@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} } @article {BF-4303, title = {Social Connectedness and Adaptive Team Coordination During Fire Events}, journal = {Fire Safety Journal}, volume = {59}, year = {2013}, month = {07/2013}, pages = {30-36}, chapter = {30}, abstract = {Preparing for fire-related emergencies and consequence management is considered to be dynamic and challenging in managing crises, preventing losses, and in the allocation of resources. In this study, we argue that improving plans and operations of personnel involved in managing fire-related emergencies is an important area of investigation. Here, we investigate the effects of social connectedness among different team members to manage bushfires. We further analyze response coordination by exploring variables such as participants{\textquoteright} preparedness quality, quality of incident action planning, and quality of accessibility of resources. In doing so, we also test the effects of these variables on improved adaptive behavior. Our results show high positive correlation between social connectedness for team members and coordination quality and also adaptive behavior. We also found significant relationship between coordination and adaptive behavior. Applying regression analyses, the results indicate positive significant effect of social connectedness on coordination and adaptive behavior and also the effect of coordination on adaptive behavior. By exploring the proposed model, we are able to develop a better understanding of the factors that support adaptive behavior in incident management teams responding to bushfire events.}, keywords = {Coordination quality, Disaster management, Incident management team, Participants adaptive behavior, Team connectedness}, doi = {http://dx.doi.org/10.1016/j.firesaf.2013.03.016}, url = {http://www.sciencedirect.com/science/article/pii/S0379711213000751}, author = {Alireza Abbasi and Owen, Christine and Liaquat Hossain and Jafar Hamra} } @article {BF-4278, title = {Effects of networks on learning during emergency events}, journal = {Disaster Prevention and Management}, volume = {21}, year = {2012}, month = {2012}, pages = {584-598}, chapter = {584}, abstract = {Purpose {\textendash} This paper aims to explore the relationship between learning and the social networks employed within the context of emergency management. It hypothesises, using social network theory as a framework for analysis, that changes to interconnectedness between actors are implicated in the potential for those actors to learn and improvise in dynamically changing and emergent conditions. Design/methodology/approach {\textendash} To test the hypotheses, survey data were investigated which were collected as part of a research study with the support of the Australian Bushfire Co-operative Research Centre (CRC). This survey was completed by experienced personnel reflecting on a number of indicators in an emergency event. Findings {\textendash} Results show that increases in actors{\textquoteright} involvement within the social emergency management network influences the ability of those actors to engage in learning-related work activity. The paper infers that by developing learning related resources within the context of their social interactions these emergency personnel are better able to adapt and improvise in complex emergency events. Research limitations/implications {\textendash} As an area of further research, it would be useful to apply the existing theoretical model to the context of another domain, preferably one that shares characteristics of uncertainty and unstable environments. Originality/value {\textendash} Most existing studies of learning theory in human networks have focused on learning in situations requiring stable working relationships with no environmental uncertainties. In this paper, it is argued that the designs of existing models are useful as a building block, yet flawed for application within the context of disaster management. By presenting a model of learning-related work activity, as an ongoing aspect of network connectedness, personnel within emergency services organisations can strengthen their capacity to be flexible and adaptable.}, keywords = {Adaptability, Australia, Bushfire, Emergency management, Fire, Learning, Social networks}, issn = {0965-3562}, doi = {10.1108/09653561211278716 }, url = {http://www.emeraldinsight.com/journals.htm?articleid=17063627}, author = {Jafar Hamra and Liaquat Hossain and Owen, Christine and Alireza Abbasi} }