MétaCan
Menu
Back to cohort

Relational Conflict Resolution as an Ambicultural Approach to Conflict Management under Ambiguity

2015· article· en· W2597671046 on OpenAlexaff
Leigh Anne Liu, Wendi L. Adair

Bibliographic record

VenueAcademy of Management Proceedings · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicConflict Management and Negotiation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAmbiguityDialecticMediationConflict resolutionConflict managementRole conflictEpistemologyPerspective (graphical)SociologySocial psychologyResolution (logic)Knowledge managementPsychologyComputer scienceSocial scienceArtificial intelligence

Abstract

fetched live from OpenAlex

An essential challenge of resolving conflict is to manage relationships. We introduce relational conflict resolution (RCR), define its conceptual dimensions, and provide a theory of its boundary conditions under ambiguity. Using a grounded inductive cross-cultural approach, with both qualitative and quantitative data in three studies conducted in the U.S. and China, we find that RCR– rooted in eastern cultural traditions of holistic, dialectical, and paradoxical thinking, as well as western practices of collaborative problem solving and mediation – is an approach to conflict management that is inclusive and non-confrontational. RCR integrates five facets: 1) the complexity of a long-term perspective, 2) relationship concern, 3) contextual factors, 4) balance of interests, and 5) flexible and informal communication. Although Chinese are more comfortable with RCR, U.S. Americans are also open to such an approach to resolving conflicts in ambiguous and complex organizational settings. In three conditions of ambiguity, both U.S. and Chinese participants preferred RCR more than approaches found in the dual concern model. We propose that RCR bridges and integrates styles of East and West, and offers new insights to managing conflicts in both eastern and western cultures, especially under complex and ambiguous situations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.147
GPT teacher head0.347
Teacher spread0.201 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueAcademy of Management ProceedingsSame topicConflict Management and NegotiationFrench-language works237,207