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.

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.

Use and Methods of Social Network Analysis in Knowledge Management

2011 ◽  
pp. 565-574 ◽  
Author(s):  
Tobias Müller-Prothmann
Keyword(s):  
Knowledge Management ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Theoretical Analysis ◽  
Knowledge Flows ◽  
Knowledge Networks ◽  
Personal Networks ◽  
Informal Communities ◽  
Analysis Of Knowledge

Whilst the primary importance of informal communities of practice and knowledge networks in innovation and knowledge management is widely accepted (see Armbrecht et al., 2001; Brown & Duguid, 1991; Collinson & Gregson, 2003; Jain & Triandis, 1990; Lesser, 2001; Liyanage, Greenfied & Don, 1999; Nahapiet & Ghoshal, 1998; Nohria & Eccles, 1992; Wenger, 1999; Zanfei, 2000), there is less agreement on the most appropriate method for their empirical study and theoretical analysis. In this article it is argued that social network analysis (SNA) is a highly effective tool for the analysis of knowledge networks, as well as for the identification and implementation of practical methods in knowledge management and innovation. Social network analysis is a sociological method to undertake empirical analysis of the structural patterns of social relationships in networks (see, e.g., Scott, 1991; Wasserman & Faust, 1994; Wellman & Berkowitz, 1988). This article aims at demonstrating how it can be used to identify, visualize, and analyze the informal personal networks that exist within and between organizations according to structure, content, and context of knowledge flows. It will explore the benefits of social network analysis as a strategic tool on the example of expert localization and knowledge transfer, and also point to the limits of the method.

scholarly journals Mapping the knowledge flow in sustainable construction project teams using social network analysis

2017 ◽  
Vol 24 (2) ◽  
pp. 229-259 ◽  
Author(s):  
Veronika Lilly Meta Schröpfer ◽  
Joe Tah ◽  
Esra Kurul
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Construction Projects ◽  
Construction Project ◽  
Research Strategy ◽  
Knowledge Flows ◽  
Sustainable Construction ◽  
Content Type ◽  
Novel Approach

Purpose The purpose of this paper is to examine knowledge transfer (KT) practices in five construction projects delivering sustainable office buildings in Germany and the UK by using social network analysis (SNA). Design/methodology/approach Case studies were adopted as research strategy, with one construction project representing one case study. A combination of quantitative data, social network data and some qualitative data on perceptions of the sustainable construction process and its KT were collected through questionnaires. The data were analysed using a combination of descriptive statistics, cross-tabulations, content analysis and SNA. This resulted in a KT map of each sustainable construction project. Findings The findings resulted in a better understanding of how knowledge on sustainable construction is transferred and adopted. They show that large amounts of tacit knowledge were transferred through strong ties in sparse networks. Research limitations/implications The findings could offer a solution to secure a certain standard of sustainable building quality through improved KT. The findings indicate a need for further research and discussion on network density, tie strength and tacit KT. Originality/value This paper contributes to the literature on KT from a social network perspective. It provides a novel approach through combining concepts of network structure and relatedness in tie contents regarding specialised knowledge, i.e. sustainable construction knowledge. Thereby it provides a robust approach to mapping knowledge flows in office building projects that aim to achieve high levels of sustainability standards.

Use and Methods of Social Network Analysis in Knowledge Management

2011 ◽  
pp. 1096-1106 ◽  
Author(s):  
Tobias Muller-Prothmann
Keyword(s):  
Knowledge Management ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Theoretical Analysis ◽  
Knowledge Flows ◽  
Knowledge Networks ◽  
Personal Networks ◽  
Informal Communities ◽  
Analysis Of Knowledge

Whilst the primary importance of informal communities of practice and knowledge networks in innovation and knowledge management is widely accepted (see Armbrecht et al., 2001; Brown & Duguid, 1991; Collinson & Gregson, 2003; Jain & Triandis, 1990; Lesser, 2001; Liyanage, Greenfied & Don, 1999; Nahapiet & Ghoshal, 1998; Nohria & Eccles, 1992; Wenger, 1999; Zanfei, 2000), there is less agreement on the most appropriate method for their empirical study and theoretical analysis. In this article it is argued that social network analysis (SNA) is a highly effective tool for the analysis of knowledge networks, as well as for the identification and implementation of practical methods in knowledge management and innovation. Social network analysis is a sociological method to undertake empirical analysis of the structural patterns of social relationships in networks (see, e.g., Scott, 1991; Wasserman & Faust, 1994; Wellman & Berkowitz, 1988). This article aims at demonstrating how it can be used to identify, visualize, and analyze the informal personal networks that exist within and between organizations according to structure, content, and context of knowledge flows. It will explore the benefits of social network analysis as a strategic tool on the example of expert localization and knowledge transfer, and also point to the limits of the method.

scholarly journals Trends in distance education research: A content analysis of journals 2009-2013

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Aras Bozkurt ◽  
Ela Akgun-Ozbek ◽  
Sibel Yilmazel ◽  
Erdem Erdogdu ◽  
Hasan Ucar ◽  
...  
Keyword(s):  
Distance Education ◽  
Education Research ◽  
Content Analysis ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Descriptive Analysis ◽  
Research Areas ◽  
Current Trends ◽  
E Learning

<p>This study intends to explore the current trends in the field of distance education research during the period of 2009-2013. The trends were identified by an extensive review of seven peer reviewed scholarly journals: <em>The American Journal of Distance Education</em> (AJDE), <em>Distance Education</em> (DE), <em>The European Journal of Open, Distance and e-Learning</em> (EURODL), <em>The Journal of Distance Education</em> (JDE), <em>The Journal of Online Learning and Technology</em> (JOLT), <em>Open Learning: The Journal of Open, Distance and e-Learning</em> (OL) and <em>The International Review of Research in Open and Distributed Learning</em> (IRRODL). A total of 861 research articles was reviewed. Mainly content analysis was employed to be able to analyze the current research. Also, a social network analysis (SNA) was used to interpret the interrelationship between keywords indicated in these articles. Themes were developed and the content of the articles in the selected journals were coded according to categories derived from earlier studies. The results were interpreted using descriptive analysis (frequencies) and social network analysis. The reporting of the results were organized into the following categories: research areas, theoretical and conceptual frameworks, variables, methods, models, strategies, data collection and analysis methods, and the participants. The study also identified the most commonly used keywords, and the most frequently cited authors and studies in distance education. The findings obtained in this study may be useful in the exploration of potential research areas and identification of neglected areas in the field of distance education.  </p>

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.

scholarly journals Social network analysis of construction companies operating in international markets: case of Turkish contractors

2016 ◽  
Vol 23 (3) ◽  
pp. 327-337 ◽  
Author(s):  
Bartug Kemal AKGUL ◽  
Beliz OZORHON ◽  
Irem DIKMEN ◽  
M. Talat BIRGONUL
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Construction Projects ◽  
International Markets ◽  
Project Networks ◽  
Construction Companies ◽  
International Construction ◽  
Multiple Partners ◽  
Complex Projects

Investigation of market entry strategies is critical for the success of international contractors. Establishing partnerships is among the most effective vehicles of operating in international markets. The major objective of this paper is to analyze the partnership behavior of contractors in overseas projects. In this respect, social network analysis (SNA) was used to better understand the collaborative project networks in different markets and for projects of differing sizes. A database was developed based on the collaborative international construction projects where Turkish firms and their non-Turkish partners were involved. A total of 449 projects carried out in 46 countries were used for the analysis. The findings of the study suggest that contractors adopt different strategies depending on the market and project character­istics. The majority of the companies tend to remain in the same markets; they keep working with the same partners or choose local partners; and engage with multiple partners in more complex projects. This study is expected to help contractors reflect on their internationalization decisions and devise appropriate strategies to establish project networks.

FinTech innovation and knowledge flows in Hong Kong’s financial sector: a social network analysis approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anson Au
Keyword(s):  
Hong Kong ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Commercial Banks ◽  
Financial Sector ◽  
Knowledge Flows ◽  
Foreign Firms ◽  
Content Type ◽  
Network Position

Purpose This paper aims to examine how financial technology (FinTech) knowledge from foreign firms flows into and among elite commercial banks in Hong Kong’s financial sector to drive innovation. Design/methodology/approach Using social network analysis and regression analysis on a novel database of patents held by Hong Kong’s elite commercial banks, this paper examines the relationships between network position and FinTech knowledge flow. Findings This paper finds four untold patterns of innovation and inequality in Hong Kong’s financial sector: only three banks are responsible for all the FinTech knowledge entering Hong Kong; most foreign FinTech comes from the USA through Hong Kong and Shanghai Banking Corporation, whereas most FinTech from China enters through Fubon Bank and Development Bank of Singapore; older banks and banks with more connections to firms inside Asia are more likely to import FinTech; the most beneficial sources of FinTech for a bank’s network position are firms from outside Asia. Originality/value Despite the well-documented volumes of cross-border and cross-continental movement of financial institutions in Hong Kong, there is little work on the knowledge flows that underwrite this mobility. This paper addresses this gap by using FinTech knowledge flows to map the distribution of innovation, network position and competitive advantage in Hong Kong’s financial sector.

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.

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