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Journal Article

Citation

Xenidis Y, Kaltsidi G. Safety Sci. 2022; 152: e105773.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.ssci.2022.105773

PMID

unavailable

Abstract

The importance of the human factor during catastrophic events is increasingly being highlighted in the field of disaster management as human behaviors critically affect the propagation of disasters and the extent of damage they cause. Despite the attempted efforts, the complexities and the inherent difficulties in modeling human behaviors still prevent from an effective tool for predicting them during disasters. This paper contributes to this course of research by proposing a methodology to construct a predictive indicator, namely the Behavioral Pattern during Disaster Indicator (BPDI). First, a systematic literature review on the identification and the relations between behavioral factors, behavioral patterns and humans' reactions during catastrophic events is performed. The review presents also the main typologies for modeling the human factor in the context of disaster management. Then based on the review findings, the paper proposes a statistical methodology that comprises correlation and ordinal regression analysis for constructing a predictive indicator. The methodology is applied to a case study that was created in the context of this research. A questionnaire survey that yielded 573 responses is used for identifying the effect of 18 factors on the adoption of one or more among three behavioral patterns. Several conclusions are drawn through the methodology's application with the most significant one being the potential of a lack of significant correlations between behavioral factors and patterns during catastrophic events. The final output of this research, i.e., the BPDI indicator can be an effective tool that is easy for disaster management authorities to apply towards predicting human behaviors during disastrous events.


Language: en

Keywords

Behavioral patterns; Disaster management; Human behavior; Indicator; Statistical analysis

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