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Record W1986586706 · doi:10.1177/1470595807079380

Cultural Influences in Negotiations

2007· article· en· W1986586706 on OpenAlexafffund
Lynn E. Metcalf, Allan Bird, Mark Peterson, Mahesh N. Shankarmahesh, Terri R. Lituchy

Bibliographic record

VenueInternational Journal of Cross Cultural Management · 2007
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsConcordia University
FundersInstituto Tecnológico y de Estudios Superiores de MonterreyYork University
KeywordsOperationalizationNegotiationScale (ratio)Sample (material)SociologyCross-culturalHofstede's cultural dimensions theorySocial scienceGeographyEpistemology

Abstract

fetched live from OpenAlex

Empirical work systematically comparing variations across a range of countries is scarce. A comprehensive framework having the potential to yield comparable information across countries on 12 negotiating tendencies was proposed more than 20 years ago by Weiss and Stripp; however, the framework was never operationalized or empirically tested. A review of the negotiation and cross cultural research that have accumulated over the last two decades led to refinements in the definition of the dimensions in the framework. We operationalized four dimensions in the Negotiation Orientations Framework and developed the Negotiation Orientations Inventory (NOI) to assess individual orientations on those four dimensions. Data were collected from a sample of 1000 business people and university students with business experience from Finland, Mexico, Turkey, and the United States. Results are presented and further scale development is discussed. Findings establish the utility of the dimensions in the framework in making comparisons between the four countries.

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)0.007
metaresearch head score (Gemma)0.025
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 score0.008
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.006
Scholarly communication0.0080.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.086
GPT teacher head0.478
Teacher spread0.392 · 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

Citations40
Published2007
Admission routes2
Has abstractyes

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