Big-data-driven safety decision-making: A conceptual framework and its influencing factors

Abstract Safety data and information are the most valuable assets for organizations’ safety decision-making (SDM), especially in the era of big data (BD). In this study, a conceptual framework for SDM based on BD, known as BD-driven SDM, was developed and its detailed structure and elements as well as strategies were presented. Other theoretical and practical contributions include: (a) the description of the meta-process and interdisciplinary research area of BD-driven SDM, (b) the design of six types of general analytics and five types of special analytics for SBD mining according to different requirements of safety management applications, (c) the analysis of influencing factors of BD-driven SDM, and (d) the discussion of advantages and limitations in this research as well as suggestions for future research. The results obtained from this study are of important implications for research and practice on BD-driven SDM.

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