Citation

Fu L, Wang X, Liu B, Li L. Safety Sci. 2020; 128: e104764.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.ssci.2020.104764

PMID

unavailable

Abstract

The pervasive human and organizational factors (HOFs) in security work at large-scale events (LSEs) contribute greatly to preventing terrorist attacks. However, as they have not been systematically analyzed, the formulation of relevant precautions could be flawed. This study aims to construct a quantitative model for: (i) systematically analyzing the HOFs in security work against terrorism at LSEs, (ii) predicting the probability of such terrorist attacks, and (iii) diagnosing the most critical HOFs. First, 30 HOFs were systematically identified by modifying the Human Factors Analysis and Classification System (HFACS) and integrating relevant historical data, literature, and expert knowledge. Second, these HOFs were statistically analyzed. Finally, by taking the Beijing 2022 Winter Olympics as an example, a hybrid HFACS-Bayesian Network model was constructed to quantitatively analyze the HOFs based on data collected through questionnaires and expert interviews. The example demonstrates the hybrid model's capabilities in probability prediction and key factor diagnosis. This study contributes to the establishment of a systematic causation model for analyzing the root causes of the failure of security against terrorism at LSEs, which will enable more holistic incident investigation and more accurate formulation of precautions, as well as helping the development of risk analysis methods in the public security field.

Language: en

Keywords

Human factor; Large-scale event; Risk analysis; Security; Terrorism