Orchestrating Your Ecosystem - CCC - Stans Foundation - Taipei - April 11 2013

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Network orchestration is key to innovation ecosystems. With examples from mediaX at Stanford University, Norway, China, and the mobile device sector, this talk explores the co-creation relationships that enable innovation.

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Orchestrating Your Ecosystem - CCC - Stans Foundation - Taipei - April 11 2013

  1. 1. Orchestra)ng     Your  Innova)on  Ecosystem  Chinese  Consumer  Center/StanS  Founda2on   mediaX  at  Stanford  University     Martha  G  Russell,  PhD   April  11,  2013  
  2. 2. INNOVATION  AND  ENTREPRENEURIAL   ENVIRONMENTS  REQUIRE     CO-­‐CREATION  &  COLLABORATION    
  3. 3. Shared Vision Transforms Iterative Impact Alignment Co-Create Value Shared   Vision   Transforma2on   Event Coalition Interact & FeedbackMartha G. Russell, Kaisa Still, Jukka Huhtamaki, and Neil Rubens, “Transforming innovation ecosystems through shared visionand network orchestration,” Triple Helix IX Conference, Stanford University, July 13, 2011.
  4. 4. Martha G Russell•  Public-private partnerships – for science, technology & development –  Tecnopolis, MAMTech, IC2 Institute, Incubators linking SMEs to Value Chain, CSATA, Agricultural Experiment Station, UNIDO•  Research consortia: University – Industry IDR Research initiatives for education, research and outreach –  Microelectronic and Information Sciences Center –  Center for the Development of Technology Leadership –  Burnt Orange Productions –  IC2 Institute (Innovation, Creativity, Capital) Think and Do Tank•  Startups in –  Genetic engineering for cyclodextrins, Needleless injection, Aquaculture –  Online market research, desktop CSAT benchmarking, market segmentation•  Technology Transfer, Marketing and Organizational Change –  Internet2 Sociotechnical Summit, 1999 –  Relationship-focused innovation ecosystems•  Catalyst – Innovator - Enabler
  5. 5. H-­‐STAR     HUMAN  SCIENCES  AND  TECHNOLOGIES    at S T A N F O R D U N I V E R S I T Y ADVANCED  RESEARCH  INSTITUTE   RELATIONSHIP  INTERFACES  FOR  DISCOVERY  COLLABORATIONS     Goal:  Do  something  together  neither  of  us  could  do  by  ourselves.     Research  on  people  and  technology  —  how  people  use  technology,  how  to  beVer  design   technology  to  make  it  more  usable,  how  technology  affects  people’s  lives,  and  the  innova)ve   use  of  technologies  in  research,  educa2on,  art,  business,  commerce,  entertainment,   communica2on,  security,  and  other  walks  of  life.    
  6. 6. The REAL Issueat S T A N F O R D U N I V E R S I T Y Deep Knowledge with Wide Applicability IN  THE  HEART  OF  SILICON  VALLEY    IN  A  CULTURE  OF  RAPID  ITERATION,  WHERE  DISRUPTION  IS  CELEBRATED    WHERE  TALENT,  INFORMATION  AND  CAPITAL  RESOURCES  FLOURISH   THE  ISSUE  IS  NOT  THE  RATE    TECHNOLOGY  TRANSFER    THE  ISSUE  IS  THE  EFFECTIVENESS  OF  INNOVATION  AND  KNOWLEDGE  TRANSFER      WE  CALL  THIS  “COLLABORATIVE  DISCOVERY”     The  Media  X  approach    WORK  ON  BOLD  IDEAS  WITH  BUSINESS,  TEST  SUCCESS/FAILURE  CONDITIONS,      ITERATE  RESULTS  QUICKLY,  TRANSFER  INSIGHTS  AT  EVERY  STAGE  
  7. 7. Stanford University Medical Media ! & Information Technology ! SUMMIT Distributed Vision Lab ! a t S T A N F O R D U! I V E R S I T Y N DVL Discovery Collaborations ! Electrical Engineering Psychology Span Stanford Labs! Computer Science EE Psy Linguistics Communication Between HumansPhilosophy Ling and Interactive Media CS CHIMe Phil SHL Stanford Humanities Lab Graduate School VHIL GSB Of BusinessVirtual Human Stanford CenterInteraction Lab SCIL for Innovations in Learning Center for the Study Of CSLI Language & Information Art Digital Art CenterEngineeringEng & Product Design School of Education; Ed Education and PBLL Law Learning SciencesWorkTechnology & Center forOrganization SSP Legal Des Stanford Joint PBLL Program in Design Project Based Informatics d.school Learning Symbolic LIFE Laboratory Systems Program Learning in Informal and Formal Environments
  8. 8. Media  X’s  Unique  proposi2on  •  Pose  a  ques2on  to  the  Stanford  thought  leaders   that  will  create     –  Opportuni2es  for  discovery  collabora2ons     –  On  novel  research   –  That  leverages  the  latest  research  interests   –  To  iden2fy  the  new  ques2ons  that  will  lead  to   –  Insights  that  address  edge  ques2ons     –  3  to  5  years  out  •  Par2cipate  in  the  discovery  process  to  learn  •  The  best  ques2ons  and  how  to  pursue  them  •  Ra2onale  of  research  pathways  –  why?  why  not?   at S T A N F O R D U N I V E R S I T Y
  9. 9. Build  Capacity  for  Insights  -­‐  Sooner  •  Time  advantage     –  3  years  ahead  of  reading  the  latest  publica2ons  •  Relevance  advantage   –  Ques2ons  relevant  to  strategic  partner’s  future  •  Lower  risk  of  explora2on   –  Rapid  itera2on   –  Know  sooner  what  works   –  Externalizes  high  risk  •  Capacity  building   –  Iden2fy  new  exper2se  needed   –  Enhance  exis2ng  exper2se   –  Leverage  the  Stanford  network   at S T A N F O R D U N I V E R S I T Y
  10. 10. •  Membership MediaX connects businesses with•  Visiting Scholars Stanford University’s world-renowned•  Research Initiatives faculty to study new ways for people and•  Workshops technology to intersect.•  Seminars•  Conferences at S T A N F O R D U N I V E R S I T Y
  11. 11. A Revolution is Coming In the Productivity of Knowledge Work
  12. 12. Augmenting the Brain 13
  13. 13. Creativity 14
  14. 14. Collaboration in Teams 15
  15. 15. Knowledge in Practice 16
  16. 16. Reinventing Workflow 17
  17. 17. Agile Networks Network Orchestration 18
  18. 18. Knowledge Worker Productivity Productivity of Knowledge Workers 7 projects selected from 25 proposals Measuring  &  Increasing  Knowledge  Worker  Produc2vity   Detecting States of Mind Through Technologically Mediated Non-Verbal Behavior Cooperation and Collaboration The Utility of CalmingEteRNA: Accelerating Knowledge Creation for Technologies in Improving Creativity and Culture: UnderstandingRNA Bioengineering through Internet-Scale Productivity Team Creativity and What Fosters ItGaming A Journey from Islands of Knowledge to Process Integration Platform: Enabling Mutual Understanding In Global Business Process Transparency Within Teams and Meetings scaling of Process Knowledge Across the Entire Firm 19 at S T A N F O R D U N I V E R S I T Y
  19. 19. Knowledge Worker Productivity Premise   team  interac2ons  with  files  reveal  workflows    © 2012 by Reid Senescu January 22, 2012 20
  20. 20. Knowledge Worker ProductivityTotal  Engagement  at  Work  and  Play  
  21. 21. Knowledge Worker ProductivityThe  U)lity  of  Calming  Technologies  in  Improving  Produc)vity     Perform.  Produce.  Don’t  burn   out.  Be  creaIve.  Chill.  Focus.   Heal.  Relax.  Recover.  Take  care   of  yourself.  Don’t  stress  out.   Stay  healthy.  Be  present.  Live  in   the  moment.       Goal:    Devise  and  evaluate  ways   to  augment  human  self-­‐ regula2on  with  technology  =   calming  technology.        
  22. 22. Knowledge Worker ProductivityTechnologically  Mediated  Coopera)on  and  Collabora)on     Goal:  Understand  how   informa2on  search   interfaces  can  be   designed  to  facilitate   beVer  decision-­‐making.     Premise:  It  should  be   possible  to  reduce  selec2ve   exposure  bias  by  strategically   par22oning  self-­‐consistent   and  self-­‐inconsistent   informa2on  to  different   degrees.  
  23. 23. Knowledge Worker ProductivityDetec)ng  States  of  Mind  Through  Nonverbal  Behavior  •  Impact:  Preliminary  results  imply  that  gesture   can  predict  the  quality  of  a  two-­‐person,  face-­‐ to  face-­‐interac2on      
  24. 24. Worker Productivity When  People  Become  the  Content  of  Media  Par2cipa2on,  Personaliza2on  and  Emo2on        for  Persuasion,  Risk,  and  Reward   Infinite  Reality  
  25. 25. Data – Integration - Semantics •  Personal Area Networks: New Rules, New Metrics •  Semantic and functional integration across –  TV –  Computer –  Phone –  Home –  Car •  From clouds to the edge •  Ambient and intelligent •  Personalized •  Privacy-controlled •  Fluid media –  With many IP issues and measurement challengesRussell, M.G. 2009 A Call for New Metrics for New Media,http://jiad.org/article117
  26. 26. Quantified Self On the Horizon: The Quantified Self
  27. 27. Semantic Integration Technologies •  Sensors •  Mobile devicesOn the Horizon: The Intelligent Coach for Health and Well-being
  28. 28. On  the  Horizon:  Transparency,  Iden2ty  &  Persuasion  
  29. 29. Preferences  &  Permissions  for  Liquid  Flow  of  Informa2on   Inconsistency  Robustness  
  30. 30. Online  biz  models  based  on  access  to   in2mate  personal  data  
  31. 31. social  media  ecosystem   compu2ng  plajorm,  human   behavior,  and  content    Social Network   Technology Infrastructure and Support  Compu2ng  Infrastructure   Service Operations hVp://www.alpheuscommunica2ons.com  hVp://www.vaqueronet.com/coloca2ng.php   hVp://mashable.com/2009/04/30/facebook-­‐friends-­‐page/  
  32. 32. Can Health Spread As Well As Disease? Transmitting Relationships Happiness NetworksSalathe´ M, Jones JH (2010) Dynamics and Control of Diseases in Networks with Community Structure. PLoS Comput Biol 6(4): e1000736. doi:10.1371/ journal.pcbi.1000736 Contact – Vulnerability - Conditions Access - Trust - Relevance James H Fowler and Nicholas A Christakis, “Dynamic Spread ofHappiness in a Large Social Network: longitudinal analysis over 20 years in the Framingham Heart Study network,” BMJ 2008;337
  33. 33. OTHER RESEARCH THEMESat S T A N F O R D U N I V E R S I T Y•  Publish  on  Demand  •  The  Future  of  Content  •  Personalized  Learning  at  Scale  •  Contextual  Ambient  Intelligent  •  Mul2-­‐modal  Communica2ons  •  Changing  Consumers’  Energy  Behavior  •  Innova2on  Ecosystems  
  34. 34. Ecosystem   Heterogeneous  and  con2nuously   evolving  set  of  firms  that  are   interconnected  through  a  complex,   global  network  of  rela2onships.   [Basole  et  al.,  2012]  
  35. 35. Many Stakeholders in Innovation Ecosystem Startups   U2li2es,   Angels,     Industry   VC  firms,   Associa2ons   Incubators   Ecosystem   Banks  and   Law  Firms,   Financial   Accoun2ng   Ins2tu2ons   Firms   Universi2es  
  36. 36. Shared Vision Transforms Iterative Impact Alignment Co-Create Value Shared   Vision   Transforma2on   Event Coalition Interact & FeedbackMartha G. Russell, Kaisa Still, Jukka Huhtamaki, and Neil Rubens, “Transforming innovation ecosystems through shared visionand network orchestration,” Triple Helix IX Conference, Stanford University, July 13, 2011.
  37. 37. Distance Old New
  38. 38. Infrastructures  for  Resource  Flows                                                                                -­‐  -­‐  -­‐  Rela2onships   The Way We USED to Think About Organizations New  Organiza2onal  Chart  Based  on  Rela2onships   Relationship-Focused Co-Creation Infrastructure CreaIve  collaboraIons    -­‐  -­‐  -­‐  -­‐  -­‐  -­‐  -­‐  -­‐  -­‐  -­‐  -­‐   are  interlocked  through  key  people  –   informaIon  flow,  norms,  mental  models. (Davis,1996)  
  39. 39. Rela2onship  Interlocks  •  Execu2ves  and  key  employees   –  Transfer  of  technologies  and  knowledge,  professional  networks,  business   culture,  value-­‐chain  resources    •  Directors   –  US  Fortune  500  firms  interlocked  (shared  directors)  with  average  7  other  firms   •  Corporate  governance  embedded  and  filtered  through  social  structures     –  Execu2ve  compensa2on,  strategies  for  takeovers,  defending    against  takeovers   •  Gerald  F.  Davis,  “The  Significance  of  Board  Interlocks  for  Corporate  Governance,”  Corporate  Governance  4:3,  1996  •  Investors  and  service  providers   –  Awareness  of  external  forces,  compe22ve  insights,  resource  leverage  •  Rela2onship  interlocks  provide   –  Social  rela2onship  “filter”  for  governance,  informa2on  flow  &  norms   –  Transfer  of  implicit  and  explicit  know-­‐how   –  Mental  models   hVp://fusionenterprises.ca/Business_Training.php  
  40. 40. Stanford  spin-­‐offs  Over  2000  companies  started  by  faculty  students  and  alumni   •  Abrizio   •  NVIDIA   •  ASK  Computer  systems   •  Orbitz   •  Cisco  Systems,  Inc.   •  Octel  Communica)ons  Corp.   •  Dolby  Systems   •  Odwalla   •  eBay   •  ONI  Systems   •  E*Trade   •  PayPal   •  Electronic  Arts   •  Pure  SoZware,  Inc.   •  Excite,  Inc.   •  Rambus,  Inc.   •  Gap   •  Ra)onal  SoZware   •  Google   •  Silicon  Graphics,  Inc.   •  HewleW-­‐Packard   •  Sun  Microsystems   •  IDEO   •  Tandem  Computers,  Inc.   •  Intuit,  Inc.   •  Taiwan  Semiconductor   •  Learning  Company   •  Tensillica   •  Linked-­‐In   •  Tesla  Motors   •  Logitech   •  Trilogy   •  Mathworks   •  Varian  Associates,  Inc.   •  MIPS  Technologies,  Inc.   •  Vmware   •  Nike   •  Whole  Earth  Catalog   •  NeYlix   •  Yahoo!  Inc.  
  41. 41. Alumni  Leadership  Networks  
  42. 42. hVp://www.slowtrav.com/blog/chiocciola/Geirangertord.jpg  
  43. 43. The  Norwegian  Puzzle  •  Norway  is  a  wealthy  country  with  high  standard   of  living  and  almost  NO  unemployment  •  Yet,  low  rate  of  technology-­‐based  innova2on  •  What  is  the  future  of  Norway  aver  oil  reserves   have  been  extracted?  •  Given  technology  targets:   –  How  can  innova2on  be  catalyzed?   –  How  can  establishing  global  rela2onships  be   accelerated?  
  44. 44. Norwegian  Tech-­‐based  Companies   Their  Branch  Offices  and  Their  Financial  Orgs   Example  view  to  IEN  dataset  in  Gephi.  Companies  are  selected  with   keyword  search  “Norway  +  Norwegian;”  the  funding  organiza2ons  Links  show  rela2onships   associated  with  those  companies  are  added    Nodes  represent   companies  and  their  investors;  edges  indicate  resource  flows.    The   network  layout  is  created  with  Yifan  Hu  Mul2level  algorithm  and  nodes   are  inflated  according  to  their  indegree,  i.e.  the  number  of  the   connected  investors.    
  45. 45. Advisors & Investors Expand AccessInvestors  leverage  co-­‐crea2on  opportuni2es  with  investments  in  mul2ple  companies.  Intl  companies  not  shown.    Companies  leverage  value  co-­‐crea2on  opportuni2es  through  rela2onships  with  mul2ple  investors.  Some  investors  are  interna2onal.    Timeline  analysis  of  investment  events  reveals  paVerns  of  co-­‐investment  –  an  indica2on  of  inten2on  to  co-­‐create  value  and,  perhaps,  s2mulus  programs.   IEN  Dataset,  July  2010  
  46. 46. International Relationships forValue Co-CreationHuge opportunities forinternationalrelationships lie 2 & 3degrees out fromNorwegian companies IEN  Dataset,  July  2010   Example  view  to  IEN  dataset  for  keyword  search.  Nodes  represent  companies  and  their  previous  and  current  employees.  The   network  layout  is  created  with  Fruchterman  Reingold  algorithm  and  nodes  are  inflated  according  to  their  outdegree.  Protocols   for  anonymity  are  evolving.  
  47. 47. Globalization of Norwegian ICTNorwegian  tech-­‐based  companies  with  financing  are  more  likely  to  have  networked  rela2onships.    Norwegian  tech-­‐based  companies  have  access  to  global  rela2onships  through  current  board  members,  investors,  and  key  personnel.   IEN  Dataset,  July  2010    
  48. 48. Insights  About  Norway  •  Dual  offices:  regional  and  Oslo  •  In  sectors  we  studied   –  Business  loca2ons  parallel  technical  university  programs     –  Investor  rela2onships  have  strong  local  links     •  Some  inves2ng  organiza2ons  are  governmental  programs   •  Expands  to  Oslo  when  offices  are  in  Oslo   •  Interna2onal  rela2onships  linked  to  small  set  of  personal   rela2onships  at  execu2ve  level   –  Interna2onal  investors  drawn  through  execu2ve   rela2onships   •  Rela2onships  through  execs  at  Google  and  AOL  provide  channels   for  global  network  expansion  
  49. 49. Sørlandet is world leading in offshore oil drilling technologyTorger Rev, Innovation Ecosystems Summit, Stanford University, July 11, 2011
  50. 50. Offshore   oil  and  gas   Mari2me   policies   industry   Ship design Maritime Mari2me  lawyers   education Specialized Advanced   ship yards fisheries   Advanced Maritime ship equip- R&D SHIPPING   ment Logis2cs   Shipping   Effective systems   finance   Maritime ports and IT terminals Shipping brokers Ship management Marine   insurance   Environmental   standards   Ship   classifica2on   services  Mari)me:    From  ship  tonnage  to  mari)me  technology  and  finance  Torger Rev, Innovation Ecosystems Summit, Stanford University, July 11, 2011
  51. 51. SHIPPING  Mari)me:    From  ship  tonnage  to  mari)me  technology  and  finance  Torger Rev, Innovation Ecosystems Summit, Stanford University, July 11, 2011
  52. 52. Global  Mobile  Broadband  Explosion   85%  of  the  World  Will   Have  High-­‐Speed  Mobile   Internet  by  2017   Urban  areas    are   es2mated  to  generate   around  60  percent  of   mobile  traffic  by  2017   Mobile  Data  Traffic  for   smartphones  will  grow  by  ~20  2mes  by  the  end   of  2017   Video  represents  the   largest  data  traffic   volume   Source:  hVp://www.ericsson.com/res/docs/2012/traffic_and_market_report_june_2012.pdf  
  53. 53. Smartphone  Explosion  2002   2003   2004   2005   2006   2007   2008   2009   2010   2011   2012   Source:  Basole  (2011)  
  54. 54. BaVle  of  the  PlaYorms  2002   2003   2004   2005   2006   2007   2008   2009   2010   2011   2012   Source:  Basole  and  Karla  (2012a)  
  55. 55. Emergence  of  Apps  &  App  Stores  2002   2003   2004   2005   2006   2007   2008   2009   2010   2011   2012   Source:  Basole  and  Karla  (2012b)  
  56. 56. Products  &  Services  Delivered  through  Complex  Value  Networks   Game   Photography  &   Developers   Digital  Imaging   Media  &   Entertainment   Cable   Providers   Providers   Internet   Content   Service   Providers   Providers  Silicon  Vendors  &   Mobile  Other  Component   Products   Network   Providers   Consumers   &  Services   Operators   Device   Manufacturers   System   Integrators   Network  &   Infrastructure   Service  &   Providers   Billing   Providers   SoZware   Applica)on   Providers   Developers   Source:  Basole  (2009)  
  57. 57. Source:  iPhone  3G  Teardown  -­‐  Semiconductor  Insights  (2011)  
  58. 58. Source:  iPad  Teardown  –  UBM  TechInsights  (2010)  
  59. 59. How  do  you  u2lize  and  leverage  today’s  tsunami  of  available  data  sources  to  describe,  make  sense,  and   discover  the  dynamics  of  ecosystems?  
  60. 60. Innova2on  Ecosystem  Network   Approach   DATA   SDC     IEN   Deals  &     Execu)ves  &   Alliances     Funding   IdenIfy,  extract,  and   determines     curate     Public     Opinion  &     Discourse   NL   feeds   STEP  1   STEP  2   STEP  3   STEP  4   Boundary   Metrics   Computa2on,  Analysis  &   Sense  Making  &  Storytelling   Specifica2on   Iden2fica2on   Visualiza2on  Basole,  Russell,  Huhtamäki,  S2ll  and  Rubens,  “Understanding  Mobile  Ecosystem  Dynamics:  A  Data-­‐Driven  Approach,”   SubmiVed  to  JIT  March  2013.  
  61. 61. Apple  Samsung  Co-­‐ope22on   Deal  &  Alliance  Rela2onships   Financial  &  Execu2ve  Rela2onships  Basole,  Russell,  Huhtamäki,  S2ll  and  Rubens,  “Understanding  Mobile  Ecosystem  Dynamics:  A  Data-­‐Driven  Approach,”   SubmiVed  to  JIT  March  2013.  
  62. 62. Microsov  Nokia  Alliance   Deal  &  Alliance  Rela2onships   Financial  &  Execu2ve  Rela2onships  Basole,  Russell,  Huhtamäki,  S2ll  and  Rubens,  “Understanding  Mobile  Ecosystem  Dynamics:  A  Data-­‐Driven  Approach,”   SubmiVed  to  JIT  March  2013.  
  63. 63. Google  Acquisi2on  of  Motorola   Deal  &  Alliance  Rela2onships   Financial  &  Execu2ve  Rela2onships  Basole,  Russell,  Huhtamäki,  S2ll  and  Rubens,  “Understanding  Mobile  Ecosystem  Dynamics:  A  Data-­‐Driven  Approach,”   SubmiVed  to  JIT  March  2013.  
  64. 64. Mobile  Device  Ecosystem  Basole,  Russell,  Huhtamäki,  S2ll  and  Rubens,  “Understanding  Mobile  Ecosystem  Dynamics:  A  Data-­‐Driven  Approach,”   SubmiVed  to  JIT  March  2013.  
  65. 65. Mobile  Device  Ecosystem  Basole,  Russell,  Huhtamäki,  S2ll  and  Rubens,  “Understanding  Mobile  Ecosystem  Dynamics:  A  Data-­‐Driven  Approach,”   SubmiVed  to  JIT  March  2013.  
  66. 66. Context  of  Investments  into/from  China   Socially  constructed  dataset,    in  English,  openly     available–  all  challenges  in  China       Socially   Constructed   Data         Innova2on  Ecosystems  Dataset:   • 323  technology-­‐based  companies  with  one  or     more  loca2ons  in  China   • 42  Chinese,  77  foreign  investment  firm   • Investment  into  China  US$  5.4  B     Social   Network   • Investment  origina2ng  from  China   Analysis   US$  3.1  B     Insights    explored:   Insights  into   Innova2on     The  flow  of  financial  resources  into  and  out  of   China       More  illustra)ve  than  descrip)ve/prescrip)ve   results        Neil  Rubens,  Kaisa  S2ll,  Jukka  Huhtamaki,  Martha  G.  Russell,  A  Network  Analysis  of  Investment  Firms  as  Resource  Routers  in  Chinese  Innova2on  Ecosystem,  Journal  of  Networks,  Fall,  2010.   Innova2on  Ecosystem  Network    
  67. 67.   More  Specific:  Context  of  eCIS  sector   eCommerce  and  electronic  security=   eCommerce,  sovware  search,  network  hos2ng,  mobile,  games  &video,  enterprise         Ini2al  Data  Analysis:   53%  (113)  of  the  Chinese  companies   Socially   Constructed   from  eCIS  business  sector   Data     50  %  (66)  of  the  foreign  companies   are  from  the  eCIS  business  sector   Social     Network   Toward  Insights  about:   Analysis     PaVerns  and  differences  in  the   Insights  into   characteris2cs  of  investment  flows   Innova2on   into  and  from  China    Neil  Rubens,  Kaisa  S2ll,  Jukka  Huhtamaki,  Martha  G.  Russell,  A  Network  Analysis  of  Investment  Firms  as  Resource  Routers  in  Chinese  Innova2on  Ecosystem,  Journal  of  Networks,  Fall,  2010.   Innova2on  Ecosystem  Network    
  68. 68. CULTIVATION   Investments  into  China     (receiving  investments)  Neil  Rubens,  Kaisa  S2ll,  Jukka  Huhtamaki,  Martha  G.  Russell    A  Network  Analysis  of  Investment  Firms  as  Resource  Routers  in  Chinese  Innova2on  Ecosystem,  Journal  of  Networks,  Fall,  2010.   Innova2on  Ecosystem  Network  
  69. 69. Emerging  Chinese  business  clusters  linked   by  firms’  rela2onships    Neil  Rubens,  Kaisa  S2ll,  Jukka  Huhtamaki,  Martha  G.  Russell    A  Network  Analysis  of  Investment  Firms  as  Resource  Routers  in  Chinese  Innova2on  Ecosystem,  Journal  of  Networks,  Fall,  2010.  
  70. 70. HARVEST   Investments  from  Chinese     (making  investments)  Neil  Rubens,  Kaisa  S2ll,  Jukka  Huhtamaki,  Martha  G.  Russell,  A  Network  Analysis  of  Investment  Firms  as  Resource  Routers  in  Chinese  Innova2on  Ecosystem,  Journal  of  Networks,  Fall,  2010.   Innova2on  Ecosystem  Network    
  71. 71. Topline  Findings   •  Modes  of  value  co-­‐crea2on   •  Cul2va2on     •  Harves2ng   •  Chinese  interlocks  at  the  investment  firm  level   –  Government-­‐led  investment  firms   –  Knowledge  of  government  guarantees   –  Investments  in  firms  that  return  benefits  to  China   •  Global  interlocks  at  both  investment  firm  and   enterprise  levels   •  Opportunity  network  &  value  co-­‐crea2on  hVp://successbeginstoday.org/wordpress/wp-­‐content/unexpected2.jpg  
  72. 72. A  Western  View:     pretty picture:China’s  Internal  Innova2on  Ecosystem   1 description level 1: entities with HQ in level2: people that worke level3: expanding l1, l2; case intl ones (chn were alre l4; expanding l1-l3 2 l4-type  Chinese     level 4 colored by typeinvestment  organiza2ons,    companies,    branch  offices  
  73. 73. Shared Vision Transforms Iterative Impact Alignment Co-Create Value Shared   Vision   Transforma2on   Event Coalition Interact & FeedbackMartha G. Russell, Kaisa Still, Jukka Huhtamaki, and Neil Rubens, “Transforming innovation ecosystems through shared visionand network orchestration,” Triple Helix IX Conference, Stanford University, July 13, 2011.
  74. 74. Lessons  from  the  Valley   Five  Rules  for  Successful  Failure  •  Iterate  quickly   –  If  it  doesn’t  work,  change  something  –  ASAP  •  Take  personal  responsibility   –  Don’t  blame  anyone  •  Share  what  you  learned   –  Each  failure  includes  lessons  for  success  •  Start  again     –  Immediately!  •  Don’t  do  it  alone   –  Know,  cul2vate  and  orchestrate  your  network  
  75. 75. Transform Through Shared Vision at S T A N F O R D U N I V E R S I T Y• Know• Cultivate• Orchestrate 80  
  76. 76. Crea2ve  Collabora2ons  #1   TWO  PIZZAS  •  GOAL:  The  goal  of  crea2ve  collabora2ons  is  to  do   something  together  that  neither  could  do  independently.     –  Team  and  network  are  both  important.   •  Two  pizzas   –  Build  on  strengths     •  Jazz  band   •  Morph  and  improvise  •  REQUIREMENTS:  Dynamic    crea2ve  environments  require   –   Permission  to  experiment   –   Belief  in  the  upside  poten2al  •  MINDSET:  successful  failure  =  learning   –  Rules  of  thumb  
  77. 77. Crea2ve  Collabora2ons  #2   LIKE  A  JAZZ  BAND  
  78. 78. What Can We Do TogetherThat Neither of Us Could Do Alone? at S T A N F O R D U N I V E R S I T Y Thank You Martha.Russell@stanford.edu www.innovation-ecosystems.org http://mediax.stanford.edu

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