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Record W2106999253 · doi:10.7202/1008679ar

PME, gestion internationale et systèmes d'information marketing : au-delà des évidences technologiques

2012· article· fr· W2106999253 on OpenAlexvenueno aff
Martine Boutary

Bibliographic record

VenueRevue internationale P M E Économie et gestion de la petite et moyenne entreprise · 2012
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

La connaissance des marchés a souvent été mise en évidence comme étant indispensable à la réussite internationale. Les structures exportatrices de taille moyenne sont particulièrement sensibles à ce problème : leur besoin informationnel est rendu important par des marchés géographiquement dispersés, alors que leurs systèmes d'information sont le plus souvent peu formalisés, peu sophistiqués et vite surchargés. La tentation d’un discours normatif est alors grande et de nombreux conseils de gestion de Information, soutenus par les informaticiens, sont mis en place. La problématique abordée est celle d'une performance sur le marché international soumise à des conditions de traitement de l'information sur les marchés. Cet article présente une recherche destinée à décrire le comportement informationnel des entreprises de taille moyenne en vue d'un développement international plus ou moins intense. Une étude a été réalisée dont la méthodologie repose sur des méthodes descriptives mettant en évidence les caractéristiques de comportement informationnel d'entreprises exportatrices de taille moyenne. Deux catégories de résultats, issus de deux phases méthodologiques, ressortent de cette étude : La conclusion de l’article oriente le lecteur vers la validation de l’importance des systèmes d’information marketing dans les PME, mais aussi vers la prise en compte des spécificités de la PME pour dénouer le paradoxe informationnel évoqué en introduction.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
grokno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
opusno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.023
metaresearch head score (Gemma)0.085
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.085
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.012
Science and technology studies0.0020.006
Scholarly communication0.0160.019
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0180.002

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 head0.021
GPT teacher head0.243
Teacher spread0.222 · 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

Labeled directly by 3 models reading the full record.

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
Published2012
Admission routes1
Has abstractyes

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