Winners' Circles (ZhengZhi)

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Winners' Circles (ZhengZhi)

  1. 1. Winners’ Circles SNA-driven ethnography of award-winning creative teams in Japan
  2. 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. 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. 4. Background
  5. 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. 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. 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. 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. 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. 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. 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. 12. Research Design
  13. 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. 14. The Question • How connected is this industry? • Stovepiped by agency (keiretsu model) • Everybody knows everybody • But, in terms of people working together?
  15. 15. And then? • Who are these people? • What roles do they play? • How have things changed since the start of the 1980s?
  16. 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. 17. Data and Tools
  18. 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. 19. The Tools • Filemaker Pro (Data Storage/Retrieval) • Pajek (SNA) • DataDesk (Statistics/Modeling) • Text2Pajek & BBedit (Data Conversion) • iWork09 (Presentation)
  20. 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. 21. Filemaker Data-Ads
  22. 22. Filemaker Data-Roles
  23. 23. Pajek Report/Dashboard
  24. 24. Networks
  25. 25. Whole Network - 1981
  26. 26. Whole Network - 2007
  27. 27. Analysis
  28. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 38. Back to Basics
  39. 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. 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. 41. Isolate Multi-Agency Creators
  42. 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. 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. 44. Next Steps
  45. 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. 46. And Then Ethnography To Read To Interview

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