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Record W2802743937 · doi:10.1002/tie.21981

Hidden influences in international negotiations: The interactive role of insecure cultural attachment, risk perception, and risk regulation for sellers versus buyers

2018· article· en· W2802743937 on OpenAlexaff
Tuvana Rua, Zeynep G. Aytug, Mary C. Kern, Sujin Lee, Wendi L. Adair

Bibliographic record

VenueThunderbird International Business Review · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicConflict Management and Negotiation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNegotiationPerceptionRisk perceptionSocial psychologyStyle (visual arts)Attachment theoryPsychologyBusinessPolitical scienceLaw

Abstract

fetched live from OpenAlex

This research examines the previously unstudied role of cultural attachment in international negotiations. Specifically focusing on the fearful attachment style, this article reveals the intricate interaction of cultural attachment, risk perception, and risk regulation on negotiators' ability to claim value in international negotiation. Supporting our theorizing based on cultural attachment and prospect theory, findings show that risk‐averse sellers with fearful attachment to their national culture perceive greater risk and in turn are more motivated to regulate risk through relationship‐building with their counterpart (Study 1). Moreover, these individuals achieve lower economic gains when they regulate relational risk by making fewer threats to walk away (Study 2). We discuss the implications and the importance of understanding one's attachment to own national culture as its interplay with role and risk mechanisms impacts effectiveness in international negotiations.

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)0.006
metaresearch head score (Gemma)0.018
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 score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0050.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.033
GPT teacher head0.363
Teacher spread0.330 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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