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Record W2561568493 · doi:10.1002/tie.21884

Why Do European Companies Have Innovation Hubs in Silicon Valley—Best Practice Examples and Key Takeaways

2016· article· en· W2561568493 on OpenAlexaff
Andreas Berger, Alexander Brem

Bibliographic record

VenueThunderbird International Business Review · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsSeneca Polytechnic
Fundersnot available
KeywordsSilicon valleyKey (lock)BusinessAccountingProcess managementRegional scienceIndustrial organizationMarketingComputer scienceGeographyFinanceEntrepreneurship

Abstract

fetched live from OpenAlex

Innovation hubs are gaining high attention in recent years, especially from European companies. Silicon Valley has been deemed as one of the most attractive and successful environments for establishing innovation hubs. This article highlights examples of companies from Europe that made the step to California—namely, Volkswagen, Swisscom, BMW , Axel Springer, Munich Re, and Innogy SE ( RWE Group). Based on these companies’ experiences, recommendations are given on how companies might approach a setup in Silicon Valley for long‐term success. © 2016 Wiley Periodicals, Inc .

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score0.900

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 communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.055
GPT teacher head0.289
Teacher spread0.235 · 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 designNot applicable
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
Published2016
Admission routes1
Has abstractyes

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