Enhancing Organizational Resilience in Emergency Management: A Cross-Organizational Intelligence System for Sustainable Response to Crisis
Year: 2025
Type: article
Abstract: In today’s urban environment, disasters are not isolated events but part of continuous, complex processes that threaten both sustainable urban development and effective emergency management. Traditional emergency management practices are hindered by departmental silos and fragmented information exchanges, which often lead to conflicting interests, unclear responsibilities, ineffective tools, and imprecise task divisions. In response, our study repositions emergency management within the broader context of sustainable urban development by emphasizing resource optimization, strengthened inter-agency coordination, and strategic decision support to achieve UN Sustainable Development Goal 11. Based on observations from 31 departments in Dongtai City, we identified key contradictions within the current activity system. Guided by activity theory, we designed the Cross-Organizational Emergency Intelligence System (COEIS), which synchronizes real-time data across agencies via a novel information exchange mechanism. Implementation in a real-world setting and evaluation using grounded theory demonstrated that the COEIS enhances collaborative efficiency and decision support capabilities, thereby improving inter-organizational resilience. This study makes both theoretical and practical contributions by integrating the DSRM, activity theory, and grounded theory, offering a replicable pathway for transforming fragmented crisis management infrastructures into sustainable and resilient networks aligned with urban development strategies.
Source: Sustainability
DOI: 10.3390/su17115000
Authors: Hua Guo, Ying Jiang, Eldon Y. Li
Institutions: Hohai University, National Chung Cheng University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4410860010
