scholarly journals Time for a Real Shift to Relations: Appraisal of Social Network Analysis Applications in the UK Construction Industry

2013 ◽  
Author(s):  
Ximing Ruan ◽  
Dr Edward G Ochieng ◽  
Andrew David Price ◽  
Charles O Egbu
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Construction Industry ◽  
The Uk

scholarly journals Time for a real shift to relations: appraisal of Social Network Analysis applications in the UK construction industry

2013 ◽  
Vol 13 (1) ◽  
pp. 92-105 ◽  
Author(s):  
Ximing Ruan ◽  
Edward Godfrey Ochieng ◽  
Andrew David Freeman Price ◽  
Charles Egbu
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Construction Industry ◽  
Construction Management ◽  
Project Teams ◽  
The Social ◽  
Collaborative Working ◽  
Relational Concepts ◽  
The Uk

The Social Network Analysis (SNA) has been adopted in the UK construction management research and generated meaningful insights in analysing project management organisations from network perspectives. As an effective tool, social network analysis has been used to analyse information and knowledge flow between construction project teams which is considered as foundation for collaborative working and subsequently improving overall performance. Social network analysis is based on an assumption of the importance of relationships among interacting units. The social network perspective encompasses theories, models and applications that are expressed in terms of relational concepts or processes. Many believe, moreover, that the success or failure of organisations often depends on the patterning of their internal structure. This paper reviewed existing literatures on SNA applications in construction industry from three leading construction management journals.  From the review, the research proposed some advance in the application of SNA in the construction industry. 

scholarly journals Supply Chain Partner Communication in a Managed Programme in the UK Water Industry: A Case Study with Social Network Analysis

2019 ◽  
Vol 16 (21) ◽  
pp. 4211 ◽  
Author(s):  
Qing Li ◽  
Shengqiao Wang ◽  
Nicky Shaw ◽  
Victor Shi
Keyword(s):  
Supply Chain ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Water Industry ◽  
Partner Communication ◽  
Cross Sectional ◽  
Water Utility ◽  
Supply Chain Networks ◽  
The Uk

The water industry in every country aims to effectively and efficiently provide water with satisfactory quality in a sustainable and environmentally friendly manner. To this end, it is critical to achieve effective communication among the partners in water supply chain networks. In this paper, we focus on one of the UK’s largest water utility companies and its eight main contractors and analyze the factors influencing partner and network communication in a managed programme of their asset supply chain. We employ social network analysis to conduct the cross-sectional and longitudinal analysis of partner communication. Factors found to influence the communication network are grouping of projects within the programme, individual’s organisational affiliation, status, tenure, elapsed time through the programme lifecycle, and co-location. Our contributions to practice include demonstrating water programme management factors that influence communication and trust and how social network analysis can better inform them about intra- and interorganisational relationships. Moreover, the methodology introduced in this study may be applied to water management in other parts of the world.

scholarly journals Visualization of clinical teaching citations using social network analysis

2020 ◽  
Author(s):  
Hakimeh Hazrati ◽  
Shoaleh Bigdeli ◽  
Seyed Kamran Soltani Arabshahi ◽  
Vahideh Zarea Gavgani ◽  
Nafiseh Vahed
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Clinical Teaching ◽  
Clustering Coefficient ◽  
Research Network ◽  
Centrality Measures ◽  
Academic Networks ◽  
The Usa ◽  
The Uk

Abstract Background This study aimed to visualize the co-authorship networks and scientific map of research outputs of clinical teaching and medical education by Social Network Analysis (SNA). Methods We Identified 858 publications on clinical teaching through a systematic search strategy in the Scopus (Elsevier), Web of Science (Clarivate Analytics) and Medline (NCBI/NLM) through PubMed. Date of publication was limited to 1980 to 2018.The Ravar PreMap, Netdraw, UCINet and VOSviewer software were used for data visualization and analysis. Results Based on the findings of study the network of clinical teaching was weak in term of cohesion and the density in the co-authorship networks of authors (clustering coefficient (CC): 0.749, density: 0.0238) and collaboration of countries (CC: 0.655, density: 0.176). In regard to centrality measures; the most influential authors in the co-authorship network was Rosenbaum ME, from the USA (0.048). More, the USA, the UK, Canada, Australia and the Netherlands have central role in collaboration countries network and has the vertex co-authorship with other that participated in publishing articles in clinical teaching. Nineteen subject clusters were identified in the clinical teaching research network, seven of which were related to the expected competencies of clinical teaching and three related to clinical teaching skills. Conclusions Co-authorship between clinicians and medical educator specialists need to be strengthened through research development policies, appropriate academic networks and use of encouraging grants. In addition humanitarian and clinical reasoning need to consider in education of clinical teaching to empower the scientific map from thematic aspects.

scholarly journals Global Collaboration in Artificial Intelligence: Bibliometrics and Network Analysis from 1985 to 2019

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Haotian Hu ◽  
Dongbo Wang ◽  
Sanhong Deng
Keyword(s):  
Artificial Intelligence ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
International Collaboration ◽  
Edge Weight ◽  
Global Collaboration ◽  
The Us ◽  
Collaboration Patterns ◽  
The Uk

AbstractPurposeThis study aims to explore the trend and status of international collaboration in the field of artificial intelligence (AI) and to understand the hot topics, core groups, and major collaboration patterns in global AI research.Design/methodology/approachWe selected 38,224 papers in the field of AI from 1985 to 2019 in the core collection database of Web of Science (WoS) and studied international collaboration from the perspectives of authors, institutions, and countries through bibliometric analysis and social network analysis.FindingsThe bibliometric results show that in the field of AI, the number of published papers is increasing every year, and 84.8% of them are cooperative papers. Collaboration with more than three authors, collaboration between two countries and collaboration within institutions are the three main levels of collaboration patterns. Through social network analysis, this study found that the US, the UK, France, and Spain led global collaboration research in the field of AI at the country level, while Vietnam, Saudi Arabia, and United Arab Emirates had a high degree of international participation. Collaboration at the institution level reflects obvious regional and economic characteristics. There are the Developing Countries Institution Collaboration Group led by Iran, China, and Vietnam, as well as the Developed Countries Institution Collaboration Group led by the US, Canada, the UK. Also, the Chinese Academy of Sciences (China) plays an important, pivotal role in connecting the these institutional collaboration groups.Research limitationsFirst, participant contributions in international collaboration may have varied, but in our research they are viewed equally when building collaboration networks. Second, although the edge weight in the collaboration network is considered, it is only used to help reduce the network and does not reflect the strength of collaboration.Practical implicationsThe findings fill the current shortage of research on international collaboration in AI. They will help inform scientists and policy makers about the future of AI research.Originality/valueThis work is the longest to date regarding international collaboration in the field of AI. This research explores the evolution, future trends, and major collaboration patterns of international collaboration in the field of AI over the past 35 years. It also reveals the leading countries, core groups, and characteristics of collaboration in the field of AI.

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