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Effective disaster response requires well-coordinated efforts among individuals and agencies. Although collaborative disaster response increases in popularity, little has been accomplished within the hierarchical, centralized command and control context of China. This study examined collaborative disaster response in China based on the case of extraordinary serious cryogenic freezing rain and snow disaster. In addition, public managers were surveyed to investigate network establishment, with preliminary analysis on whole network using centrality measures. Subsequently, the blockmodel was employed to discuss the whole network structure followed by analysis on structural holes and intermediaries. Lastly, issues such as obstacles to effective collaboration and propositions proposed for further research were discussed.

Purpose -The purpose of this paper is to propose a modified network performance assessment method to address issues of coordination among organizations in the context of disaster response. Design/methodology/approach -The study introduces affiliation networks to map disaster response system integrated with task/function and unit identifications. The study also assesses performance in two successive procedures including ineffective networks identification and assessment on organizational effectiveness. Findings -The study emphasizes problems in the current emergency management system in China with a focus on Sichuan earthquake experience. Specifically, some problems with the traditional central planning and control mechanisms in coordination across boundaries are highlighted, including participants' inexact perception of information or resource accessibility, confusion of functional designation of organizations. Originality/value -The study builds network performance assessment method to other contexts where different emergency management systems exist and modifies them to facilitate quick complex networks evaluation. The theoretical insights from affiliation networks and cognitive accuracy provide additional contributions to the literature.

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Corporate credit-rating prediction using statistical and artificial intelligence techniques has received considerable attentions in the literature. Different from the thoughts of various techniques for adopting support vector machines as binary classifiers originally, a new method, based on support vector domain combined with fuzzy clustering algorithm for multiclassification, is proposed in the paper to accomplish corporate credit rating. By data preprocessing using fuzzy clustering algorithm, only the boundary data points are selected as training samples to accomplish support vector domain specification to reduce computational cost and also achieve better performance. To validate the proposed methodology, real-world cases are used for experiments, with results compared with conventional multiclassification support vector machine approaches and other artificial intelligence techniques. The results show that the proposed model improves the performance of corporate credit-rating with less computational consumption.