SAFETYLIT WEEKLY UPDATE

We compile citations and summaries of about 400 new articles every week.
RSS Feed

HELP: Tutorials | FAQ
CONTACT US: Contact info

Search Results

Journal Article

Citation

Sun C, Ahn S, Ahn CR. J. Constr. Eng. Manage. 2020; 146(7): e04020078.

Copyright

(Copyright © 2020, American Society of Civil Engineers)

DOI

10.1061/(ASCE)CO.1943-7862.0001863

PMID

unavailable

Abstract

Many construction accidents are caused by workers' unsafe behavior, and evidence suggests that individuals who are not sensitive to risks are accident-prone. Therefore, identifying workers with certain personal characteristics related to habitual insensitive response to safety risks potentially can help construction managers develop individualized safety training and interventions. Although the methods by which construction managers could identify those workers with specific personal characteristics have been limited, wearable sensors provide an opportunity for construction managers to monitor individual workers' behavior without any interruptions. This study investigated whether and how sensing data about workers' behavior around hazards can reveal their personality traits related to risk-taking behavior. In particular, this research focused on a personality trait well known to be related to risk-taking behavior, locus of control (LOC). An experiment was designed to collect workers' behavioral response to various hazards in terms of gait patterns; LOC was measured using standard instruments. The analysis showed that the degree of gait change around hazards is significantly correlated with LOC score. Additionally, those with internal LOC had more consistent gait patterns over a number of repetitive exposures to the same hazard (i.e., sustained sensitivity to safety risks) than those with an external LOC. These results demonstrate the potential of using wearable sensors to identify workers with personality traits associated with unsafe behavior.


Language: en

NEW SEARCH


All SafetyLit records are available for automatic download to Zotero & Mendeley
Print