The Application of Social Network Analysis: Case of Smart Roofing

The use of social network analysis (SNA) becomes popular in social science research in the recent years. It is a practical application because it helps organizations to have better conceptualized and new understandings of the interactions. It could help organizations interpret and understand complexity, systems, pattern of changes, and structure of interactions. Moreover, SNA applications have been applied in many complicated fields to identify knowledge leaders in organizations, measure collaboration of teams, illustrate the hidden patterns of structure, and exploring the paths of interactions. In addition, many software programs were developed for personal or limited distribution by mathematicians, sociologists, graph theorists, and information technology specialists enabling SNA to facilitate the analysis of data and the creation of sociograms easier than before. Applying SNA in organizations could benefit many internal activities. It could help organizations to identify the group of experts for technology roadmapping (TRM) or R&D related activities, to know who the most appropriate expert for future collaboration may be, and to see the pattern of the interactions for future R&D planning. This chapter proposes an analysis of smart roofing using SNA to identify the group of experts, the interactions among experts, and the patterns of these interactions to help researchers to gain a better understanding of the current situation of smart roofing research and development programs and also to help them to prepare related future plans in order to promote the progress of smart roofing research and development programs.

[1]  Ronald Rousseau,et al.  Social network analysis: a powerful strategy, also for the information sciences , 2002, J. Inf. Sci..

[2]  Steffen Staab,et al.  Social Networks Applied , 2005, IEEE Intell. Syst..

[3]  Ben Shneiderman,et al.  Do You Know the Way to SNA?: A Process Model for Analyzing and Visualizing Social Media Network Data , 2012, 2012 International Conference on Social Informatics.

[4]  J. Krause,et al.  Social network theory in the behavioural sciences: potential applications , 2007, Behavioral Ecology and Sociobiology.

[5]  Kimberly A. Fredericks,et al.  An introduction to social network analysis , 2005 .

[6]  Silvia Ulli-Beer,et al.  How do we know who to include in collaborative research? Toward a method for the identification of experts , 2012, Eur. J. Oper. Res..

[7]  Soon-Young Huh,et al.  Automatic expert identification using a text categorization technique in knowledge management systems , 2008, Expert Syst. Appl..

[8]  R. Hanneman.  Introduction to Social Network Methods , 2001 .

[9]  John Scott.  What is social network analysis , 2010 .

[10]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[11]  M. Duijn,et al.  Software for social network analysis , 2005 .

[12]  Stephen P. Borgatti,et al.  Centrality and network flow , 2005, Soc. Networks.

[13]  Tugrul U. Daim,et al.  Implementing technology roadmap process in the energy services sector: A case study of a government agency , 2008 .

[14]  Martin G. Everett,et al.  Network analysis of 2-mode data , 1997 .

[15]  B. Wellman,et al.  Social Networks, Kinship, and Community in Eastern Europe , 1994 .

[16]  M. Fritsch,et al.  The impact of network structure on knowledge transfer: an application of social network analysis in the context of regional innovation networks , 2010 .

[17]  M. Aldenderfer,et al.  Cluster Analysis. Sage University Paper Series On Quantitative Applications in the Social Sciences 07-044 , 1984 .