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Record W2038166832 · doi:10.1016/j.jom.2014.01.004

The impact of cultural differences on buyer–supplier negotiations: An experimental study

2014· article· en· W2038166832 on OpenAlexaff
Dina Ribbink, Curtis M. Grimm

Bibliographic record

VenueJournal of Operations Management · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal and Cross-Cultural Management
Canadian institutionsWestern University
Fundersnot available
KeywordsNegotiationDyadContext (archaeology)Transaction costBusinessRelational viewCultural diversityMarketingQuality (philosophy)Industrial organizationMicroeconomicsEconomicsSocial psychologyPsychologySociology

Abstract

fetched live from OpenAlex

Abstract In today's global economy, an ever‐increasing number of companies are dealing with international partners, instigating a need to understand the impact of cultural differences on business interactions. Using Hall's distinction of high‐ and low‐context culture, this study investigates the direct and moderating effects of cultural differences in dyadic buyer–supplier negotiations. Theory is developed regarding the impact of culture on joint profits, juxtaposing Transaction Cost Economics and the Relational View. The theory is tested with a negotiation experiment. Participants, classified by their country of origin, negotiate prices and quality levels for three products. This study finds that cultural differences within the negotiation dyad reduce joint profits when compared to dyads of participants with similar cultural backgrounds. Cultural differences also moderate the impact of trust and bargaining strategy on joint profits. Overall, this study concludes that cultural differences, as encountered in day‐to‐day business interactions in global supply chains, significantly impact negotiation outcomes.

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)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.031
GPT teacher head0.387
Teacher spread0.355 · 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 designNon-randomized trial
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

Citations124
Published2014
Admission routes1
Has abstractyes

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