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Record W2342459200 · doi:10.1509/jim.15.0056

Psychic Distance and Country Image in Exporter–Importer Relationships

2016· article· en· W2342459200 on OpenAlexaff
Aurélia Durand, Ekaterina Turkina, Matthew J. Robson

Bibliographic record

VenueJournal of International Marketing · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsArgument (complex analysis)Expectancy theoryProduct (mathematics)Perspective (graphical)Structural equation modelingValue (mathematics)Sample (material)BusinessPsychicMarketingEconomicsPsychologySocial psychologyComputer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

Conflicting evidence on the issue of psychic distance (PD) in international business relationships has suggested the existence of misunderstood boundary conditions to its effect. This article argues that country image (CI) is a contingent factor to the effect of PD. Expectancy–value theory provides the theoretical foundations, and structural equation modeling analyses for a sample of 358 exporter–importer relationships in the global wine industry provide empirical support for this argument. Product-related CI mitigates the negative impact of PD on the relational exchange orientation (REO) between firms. Specifically, a high level of PD dampens REO when product-related CI is poor, whereas a strong product-related CI helps firms facing such PD conditions to build REO. People-related CI has an indirect effect on REO through product-related CI. This study helps explain the “paradox of distance” and offers a fresh perspective on how to handle the issue of PD when relevant.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.014
GPT teacher head0.231
Teacher spread0.217 · 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

Citations39
Published2016
Admission routes1
Has abstractyes

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Same venueJournal of International MarketingSame topicWine Industry and TourismFrench-language works237,207