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Record W4229875982 · doi:10.1080/1046669x.2011.558828

How to Attain Desired Outcomes Through Channel Conflict Negotiation

2011· article· en· W4229875982 on OpenAlexaff
Annie Liu, Dheeraj Sharma

Bibliographic record

VenueJournal of Marketing Channels · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicConflict Management and Negotiation
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsNegotiationOutcome (game theory)Channel (broadcasting)Negotiation theoryConflict resolutionProcess (computing)Dependency (UML)Power (physics)BusinessKnowledge managementProcess managementComputer sciencePsychologySocial psychologyPolitical scienceMicroeconomicsEconomicsTelecommunications

Abstract

fetched live from OpenAlex

Channel relationships are dynamic and complex. Though much of channel literature has dealt with power, dependency, and conflict resolution, relatively little research focuses on how channel members apply different modes of negotiation to resolve channel conflicts and, most important, how they finagle their ways through different stages of negotiation to obtain desirable outcomes. This article suggests that in deciding which strategy to adopt to effectively negotiate with others, channel members should take into account two vital outcomes during the negotiation process: substantive gain and relationship outcome. Integrating high versus low levels for each of these two types of outcomes, this study develops a framework for channel conflict negotiation in an international setting and recommends appropriate negotiation strategies for various scenarios and phases of negotiation.

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)0.016
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.005
Scholarly communication0.0120.015
Open science0.0020.006
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0100.004

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 head0.120
GPT teacher head0.321
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations6
Published2011
Admission routes1
Has abstractyes

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