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Record W4239237285 · doi:10.3917/sim.202.0007

Making big data analytics perform: the mediating effect of big data analytics dependent organizational agility

2020· article· fr· W4239237285 on OpenAlex
Samuel Fosso Wamba, Shahriar Akter, Cameron Guthrie

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSystèmes d information & management · 2020
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsHumanitiesBig dataAnalyticsPolitical scienceArtComputer scienceData scienceData mining

Abstract

fetched live from OpenAlex

Bien que le big data analytics (BDA) ait fortement cristallisé l’attention des spécialistes et des praticiens, il est encore difficile de bien cerner la façon dont les organisations peuvent mobiliser et gérer efficacement cet outil. En se fondant sur des travaux réalisés sur le big data analytics et l’agilité organisationnelle, la présente étude explore l’impact de la capacité du BDA sur la performance des entreprises ainsi que l’effet d’intermédiation de l’agilité organisationnelle sur cette relation. Des données collectées auprès de 202 entreprises américaines ont servi à tester le modèle proposé en recourant à l’approche PLS. Les résultats obtenus soutiennent le modèle de recherche choisi et confirment que l’agilité organisationnelle contribue fortement à attirer des investissements dans le BDA en vue de créer une valeur commerciale stratégique et améliorer la performance des entreprises.

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.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0010.008
Open science0.0050.009
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.158
GPT teacher head0.301
Teacher spread0.143 · 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