Limiting fear and anger responses to anger expressions
Bibliographic record
Abstract
Purpose The purpose of this study is to test how individuals’ emotion reactions (fear vs anger) to expressed anger influence their intended conflict management styles. It investigates two interventions for managing their reactions: hot vs cold processing and enhancing conflict self-efficacy. Design/methodology/approach Hypotheses were tested in two experiments using an online simulation. After receiving an angry or a neutral message from a coworker, participants either completed a cognitive processing task (E1) or a conflict self-efficacy task (E2), and then self-reported their emotions, behavioral activation/inhibition and intended conflict management styles. Findings Fear is associated with enhanced behavioral inhibition, which results in greater intentions to avoid and oblige and lower intentions to dominate. Anger is associated with enhanced behavioral activation, which results in greater intentions to integrate and dominate, as well as lower intentions to avoid and oblige. Cold (vs hot) processing does not reduce fear or reciprocal anger but increasing individuals’ conflict self-efficacy does. Research limitations/implications The studies measured intended reactions rather than behavior. The hot/cold manipulation effect was small, potentially limiting its ability to diminish emotional responses. Practical implications These results suggest that increasing employees’ conflict self-efficacy can be an effective intervention for helping them manage the natural fear and reciprocal anger responses when confronted by others expressing anger. Originality/value Enhancing self-efficacy beliefs is more effective than cold processing (stepping back) for managing others’ anger expressions. By reducing fear, enhanced self-efficacy diminishes unproductive responses (avoiding, obliging) to a conflict.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".