Adoption of Big Data Analytics (BDA) Technologies in Disaster Management: A Decomposed Theory of Planned Behavior (DTPB) Approach

Abstract

Big data analytics (BDA) technologies have emerged as a cornerstone for predicting, preparing, and preventing natural disasters, that directly save millions of human lives. The current study takes the initial step to analyze various antecedents of using BDA technologies that support real-time and offline decisions, before the occurrence of a disaster event. The model has been underpinned based on the Decomposed Theory of Planned Behavior (DTPB) and offers generic, pro-active, and timely solutions for disaster management. A self-administered survey collected data from 361 active members of the National Disaster Management Authority and Response Units in Pakistan. Partial least square structural equation modeling (PLS-SEM) empirically tested the conceptual model and hypothesized relationships. The study findings provide significant evidence on the positive influence of attitudes, subjective norms, and behavioral control of disaster management officials on their intention to adopt BDA technologies. Using DTPB, the current study makes a unique contribution to the literature and offers invaluable insights to researchers, practitioners, and stakeholders in addressing some novel and preemptive measures in disaster management.

Keywords

• disaster management
• big data analytics (bda)
• decompose theory of planned behavior (dtpb)