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Record W3125705093 · doi:10.1080/19186444.2018.1551593

An empirical investigation into the internationalisation patterns of UK firms

2018· article· en· W3125705093 on OpenAlexvenueno aff
Jenny Berrill, Martin Hovey

Bibliographic record

VenueTransnational Corporation Review · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
Fundersnot available
KeywordsMultinational corporationInternationalizationSample (material)BusinessIndustrial organizationMarketingInternational tradeFinance

Abstract

fetched live from OpenAlex

We conduct an unique longitudinal analysis on internationalisation patterns for a sample of 600 UK firms over a 23-year sample period from 1990 to 2012. We use three measures of multinationality – the classification system for firm-level multinationality of Aggarwal, Berrill, Hutson and Kearney , foreign sales as a percentage of total sales and the number of geographic segments in which material sales occur as disclosed in the firm’s consolidated accounts. Our results show that multinational levels have increased over time. We find little evidence that firms are regional in their sales, but equally few firms are categorised as global. We also find evidence to support de-internationalisation in some sample years. The industrial analysis shows that health care and basic materials are the most international industries, while Financials, Consumer Services and Utilities are the least multinational. Non-services firms are more multinational than services firms.

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)0.010
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 score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.013
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.309
Teacher spread0.254 · 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

Citations4
Published2018
Admission routes1
Has abstractno

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