Communication, Leadership, and Organizational Skills in Emergency Response

Abstract

The development of timely and effective emergency management (EM) systems has become increasingly attractive, the primary aim of which is to help and enable emergency managers to prepare for disasters and respond to urgent events. The EM system's general framework comprises a series of decision-making problems in three phases: pre-event forecasting and preparation, in-event response and evacuation, and post-event recovery, which reflect the leadership competencies required during and post-times of crisis and the roles envisioned to support their respective organizations. However, the communication issues, primarily technological-based, uncovered the need to understand how leaders collect, disseminate, and adapt critical information through understanding crisis type and community needs. Additionally, the emergence of intelligence EM systems emphasizes learning from previous experiences when a new emergency occurs by analyzing historical data of similar events or scenarios to provide improved forecasts for affected areas, populations, and, precisely, the demand for relief resources. Furthermore, the rapid progress of big data, Artificial Intelligence (AI), and the Internet of Things (IoT) permits the development of a prediction system for emergency occurrence and resource demand, thereby improving communication between leaders, agencies, and community members. The capabilities of AI techniques to make full use of acquired data and deal with imprecise or uncertain information are widely recognized, especially in forecasting the occurrence of emergency events and evaluating their impacts on the economy and society. Therefore, improving the existing emergency preparedness for a more robust emergency response and the effects accumulated remains ideal.