Dispute resolution patterns and organizational dispute states
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the influence of conflict management on conflicts at work. Design/methodology/approach – A total of 148 post‐graduate students in management responded to a questionnaire online. Two cluster analyses were performed to identify dispute resolution patterns and organizational dispute states. Then, cross tabulation between the two clusters was performed (Pearson's chi‐square coefficient and Sommer's D statistic). Findings – Cluster analyses identified three styles of dispute resolution pattern – interest‐based, based on controlled power, and power‐based – and three different organizational dispute states: harmony, dissonance, and conflict. Finally, the influence of resolution patterns on dispute states was been confirmed by the cross tabulation. Research limitations/implications – Firstly, Ury et al.'s theoretical typology should be revised, especially for the rights‐based approach. Secondly, the results of our cluster analysis indicate that it might not be necessary to measure the emotional and behavioral dimension of conflict separately. Thirdly, our research confirms the impact of conflict management on conflicts at work. Practical implications – The results show that dispute resolution patterns have a non‐negligible influence on organizational conflict states. In order to increase the likelihood of a harmony state, an interest‐based dispute resolution pattern should be adopted. Originality/value – First, the statistical technique used – cluster analysis – is somewhat innovative. Secondly, this research shows that dispute resolution patterns may affect organizational dispute states.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 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".