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Record W4410507885 · doi:10.1002/job.2893

Disrupting the Chain of Displaced Aggression: A Review and Agenda for Future Research

2025· review· en· W4410507885 on OpenAlexaff
Constantin Lagios, Simon Lloyd D. Restubog, Pauline Schilpzand, Kohyar Kiazad, Karl Aquino

Bibliographic record

VenueJournal of Organizational Behavior · 2025
Typereview
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAggressionPsychologyChain (unit)Social psychologyCriminology

Abstract

fetched live from OpenAlex

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 I 3 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.865
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.112
GPT teacher head0.510
Teacher spread0.398 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2025
Admission routes1
Has abstractyes

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