question pertains to the consequences for bad actors: How does engaging in negative behavior impact one’s thoughts, feelings, and subsequent behaviors? Moreover, do organizational members experience costs or benefits from engaging in negative acts? We address these questions with an integrative review of empirical findings on various actor-centric consequences of a wide range of negative behaviors. We organize these findings into five dominant theoretical perspectives: affective, psychological-needs, relational, psychological-resources, and cognitive-dissonance perspectives. For each perspective, we provide an overview of the theoretical arguments, summarize findings of relevant studies underlying it, and discuss observed patterns and contradictory findings. By doing so, we provide a very tentative answer to our initial questions, contending that engaging in negative acts is a two-edged sword for actors and its costs seem to slightly prevail over its benefits. Nevertheless, we make this preliminary conclusion based upon an incomplete knowledge base. In order to further our understanding of actor-centric outcomes of negative behavior, we also identify several important research gaps and needed future research directions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".