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Record W4386223711 · doi:10.1177/02662426231175878

Governance considerations and non-linear international scale-up behaviour among INVs

2023· article· en· W4386223711 on OpenAlexaff
James M. Crick, Dave Crick, Shiv Chaudhry

Bibliographic record

VenueInternational Small Business Journal Researching Entrepreneurship · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsInternationalizationCorporate governanceCausationCredibilityScale (ratio)UnderpinningInternational businessEmerging marketsBusinessMarketingSet (abstract data type)Industrial organizationEconomicsPolitical scienceInternational tradeManagementFinance

Abstract

fetched live from OpenAlex

Underpinning this instrumental case study is an effectuation lens. It investigates how a firm’s governance affects decision-making within international new ventures (INVs), which rapidly withdrew from markets abroad, regarding their re-internationalisation activities. Interviews with founding owners, exhibiting growth-oriented objectives, provide unique insights regarding a combination of effectuation and causation-oriented decision-making. In comparison to earlier studies that focus on the role and mind-set of the founding management team, findings suggest stakeholders like angel investors may exhibit an influence on certain INVs’ internationalisation decisions. Some decision-makers view risks/rewards against objectives in subjective ways like ‘loss of credibility’ and the ‘fear of missing out,’ rather than simply economic terms like growth. New light is shed on the importance of decision-makers validating internationalised business models and exhibiting an ability to pivot product-market strategies. Non-linear international scale-up behaviour may include a temporary domestic market focus and potentially re-internationalising to different countries targeted prior to de-internationalisation.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0000.001
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 head0.050
GPT teacher head0.298
Teacher spread0.248 · 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

Citations18
Published2023
Admission routes1
Has abstractyes

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