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Record W2073619362 · doi:10.1108/02651330910971940

Are supply chains global or regional?

2009· article· en· W2073619362 on OpenAlexaff
Alan M. Rugman, Jing Li, Chang Hoon Oh

Bibliographic record

VenueInternational Marketing Review · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsBrock UniversitySimon Fraser University
Fundersnot available
KeywordsSupply chainUpstream (networking)BusinessDownstream (manufacturing)Industrial organizationOriginalitySample (material)Economic geographyPerspective (graphical)Value (mathematics)MarketingEconomicsComputer science

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is to investigate the following questions: Are supply chains global or regional? What are the performance implications for firms? Design/methodology/approach This paper classifies 183 large North American firms into home‐region oriented, host‐region oriented, bi‐regional, and global firms by using geographic distributions of their upstream and downstream activities. The performance implications of the regional supply chains of a broader set of 273 firms by using Tobin's Q and data on intra‐regional sales or assets are further evaluated. Findings It is found that the evidence to support the regional nature of supply chains – that is, over 85 percent of firms in our sample – have their supply chains within North America. The paper also finds that a regional focus of firms in terms of sales contributes to improved performance as measured by Tobin's Q. Originality/value The regionalization perspective proposed by Rugman and Verbeke to develop and test the regional nature of supply chains is applied.

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)0.002
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0000.001
Research integrity0.0010.001
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 head0.097
GPT teacher head0.279
Teacher spread0.182 · 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

Citations83
Published2009
Admission routes1
Has abstractyes

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