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Record W2045497062 · doi:10.3917/qdm.143.0079

La conduite du changement pour et avec les technologies digitales

2014· article· fr· W2045497062 on OpenAlexaff
David Autissier, Kevin Johnson, Jean-Michel Moutot

Bibliographic record

VenueQuestion(s) de management · 2014
Typearticle
Languagefr
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Avec le développement des projets informatiques visant à implanter des applications digitales dans les entreprises, un concept émerge comme une réponse à un besoin sans que le périmètre et le contenu de celui-ci soit complétement stabilisé. Il s’agit du concept de Change Digital. L’anglicisme utilisé est en adéquation avec la dimension internationale et mondialisée de la vague digitale. En français nous pourrions ainsi parlé de conduite du changement digitale. Cet article ambitionne de donner une définition du Change Digital de manière exploratoire à partir des premiers travaux théoriques et empiriques sur le sujet. Le change digital apparaît comme une manière de conduire le changement pour les projets des technologies digitales dans un contexte de révolution digitale. C’est aussi une manière de mobiliser les technologies digitales pour réaliser les actions de conduite du changement et en envisager de nouvelles. En lien avec les théories des usages et de la construction sociale des solutions technologiques, le change digital participe à la stratégie digitale des organisations en y jouant un rôle structurant.

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)0.016
metaresearch head score (Gemma)0.031
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: none
Teacher disagreement score0.022
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0060.025
Scholarly communication0.0220.023
Open science0.0020.009
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0110.002

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 head0.317
Teacher spread0.296 · 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

Citations27
Published2014
Admission routes1
Has abstractyes

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