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.

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.

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.

Social Network Analysis

2011 ◽  
pp. 818-825
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.

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

2011 ◽  
pp. 2060-2069
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.

Understanding Knowledge Networks Through Social Network Analysis

2017 ◽  
Vol 13 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Ronel Davel ◽  
Adeline S. A. Du Toit ◽  
Martie M Mearns
Keyword(s):  
Social Capital ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Communities Of Practice ◽  
Knowledge Networks ◽  
Informal Networks ◽  
Process Map ◽  
Knowledge Maps

Social network analysis (SNA) is being increasingly deployed as an instrument to plot knowledge and expertise as well as to confirm the character of connections in informal networks within organisations. This study investigated how the integration of networking into KM can produce significant advantages for organisations. The aim of the research was to examine how the interactions between SNA, Communities of Practice (CoPs) and knowledge maps could potentially influence knowledge networks. The researchers endeavour to illustrate via this question that cultivating synergies between SNA, CoPs and knowledge maps will enable organisations to produce stronger knowledge networks and ultimately increase their social capital. This article intends to present a process map that can be useful when an organisation wants to positively increase its social capital by examining influencing interactions between SNA, CoPs and knowledge maps, thereby enhancing the manner in which they share and create knowledge.

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.

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