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Record W2401188333 · doi:10.5539/ijms.v8n3p76

Types of Asymmetries in Exporter-Importer Relationships and Alignment Behaviour

2016· article· en· W2401188333 on OpenAlexvenueno aff
Cagri Talay, Volkan Alptekin

Bibliographic record

VenueInternational Journal of Marketing Studies · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
Fundersnot available
KeywordsAsymmetrySupply chainInformation asymmetryBusinessOrder (exchange)Exploratory researchIndustrial organizationConnotationMarketingSociology

Abstract

fetched live from OpenAlex

The purpose of this paper is to explore the phenomenon of asymmetry in international supply chain relationships and investigate how small exporter firms manage these asymmetric relationships. Prior studies have conceptualized asymmetry as a relationship attribute and concentrated on causal effects of asymmetry in supplier-buyer relationships by highlighting the extensive amount of reasons why asymmetry occurs in dyadic relationships. However, those extensive reasons intent to articulate why asymmetry occurs in relationships, have not provided enough in-depth to understand the complexity of asymmetry in international supply chain relationships, therefore, this study aims to explore the concept of asymmetry by focusing on structure and exercise rather than simply discovering reasons. This research explores the four distinguishable types of relational asymmetry between exporters and importers suggest different implications for international supply chain relationships. As opposed to the existing literature, which has considered and largely agreed that asymmetry is related to a negative connotation, this research suggests that different types of asymmetries also have a positive relational outcome for small export firms. This exploratory paper provides managers with additional insight into the types of asymmetry in international supply chain relationships and suggests that asymmetric relationships must be examined carefully in order to overcome difficulties that distract long-term relationships.

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)0.003
metaresearch head score (Gemma)0.015
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 score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.035
GPT teacher head0.277
Teacher spread0.242 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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