Enhancing disaster management effectiveness: An integrated analysis of key factors and practical strategies through Structural Equation Modeling (SEM) and scopus data text mining

Abstract

In the 21st century, the surge in natural and human-induced disasters necessitates robust disaster management frameworks. This research addresses a critical gap, exploring dynamics in the successful implementation and performance monitoring of disaster management. Focusing on eleven key elements like Vulnerability and Risk Assessment, Training, Disaster Preparedness, Communication, and Community Resilience, the study utilizes Scopus Database for secondary data, employing Text Mining and MS-Excel for analysis and data management. IBM SPSS (26) and IBM AMOS (20) facilitate Exploratory Factor Analysis (EFA) and Structural Equation Modeling (SEM) for model evaluation.

The research raises questions about crafting a comprehensive, adaptable model, understanding the interplay between vulnerability assessment, training, and disaster preparedness, and integrating effective communication and collaboration. Findings offer actionable insights for policy, practice, and community resilience against disasters. By scrutinizing each factor's role and interactions, the research lays the groundwork for a flexible model. Ultimately, the study aspires to cultivate more resilient communities amid the escalating threats of an unpredictable world, fostering effective navigation and thriving.

Keywords

Disaster Management / Structural Equation Modeling (SEM) / Text Mining / Scopus Data / Exploratory Factor Analysis (EFA)

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