Understanding Knowledge Networks Through Social Network Analysis

Understanding Knowledge Networks Through Social Network Analysis

Ronel Davel (University of Pretoria, South Africa), Adeline S. A. Du Toit (University of Pretoria, South Africa) and Martie Mearns (University of Johannesburg, South Africa)
DOI: 10.4018/978-1-7998-2189-2.ch004

Abstract

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, 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 chapter 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.
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Advancing Km Through Social Capital

The importance of social capital for KM has been debated by several authors including Swan et al (1999), Lesser and Prusak (1999), Liebowitz (2005), Inkpen and Tsang (2005), McElroy et al. (2006), Smedlund (2008) and Manning (2010), to name a few. It has also been hypothesised that social capital can increase an organisation’s KM capability as it has the capacity to influence KM in various ways (Hoffman et al., 2005, p.98).

Key Terms in this Chapter

Social Network Analysis: SNA has been described as “ the mapping and measuring of relationships and flows between people, groups, organisations, computers or other information/knowledge processing entities. The nodes in the network are the people and groups, while the links show relationships or flows between the nodes” ( Krebs, 2006 ). SNA deems relationships significant, and maps and evaluates both formal and informal connections in order to obtain an understanding of what assists or hampers knowledge flow within cooperating divisions. According to Mertens et al. (2013) , SNA employed within an organisation is occasionally referred to as organisational network analysis (ONA). In such an instance, the emphasis is placed on “identifying key networks within organisational boundaries, understanding the structure of personal and group relationships within these networks, and using this understanding to make a difference to business performance” (p. 3). SNAs can therefore be regarded as visual and mathematical tools and techniques that are utilised to identify and analyse relationship patterns among actors within a network. It can be implemented in order to help organisations develop their strategic decisions, promote innovation and to advance the flow of information and knowledge for example.

Knowledge Networks: According to Du Preez (cited in Perry et al., 2010 ), knowledge networks imply a number of actors and resources, where the relationships between them bring about knowledge capturing, knowledge transfer and knowledge creation for the purpose of creating value. Du Preez elaborates further on this notion by maintaining that “integrated knowledge networks span all domains, communities, and trust relationships with the goal of fostering sustainable innovation that will continue to promote the competitiveness of its users” (p. 79). Although a knowledge network has the same composition as a social network, knowledge networks are as a rule more complex and dynamic. Knowledge networks aim to facilitate the flow and sharing of knowledge as well as to create new knowledge and to ensure the application thereof ( Denner, 2012 ). Knowledge networks differ from social networks in that they accentuate joint value creation by its members–shifting from information sharing to knowledge creation; it reinforces its members’ innovation and communication skills; it implements strategies in order to engage decision makers more directly ( Creech, 2001 ). Hence, knowledge networks could be described as social networks from a KM perspective. These networks form with the purpose to collect and implement knowledge—mainly via knowledge creation and knowledge sharing processes—in order to create value.

Communities of Practice: The term CoPs can be described as groups of people (either self-organised or sponsored) with a shared concern or passion for what they do and who learn how to do it better through frequent interaction. These people learn from each other as they share their knowledge and experiences with the group. The longevity of such a community will depend on its sustained value. Brown (cited in Allee, 2000 ) describes CoPs as “peers in the execution of real work” (p. 5). Bonds are formed by a common sense of purpose and a desire to discover members’ knowledge. These communities are defined by knowledge rather than duties and their life cycles depend on the sustained value as opposed to project deadlines ( Allee, 2000 ). According to Tzagarakis et al. (2009) CoPs are regarded as one of the most efficient approaches to promote collective intelligence also known as organisational memory. Adding to this, Schenkel, Teigland, and Borgatti (cited in Assimakopoulos & Yan, 2005 ) maintain that “every CoP consists of one (or many) social networks, but not every social network forms a CoP” (p. 475). Social networks thus serve as a potential base for CoPs.

Social Capital: The term social capital was originally introduced by social economists and it attempts to link social relationships to the creation of economic substance. In essence, social capital deals with the belief that “social relationships have value” (Putnam, cited in Smedlund, 2008 , p. 65). According to the literature, two general views regarding social capital exist ( Bakker et al., 2006 ). One belief declares mere social relations to be social capital, where the size of one’s social capital is measured by the number of ties one maintains. Burt (cited in Edelman et al., 2005 ) describes social capital as “know-who” and maintains it is about “everyone you now know, everyone you knew and everyone who knows you even though you do not know them” (p. 161). In addition, Smedlund (2008) maintains that social capital can be seen as “a collective good” located in the relations between people rather than in the people themselves (p. 65). The fundamental aspect of social capital reflects the need for individuals to connect with others in order to look for resources that they do not have at their own disposal ( Lesser & Prusak, 1999 ). Thus, in order to possess social capital, one has to be connected to others, and it is those others, who are the actual source of one’s advantage (Portes cited in Edelman et al., 2005 ). Nahapiet and Ghosal (cited in Järvenpää & Immonen, 2004 ) maintain that social capital has three dimensions namely: structural (signifying network ties and relationships, as well as the ease with which one can join and integrate into a network), cognitive (referring to a shared language and history) and relational (implying trust and norms, as well as responsibilities within a network). These dimensions enhance the enthusiasm and ability of organisations to exchange and transfer knowledge, therefore increasing their intellectual capital ( Widén-Wulff & Ginman, 2004 ; Järvenpää & Immonen, 2004 ).

Knowledge Network Analysis: KNA can be considered an extension of SNA ( Helms & Buijsrogge, 2006 ) as SNA provides a systematic method to pinpoint, study and verify processes of knowledge sharing in social networks ( Müller-Prothmann, 2007 ). KNA helps to disclose what facilitates or hampers knowledge flows, who knows whom and who shares what information and knowledge with whom ( Al-Hashem & Shaqrah, 2012 ). It also enables one to observe different types of knowledge networks based on the type of knowledge exchange (e.g. obtaining advice vs. learning; Helms et al., 2010 ). KNA can thus be described as an SNA with a KM approach. It explores social networks beyond a regular information-flow angle and focuses on what actors know, whether they have access to one another and most importantly, whether learning takes place and new knowledge is created.

Social Networks: Social networks have been described as “a finite set or sets of actors and the relation or relations defined on them” ( Wasserman & Faust, 1994 , p. 20). In short, social networks reflect communication, collaboration and loose acquaintances in networked communities ( Reinhardt et al., 2009 ). A social network refers thus to an informal body consisting of a set of actors (e.g., individuals or groups) and the relationships between them. These relationships can be either weak or strong and has an effect on the creation and distribution of knowledge among its members.

SNA Metrics: SNA metrics provide an unbiased way to interpret relationships ( Mohr, 2014 ) and are applied to measure network properties ( Tubaro, 2012 ). In social networks, where actors are linked by means of one or several relationships, SNA provides structural measurements: to describe the network as a whole; and to provide information on the participation of each actor in the network ( Martinez et al., 2003 ).

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