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Record W2101442534 · doi:10.1504/ijccm.2012.046031

Effects of cognitive diversity on relationship conflict, agreement-seeking behaviour and decision quality: a study of Chinese management teams

2012· article· en· W2101442534 on OpenAlexaff
Satyanarayana Parayitam, Bradley J. Olson, Yongjian Bao

Bibliographic record

VenueInternational Journal of Chinese Culture and Management · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicConflict Management and Negotiation
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsAgreementDiversity (politics)ChinaCognitionConflict managementQuality (philosophy)Decision qualityPsychologyBusinessSocial psychologyKnowledge managementPolitical scienceComputer scienceSociologySocial scienceTeam effectivenessAnthropologyEpistemologyPsychiatry

Abstract

fetched live from OpenAlex

This study investigates (a) the effects of cognitive diversity on agreement seeking behaviour and relationship conflict, and (b) effects of agreement-seeking behaviour and relationship conflict on decision quality. Using structured survey instrument, this paper gathered data from 252 senior executives from mainland China and analysed the data using the regression techniques to test the hypotheses. The results support that cognitive diversity is positively related to relationship conflict and negatively related to agreement-seeking behaviour. The results also support the hypotheses that agreement-seeking behaviour is positively related to decision quality and relationship conflict is negatively related to decision quality. Though the study is related to Chinese executives, the findings from the study that cognitive diversity enhances interpersonal conflict and discourages agreement-seeking behaviour contribute to the strategic decision-making literature.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.356
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2012
Admission routes1
Has abstractyes

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