AbstractThe relationship patterns of organizations participating in disaster response projects have a significant impact on their performance. However, research on appropriate relationship patterns of disaster response organizations is lacking both in theory and practice. To fill this gap, we conducted a comparative study on three interorganizational relationship patterns: government-centric, decentralized participatory, and partially collaborative. Drawing on the complex network modeling method, the mechanism of the interorganizational network composed of participating organizations in disaster response projects are conceptualized to depict different relationship patterns. Then, we conducted numerical simulation to further explore the optimal relationship pattern from the perspective of disaster response project effectiveness, efficiency, and fairness. The results reveal that the optimal organizational relationship pattern varies with changes in disaster response conditions. In the initial situation of an emergency with insufficient resources, government agencies should coordinate nongovernmental organizations (NGOs) to participate in disaster response in an orderly manner. When the demand for emergency resources drops, or the supply of emergency resources gradually becomes sufficient as disaster response projects progress, the government-centric pattern becomes the optimal relationship pattern. Moreover, the relationship between the response network structure and disaster response performance turns out to be inconsistent. At the early stage, response performance can be improved by reducing the number of steps to complete disaster relief and by appropriately increasing the connections between government agencies and NGOs. When a disaster situation is mitigated, the centralization of response power and unified coordination should be emphasized to improve emergency response performance. This study contributes to improving disaster response performance from the organizational relationship perspective and enriching disaster management theories.