Investigation into the role of human and organizational factors in security work against terrorism at large-scale events. (August 2020)

• Record Type: Journal Article
• Authors: Fu, Lipeng; Wang, Xueqing; Liu, Bingsheng; Li, Ling
• Is Part Of: Safety science. Volume 128(2020)
• Publication Date: 2020-08
• Subjects: Terrorism -- Security -- Human factor -- Large-scale event -- Risk analysis; Industrial accidents -- Periodicals; Accident Prevention -- Periodicals; Safety -- Periodicals; Travail -- Accidents -- Périodiques; 363.11
• DOI: 10.1016/j.ssci.2020.104764
• Languages: English
• ISSNs: 0925-7535

Abstract

Highlights: Human and organizational factors of large-scale event security need systematic analysis. The Human Factors Analysis and Classification System can be modified for this purpose. Integrating Bayesian Network theory also enables terrorism probability prediction and key factors diagnosis. Hybrid model's capacities tested by analyzing security for the 2022 Winter Olympics. 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.