An improved WINGS-ISM approach with consensus model in group decision-making for addressing critical success factors of collaborative emergency management

Since the variety and destructiveness of unexpected natural disasters and health emergencies, and the weakness of emergency systems around the world, studies on collaborative emergency management (CEM) are receiving increasingly attention recently. The existing literature has proved that with respect to the constraint of limited resources, it's usually unrealistic for CEM practitioners to push forward all influential factors that affect the comprehensive performance of CEM simultaneously. Thus, identifying the most critical success factors (CSFs) is of great significance to promote the effectiveness of CEM practices. To achieve this intention, we proposed a novel multi-granularity extended probabilistic linguistic weighted influence nonlinear gauge system and interpretative structural modeling (WINGS-ISM) approach with a consensus model optimized by the convolutional neural network algorithm. The new technique specializes in addressing the CSFs of episodes involving CEM characterized by the combination of uncertainty and complexity in group decision-making process. To validate its feasibility and robustness, we mainly investigate 10 CSFs to conduct an illustrative CEM case, the result shows that 'Applicable emergency response plan and regulations', 'Government unity of leadership and coordination' and 'Reasonable organizational structure and clear functions' are identified out of 10 influential factors. Besides, the cause-effect and hierarchal relationship digraphs indicate that the CSFs of CEM are interconnected, and this finding can support CEM practitioners to devote more attention to the most important factors, which may yield more improvements in CEM practices. Finally, some research implications are provided for better improving the effectiveness of CEM practices based on the findings of this study.