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Record W1986739896 · doi:10.1108/01437730510624584

Framing effects in managerial third‐party intervention: an exploratory study

2005· article· en· W1986739896 on OpenAlexaff
A. R. Elangovan

Bibliographic record

VenueLeadership & Organization Development Journal · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicConflict Management and Negotiation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsFraming (construction)OriginalityPublic relationsFraming effectPsychologySociologyMarketingSocial psychologyPolitical scienceBusinessEngineeringCreativity

Abstract

fetched live from OpenAlex

Purpose This paper seeks to examine the role of framing effects and the third‐party's need for consistency in intervention strategy selection in managerial dispute intervention. The objective is to move research forward by adopting a decision‐making perspective of dispute intervention and examining the role of framing in such a context. Design/methodology/approach A scenario‐based experimental approach was used and data were collected on 318 intervention cases from 106 students majoring in business, and enrolled in a medium‐sized public university. Findings Results suggest that framing does influence the selection of intervention strategies to some extent, but the third‐party's need for consistency between his/her preferred settlement and the actual final settlement plays a bigger role in influencing strategy selection. Research limitations/implications This study higlights the merits of adopting a decision‐making perspective to understand managerial dispute intervention and points to the need for extending and testing more of the key concepts from that area of research. Practical implications The results indicating support for a need for consistency on the part of managerial third‐parties as well as the influence of framing underscore the need for managers to be aware of these factors influencing their conflict management behaviours and to strive to “rise above the fray”. Originality/value The results of this paper challenge the conventional view that third‐parties in disputes are generally more objective and can see the “big picture”, and represents a valuable first step towards gaining a better understanding the role of cognitive biases and heuristics in managerial dispute intervention.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
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 head0.057
GPT teacher head0.302
Teacher spread0.244 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations4
Published2005
Admission routes1
Has abstractyes

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