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Record W3039381369 · doi:10.1111/ijmr.12238

Collaboration and Internationalization of SMEs: Insights and Recommendations from a Systematic Review

2020· review· en· W3039381369 on OpenAlexaff
Nadia Zahoor, Omar Al‐Tabbaa, Zaheer Khan, Geoffrey Wood

Bibliographic record

VenueInternational Journal of Management Reviews · 2020
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsWestern University
Fundersnot available
KeywordsInternationalizationExtant taxonScope (computer science)Fragmentation (computing)Work (physics)Process (computing)Systematic reviewScale (ratio)BusinessKey (lock)Knowledge managementPolitical scienceComputer scienceGeographyInternational tradeEngineering

Abstract

fetched live from OpenAlex

Abstract This paper performs a systematic literature review of the undeniably diverse – and somewhat fragmented – current state of research on the collaborations and internationalization of small and medium‐sized enterprises (SMEs). We analyze key works and synthesize them into a framework that conceptually maps key antecedents, mediators, and moderators that influence the internationalization of SMEs. In addition, we highlight limitations of the literature, most notably in terms of theoretical fragmentation; extant theories are deployed and illustrated but rarely extended in a manner that significantly informs subsequent work. At an applied (but related) level, we argue the need for supplementary work that explores the distinct stages of internationalization – and the scope and scale of this process – rather than assuming closure around particular events. With this, we highlight the need for more rigorous and empirically informed explorations of contextual effects that take account of the consequences of developments in the global economic ecosystem.

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)0.051
metaresearch head score (Gemma)0.158
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.051
Threshold uncertainty score0.270

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.158
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0230.018
Science and technology studies0.0010.002
Scholarly communication0.0060.008
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.037
GPT teacher head0.323
Teacher spread0.286 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations175
Published2020
Admission routes1
Has abstractyes

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