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Record W1572952462 · doi:10.7202/1027864ar

Le déploiement d’innovations inter-filiales au sein d’une multinationale1

2014· article· fr· W1572952462 on OpenAlexvenueno aff
Sihem Ben Mahmoud‐Jouini, Florence Charue‐Duboc

Bibliographic record

VenueManagement international · 2014
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Les filiales d’une multinationale doivent concilier adaptation locale et intégration globale. Pour analyser cela, nous proposons la notion de déploiement d’innovations inter-filiales que nous définissons comme l’enchaînement des commercialisations d’une innovation par des filiales situées dans des contextes locaux différenciés, et nécessitant ainsi à chaque fois son adaptation. A partir d’une analyse longitudinale de cinq déploiements d’innovations dans une multinationale, nous mettons en évidence quatre facteurs critiques que nous relions à des dispositifs de construction et de partage de connaissances. Nous discutons ainsi le modèle théorique de transfert de connaissances de Tallmann et Chacar (2011) et en précisons les conditions de mise en oeuvre.

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)0.005
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.006
Scholarly communication0.0080.007
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.026
GPT teacher head0.249
Teacher spread0.223 · 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 designQualitative
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

Citations8
Published2014
Admission routes1
Has abstractyes

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