ABSTRACT Cross‐organizational governance for extreme disaster risk represents a critical challenge for modern society. This study develops an integrated theoretical framework examining how emerging technologies transform collaborative governance for extreme disaster risks through complex adaptive mechanisms. Employing an innovative methodological triangulation approach combining qualitative comparative analysis (QCA), machine learning (XGBoost with SHAP), and agent‐based modeling‐systems dynamics (ABM‐SD), we analyze disaster cases in China to identify and validate key technology–organization configurations that enhance system resilience. Initially, QCA analysis of 12 representative cases reveals that data analysis precision and inter‐organizational links are necessary foundations for high‐performance collaborative governance, with three distinct configuration pathways identified: non‐pressure‐responsive type, pressure‐state type, and pressure‐responsive type. Machine learning validation across an expanded sample of 120 cases confirms the robustness of these configurations while revealing their temporal evolution from network‐dominated to data‐driven patterns. The ABM‐SD simulation demonstrates that proactive policies with cyclical technological upgrading significantly enhance system resilience, while loosely coupled networks with high heterogeneity better prevent “complexity traps” during extreme events. This research makes unique contributions by (1) establishing a systematic framework for analyzing technology–organization interactions in disaster contexts; (2) identifying equifinal pathways to effective collaborative governance; and (3) developing a theoretical model that illustrates how technological empowerment and organizational collaboration dynamically interact across threshold conversion areas to generate system emergence and reconstruction under varying pressure levels. Practical implications include configuration selection strategies for policy‐makers based on regional development levels and disaster characteristics. Study limitations include the focus on Chinese cases, which may limit generalizability to different institutional contexts, and the need for longitudinal studies to further validate the proposed adaptation mechanisms.
Dong et al. (Sun,) studied this question.