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Record W4210858068 · doi:10.1386/tmsd_00040_1

The correlation between internationalization and creativity: An exploratory study of Canadian SMEs

2021· article· en· W4210858068 on OpenAlexaboutno aff
Mélody Roussy-Parent

Bibliographic record

VenueInternational Journal of Technology Management and Sustainable Development · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
Fundersnot available
KeywordsMindsetInternationalizationCreativityContext (archaeology)Process (computing)BusinessExploratory researchPhenomenonMarketingOrder (exchange)Knowledge managementIndustrial organizationSociologyPolitical scienceComputer scienceInternational trade

Abstract

fetched live from OpenAlex

This research evaluates the internationalization process of small enterprises from Canada and identifies the point where a creative act occurs. It suggests a holistic view and a better understanding of the correlation between internationalization and creative thinking. Semi-structured interviews were conducted with sixteen small and medium enterprises (SMEs) from Canada and six representative organizations in order to collect reliable quality data that define a model explaining this correlation. The enterprises operate in six industry sectors: gaming, artificial intelligence, information and communication technology, cleantech, life sciences and consumer goods. The case study approach was used to investigate the phenomenon in a real-life context and to present a conceptual framework that generalizes the findings. Consequently, this study finds that the process of creative thinking takes place when opportunities and cultural, economic and legal barriers appear during the internationalization process. The findings presented have implications for policy-makers, for consultants in international trade and for education systems. Challenging times and contexts of uncertainty require a creative mindset that will help the enterprise navigate safely and make the best decisions when unexpected events occur.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.414
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.233
Teacher spread0.219 · 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.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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