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Record W4312045894 · doi:10.1142/s1363919622500517

THE LIMITS TO INTERNATIONAL OPEN INNOVATION WITHIN SMEs: THE ROLE OF DISTANCE

2022· article· en· W4312045894 on OpenAlexaffabout
Carène Tchuinou Tchouwo, Sophie Veilleux, Diane Poulin

Bibliographic record

VenueInternational Journal of Innovation Management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsUniversité LavalUniversité du Québec à Montréal
Fundersnot available
KeywordsSeniorityContext (archaeology)Function (biology)BusinessBorder crossingDistance decayGeographical distanceMarketingEconomic geographyEconomicsSociologyPolitical scienceGeographyPoliticsLaw

Abstract

fetched live from OpenAlex

This paper examines the limits to adoption of open innovation (OI) within SMEs in an in-ternational context. In-depth interviews were conducted with managers at 40 Canadian SMEs that operate internationally. The results show that these limits are a function of in-ternational distance, which has cultural, institutional, economic, and geographic dimen-sions. We also found that individual factors (international experience, communication, personal values), organisational factors (economic sector, size, international seniority, international entry mode, available resources, dynamic capabilities, organisational culture), and contextual factors (laws and regulations) can increase or decrease the impact of cultural, institutional, economic, or geographic distance. We contribute to the OI literature by describing the limits to OI in an international context. These findings will help managers identify the limits to their use of international OI, as well as the factors that strengthen or mitigate those limits.

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)0.007
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.011
Scholarly communication0.0090.005
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.283
Teacher spread0.260 · 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 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

Citations4
Published2022
Admission routes2
Has abstractyes

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