Task conflict, relationship conflict and agreement‐seeking behavior in Chinese top management teams
Bibliographic record
Abstract
Purpose This paper aims to investigate the effects task conflict has on agreement seeking behavior and interpersonal conflict. In addition, it seeks to examine the moderating role of trust on the effects of agreement seeking behavior and interpersonal conflict on the styles of handing conflict, namely, avoidance, collaboration, and third party intervention. Design/methodology/approach Using a structured survey instrument, this paper gathered data from 252 senior executives from Mainland China and analyzed these using the regression technique to see how interpersonal trust between executives moderates the relationship between conflict and conflict response mechanisms. The study also investigates the relationship between task and relationship conflict as well as agreement‐seeking behavior among Chinese executives in Mainland China. Findings Results show that the presence of interpersonal trust among executives affects the conflict responses for the benefit of organization. The results show that task conflict in top management teams is positively related to relationship conflict and negatively related to agreement‐seeking behavior. The data support the view that intra‐group trust moderates the relationship between agreement‐seeking behavior and collaborating responses such that high‐trust groups will have greater collaboration than low‐trusting teams. Results also support that intra‐group trust moderates the relationship between agreement‐seeking behavior and third party responses such that high‐trust groups will have greater third‐party responses than low‐trusting teams. Research limitations/implications Self‐report measures may have some inherent social desirability bias. Despite this potential weakness, this study examines Chinese executives and therefore provides insights into top management team literature. Practical implications This study contributes to both practicing managers as well as to strategic management literature. This study suggests that administrators need to focus on interpersonal trust while dealing with the outcomes of task and relationship conflict. Originality/value Although the study is related to Chinese executives, the findings from the study, that task conflict has its affect on interpersonal conflict and agreement‐seeking behavior, contribute to the strategic decision making literature.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".