Negotiated Studies Presentation on Social Network Analysis of Knowledge Networks


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Negotiated Studies Presentation -Social Network Analysis of Knowledge Networks

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Negotiated Studies Presentation on Social Network Analysis of Knowledge Networks

  1. 1. Negotiated Studies Presentation on PREM SANKAR C M.Tech [T.M]
  2. 2. Oli Mould & Sian Joel * Dept of Geography ,Loughborough University; ** Dept of Computing and creative industries ,Napier University       Publisher : Royal Geographical Society Theme :Knowledge Network in Industry Topics :Clustering & networking Sector :Advertising Key words: Economic Geography, London, Buzz, Advertising Industry, Social Network Analysis
  3. 3.  Introduction ◦ ‘Buzz’ ◦ Social Network Analysis Data Source  Methodology  Key Findings  Conclusion 
  4. 4.  To highlight how SNA can provide a flow of ‘buzz', on London's advertising industry.  This study states that the SNA can be used as a viable research tool for highlighting networks within a particular industry and location
  5. 5.  Recent Economic Geography literature concerned with : ‘Firm as complex network of actors ‘(Jones 2007) ‘Tacit knowledge transfer ‘(Gertler and Levitte, 2005) –on innovation in the Canadian Biotechnology Industry.  ‘Local Noise in London adv Industry’ (Grabher, 2002) ‘ Communities of knowledge’ (Storper and Venables, 2004) Importance of Social ,Intra and inter-firm networks in Advertising/Creative industries (Caves 2000,Cunnningham 2004) Serendipitous nature of buzz Grabher(2002)
  6. 6. “ Buzz refers to the information and communication knowledge created by face-to-face contacts, co-presence and co-location of people and firms within the same industry and place or region”. (Bathelt et al., 2004: 38)  Buzz consists of specific information and continuous updates of this information through organised and accidental meeting  Buzz is based on network of communication and information linkage within a industry  This occurs in negotiations with suppliers ,in phone calls during office hours ,while talking to neighbors or when having lunch with other employees and so on
  7. 7.     Is a network structure that is highly clustered with small path lengths Introduced by Milgram(1967) – concluded that “six degree of separation” in friendship network - Avg 6 intermediaries between 2 strangers Same in Scientific Co-authoring Davis and Newman (2003) Small world network are represented graphically using SNA tools
  8. 8.  Attempts have been made to quantify/qualify ‘buzz’ ◦ Gertler and Levitte (2005) focused on local networks in the Canadian biotech industry ,suggested that local knowledge and global circulation for success in innovation. ◦ Watson (2008) described the local buzz/global pipeline dualism for knowledge exchange in London’s music industry …
  9. 9.    Social Network Analysis offers a way to visualize networks and highlight important characteristics of those networks . SNA is under utilized in economic geography literature. Connection between firms and particular individuals shows the path of knowledge flow within the industry and can highlight the key gate keepers (individuals sit on many of company board of directors).
  10. 10.    Historically Alfred Marshall (1920),suggested strong centre of specialized industry attracts new energy and maintain its lead(for heavy manufacturing industry of that time but still it hold true for knowledge economy of today ) Inter-locking board members data can be used as it shows the connectivity levels within any given industry or locale (O’Hagan and Green, 2003) Knowledge shared informally between people and firms increases with technological connectivity(Jones 2007)
  11. 11.    Grabher noted that a key skill of (the most successful) advertisers is converting the ‘noise’ into ‘signals’, or what can be roughly translated as ‘tacit’ into ‘codified’ How buzz is geographically spread through intra-inter firm networks -Glucker (2006) This study use data on on interlocking board members of advertising companies in London ,and visualize their networks using sociograms. And highlights the intensity of connections between companies and particular individuals, showing the paths of knowledge flow within the industry and the ‘gatekeepers'.
  12. 12.       The data for the SNA were obtained from the UK Companies House database. Data was filtered to remove those companies with less than 4 people on the board Empty and not valid data also removed Network maps were visualised as sociograms Software packages used -UCINET and Netdraw. Spearman's Rank Correlation Coefficient (SRCC) was used.
  13. 13.  UCINET— is produced by Analytic Technologies. It offers a very user-friendly, reasonably priced software system for network analysis  NetDraw -, a Windows program for visualizing social network data. The program is free.  Spearman's rank correlation coefficient or Spearman's rho, named after Charles Spearman and is measure of statistical dependence between two variables. It assesses how well the relationship between two variables can be described using a function
  14. 14.      Rectangular matrix –shows 2 mode network of Company to director Adjacency matrix –shows one mode network of company to company and director to director Rank of company by net asset is calculated – highlights it is not always largest companies are most connected or most central Based on the holding of shares ,Company family tree also obtained SNA tools are used to find density and centralization (Degree and Eigen vector)
  15. 15.  Networks that are produced have certain characteristics: ◦ People-centric ◦ Top-heavy ◦ People-centric: Selects people on many boards rather than boards of directors that have many people. This was done as the advertising industry (creative industries) tend to be ‘fluid’ (in other words, high degree of freelancing, sub-contracting etc) so the individual is important ◦ Top-heavy: Selects those people who sit on the board of larger companies (i.e. with more board members)
  16. 16. DIRECTOR AND COMPANY NETWORK PEOPLE COMPANY A Well Connected Industry Knowledge can transfer quickly
  17. 17. THE NETWORK  In this network;  64 people sit on 286 company boards of directors  From all the possible company connections, there exists 5.32% of ties  From the possible individual connections there is 1.15% of ties  Therefore connections could be considered strategic – quality not quantity
  19. 19. KEY CUT POINTS PEOPLE COMPANY Identified ‘cut-points’ or gate keepers Or Trend Setters For knowledge transfer
  20. 20. STRATEGIC CUT POINTS  Companies have their headquarters in same physical location  Those board of directors are friends or have worked together in the past  Companies may belong to the same holding company (hypothesis tested by subsidiary company data) then people who sit on the board of a holding company also sit on the board of their subsidiaries (company family)  Cut points can transmit (restrict) knowledge between company family trees
  21. 21. DENSITY PEOPLE COMPANY Some people sit on similar companies
  22. 22. PEOPLE AND COMPANIES PEOPLE COMPANY Does show which are the people who sit on the largest companies
  23. 23. COMPANY SIZE BY NET ASSET PEOPLE COMPANY Company size –size does not equate to connectivity importance
  24. 24. PERFORMANCE INDICTORS  Based of net asset variable company was ranked and compared with centrality rank for that company to check correlation between ranks  Shown in scatter diagram  Findings: Being a certain size company has no guarantee on that company being centralized  Firm size is not an indictor of network importance
  25. 25.    People's importance (the intensity of an individual in the network), shown by degree centrality measures and the eigenvector index. Degree centrality is a simple measure that counts the number of ties a person has. Eigenvector index is a mathematical measurement of the ‘connectedness' of any particular node in a social network
  26. 26.  Top 10 most connected people in the social network (degree centrality) Person A 28 B 27 AL 12 AM 11 AE 11 AG 10 C 6 I 6 AV 5 AU  Number of company boards 5 Top 10 most connected people in the social network (degree centrality) Person Number of company boards A 28 B 27 AN 16 Q 12 AG 11 R 11 P 8 L 7 AW 5 BJ 5
  27. 27.  Based on the postal address  5 of the most ‘connected’ companies are located at one address, 121-141 Westbourne Terrace, London, London, W2 6JR  Those 5 companies are not large, but reside in the same building as Fitch, who are owned by the WPP Group, one of the largest advertisers in the world  These 5 companies are not direct subsidiaries of Fitch or WPP, so the connection between these companies goes beyond a simple ‘company family ties’ (Incubator)  May therefore share board members more readily and act as pipelines from global into local buzz for exchange of tacit knowledge
  28. 28.        Sociograms showed that that the advertising sector is relatively well connected. Highlighted the key individuals /gate keepers who connect between clusters for spreading knowledge between them Comparing members according to their net assets, not particularly large companies are hugely important in providing connection and cohesion to the network. finding suggests that being in a certain size company has no bearing on that company being centralised within London's advertising network. Small, medium or large companies can play an important part in connecting companies and groups. The study showed that there is strong geographical effect on the networked-centrality of London's advertising companies - more so than size.
  29. 29.     The advertising industry is relatively connected via board of directors . Advertising is dynamic and is dependent on trends and so the exchange of knowledge is crucial to economic viability. SNA as research tool for Economical geographers Highlighted well connected and important people in London advertising Industry