Managing aggression in organizations: what leaders must know
Bibliographic record
Abstract
Purpose The paper aims to incorporate a sensemaking framework that augments research on organizational justice and aggression. Design/methodology/approach Sensemaking is used as a basis for designing an aggression model. Organizational justice and attribution theory are key components of sensemaking triggers. In addition, the model includes both organizational and personal influences on the sensemaking process. Finally, information processing theory provides explanations as to the importance of retrospect in sensemaking. Findings The sensemaking framework: presents the workplace antecedents of the sensemaking process; specifies the sensemaking triggers that provoke aggressive responses; identifies the individual and organizational factors that affect both the sensemaking triggers and the link between triggers and aggressive behaviors; and incorporates a full range of aggressive behaviors (e.g. violence, verbal abuse, or refusal to return telephone calls) that occur in organizations. Practical implications The paper proposes that by taking a sensemaking perspective, leaders can understand and proactively manage aggressive behavior in the workplace. Originality/value This paper provides a comprehensive aggression model that can assist both researchers and practitioners regarding the sensemaking process and its role in workplace aggression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.021 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".