Framing effects in managerial third‐party intervention: an exploratory study
Bibliographic record
Abstract
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.
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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 | 0.031 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".