@article {bnh-1838, title = {Exponential random graph modelling of emergency collaboration network}, journal = {Knowledge-Based Systems}, volume = {77}, year = {2015}, month = {03/2015}, pages = {68-79}, chapter = {69}, abstract = {

Effective response to bushfires requires collaboration involving a set of interdependent complex tasks that need to be carried out in a synergistic manner. Improved response to bushfires has been attributed to how effective different emergency management agencies carry out their tasks in a coordinated manner. Previous studies have documented the underlying relationships between collaboration among emergency management personnel on the effective outcome in delivering improved bushfire response. There are, however, very few systematic empirical studies with a focus on the effect of collaboration networks among emergency management personnel and bushfire response. Given that collaboration evolves among emergency management personnel when they communicate, in this study, we first propose an approach to map the collaboration network among emergency management personnel. Then, we use Exponential Random Graph (ERG) models to explore the micro-level network structures of emergency management networks and their impact on performance. ERG Models are probabilistic models presented by locally determined explanatory variables and that can effectively identify structural properties of networks. It simplifies a complex structure down to a combination of basic parameters such as 2-star, 3-star, and triangle. By applying our proposed mapping approach and ERG modeling technique to the 2009 Royal Commission Report dataset, we construct and model emergency management response networks. We notice that alternative-k-star, and alternative-k-two-path parameters of ERG have impact on bushfire response. The findings of this study may be utilized by emergency managers or administrators for developing an emergency practice culture to optimize response within an emergency management context.

}, doi = {DOI: 10.1016/j.knosys.2014.12.029}, url = {http://www.researchgate.net/publication/269391070_Exponential_Random_Graph_Modeling_of_Emergency_Collaboration_Network}, author = {Liaquat Hossain and Jafar Hamra and Rolf Wigand and Sven Carlsson} } @article {BF-4285, title = {Network effects on learning during emergency events}, journal = {Knowledge Management Research \& Practice}, year = {2013}, month = {01/2013}, abstract = {Understanding the factors that enhance or impede learning of individuals is instrumental in achieving organizational performance goals. In this study, the effect of social network structures on the learning attitudes of emergency personnel during an emergency event was investigated. On the basis of a social influence model of learning, a theoretical framework has been proposed to investigate the effects of network structure on learning outcomes of bushfire incident management teams. To test our framework, we investigated social network data, which were extracted from the transcripts of the 2009 Victorian Bushfires Royal Commission report. Empirical results suggest that a network structure of emergency personnel can be identified, which plays a key role in the ability of those actors to engage in learning-related work activity, allowing them to adapt and improvise in complex emergency events. By presenting a model of learning-related work activity, based on a social network analysis of its structure, emergency staff members can strengthen their capacity to be flexible and adaptable.}, doi = {10.1057/kmrp.2012.65}, url = {http://www.palgrave-journals.com/kmrp/journal/vaop/ncurrent/full/kmrp201265a.html}, author = {Jafar Hamra and Rolf Wigand and Liaquat Hossain and Owen, Christine} }