The psychosocial costs of conflict management styles
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the interactive effect of interpersonal conflict at work and adopting an integrating/compromising conflict style on workers' psychosocial wellbeing. Design/methodology/approach A total of 311 employed young adults completed an online questionnaire. Findings Moderated hierarchical multiple regression analyses support the hypothesis that integrating/compromising interacts with interpersonal conflict at work to predict psychosocial strain. Specifically, it was found that integrating/compromising is related to psychosocial strain in a U-shaped fashion when work conflict is high. Although a moderate degree of integrating/compromising is psychosocially beneficial for workers and can buffer the negative impact of work conflict, beyond a certain point, integrating/compromising is associated with an increase in psychosocial strain when work conflict is high. Research limitations/implications The results of the study suggest that investigations of conflict styles should focus not only on managing the occurrence of conflict – or resolving it when it does occur – but also on the psychosocial costs of adopting particular conflict styles. The data are cross-sectional; therefore, inferences about causality are limited. Originality/value The study is one of the few to empirically test the psychosocial costs of adopting particular conflict styles. In addition, compared with similar studies, more complex relationships (i.e. nonlinear) between the variables are assessed.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".