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Record W2921503843 · doi:10.5539/ibr.v12n4p1

Impact of Digitalization on the Speed of Internationalization

2019· article· en· W2921503843 on OpenAlexvenueno aff
Yan-Yin Lee, Mohammad Falahat

Bibliographic record

VenueInternational Business Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
FundersKementerian Pendidikan MalaysiaMinistry of Education, IndiaUniversiti Tunku Abdul Rahman
KeywordsInternationalizationGlobalizationBusinessInternational businessIndustrial organizationPhenomenonField (mathematics)Economic geographyInternational tradeEconomicsMarket economyManagement

Abstract

fetched live from OpenAlex

Digitalization combined with globalization is the current megatrend that impacting the international business landscape and creates opportunities for new business models. Embracing digitalization enables firms for speedy internationalization. Although the phenomenon of early internationalization has received increasing attention in the field of International Entrepreneurship over the past decades, however, there is a lack of focus on the role of digitalization that allows a higher speed of internationalization. This paper proposes a model that highlights the moderating role of digitalization on international business competencies and speed of internationalization. We argue that small and medium enterprises able to enter international markets more rapidly due to the impact of digitalization. This study addresses a gap in the literature and practical development needs for better understanding the impact of digitalization on the speed of internationalization. The limitations and implications of this study will be discussed for theoretical development and future research direction.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.060
GPT teacher head0.359
Teacher spread0.299 · 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 teacher head, not a consensus.

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

Citations85
Published2019
Admission routes1
Has abstractyes

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