What Happens to Bad Actors in Organizations? A Review of Actor-Centric Outcomes of Negative Behavior
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it