Conflict management styles as indicators of behavioral pattern in business negotiation
Bibliographic record
Abstract
Purpose The purpose of this study is to examine whether conflict management styles are able to predict actual behaviors in business negotiation in two different countries. Design/methodology/approach Subjects were recruited from both Canada and China to participate in a laboratory study. Three simulated business negotiations were used for participants to negotiate deals in both countries in order to compare the validity of conflict management styles in predicting negotiation behaviors. Findings This study shows that conflict management styles are valid predictors of actual negotiation behaviors in Canada, but not in China. The results also show that Chinese people use a more avoiding approach and demonstrate a higher level of integrativeness during business negotiation simulations, while Canadians use a more compromising approach and show a higher level of distributiveness. Practical implications Practical implications of the findings are discussed in terms of the usefulness of self‐reported conflict management styles for negotiation researchers and practitioners in training seminars and in terms of the effectiveness of first offer as one negotiation strategy to achieve better negotiation outcomes. Originality/value This study is particularly pertinent, given that the relationship between conflict management styles and actual behaviors in negotiation receives little attention and that even less attention is given to this relationship in a cross‐cultural context.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".