Process, challenges, and optimization strategies of digital intelligence-driven emergency collaboration in China

Abstract

Existing research on digital intelligence (DI) in emergency management has primarily focused on the functional advantages of digital technologies, such as accelerating information processing, enhancing risk monitoring and early warning, and supporting emergency decision-making. These studies generally assume that digital technologies can improve emergency collaboration by overcoming spatial and temporal constraints. However, growing empirical evidence suggests that the deployment of DI does not automatically translate into effective collaboration among heterogeneous actors, and the mechanisms through which digital technologies are embedded in emergency collaboration processes remain insufficiently understood. To address this gap, this study investigates how DI-driven emergency collaboration unfolds in practice and why its effectiveness varies across contexts. Drawing on in-depth interviews with government agencies, enterprises, social organizations, and technical actors in Hunan Province, the study adopts an inductive qualitative approach to examine the process, challenges, and improvement pathways of DI-enabled emergency collaboration. The analysis shows that DI-driven emergency collaboration emerges from the interaction among multiple interrelated collaborative capabilities, forming a process-oriented and mutually reinforcing collaboration system rather than a set of independent technological functions. The study further reveals that the effectiveness of DI-driven emergency collaboration is shaped by both technical and managerial conditions, which influence how digital technologies are integrated into organizational routines and collaborative practices. By clarifying the process logic and constraints of DI-enabled emergency collaboration, this research advances understanding of how DI can more effectively support collaborative emergency governance and offers practical insights for improving emergency collaboration performance.