BackgroundThe purpose of this study was to examine the cohesions status of the coordination within response teams in the emergency response team (ERT) in a refinery.MethodsFor this study, cohesion indicators of social network analysis (SNA; density, degree centrality, reciprocity, and transitivity) were utilized to examine the coordination of the response teams as a whole network. The ERT of this research, which was a case study, included seven teams consisting of 152 members. The required data were collected through structured interviews and were analyzed using the UCINET 6.0 Social Network Analysis Program.ResultsThe results reported a relatively low number of triple connections, poor coordination with key members, and a high level of mutual relations in the network with low density, all implying that there were low cohesions of coordination in the ERT.ConclusionThe results showed that SNA provided a quantitative and logical approach for the examination of the coordination status among response teams and it also provided a main opportunity for managers and planners to have a clear understanding of the presented status. The research concluded that fundamental efforts were needed to improve the presented situations.
The purpose of this paper is to examine the status of the preparedness within emergency response team (ERT) in a refinery. Preparedness was investigated through trust and coordination relationships. Social network analysis as a quantitative approach was utilized in this research. To do this, social network analysis (SNA) indicators including density, degree, reciprocity and transitivity were utilized as a whole network. These indicators were calculated at the levels of first-line, supportive and whole teams. The required data were collected through structured interviews and were analyzed using UCINET 6.0 social network analysis program. The results of this study indicate that first-line teams can play a critical role in ERT, which is related to a higher level of SNA indicators and consequently the preparedness between team members can be easily achieved. In addition, the findings for the supportive teams revealed that they had relatively a low level of cohesion. However, the results of whole networks among all of teams had low level of cohesion that is a key challenge for performance of ERT. According to statistical results, there is a high correlation (82%) between trust and coordination networks. The finding of SNA provides a main opportunity for managers and planners to detect preparedness challenges based on coordination and trust ties among response teams of emergency management. This research suggests that fundamental efforts along with evaluation of the effectiveness of programs are needed to improve the presented situations and in order to optimize preparedness between response teams.
Predicting missing links and links that may occur in the future in social networks is an attention grabbing topic amid the social network analysts. Owing to the relationship between human‐based system and social sciences in this field, granular computing can help us to model the systems more effectively. The present study aims to propose two new similarity indices, based on granular computing approach and fuzzy logic. It also presents a new hybrid model for creating synergy between various link prediction models. Results show that fuzzy system analysis, in comparison with the crisp approach, can make more effective predictions through better expression of network characteristics. The indices are tested on collaboration networks. It is found that the accuracy of predictions is significantly higher than the crisp approach. It can modify the models for computing the strength of the links and/or predicting the evolutions of the social networks.
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