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    Using Pajek and Centrality Analysis to Identify a Social Network of Construction Trades

    Source: Journal of Construction Engineering and Management:;2012:;Volume ( 138 ):;issue: 010
    Author:
    Brad W. Wambeke
    ,
    Min Liu
    ,
    Simon M. Hsiang
    DOI: 10.1061/(ASCE)CO.1943-7862.0000524
    Publisher: American Society of Civil Engineers
    Abstract: Construction project managers are often faced with the challenge of managing a complex construction process consisting of multiple trades working on a large number of interdependent tasks. A social network is a pattern of ties that exist between different entities (i.e., people, organizations, countries). There is an underlying social network of trades that exists with a construction project and recognizing it can help a management team succeed in this challenging environment. A $50 million project involving 43 trades was studied over a 28-week period. Pajek, a social network analysis program, was used to generate a series of 14 social networks for the trades involved. Both degree and eigenvector centrality were analyzed to reflect the distribution of relationships through the network and to identify the key trades. This research is useful to project managers and is significant as it outlines and illustrates a method of identifying the underlying network and associated key trades of a construction project based on spatial proximity. While this research is based upon an individual case study, the apsects of this research are repeatable. The methods presented in this paper will enable others to develop a social network that is tailored for a specific aspect of a project, ranging from contract development teams to the individual trades using a critical path method (CPM) schedule.
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      Using Pajek and Centrality Analysis to Identify a Social Network of Construction Trades

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    contributor authorBrad W. Wambeke
    contributor authorMin Liu
    contributor authorSimon M. Hsiang
    date accessioned2017-05-08T21:39:44Z
    date available2017-05-08T21:39:44Z
    date copyrightOctober 2012
    date issued2012
    identifier other%28asce%29co%2E1943-7862%2E0000530.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/58687
    description abstractConstruction project managers are often faced with the challenge of managing a complex construction process consisting of multiple trades working on a large number of interdependent tasks. A social network is a pattern of ties that exist between different entities (i.e., people, organizations, countries). There is an underlying social network of trades that exists with a construction project and recognizing it can help a management team succeed in this challenging environment. A $50 million project involving 43 trades was studied over a 28-week period. Pajek, a social network analysis program, was used to generate a series of 14 social networks for the trades involved. Both degree and eigenvector centrality were analyzed to reflect the distribution of relationships through the network and to identify the key trades. This research is useful to project managers and is significant as it outlines and illustrates a method of identifying the underlying network and associated key trades of a construction project based on spatial proximity. While this research is based upon an individual case study, the apsects of this research are repeatable. The methods presented in this paper will enable others to develop a social network that is tailored for a specific aspect of a project, ranging from contract development teams to the individual trades using a critical path method (CPM) schedule.
    publisherAmerican Society of Civil Engineers
    titleUsing Pajek and Centrality Analysis to Identify a Social Network of Construction Trades
    typeJournal Paper
    journal volume138
    journal issue10
    journal titleJournal of Construction Engineering and Management
    identifier doi10.1061/(ASCE)CO.1943-7862.0000524
    treeJournal of Construction Engineering and Management:;2012:;Volume ( 138 ):;issue: 010
    contenttypeFulltext
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