Your SlideShare is downloading. ×
0
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

The Transformation of Innovation Ecosystems in Global Metropolitan Areas A Data-Driven Perspective

521

Published on

This study provides a comparative multiscopic study of the structural transformation of innovation ecosystems in select major US and worldwide areas from 1990-2013. Our results reveal distinct …

This study provides a comparative multiscopic study of the structural transformation of innovation ecosystems in select major US and worldwide areas from 1990-2013. Our results reveal distinct patterns of ecosystem formation, growth and evolution. We complement our findings using an interactive network visualization approach. Martha Russell Rahul Basole,

0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
521
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
14
Comments
0
Likes
1
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. The  Transforma,on  of     Innova&on  Ecosystems     in  Global  Metropolitan  Areas     A  Data-­‐Driven  Perspec,ve   Martha  G  Russell   Innova,on  Ecosystems  Network   2013  Annual  INFORMS  Conference,  October  8,  2013   Rahul  C.  Basole,  Neil  Rubens,  Jukka  Huhtamäki,  Kaisa  S,ll    
  • 2. People, Technology & Media 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   at S T A N F O R D U N I V E R S I T Y
  • 3. Twin  Ci,es  
  • 4. Twin  Ci,es   Aus,n   Paris   Norway                            EU  
  • 5. Innova,on  Ecosystems   Innova&on  Ecosystems  refer  to  the  inter-­‐organiza,onal,   poli,cal,  economic,  environmental,  and  technological   systems  through  which  a  milieu  conducive  to  business   growth  is  catalyzed,  sustained,  and  supported.     A  dynamic  innova,on  ecosystem  is  characterized  by  a  con,nual  realignment   of  synergis,c  rela,onships  that  promote  growth  of  the  system.    In  agile   responsiveness  to  changing  internal  and  external  forces,  knowledge,  capital   and  other  vital  resources  flow  through  these  rela,onships.     Heterogeneous  and  con,nuously  evolving  set  of  firms  that  are   interconnected  through  a  complex,  global  network  of  rela,onships.  [Basole  et  al.,  2012       Martha G. Russell, Kaisa Still, Jukka Huhtamaki, and Neil Rubens, “Transforming innovation ecosystems through shared vision and network orchestration,” Triple Helix IX Conference, Stanford University, July 13, 2011.
  • 6. ???  Who  –  Why  –  What  ???   WHO  WANTS  TO  KNOW?   •  Program  managers   •  Policy  analysts   •  Business  execu,ves   •  Entrepreneurs   WHY  DO  THEY  CARE?   •  Reduce  uncertainty   •  Allocate  resources   •  Improve  quality  of  decisions   WHAT  ARE  THEIR  QUESTIONS?   •  What  interven,ons  have   greatest  impact  poten,al?   •  What  alterna,ve  or   concurrent  interven,ons  will   improve  the  likelihood  of   sustainable  change?   •  Which  systemic  factors  may     produce  counterintui,ve   results  in  the  short  term?   •  What  ,me  is  required  to  see   impact?  
  • 7. Our  Key  Contribu,ons   Mul&ple  Data  Sources   •  Deals  &  Alliances   •  Execu,ves  &  Finance   •  Startups  and  Angels   Focus  on  Rela&onships   •  Conduits  for  resource   flows   •  Co-­‐create  shared  vision   •  Co-­‐create  shared  value   Ecosystem  Scope   •  Products,  services,   educa,onal  ins,tu,ons   •  Mul,-­‐na,onal,  English   language   •  To  2013,  with  history   added   •  Data-­‐driven  visualiza,ons   •  For  analysts  and  decision   makers  
  • 8. Actors & Events Impact over time Coalitions Transforma,on   Measure & Track Interact & Feedback Co-Create Value Measuring Impact of Transformative Coalitions in Innovation Ecosystems Martha G. Russell, Kaisa Still, Jukka Huhtamaki, and Neil Rubens, “Transforming innovation ecosystems through shared vision and network orchestration,” Triple Helix IX Conference, Stanford University, July 13, 2011. Shared   Vision  
  • 9. Employee   Execu&ve   Advisor   Founder   Investor     Acquisi&on   Deal   Alliance   Financing   Investor   Angel  
  • 10. Shared  Vision  Based  on  Rela,onal  Capital   We  are  connected   Employee        Execu,ve   Advisor        Founder   Acquisi,on   Deal                                                    Alliance   Investment   Angel   People   Company   Financing   University   Research  project   Publica,ons   Talent   Company   These  rela,onships  can  be  represented  in  many  ways   List                      Matrix                              Network   Basole,  Russell,  Huhtamäki,  S,ll  and  Rubens,  (2013)   “Understanding  Mobile  Ecosystem  Dynamics:  A  Data-­‐Driven  Approach,”  Under  Review  
  • 11. Innova,on  Ecosystem  Network     Data-­‐driven  Visualiza,ons   Basole,  Russell,  Huhtamäki,  S,ll  and  Rubens,  (2013)   “Understanding  Mobile  Ecosystem  Dynamics:  A  Data-­‐Driven  Approach,”  Under  Review   STEP 1 Boundary Specification STEP 2 Metrics Identification STEP 3 Computation, Analysis & Visualization STEP 4 Sense Making & Storytelling DATA determines feeds Deals & Alliances Executives & Funding Public Opinion & Discourse IEN NL SDC identify, extract, and curate
  • 12. hfp://www.slowtrav.com/blog/chiocciola/Geirangeriord.jpg   Norwegian  Example  
  • 13. International Relationships for Value Co-Creation Huge opportunities for international relationships lie 2 & 3 degrees out from Norwegian companies 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.   IEN  Dataset,  July  2010   Knowledge-­‐Based  Norway,  Presented  at  Business  Ins,tute,  October  6,  2010,  Martha  Russell  and  Neil  Reubens  
  • 14. Aus,n  Ecosystem     Enterprises,  Deals  &  Alliances   678  companies  and  498  interconnec,ons   Russell,  Huhtamäki,  Basole,  S,ll,  Yu  &  Rubens,  IC2  Ins,tute  Global  Fellows  Mee,ng,  April,  2013  
  • 15. Aus&n’s  Young  Growth  Companies   Primary  Office  in  Aus&n   Investors     Aus,n  Ventures     Silverton  Partners     DFJ  Mercury     Bafery-­‐  Ventures     Floodgate     S3  Ventures     New  Enterprise  Associates     Rho  Capital  Ventures     Texas  Emerging  Technology  Fund     Interwest  Partners     Companies  =  801   Investors  –  286   Leadership  Indiv’s  =  1355     Key  Individuals     Joshua-­‐  Baer     Kevin-­‐  Cunningham     John  Hime     Kip  Mcclanahan     Bill  Boebel     Larry  Warnock     Robert  Reeves     Jason  Calacanis     Mike  Maples     Ron  Conway     Companies   Da,cal     Homeaway     Sailpoint-­‐technologies     Mass-­‐relevance     Digby     B-­‐side     Bazaarvoice     Wp-­‐engine     Convio-­‐2     Gowalla     Actacell     IEN  Dataset,  March,  2013   Russell,  Huhtamäki,  Basole,  S,ll,  Yu  &  Rubens,  IC2  Ins,tute  Global  Fellows  Mee,ng,  April,  2013  
  • 16. Colleges/Universi&es  =    200+   University  of  Texas,  Aus,n     Stanford  University     Massachusefs  Ins,tute  of  Technology     University  of  California,  Berkeley   Georgia  Ins,tute  of  Technology     University  of  Michigan     Harvard  University   Purdue  University     University  of  Colorado,  Boulder     University  of  Chicago     Loca&ons  =  200+   San  Francisco   New  York,  NY   Los  Angeles   Houston   Palo  Alto     Dallas   London     Boston   Chicago   Silicon  Valley     Sectors/Markets  =  374   Social  media     Mobile   SaaS   e-­‐commerce   Digital  media     Social  commerce   Consumer  internet     Marketplaces   Small  and  medium  businesses   Educa,on     Companies  =  2000+   Latakoo     Bazaarvoice   Infochimps     SalesVu   OwnLocal     Predictable  Data     Mass  Relevance     Forcas,x   SQMOS   Vivogig       Network  of  Aus,n  Start-­‐ups,    Founders  &  Angels   IEN  Dataset,  March,  2013   692  companies   3500+  investors     Russell,  Huhtamäki,  Basole,  S,ll,  Yu  &  Rubens,  IC2  Ins,tute  Global  Fellows  Mee,ng,  April,  2013  
  • 17. Aus,n  ICT  Innova,on  Ecosystem     at  a  Glance   Russell,  Huhtamäki,  Basole,  S,ll,  Yu  &  Rubens,  IC2  Ins,tute  Global  Fellows  Mee,ng,  April,  2013   Startups  &  Angels                  Execu,ves  &  Finance                      Deals  &  Alliances     SDC  Data  2013.  Aus,n  companies   With  Deals  &  Alliances   Nodes  =  678  Edges  =  498   IEN  Data  2013.    Aus,n  companies   Linked  people  &  venture/financing  en,,es   Nodes=  2442;  Companies=802   Startup/Angel  Data  2013.     Selected  Aus,n  companies   Nodes=  5062;  Startups=2200;  Edges=11837  
  • 18. Twin  Ci,es  
  • 19. Twin  Ci,es/MN  ICT  Innova,on   Ecosystem  at  a  Glance   SDC  Data  2013.  Selected  MN  companies   With  Deals  &  Alliances   Nodes  =  1406;  Edges  =  1226   IEN  Data  2013.  Selected  MN  companies   Linked  people  &  venture/financing  en,,es   Nodes=  1358;  Edges=978   Startup/Angel  Data  2013.     Selected  Minnesota  companies   Nodes=  885;  Edges  =  1185   Startups  &  Angels                  Execu,ves  &  Finance                      Deals  &  Alliances    
  • 20. CapDigital  -­‐  Regional  Sector  Catalyst   Vision   To  catalyze  the  new  digital  infrastructure  in  France  with  global  connec,ons   To  create  an  ecosystem  to  facilitate  the  rela,onship  between  France  and  global   market   Enable  Paris  to  become  global  region  of  the  market  for  digital  services         How  do  you  spend  money   locally  to  enhance  global   par,cipa,on  in  a  way  that   returns  the  benefit  back   home?   • CapDigital  members:   –  Small  startups   –  Large  companies   –  Support  programs   21  
  • 21. Paris  ICT  Innova,on  Ecosystem     at  a  Glance   SDC  Data  2013.  Selected  French  companies   With  Deals  &  Alliances,  Highlight  CapDigital     Nodes  =  3192;  Edges  =  3441   IEN  Data  2013.  Selected  FRENCH  companies   Linked  people  &  venture/financing  en,,es   Nodes=  2858;  Edges=2333   Startup/Angel  Data  2013.  Selected  French  companies   Highlight  CapDigital  Members   Nodes=  990;  Edges  =  530   Startups  &  Angels                      Execu,ves  &  Finance                  Deals  &  Alliances     Russell,  Huhtamaki,  S,ll,  Basole,  and  Reubens,    “Orchestra,ng  Ecosystem  Transforma,ons  with  Data-­‐Driven     Network  Visualiza,ons,”Keynote  Presenta,on,  Futur  en  Seine,  June  14,  2013.  
  • 22. Analysis  of  EIT   ICT  Labs:  Trento   included  as  the   sixth  node,  more   ci,es  connected   to  coloca,on   centers,  updated   data  and   transforma,on  in   place     S,ll,  Huhtamäki,  Russell,  Rubens   (2013)  ”Transforming  Innova,on   Ecosystems  Through  Network   Orchestra,on:  Case  EIT  ICT  Labs,”   IJTM  Special  Issue.  
  • 23. Adding  San   Francisco  Bay   Area  as  “the   seventh  EIT  ICT   Labs  node”  for   contrast,   interconnec,ons,   comparison  and   benchmark   S,ll,  Huhtamäki,  Russell,  Rubens   (2013).  Transforming  InnovaLon   Ecosystems  Through  Network   OrchestraLon:  Case  EIT  ICT  Labs  
  • 24. 26 Skill  of  the  Future  =     Network  Orchestra,on  
  • 25. Actors & Events Impact over time Coalitions Transforma,on   Measure & Track I E Health & Resilience Intra-network linkages Betweenness Density Size Interact & Feedback Co-Create Value Measuring Impact of Transformative Coalitions in Innovation Ecosystems Martha G. Russell, Kaisa Still, Jukka Huhtamaki, and Neil Rubens, “Transforming innovation ecosystems through shared vision and network orchestration,” Triple Helix IX Conference, Stanford University, July 13, 2011. Shared   Vision  
  • 26. •  Thank  you           •  Martha  Russell   •  Innova,on-­‐ecosystems.org   •  marthar@stanford.edu  

×