Exploring a data-driven framework for safety performance management: A theoretical investigation at the enterprise level

Abstract

The effective utilization of massive safety performance data has become an urgent necessity in response to the growing complexity and scale of work safety systems. Data-driven technologies and theories have emerged as a promising avenue for enhancing safety performance management with the rapid development of big data, which urgently need to develop a comprehensive data-driven framework to continuously improve the performance of work safety systems. Firstly, the discipline support and rationale behind safety performance management in the information age are analyzed, and the research stages and strengths of data-driven are clarified. Subsequently, the three-dimensional structure of safety performance management under data-driven is depicted, the three-level data flow in the work safety process is summarized, and the data feedback process for safety performance management were described. Lastly, the overall framework of data-driven safety performance management is constructed, and the sub-frameworks are analyzed from four aspects: infrastructure, data collection and storage, data analysis, and information services. Framework advantages, development strategies, and research limitations for data-driven safety performance management are also provided. Overall, this study aims to advance the informatization and digitization construction of work safety performance management at the enterprise level through the synthesis of existing data-driven theoretical views and data analytic techniques.