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Record W4282833245 · doi:10.1177/14657503221106181

Institutional and organizational capabilities as drivers of internationalisation: Evidence from emerging economy SMEs

2022· article· en· W4282833245 on OpenAlexaff
Mahfuzur Rahman, Dieu Hack‐Polay, Sujana Shafique, Paul Agu Igwe

Bibliographic record

VenueThe International Journal of Entrepreneurship and Innovation · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsCrandall University
Fundersnot available
KeywordsInternationalizationBusinessEmerging marketsContext (archaeology)Developing countrySmall and medium-sized enterprisesConstruct (python library)Industrial organizationSurvey data collectionResource-based viewMarketingEconomic geographyKnowledge managementCompetitive advantageEconomic growthEconomicsInternational trade

Abstract

fetched live from OpenAlex

Internationalisation, the notion of cross border business, has become one of the key strategies for growth for small and medium enterprises (SMEs) in developing countries in recent years. Although many SMEs have used internationalisation strategies, there remains a gap of understanding the relative importance of factors influencing SMEs internationalisation particularly from developing countries perspectives. Drawing on the Resource Based View of the firm (RBV) theory, this research develops and validates the dimensions and sub-dimensions of the drivers of internationalisation. The study further identifies the relative importance of these dimensions from a developing country context. The study used a questionnaire survey to collect primary data from 212 Bangladeshi SMEs based on area wise cluster sampling. This study used partial least square based structural model (PLS-SEM) to assess the key drivers for foreign market entry by developing country SMEs. The findings confirm that the drivers of internationalisation represent a hierarchical construct consisting of three primary and eight sub-dimensions. The study suggests that SME internationalisation in a developing country is contingent upon two categories of capabilities: critical organisational capabilities or resources (linked to internal processes largely associated with human resources) and critical institutional capabilities (associated with state level provision and the cultural fabric).

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.241
Teacher spread0.217 · 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 designTheoretical or conceptual
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

Citations18
Published2022
Admission routes1
Has abstractyes

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