Social Network Analysis in Knowledge Management Research

2011 ◽  
Vol 474-476 ◽  
pp. 1007-1011
Author(s):  
Bing Wu ◽  
Jun Ge ◽  
Wen Xia Xu
Keyword(s):  
Knowledge Management ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
The United States ◽  
Library Science ◽  
Knowledge Network ◽  
Future Research ◽  
Management Research ◽  
Main Research

This study is a productivity review on the literature gleaned from SSCI, SCIE databases concerning social network analysis in knowledge management research. The result indicates that the number of related literature is still growing especially in recent two years. The main research development country is the United States, then England and German, and from the analysis of the subject area, Information Science & Library Science is the most popular subject. Concerning source title, Knowledge Management Research & Practice is in the priority. Moreover the research focuses on this topic are mainly in close relationship with knowledge network. Typical references were analyzed in detail, including limitations and future research.

Current Trends and Future Directions in Knowledge Management in Construction Research Using Social Network Analysis

Buildings ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 599
Author(s):  
Sepani Senaratne ◽  
Muhandiramge Nimashi Navodana Rodrigo ◽  
Xiaohua Jin ◽  
Srinath Perera
Keyword(s):  
Knowledge Management ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Construction Projects ◽  
Knowledge Flows ◽  
Future Research ◽  
Research Areas ◽  
Current Trends ◽  
Improved Performance

The growing interest in Knowledge Management (KM) has led to increased attention to Social Network Analysis (SNA) as a tool to map the relationships in networks. SNA can be used to evaluate knowledge flows between project teams, contributing to collaborative working and improved performance. Similarly, it has the potential to be used for construction projects and organisations. This paper aims at identifying current trends and future research directions related to using SNA for KM in construction. A systematic review and thematic analysis were used to critically review the existing studies and identify potential research areas in construction specifically related to research approaches and explore the possibilities for extension of SNA in KM. The findings revealed that there are knowledge gaps in research approaches with case study-based research involving external stakeholders, collaborations, development of communication protocols, which are priority areas identified for future research. SNA in KM related to construction could be extended to develop models that capture both formal and informal relationships as well as the KM process in pre-construction, construction, and post-construction stages to improve the performance of projects. Similarly, SNA can be integrated with methodological concepts, such as Analytic Hierarchy Process (AHP), knowledge broker, and so forth, to improve KM processes in construction. This study identifies potential research areas that provide the basis for stakeholders and academia to resolve current issues in the use of SNA for KM in construction.

scholarly journals Social Network Analysis and the Study of University Industry Relations

2019 ◽  
pp. 550-562
Author(s):  
Fernando Cabrita Romero
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Methodological Approach ◽  
Future Research ◽  
Main Research ◽  
Differential Outcomes ◽  
Research Questions ◽  
Industry Relations ◽  
University Industry

The aim of this chapter is to give an overview of the use of social network analysis in the study of university industry relations. The structure of networks can be analyzed through the lens of social network analysis. This methodological approach is briefly described, and its fundamental concepts are presented. The chapter reviews the applications of this approach on the study of university industry relations. Different structures in the relations may result in different innovation outcomes, and the use of SNA may be particularly useful to understand differential outcomes. This chapter is based on a review of available literature on the topics. The chapter aims at systematizing the information and knowledge related to the application of SNA on university industry networks, highlighting the main research pathways, the main conclusions, and pointing to possible future research questions.

scholarly journals Social Network Analysis and the Study of University Industry Relations

2018 ◽  
pp. 7150-7160
Author(s):  
Fernando Cabrita Romero
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Methodological Approach ◽  
Future Research ◽  
Main Research ◽  
Differential Outcomes ◽  
Research Questions ◽  
Industry Relations ◽  
University Industry

The aim of this work is to give an overview of the use of social network analysis in the study of university industry relations. The structure of networks can be analysed through the lens of Social Network Analysis. This methodological approach is briefly described and its fundamental concepts are presented. The paper reviews the applications of this approach on the study of university industry relations. Different structures in the relations may result in different innovation outcomes, and the use of SNA may be particularly useful to understand differential outcomes. This work is based on a review of available literature on the topics. The paper aims at systematizing the information and knowledge related to the application of SNA on university industry networks, highlighting the main research pathways, the main conclusions and pointing possible future research questions.

scholarly journals Visualization of clinical teaching citations using social network analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hakimeh Hazrati ◽  
Shoaleh Bigdeli ◽  
Seyed Kamran Soltani Arabshahi ◽  
Vahideh Zarea Gavgani ◽  
Nafiseh Vahed
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Research Collaboration ◽  
Clinical Teaching ◽  
Clustering Coefficient ◽  
Knowledge Network ◽  
Research Network ◽  
Centrality Measures ◽  
The Usa

Abstract Background Analyzing the previous research literature in the field of clinical teaching has potential to show the trend and future direction of this field. 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 1229 publications on clinical teaching through a systematic search strategy in the Scopus (Elsevier), Web of Science (Clarivate Analytics) and Medline (NCBI/NLM) through PubMed from the year 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. Analysis of background and affiliation of authors showed that co-authorship between clinical researchers in medicine filed is weak. 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 In order to improve the cohesion of the authorship network of clinical teaching, it is essential to improve research collaboration and co-authorship between new researchers and those who have better closeness or geodisk path with others, especially those with the clinical background. To reach to a dense and powerful topology in the knowledge network of this field encouraging policies to be made for international and national collaboration between clinicians and clinical teaching specialists. In addition, humanitarian and clinical reasoning need to be considered in clinical teaching as of new direction in the field from thematic aspects.

scholarly journals Nursing Students’ Subjective Happiness: A Social Network Analysis

2021 ◽  
Vol 18 (21) ◽  
pp. 11612
Author(s):  
Eun-Joo Kim ◽  
Ji-Young Lim ◽  
Geun-Myun Kim ◽  
Seong-Kwang Kim
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Nursing Students ◽  
Network Analysis ◽  
Group Dynamics ◽  
Future Research ◽  
Subjective Happiness ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
High Level

Improving nursing students’ subjective happiness is germane for efficiency in the nursing profession. This study examined the subjective happiness of nursing students by applying social network analysis (SNA) and developing a strategy to improve the subjective happiness of nursing. The study adopted a cross sectional survey to measure subjective happiness and social network of 222 nursing students. The results revealed that the centralization index, which is a measure of intragroup interactions from the perspective of an entire network, was higher in the senior year compared with the junior year. Additionally, the indegree, outdegree, and centrality of the social network of students with a high level of subjective happiness were all found to be high. This result suggests that subjective happiness is not just an individual’s psychological perception, but can also be expressed more deeply depending on the subject’s social relationships. Based on the study’s results, to strengthen self-efficacy and resilience, it is necessary to utilize strategies that activate group dynamics, such as team activities, to improve subjective happiness. The findings can serve as basic data for future research focused on improving nursing students’ subjective happiness by consolidating team-learning social networks through a standardized program approach within a curriculum or extracurricular programs.

scholarly journals Social Network Analysis Visualization

2015 ◽  
pp. 257-270
Author(s):  
Mohana Shanmugam ◽  
Yusmadi Yah Jusoh ◽  
Rozi Nor Haizan Nor ◽  
Marzanah A. Jabar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Network ◽  
Future Research ◽  
Research Gaps ◽  
The Social ◽  
Network Studies ◽  
Academic Study

The social network surge has become a mainstream subject of academic study in a myriad of disciplines. This chapter posits the social network literature by highlighting the terminologies of social networks and details the types of tools and methodologies used in prior studies. The list is supplemented by identifying the research gaps for future research of interest to both academics and practitioners. Additionally, the case of Facebook is used to study the elements of a social network analysis. This chapter also highlights past validated models with regards to social networks which are deemed significant for online social network studies. Furthermore, this chapter seeks to enlighten our knowledge on social network analysis and tap into the social network capabilities.

Social network analysis and data visualization of MIS international collaboration in Taiwan

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yung-Ting Chuang ◽  
Yi-Hsi Chen
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
International Collaboration ◽  
Research Productivity ◽  
Research Collaboration ◽  
Clustering Algorithms ◽  
The United States ◽  
Content Type ◽  
Collaboration Patterns

PurposeThe purpose of this paper is to apply social network analysis (SNA) to study faculty research productivity, to identify key leaders, to study publication keywords and research areas and to visualize international collaboration patterns and analyze collaboration research fields from all Management Information System (MIS) departments in Taiwan from 1982 to 2015.Design/methodology/approachThe authors first retrieved results encompassing about 1,766 MIS professors and their publication records between 1982 and 2015 from the Ministry of Science and Technology of Taiwan (MOST) website. Next, the authors merged these publication records with the records obtained from the Web of Science, Google Scholar, IEEE Xplore, ScienceDirect, Airiti Library and Springer Link databases. The authors further applied six network centrality equations, leadership index, exponential weighted moving average (EWMA), contribution value and k-means clustering algorithms to analyze the collaboration patterns, research productivity and publication patterns. Finally, the authors applied D3.js to visualize the faculty members' international collaborations from all MIS departments in Taiwan.FindingsThe authors have first identified important scholars or leaders in the network. The authors also see that most MIS scholars in Taiwan tend to publish their papers in the journals such as Decision Support Systems and Information and Management. The authors have further figured out the significant scholars who have actively collaborated with academics in other countries. Furthermore, the authors have recognized the universities that have frequent collaboration with other international universities. The United States, China, Canada and the United Kingdom are the countries that have the highest numbers of collaborations with Taiwanese academics. Lastly, the keywords model, system and algorithm were the most common terms used in recent years.Originality/valueThis study applied SNA to visualize international research collaboration patterns and has revealed some salient characteristics of international cooperation trends and patterns, leadership networks and influences and research productivity for faculty in Information Management departments in Taiwan from 1982 to 2015. In addition, the authors have discovered the most common keywords used in recent years.

Social Network Analysis

2011 ◽  
pp. 1460-1469
Author(s):  
David J. Dekker ◽  
Paul H.J. Hendriks
Keyword(s):  
Knowledge Management ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Development Project ◽  
Knowledge Networks ◽  
Knowledge Processes ◽  
Social Network Structure ◽  
Product Development Project ◽  
Practice Knowledge

In knowledge management (KM), one perspective is that knowledge resides in individuals who interact in groups. Concepts as communities-of-practice, knowledge networks, and “encultured knowledge” as the outcome of shared sense-making (Blackler, 1995) are built upon this perspective. Social network analysis focuses on the patterns of people’s interactions. This adds to KM theory a dimension that considers the effects of social structure on for example, knowledge creation, retention and dissemination. This article provides a short overview of consequences of social network structure on knowledge processes and explores how the insights generated by social network analysis are valuable to KM as diagnostic elements for drafting KM interventions. Relevance is apparent for management areas such as R&D alliances, product development, project management, and so forth.

Network Analysis for Economics and Management Studies

2016 ◽  
pp. 1-60 ◽  
Author(s):  
Lucio Biggiero
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Formation ◽  
Competitive Equilibrium ◽  
Group Structure ◽  
Network Modeling ◽  
Descriptive Analysis ◽  
Economic Networks ◽  
Main Research

Sociology and other social sciences have employed network analysis earlier than management and organization sciences, and much earlier than economics, which has been the last one to systematically adopt it. Nevertheless, the development of network economics during last 15 years has been massive, alongside three main research streams: strategic formation network modeling, (mostly descriptive) analysis of real economic networks, and optimization methods of economic networks. The main reason why this enthusiastic and rapidly diffused interest of economists came so late is that the most essential network properties, like externalities, endogenous change processes, and nonlinear propagation processes, definitely prevent the possibility to build a general – and indeed even partial – competitive equilibrium theory. For this paradigm has dominated economics in the last century, this incompatibility operated as a hard brake, and presented network analysis as an inappropriate epistemology. Further, being intrinsically (and often, until recent times, also radically) structuralist, social network analysis was also antithetic to radical methodological individualism, which was – and still is – economics dominant methodology. Though culturally and scientifically influenced by economists in some fields, like finance, banking and industry studies, scholars in management and organization sciences were free from “neoclassical economics chains”, and therefore more ready and open to adopt the methodology and epistemology of social network analysis. The main and early field through which its methods were channeled was the sociology of organizations, and in particular group structure and communication, because this is a research area largely overlapped between sociology and management studies. Currently, network analysis is becoming more and more diffused within management and organization sciences. Mostly descriptive until 15 years ago, all the fields of social network analysis have a great opportunity of enriching and developing its methods of investigation through statistical network modeling, which offers the possibility to develop, respectively, network formation and network dynamics models. They are a good compromise between the much more powerful agent-based simulation models and the usually descriptive (or poorly analytical) methods.

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