Disrupting the Chain of Displaced Aggression: A Review and Agenda for Future Research
Bibliographic record
Abstract
ABSTRACT Displaced aggression refers to instances in which a person redirects their harm‐doing behavior from a primary to a secondary, substitute target. Since the publication of the first empirical article in 1948, there has been a noticeable surge in research referencing this theory in both management and psychology journals. This trend highlights the continuing relevance of displaced aggression research and its applicability to other disciplinary fields (e.g., criminology, hospitality management, information systems, and tourism). Despite the ubiquity of displaced aggression theory, however, there persists a notable lack of clarity and consensus regarding its fundamental principles, moderating factors, and underlying mechanisms. In light of these limitations, we provide a systematic and interdisciplinary review of displaced aggression theory in work settings with three key aims. First, our review offers foundational knowledge that helps unify the diverse ways in which scholars from varied disciplinary backgrounds have applied, interpreted, and operationalized displaced aggression. Second, inspired by the I3 model, we introduce an overarching theoretical framework to coherently and parsimoniously organize the displaced aggression literature. Lastly, to move the field forward, we propose a promising agenda for future research that focuses on important issues emerging from our review.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".