Human-AI collaboration in crisis response: understanding adoption barriers and enablers for generative AI in emergency decision-making

Abstract Human-AI collaboration in emergency management represents a critical socio-technical challenge that requires understanding both technological capabilities and human factors. This study examines the barriers and enablers influencing emergency decision-makers’ adoption of generative artificial intelligence, focusing on the complex interplay between individual, organizational, and technological factors in high-stakes crisis environments. Through expert interviews and intuitionistic fuzzy DEMATEL analysis involving Chinese emergency management practitioners, we identify five key dimensions affecting AI adoption: technology acceptance, decision-making processes, contextual factors, risk and trust, and individual factors. Our analysis within the Chinese emergency management context suggests that contextual factors and decision-making processes are perceived as primary drivers, while technology acceptance and individual factors serve as critical enablers shaped by organizational environments. The findings demonstrate that successful human-AI collaboration requires addressing social and technical dimensions simultaneously, with organizational support, trust-building, and individual self-efficacy emerging as fundamental requirements. These insights inform strategies for emergency management organizations to enhance human-AI integration through governance frameworks, transparency mechanisms, and targeted training programs, contributing empirical evidence to the growing literature on socio-technical aspects of AI adoption in critical decision-making contexts.