A Systematic Review on Flood Early Warning and Response System (FEWRS): A Deep Review and Analysis

Flood is the major natural hazard in Malaysia in terms of populations affected, area extent, frequency, social–economic damage, and flood duration. This shows the importance of providing comprehensive and effective flood mitigation management, which requires government agencies to enhance their employment performance that involves technological innovation. A flood early warning and response system (FEWRS) is essential to ensure that all stakeholders receive the right information and are provided with necessary actions and response information to avoid loss of lives and property. It should have some effective usability features and success components of its strategic information access and display; existing FEWRS fail and often do not effectively provide information on flood disasters to reduce their impacts at a local level to save the population’s lives. The measurement of information system (IS) success remains a top concern for researchers, managers, and practitioners. Therefore, this study seeks to provide a systematic overview of the most successful model of employment-related technical advancement for the management of flood disasters to improve its employees’ performance. In addition, examined factors that affect the successful implementation of flood management approaches, based on a systematic literature review among IS, show interrelationship of success factors. Findings from the literature suggest that the DeLone and McLean (D&M) model is the most widely employed model, recorded in 28 studies (39%). Further description of the 28 studies indicates that the D&M was either adopted alone or integrated with other theories. Moreover, results suggest the D&M is has been applied alone 39% of the time and has been extended 29% of the time; in total, 68% of the time, D&M has been applied either alone or extended in the information system domain. Additionally, 22% of the time it has been integrated with other theories. View Full-Text