This document discusses using social network analysis to study knowledge networks in London's advertising industry. It outlines how social network analysis was used to map the connections between people who sit on multiple company boards and between companies. The analysis identified several key findings: - 64 people sat on boards of 286 companies, with only a small percentage of potential connections actually present. - Five of the most connected companies were located in the same building as a large advertising firm, suggesting knowledge sharing between these companies beyond formal ownership ties. - Social network analysis provides a way to visualize networks and identify important characteristics, like central individuals and clusters, to help understand tacit knowledge transfer within an industry.

1. Knowledge networks in
London’s Advertising
Industry: A Social Network
Analysis approach
Sian Joel & Oli Mould

2. OUTLINE
• Rationale
• ‘Buzz’
• Social Network
Analysis
• Methodology
• People and
Companies
• Size
• Location
• Conclusion

3. RATIONALE
• Recent Economic Geography literature
concerned with:
•Tacit knowledge transfer (Gertler and Levitte,
2005)
•‘Project ecologies’ (Grabher, 2004)
•‘Noise’ (Grabher, 2002)
•‘Communities of knowledge’ and F2F (Storper
and Venables, 2004)
•‘Buzz’ (Bathelt et al., 2004)
•Echoes of Marshall’s (1920) ‘industrial
atmosphere’
“Buzz refers to the information and communication
ecology created by face-to-face contacts, co-presence
and co-location of people and firms within the same
industry and place or region”.

4. RATIONALE
• Grabher (2002) looked at London’s advertising
industry from a project-based perspective:
“Rather than equating the agglomeration of creative
projects in Soho simply with reduced costs of
transaction, it provides a vibrant site for ‘hanging out’,
training and thus, gaining access to networks at the
peripheries of projects… Through processes of
negotiating meaning, these networks act as local
interpretive communities which filter noise into signals”.
(Grabher, 2002: 258)
• 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’
•Could additional methodologies be utilised in
tandem to develop the measurement or

5. RATIONALE
• Social Network Analysis offers a way to visualise
networks and highlight important characteristics
of those networks
• Emanates from IT studies, but the ease of use of
software application (through open-sourcing and
Web 2.0 techniques) has increased
• Inter-locking board data can be used as it shows
the connectivity levels within any given industry or
locale (O’Hagan and Green, 2003), and also is an
increasingly popular trait in modern capitalist
tendencies (Kono et al., 1998).

6. METHODOLOGY
• Company data downloaded from a companies
house population and then cleaned
• SIC code(s) and address were used to identify
relevant companies and then each entry was
tested for relevance
• Resulting data was filtered to remove those
companies with less than 4 people on the board –
which means the networks that are produced have
certain characteristics:
• People-centric: Selects people on many boards rather than
boards of directors that have many people. This was done as
the advertising industry (and wider creative industries) tend
to be ‘fluid’ (in other words, high degree of freelancing, sub-
contracting etc) so the individual is important

7. PEOPLE AND
COMPANIES
People
Compa
ny

8. PEOPLE AND
COMPANIES
People
Compa
ny
A well-
connected
industry

9. PEOPLE AND
COMPANIES
People
Compa
ny
A well-
connected
industry

10. PEOPLE AND
COMPANIES
People
Compa
ny
Some people
sit on similar
companies

11. PEOPLE AND
COMPANIES
People
Compa
ny
Some people
sit on similar
companies

12. PEOPLE AND
COMPANIES
People
Compa
ny
Identify the
‘cut-points’ or
‘gatekeepers’

13. PEOPLE AND
COMPANIES
People
Compa
ny
Identify the
‘cut-points’ or
‘gatekeepers’

14. PEOPLE AND
COMPANIES
People
Compa
ny
Company by
size – size does
not equate to
connectivity
importance

15. PEOPLE AND
COMPANIES
People
Compa
ny
Company by
size – size does
not equate to
connectivity
importance

16. PEOPLE AND
COMPANIES
People
Compa
ny
Does show
which are the
people who sit
on the largest
companies

17. PEOPLE AND
COMPANIES
People
Compa
ny
Does show
which are the
people who sit
on the largest
companies

18. 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
• We can rank the companies by ‘degree centrality’
and map them to see clusters…

19. CLUSTERING

20. CLUSTERING

21. CLUSTERING
• 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’
• Often spinout companies form from larger ones,
but will still be housed in the original space to

22. UNPACKING ‘BUZZ’?
• In this small example, using existing information,
we have been able to identify 5 out of the 10 most
connected companies to one building
• Further research could corroborate this
evidence
• Then, qualitative information could be targeted to
gather more specific data as to these connections
• Inter-board connectivity is only one form of
connectivity that can be measured this way
• SNA allows a ‘calibration’ (i.e. a range-finder) of
the network for more targeted, descriptive data
that can elaborate on the collaborative practices