1. Winners’
Circles
SNA-driven ethnography
of award-winning creative teams in
Japan

2. John McCreery
• Anthropologist, Cornell Ph.D., 1973
• Moved to Japan in 1980
• Copywriter and creative director for Hakuhodo
Inc., 1983-1996
• Partner and Vice-President, The Word Works, Ltd.
(www.wordworks.jp)

3. Welcome to my world
Dentsu
Hakuhodo
ADK
Other
We can see the overall shape in the
distance, but what about the
underlying geology?
全面的な形が大ざっぱで見えるけど、
裏の地理学的な細かい動きと関係は、ど
うだろう？

4. Background

5. The Old Regime
• One question to one
person could be
important
• Lots of questions,
lots of people,
usually not feasible
• Hypothesis-testing or
exploratory research

6. Then, Computers
• Simulations support creation of complex models
(not our topic today)
• Data mining searches for patterns in large
quantities of data (getting close)
• Social network analysis (SNA) drives ethnographic
research (today’s proposition)

7. SNA Origins
Urban
Anthropolo
gy
Social
Psychology
Graph
Theory
• Mathematics of
structures composed
of lines and nodes
• Sociometry, balance
theory, “six degrees”
• Social network
formation among
urban migrants in
Central Africa

8. SNA Focus
• Social ties versus institutional structures
• Interactions versus rules
• Transmission of behavior, attitudes, information or
goods
• Constraints and opportunities emerging as ties are
created or destroyed

9. SNA Limitations
Cross-Sectional Analysis
• Now changing with emergence of longitudinal methods
• This study is, however, confined to a series of cross-sections,
i.e., a series of discrete networks, one for each of the annual
contests considered.

10. SNA Limitations
Thin Descriptions
• Thin descriptions constrained by data coding
• What other relationships do individual actors have?
• How else could they get to know each other?
• Local and historical context?

11. Ethnography
Thick Descriptions
• For example, in this project I take advantage of data collected for other purposes
that allow me to identify precisely the people with whom a copywriter named Maki
Jun worked on winning ads in 2001 and situate them on a map of relationships that
tie the top of an industry together. But I don't want to leave Maki as nothing more
than a labeled node in a network analysis diagram. I want people to know that, like
me, he grew up beside the sea and played the trombone in a high school band.
They should also know that he has recently published a book suggesting that
advertising copy, with its business suit removed, is a new form in a long tradition of
one-line poetry that includes haiku, tanka, and senryu, all traditional forms of poetry
for which Japanese literature is justly renowned. He is a man addicted, as I am, to
wordplay and a genuine master of the art. Maki’s latest book is prefaced with the
line kotoba no happa wa, itsuka ki ni naru mori ni naru (the leaves on words
sometimes become a forest), which pivots on his substitution of the Chinese
character for "tree" for the usual character for "breath" in the phrase ki ni
naru, turning "notice and are concerned about" into "become trees, become a
forest" (a more literal way to translate the way the line ends). The whole thing is set
off because the ba in kotoba (word) is written with the Chinese character for "leaf."
So the whole thing might have been rendered "The leaves in "spoken-
leaves" (words) sometimes become trees, become forests."

12. Research
Design

13. The Plan
データ源泉
(TCC 年鑑)
Social Network Analysis
(SNA)
資料分析
取材
（インタビュー）
DATA: Credits from winning ads in the Tokyo
Copywriters Club Annual
SNA: Explore networks linking members of
winning teams
DESK RESEARCH: Books and articles written
by or about central figures in the networks
INTERVIEWS: Conversations with central
figures using output from SNA and desk research
as stimulus material

14. The Question
• How connected is this industry?
• Stovepiped by agency (keiretsu model)
• Everybody knows everybody
• But, in terms of people working together?

15. And then?
• Who are these people?
• What roles do they play?
• How have things changed since the start of the
1980s?

16. So what?
• LATERAL PERSPECTIVE: Analysis comparable
to that performed on movie and citation index
databases,on a longitudinal series of networks.
• LOOKING FORWARD: Use of SNA to explore
the dynamics of project teams and the populations
of experts brought together to work on them.

17. Data and Tools

18. The Data
• The TCC 年鑑 (TCC Advertising Copy Annual)
• Published every year since 1963
• Credits are provided for each winning ad
• Today we will be looking at data from 1981, 1986,
1991, 1996, 2001, 2006, and 2007

19. The Tools
• Filemaker Pro (Data Storage/Retrieval)
• Pajek (SNA)
• DataDesk (Statistics/Modeling)
• Text2Pajek & BBedit (Data Conversion)
• iWork09 (Presentation)

20. Filemaker Database
Ads
Roles
Creators
4537
34926
8579
Note1: Ad production requires multiple roles
Note 2: Creators may play more than one role
Note 3: Multiple creators may play the same role

21. Filemaker Data-Ads

22. Filemaker Data-Roles

23. Pajek Report/Dashboard

24. Networks

25. Whole Network - 1981

26. Whole Network - 2007

27. Analysis

28. Facts to Consider
(Growth)
0
750
1500
2250
3000
1981 1986 1991 1996 2001 2006 2007
Winning Ads and Creators (2-mode Networks)
Total Ads Total Creators
0
2000
4000
6000
8000
1981 1986 1991 1996 2001 2006 2007
Winning Ads and Creators vs Total Ad Spend
Total Ads Total Creators Total Spend
During the period in question,
the number of winning ads
rises from 475 to 902, the
number of winning creators
from 885 to 2746
During the same period, total
ad spend in Japan rises from
2465.7 billion yen to 7019.1
billion yen

29. Facts to Consider
(Media)
0
0.125
0.250
0.375
0.500
1981 1986 1991 1996 2001 2006 2007
Share(%) of Winning Ads Per Medium
TV Radio Newspaper Magazine
0%
10.0%
20.0%
30.0%
40.0%
1981 1986 1991 1996 2001 2006 2007
Share of Ad Spend in Japan (%)
TV Radio Newspaper Magazine
TV share rises sharply from 1981 to
2001, then declines. Excluding a brief
recovery by newspaper ads in 1991, the
share of print advertising (Newspaper +
Magazine) declines throughout the
period.
Turning to ad spend, we find that TV
and newspapers are the dominant media
throughout the period, but while TV
share remains high, newspaper share
steadily declines.

30. Facts to Consider
(Teams)
Creators Ads Avg Team
TV
Radio
Newspaper
Magazine
12135 1159 10.47
1185 194 6.11
6152 1226 5.02
2284 517 4.42
•Based on the number of individuals who are given credits per ad,
creative teams for TV commercials are, on average, twice as large as
those for newspaper ads and more than twice as large as those for
magazine ads.
•A team of size n contributes n(n-1)/2 links to the network.

31. Hypotheses
(Components)
• Creator networks will exhibit giant weak components (all
vertices connected by at least one path)
• Creator networks will also exhibit giant bi-components
(all vertices connected by 2+ paths)
• As networks increase in size, the size of giant bi-
components will approach the size of giant weak
components
Adapted by author from M. E. J. Newman, Gourab Ghoshal
“Bicomponents and the robustness of networks to failure”
Phys. Rev. Lett. 100, 138701 (2008)
0.1
0
0.5

32. Giant Weak Component
(1981)
Net>Components>Weak
Draw>Partition
Layout>Energy>Fruchterman Reingold>2D
Info>Partition
Total Vertices: 885
Total Components: 73
Giant Component: 638

33. Giant Components(’81-’07)
1981 1986 1991 1996 2001 2006 2007
Total Vertices
#Components
Giant Component
GiantC/Total%
#Bi-components
Giant B-component
GiantB/Total%
885 922 1399 1824 1884 2662 2746
73 38 33 33 37 65 51
638 742 1253 1670 1692 2381 2488
72.09% 80.48% 89.56% 91.56% 89.81% 89.44% 90.60%
109 76 70 86 65 125 139
325 565 1064 1427 1547 2031 1978
36.72% 61.28% 76.05% 78.23% 82.11% 76.30% 72.03%
0%
25.00%
50.00%
75.00%
100.00%
1981 1986 1991 1996 2001 2006 2007
Giant Component & Giant Bicomponent % of Total (Creators)
GiantC/Total% GiantB/Total%
0
38
75
113
150
1981 1986 1991 1996 2001 2006 2007
Weak vs Bi-Components (# of Components)
#Components #Bi-components
•Giant components rise from
1981 to 1991 then stabilize
around 90%
•Bi-components converge
toward giant components
from 1981 to 2001 but
diverge in 2006-2007
•From 2001-2007, the number
of bi-components increases
sharply compared to the
number of components

34. Why these patterns?
0%
25.00%
50.00%
75.00%
100.00%
1981 1986 1991 1996 2001 2006 2007
GC &GBC % of Total (Creators)
GiantC/Total% GiantB/Total%
0
38
75
113
150
1981 1986 1991 1996 2001 2006 2007
Weak vs Bi-Components (# of Components)
#Components #Bi-components
0%
17.5%
35.0%
52.5%
70.0%
1981 1986 1991 1996 2001 2006 2007
% of Winning Ads (Print, Broadcast, Other)
Other Broadcast Print
•The rise of TV (1981-2001)
• The rise of Other (collateral + Web, 2001-2007)

35. Hypothesis (Degree
Distribution)
• Studies of large networks frequently find that the
degree distribution of vertices follows a power law.
• What about these networks?

36. Distribution Modeling
0
10
20
30
40
40 80 120 160
Freq1
C
l
u
s
t
e
r
1
1981 Total Network
10
20
30
40
25 50 75 100
Freq
C
l
u
s
t
e
r
2
1981 Giant Component
0.0
7.5
15.0
22.5
30 60 90 120
Freq3
C
l
u
s
t
e
r
3
1981 GC line value >1 (-0.41)
Cluster and frequency data from Pajek are analyzed using the calc>non-
linear models>power fit command in Data Desk in a three stage process: (1)
total network, (2) giant component, (3) pruned giant component (line value
>1).

37. Pruned Giant Components (’86-’07)
20
40
60
80
40 80 120
Freq3
C
l
u
s
t
e
r
3
0
20
40
60
80
75 150 225
Freq3
C
l
u
s
t
e
r
3
0
20
40
60
80
75 150 225 300
Freq3
C
l
u
s
t
e
r
3
1986 (-0.42) 1991 (-0.48) 1996 (-0.42)
0
25
50
75
100
100 200 300
Freq3
C
l
u
s
t
e
r
3
2001 (-0.49)
0
30
60
90
120
125 250 375 500
Freq3
C
l
u
s
t
e
r
3
2006 (-0.45)
0
20
40
60
80
125 250 375 500
Freq3
C
l
u
s
t
e
r
3
2007 (-0.40)

38. Back to Basics

39. Return to the
• The results so far add confirmation to well-established propositions in
network analysis.
• They don’t, however, answer questions of interest to people in the
industry
• How many and what proportion of creators work on projects for
more than one agency?
• Who are these people and what are the roles they play?
Dentsu
Hakuhodo
ADK
Other

40. Shift Networks
Ads-Creators 2001
• Net>Components>Partition>
2-mode
• Draw>Partition
• Layout>Energy>
Fruchterman Reingold>2D
Agencies-Creators 2001
• Net>Components>Bi-Components
• Edit>Partition (separate Agencies
from Creators)
• Draw>Partition
• Layout>Energy>
Fruchterman Reingold>2D

41. Isolate Multi-Agency
Creators

42. Multi Agency Creators
0
750
1500
2250
3000
1981 1986 1991 1996 2001 2006 2007
Single vs Multi Agency Creators
One Agency Multi Agency %Multi Agency
• While total creators range from 885 in 1981 to 2746 in 2007, multi agency creators
range from 36 to 251
• As a percentage of total creators, multi agency creators range from 4.07% in 1981 to
11.57% in 2001
• Note, however, that if all networks are combined, the proportion of multi agency
creators rises to 15.88%.
0%
5.00%
10.00%
15.00%
20.00%
1981 1986 1991 1996 2001 2006 2007 ALL
Multi-Agency Creatives (%)
15.88%
11.57%

43. The Roles They Play
0
0.15
0.30
0.45
0.60
Copywriter% CD% PL% AD% Designer% PH% PR% Director % CA%
% of Multi-Agency Creators by Role
1981 1986 1991 1996 2001 2006 2007
• Selected from a list of 88 roles for which credits appear in the TCC Annual, the nine roles
that appear here account for 2/3 of all roles in our networks
• The short columns to the left (Copywriter, CD, PL, AD, Designer) are core members of
the teams and more likely to be agency employees.
• The tall columns on the right are production staff added after an idea has been sold to the
client: more often freelancers (PH, Director, CA) or employed by production companies
(PR).

44. Next Steps

45. To Learn More
• Start with a 2-mode network (here Ads-Creators 1981)
• Net>k-neighbors>vertex >distance =2
• Here the selected vertex is Nak190, legendary copywriter and creative director
Nakahata Takashi.
• The yellow vertices are the ads on which he worked.
• The green vertices are the other members of the teams in which he participated.

46. And Then Ethnography
To Read To Interview