LINKING SOCIAL CAPITAL AND KNOWLEDGE MANAGEMENT

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INTRODUCTION

Knowledge and innovation have become widely recognised as strategic resources that enhance the competitive advantage of organisations (Magnier-Watanabe et al. 2011; Itzkin 2000). As Dougherty (1995) points out, an organisation’s competitive advantage is predominantly rooted in the intangible, tacit knowledge of its employees and these capabilities do not exist separate from those who acquired them. This notion is confirmed by Zhang et al. (2009) who mention that when tacit knowledge is actively obtained, created and shared within organisations, there is a higher likelihood of creating an enduring competitive advantage. In the modern day knowledge economy the ability to manage knowledge has thus become imperative as the creation and distribution of knowledge has become vital to organisations’ competitiveness (Dalkir 2011:2). Wiig (2000:3) accentuates the fact that knowledge management (KM) involves a wide range of disciplines. As a result of this multi-disciplinary nature several KM approaches and models, depicting the KM cycle, have materialised (Davenport & Prusak 1998). According to Alqahtani et al. (2012:143-144), four of the most popular KM models include:  the Karl Wiig KM model (1993) that stresses the belief that in order for knowledge to be useful and valuable, it has to be organised;  Nonaka and Takeuchi’s (1995) SECI model that categorises the KM process in relation to socialisation, internalisation, externalisation and a combination of practices;  the KM model identified by Davenport and Prusak (1998) defining the KM process as generating, codifying and transferring knowledge; and  Alavi and Leidner’s (2001) KM model that associates KM process with the creation, storage and retrieval, transfer and application of knowledge.
Song and Lee (2007) distinguish between two general KM approaches namely technological and non-technological approaches. Technological approaches to KM, also known as a techno-centric approach, uses information and communication technology to capture, codify, store, disseminate and reuse knowledge within organisations. Conversely, the non-technological approach is much more people centred and focuses more on managerial, social and cultural techniques to manage organisational knowledge. Since large amounts of tacit knowledge cannot really be captured or documented, knowledge is often created and shared through social interaction between people (Nonaka & Takeuchi 1995:8, 57, 60, 72, 85). Weick and Westley (1996) and Araujo (1998) confirm that new knowledge as well as competencies can be indirectly generated and shared via dialogue and networking activities. These interpersonal relationships form patterns which are labelled social innovation capital or social capital (McElroy 2002:30). Research indicates that relationships are critical to knowledge creation and knowledge transfer (Levin & Cross 2004:1477) and that the various forms of social networks that exist within organisations contribute fundamentally to the dissemination thereof (Murale & Raju 2013).
This notion is underlined by Thomas (cited in van den Berg & Snyman 2003), who observes that “… it is in communities that individuals develop the capacity to create, refine, share and eventually apply knowledge.” Allee (2000:5) adds to this by affirming that when knowledge work is at stake, “… people require conversation, experimentation, and shared experiences with other people who do what they do …” and that one cannot separate knowledge from “… communities that create it, use it, and transform it.” It is thus important that organisations encourage the formation of communities in order to promote knowledge sharing and learning. In the words of Dalkir (2011:2): “An organisation in the Knowledge Age is one that learns, remembers, and acts based on the best available information, knowledge, and know-how.” Amidon (2002) supports this perspective by maintaining that innovation and knowledge creation depend considerably on existing knowledge networks within organisations, and by what means these networks consider or inhibit diverse knowledge domains from being connected in new and meaningful ways. This study aims to join the non-technological KM approach, more specifically the socialisation school of thought, as reflected in the works of Hansen et al. (1999) and Nonaka and Takeuchi (1995), where the creation and sharing of knowledge occurs primarily by means of social interaction between individuals. The said interaction usually occurs through informal networks, also known as knowledge networks (Helms & Buijsrogge 2006).

THE NATURE OF THE RESEARCH PROBLEM

Since its appearance in the 1930s, SNA has grown into a practice that offers visual and statistical elements to analyse the attributes of individuals and their relationships (Scott 1988:109-110). Like KM, SNA has been employed in a variety of disciplines. The importance of SNA as an instrument that can be applied to examine the social- and knowledge networks within organisations is underscored by Badaracco (1991:13-14) who points out that “…in an age of rapidly proliferating knowledge, the central domain is a social network that absorbs, creates, transforms, buys, sells, and communicates knowledge.
Its stronghold is the knowledge embedded in a dense web of social, economic, contractual, and administrative relationships.” Seufert et al. (1999) maintain that organisations are progressively transforming from well-defined, manageable structures into interwoven network structures with blurred boundaries. As a result it is important to recognise that the creation and transfer of knowledge is increasingly taking place within a network environment as opposed to within traditional organisational boundaries. In short, network relations and the proficiency to manage networks have developed into significant drivers of a new way of conducting business. This study intends to investigate how the integration of networking into KM can produce significant advantages for organisations. The aim of the research is to examine a process or methodology that can have an effect on the interactions between SNA, CoPs and knowledge maps concerning knowledge networks. This research aspires to outline a method for organisations to strengthen their social capital by analysing, shaping and reinforcing their knowledge networks, thereby enhancing the manner in which they share and create knowledge. Consequently, the main research problem of the study was to investigate…

Table of Contents :

• ACKNOWLEDGEMENTS
• ABSTRACT
• 1 INTRODUCTION
  • 1.1 THE NATURE OF THE RESEARCH PROBLEM
  • 1.2 RESEARCH OBJECTIVES
  • 1.3 RESEARCH APPROACH
  • 1.4 VALUE OF THE RESEARCH
  • 1.5 THESIS OUTLINE
• 2 LINKING SOCIAL CAPITAL AND KNOWLEDGE MANAGEMENT
  • 2.1 EXPLAINING SOCIAL CAPITAL
  • 2.2 DEFINING KNOWLEDGE MANAGEMENT
  • 2.3 KNOWLEDGE AND SOCIAL NETWORK STRUCTURES
  • 2.4 ADVANCING KM THROUGH SOCIAL CAPITAL
    • 2.4.1 The influence of social capital on KM processes
    • 2.4.2 Social capital encourages participation
    • 2.4.3 Discovering knowledge via network relationships
  • 2.5 SUMMARY
• 3 SOCIAL NETWORKS VS KNOWLEDGE NETWORKS
  • 3.1 SOCIAL NETWORKS AT A GLANCE
    • 3.1.1 Social networks
    • 3.1.2 Social network analysis
    • 3.1.3 SNA metrics
      • 3.1.3.1 Whole network analysis
      • 3.1.3.2 Network structure analysis
      • 3.1.3.3 SNA metrics: prominence
• 3.2 REVIEWING KNOWLEDGE NETWORKS
  • 3.2.1 Knowledge networks
  • 3.2.2 Knowledge network analysis
• 3.3 THE IMPORTANCE OF SNA FROM A KM PERSPECTIVE
  • 3.3.1 Building knowledge maps
  • 3.3.2 Social networks and CoPs
• 3.4 MEASURING KNOWLEDGE RELATIONSHIPS
• 3.5 CULTURE, KM AND SOCIAL NETWORKS
• 3.6 SUMMARY

• 4 RESEARCH METHODOLOGY
  • 4.1 FRAMING THE RESEARCH PARADIGM
  • 4.2 RESEARCH DESIGN
    • 4.2.1 Foundation Theories to SNA
    • 4.2.2 Triangulation
  • 4.3 CLARIFYING RESEARCH OBJECTIVES
  • 4.4 SAMPLE DESIGN
  • 4.5 DATA COLLECTION METHODS AND INSTRUMENTS
    • 4.5.1 Semi-structured group interviews
    • 4.5.2 Online questionnaires
      • 4.5.2.1 Skills audit questionnaire
      • 4.5.2.2 Divisional Social Network Analysis
    • 4.5.3 Indirect unobtrusive measures
  • 4.6 CAPTURING AND EDITING DATA
  • 4.7 DATA ANALYSIS
    • 4.7.1 Measures of network properties applied in the analysis
      • 4.7.1.1 Analysis of individual positions
      • 4.7.1.2 Analysis of the network structure
      • 4.7.1.3 Analysis of the whole network
    • 4.7.2 Skills audit analysis
    • 4.7.3 CoP analysis
  • 4.8 SHORTCOMINGS AND LIMITATIONS
    • 4.8.1 Assumptions
    • 4.8.2 Limitations
    • 4.8.3 Delimitations
      • 4.8.3.1 Addressing biases
• 4.9 SUMMARY
• 5 COMPARATIVE RESEARCH ANALYSIS
  • 5.1 CONTEXTUALISING THE SAMPLE POPULATION
    • 5.1.1 Physical proximity of actors
  • 5.2 PRESENTING THE RESULTS
    • 5.2.1 Skills maps vs knowledge networks
      • 5.2.1.1 Commodity Control network
      • 5.2.1.2 Data Analysis and Mining network
      • 5.2.1.3 Single Registration network
      • 5.2.1.4 Service Manager Cases network
    • 5.2.2 Linking CoP participation with key network positions
      • 5.2.2.1 Commodity Control
      • 5.2.2.2 Data Analytics and Mining
      • 5.2.2.3 Single Registration
      • 5.2.2.4 Service Manager Cases
    • 5.2.3 Comparing knowledge network structures
      • 5.2.3.1 Cohesion (cliques)
      • 5.2.3.2 Cut-points
      • 5.2.3.3 Hubs
    • 5.2.4 The influence of CoPs and knowledge maps on network connectivity
      • 5.2.4.1 Size
      • 5.2.4.2 Density
      • 5.2.4.3 Reachability
      • 5.2.4.4 Centralisation
• 5.3 CONTEMPLATING THE OUTCOMES
  • 5.3.1 Linking key network positions and identified experts as per the skills audit
  • 5.3.2 Comparing CoP participation with key network positions
  • 5.3.3 The influence of CoPs and knowledge maps on knowledge network structures
  • 5.3.4 The influence of CoPs and knowledge maps on whole-network metrics
• 5.4 CONCLUDING INTERPRETATIONS
  • 5.4.1 Knowledge networks can ascertain if actual experts are approached for
  • information
  • 5.4.2 By combining knowledge networks and skills maps one can pinpoint non- expert authorities
  • 5.4.3 Fusing knowledge networks and skills maps expose the nature of specialist relationships
  • 5.4.4 Knowledge network positions influenced members’ disposition to join CoPs
  • 5.4.5 CoP participation levels can be linked to knowledge network positions
  • 5.4.6 Knowledge and information is transferred more effectively within
  • knowledge networks as a result of CoPs
  • 5.4.7 CoP activity can impact on the size of knowledge networks
  • 5.4.8 Network density can indicate if CoPs produced more trusted relationships and faster knowledge transfer
  • 5.4.9 The formation of CoPs can result in improved connectivity within
  • knowledge networks
  • 5.4.10 CoPs can influence the level of interaction within knowledge networks
  • 5.4.11 The implementation of CoPs can lead to improved dissemination of
  • knowledge
• 5.5 SUMMARY

• 6 SYNTHESIS, RECOMMENDATIONS AND CONCLUSION
  • 6.1 INTRODUCTION
  • 6.2 SYNTHESIS
  • 6.3 RECOMMENDATIONS AND SIGNIFICANCES
  • 6.4 CONCLUSION
  • REFERENCES
  • APPENDICES
  • APPENDIX 1: DIVISIONAL SOCIAL NETWORK ANALYSIS
  • APPENDIX 2: SKILLS AUDIT QUESTIONNAIRE
  • APPENDIX 3: SUMMARY OF THE CONDUCTED GROUP INTERVIEWS
  • APPENDIX 4: LETTER OF INFORMED CONSENT
  • APPENDIX 5: COP MEMBERSHIP AND LEVEL OF PARTICIPATION
  • APPENDIX 6: RESULTS OF THE SKILLS AUDIT

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