The Naturalistic Decision-Making process in big data environment: The case of a Police Information and Command Center (ICC)

Authors

  • Cécile Godé Aix-Marseille Université
  • Jean-Fabrice Lebraty Université de Lyon Jean Moulin, iaelyon, Magellan, Lyon, France
  • Jordan Vazquez Université Polytechnique Hauts de France, IDP, Valenciennes, France

Keywords:

Big data environment, Naturalistic decision making, Police, Recognition primed decision, Situational awareness

Abstract

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, ill-defined goals and time stress, 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.

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Published

2019-10-02

How to Cite

Godé, C., Lebraty, J.-F., & Vazquez, J. (2019). The Naturalistic Decision-Making process in big data environment: The case of a Police Information and Command Center (ICC). Systèmes d’Information Et Management (French Journal of Management Information Systems), 24(3), 67–96. Retrieved from https://revuesim.org/index.php/sim/article/view/992

Issue

Vol. 24 No. 3 (2019)

Section

Teaching case studies and experiences

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