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Record W3144337973

Regional and Product Diversification and the Performance of Retail Multinationals

2014· preprint· en· W3144337973 on OpenAlexaff
Chang Hoon Oh, Timo Sohl, Alan M. Rugman

Bibliographic record

VenueRePEc: Research Papers in Economics · 2014
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDiversification (marketing strategy)BusinessPanel dataEconomic geographyIndustrial organizationEconomicsEconometricsMarketing
DOInot available

Abstract

fetched live from OpenAlex

Despite the importance of geographic expansion in the services sector, few studies have analyzed the relationships between international diversification, product diversification and performance for services firms. Here we investigate whether and how firms in the retail-trade sector may benefit by spreading their boundaries within and across regional boundaries. Using a panel data set of 68 large European retailers from 19 countries for the period between 1997 and 2010, we find that intra-regional diversification has a horizontal S-curve relationship and interregional diversification has an S-curve relationship with firm performance. Moreover, the results show that unrelated product diversification has a negative moderating effect on the relationship between inter-regional diversification and firm performance. Overall, these results add support in the services sector for the three-stage paradigm of international diversification and performance

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)0.004
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.039
GPT teacher head0.272
Teacher spread0.233 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

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