Le processus de décision naturaliste en environnement big data: le cas des forces de Police au sein d’un Centre d’Information et de Commandement (CIC)
Cécile Godé (),
Jean-Fabrice Lebraty and
Jordan Vazquez ()
Additional contact information
Cécile Godé: CRET-LOG - Centre de Recherche sur le Transport et la Logistique - AMU - Aix Marseille Université
Jordan Vazquez: Centre de Recherche Magellan - Institut d'Administration des Entreprises (IAE) - Lyon - UJML - Université Jean Moulin - Lyon 3 - Université de Lyon
Post-Print from HAL
Naturalistic Decision-Making is a mainstream research paradigm to study decision-making of experts confronted to dynamic and changing conditions, ill-defined goals and time stress. For the last ten years, contributions encourage the analysis of human-technology interactions to refine our understanding of naturalistic decision-making. Drawing on this perceptive, an inductive qualitative case study is completed to understand how experts police officers in an Information and Command Center (ICC), facing with dynamic and changing conditions, make decisions in big data environment. ICC daily produces an important volume of varied and responsive data, which need to be verified. Police officers assemble these data in situation, from unintegrated technologies. The case analysis shows two distinct stages of decision-making process in big data environment: the upstream level of situational awareness, complete or partial according to the circumstances, and the recognition process. These results allow suggesting an integrated model of naturalistic decision-making, applied to ICC Police officers in big data environment.
Keywords: Police; reconnaissance immédiate; Environnement big data; Décision naturaliste; Conscience de la situation (search for similar items in EconPapers)
Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-02316038
References: Add references at CitEc
Citations: Track citations by RSS feed
Published in Systèmes d'Information et Management, Eska, 2019, 24 (3), pp.67-96. ⟨10.3917/sim.193.0067⟩
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02316038
Access Statistics for this paper
More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().