Innovation Ecosystem Transformation – Finnish Perspective

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Presentation given to a Finnish eMBA group during their visit at mediaX at Stanford.

Presenters: Kaisa Still, Jukka Huhtamäki
Session chair: Martha G. Russell

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Innovation Ecosystem Transformation – Finnish Perspective

  1. 1. The  Transforma,on  of     Innova&on  Ecosystems     in  Global  Metropolitan  Areas     A  Data-­‐Driven  Perspec,ve   Martha  G  Russell,  Jukka  Huhtamäki,  Kaisa  S,ll   Innova,on  Ecosystems  Network   TUT  eMBA  Visit  to  Stanford  University   Martha  Russell,  Rahul  C.  Basole,  Neil  Rubens,  Jukka  Huhtamäki,  Kaisa  S,ll  
  2. 2. Transforming  Innova,on   Ecosystems  Through  Network   Orchestra,on:  Case  EIT  ICT  Labs   Dr.  Kaisa  S,ll,  VTT  Technical  Research  Centre  of  Finland       In  collabora,on  with  Marko  Turpeinen  and  others  at  EIT  ICT  Labs  Helsinki  
  3. 3. Need  for  innova,on   indicators:     tradi,onal  measures  and   metrics  are  limited   Innova,on  ac,vi,es   rarely  carried  out  within   a  single  organiza,on:   Network  approach  to   understand  the  complex   systems  of  innova,on   Unprecedented   amount  of  data  about   the  complex   innova,on  system   and  its  actors:     Social  media,  socially   constructed  data   Possibili,es  of  SNA  and   visualiza,ons     Computer  power  
  4. 4. •  EIT  ICT  Labs  aims  “to  build  European  trust  based  on  mobility  of   people  across  countries,  disciplines  and  organiza,on”     •  People,  their  knowledge  and  the  financial  flows  are  networked,  all   contribu,ng  toward  poten,al  of  innova,on  -­‐>  Analysis  should  not   be  limited  to  labor  mobility   •  How  to  measure,  analyze  and  visualize  mobility  of  people,  money   and  technology  in  the  European  ICT  innova,on  ecosystem?   EIT  ICT  Labs’  mission  is  to  turn  Europe     into  a  global  leader  in  ICT  Innova,on  
  5. 5. Mobility  is  a  central  theme   5  nodes  working   together   Student  and  teacher   mobility,  Doctoral   School,   Mobility  programs   Ini,al  analysis  of  mobility     (S#ll  et  al  2010)  for  baseline:   with  geospa,al   representa,ons  of  networks   and  a  metric  of  betweenness   Highligh,ng  few  individuals,  more  investors,  less  so  of   universi,es,  and  the  role  of  Silicon  Valley  as  connectorà   (1)  new  ”requirements”  for  data/  process  of  next   network  visualiza,on,  and  (2)  ini,al  insights  for  network   orchestra,on      
  6. 6. Two  Studies   §   Using  IEN  Dataset     §   Betweenness  Centrality     §  number  of  ,mes  that  a  given  node  is  included  in  the  shortest  path   between  any  two  nodes  in  the  network  (Wasserman  and  Faust,  1994)   §  point  out  investors,  individuals  and  educa,onal  ins,tu,ons  that  operate   in  between  the  six  EIT  ICT  Labs  Nodes   §  Coupled  with  the  modeling  applied,  can  be  used  as  a  metric  for  actor   mobility  in  an  innova,on  ecosystem   §   Note:  analysis  does  not  show  the  mobility  of  people  within  individual  companies   §   Two  consecu,ve  analysis:  first  in  2011  and  the  second  in  2012,  with  refined  segng   and  updated  data   (Gray,  2012)  
  7. 7.               S,ll,  Russell,   Huhtamäki,   Turpeinen,  Rubens   (2011).  Explaining   innova#on  with   indicators  of  mobility   and  networks:   Insights  into  central   innova#on  nodes  in   Europe     Mobility  and  Educa,onal   Ins,tu,ons  2011    
  8. 8. S,ll,  Russell,   Huhtamäki,   Turpeinen,  Rubens   (2011).  Explaining   innova#on  with   indicators  of  mobility   and  networks:   Insights  into  central   innova#on  nodes  in   Europe     Mobility  and   Financial   Flows  2011  
  9. 9. Analysis  round  #2:   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  (2012).   Transforming   Innova#on  Ecosystems   Through  Network   Orchestra#on:  Case  EIT   ICT  Labs  
  10. 10. Finally,  adding   San  Francisco   Bay  Area  as  “the   seventh  EIT  ICT   Labs  node”  for   contrast,   interconnec,ons,   comparison  and   benchmark   S,ll,  Huhtamäki,   Russell,  Rubens  (2012).   Transforming   Innova#on  Ecosystems   Through  Network   Orchestra#on:  Case  EIT   ICT  Labs  
  11. 11. Conclusions   §   Geospa,al  social  network  visualiza,on  make  it  possible   to  share  and  show  special  characteris,cs,  significant   actors  and  connenc,ons  in  the  innova,on  ecosystem   §   Betweenness  centrality  (how  central  a  node  is  within  a   network)  can  be  used  to  measure  innova,on  poten,al  of   an  ecosystem   §   Our  framework  can  be  used  for  understanding  the   transforma,on  and  for  bringing  transparency   § At  the  same  ,me,  when  interpreted  in  the  context,  our   approach  can  be  used  to  suggest  possibili,es  for  network   orchestra,on      
  12. 12. Networks  of  innova&on   rela&onships:  mul&scopic   views  on  Finland   Presented  at  ISPIM  Helsinki  2012   Kaisa  S,ll,  VTT   Jukka  Huhtamäki,  TUT   Martha  G.  Russell,  Stanford  mediaX   Rahul  C.  Basole,  Georgia  Tech   Jaakko,  Salonen,  TUT   Neil  Rubens,  University  of  Electro-­‐ Communica,ons   Jukka  Huhtamäki,  Tampere  University  of  Technology  
  13. 13. Networks  of  innova,on   Approach   By    whom   The  shik  of  innova,on  from  a   single  firm  toward  an  increasingly   network-­‐centric  ac,vity   Chesbrough  2003   Importance  of  collabora,on  and   value  co-­‐crea,on   Ramaswamy  and  Goullart  2010   Resul,ng  networks  of  rela,onships   between  individual  and   organiza,onal  en,,es   Kogut  and  Zander  1996,  Vargo   2009   Studies  of  innova,on  ecosystems   Iansi,  and  Levien  2004,  Russell  et   al.  2011,  Basole  et  al.  2012,   Hwang  and  Horowio  2012,  Marts   et  al.  2012  
  14. 14. From  data  with  visualiza,on  to   insights   Sense-­‐making  and  storytelling   Boundary  specifica,on   Computa,on,  analysis  and   visualiza,on   Metrics  iden,fica,on   Analysing  a  business   ecosystem  
  15. 15. Boundary  specifica,on:     nodes  and  edges  
  16. 16. Metrics:  for  descrip,ons  
  17. 17. Visualiza,on  1:     Highligh,ng  enterprise  level  rela,onships  
  18. 18. Visualiza,on  2:   Highligh,ng  growth  companies    
  19. 19. Visualiza,on  3:   Highligh,ng  start-­‐up  companies  
  20. 20. Visualiza,on  4:   Mul,scope  with  aggregated  data  
  21. 21. Sense-­‐making  and  storytelling:     So  what?   •  Visualiza,ons  of   metrics  and  networks   can  be  seen  to  model   the  skeleton  of  an   ecosystem   •  Tacit  knowledge  about   networks  (and  the   roles  of  certain  actors)   becomes  explicit  and   shared  
  22. 22. Visualizing an Open Innovation Platform: The structure and dynamics of Demola Huhtamäki, Luotonen, Kairamo, Still, Russell TUT // New Factory // VTT // Stanford Academic MindTrek 2013: "Making Sense of Converging Media” http://bit.ly/mt2013visualizingdemola // @jnkka
  23. 23. In this presentation •  Case context description: what is Demola? •  Challenges in measuring Demola & open innovation •  Use case examples •  Method: data-driven network animation •  Results •  Discussion •  Critique •  Wrap up and future work
  24. 24. What is Demola? • Open innovation platform & ecosystem engager established in 2008 in Tampere • By 2013, 86 companies and 1200 students from 3 universities have participated in 250+ projects • The Demola network is expanding internationally • This study focuses in Demola Tampere
  25. 25. Open innovation platform & ecosystem engager established in 2008 in Tampere By 2013, 86 companies and 1200 students from 3 universities have participated in 250+ projects The Demola network is expanding internationally; this study focuses in Demola Tampere
  26. 26. Challenges in measuring Demola …and open innovation in general: • Tradition: A linear view on innovation; • Measuring inputs (money) and outputs (patents, products, new companies); • Survey-based methods, aggregate measures How does one measure the performance of an ecosystem engager? Still, K., Huhtamäki, J., Russell, M. & Rubens, N. 2012. Paradigm shift in innovation indicators—from analog to digital. Proceedings of the 5th ISPIM Innovation Forum, 9-12 December, Seoul, Korea.
  27. 27. Use case examples Who wants to measure? Why do they want to measure? What will the do with the measurement insights? Policy makers Interested in the impact that Demola has had to the surrounding ecosystem Evaluate the utility of the platform for future investments and the applicability of the approach Company representatives Utility of Demola engament Decide whether to engage or not; Select an approach suitable for their portfolio Demola operators Activity in general; Companies with changing (increasing/decreasing) Demola engagement; Ecosystem Structure General Demola introductions, marketing & sales; Demola key area development University students Reviewing opportunities that participating in a Demola project would open Decide whether to participate or not University decision- makers Impact, new developments in the ecosystem Add initiatives for students to get involved International actors Impact, engagement, transformation To evaluate the utility of the process for deciding the applicability of the approach
  28. 28. Method: data-driven network analysis (& action research) (Hansen et al., 2009) Project Detail Example Project Id Project 115 Name Koukkuniemi 2020 Started 2010-05-04 Ended 2010-10-31 Status Completed Collaboration Partner City of Tampere Type of Partner Public Project Domain Non-profit Location Tampere Key Areas well-being, knowledge management, regional studies Project Team Members uta, uta, tut, tut
  29. 29. Result 1: Project network Nodes represent projects and companies Company nodes are light green; other colors indicate cluster membership Node size shows its betweenness value Force-driven layout
  30. 30. Result 2: Project domain network Nodes represent project domains Nodes are connected through domain co- occurence Colors show cluster membership Node size shows its betweenness
  31. 31. Result 3: Project sphere animation
  32. 32. Discussion • Technical challenges exist when using internally collected data for network visualization and animation • Visualization development challenges data- collection procedures and can add value to existing data • Demola operators find value particularly in the animation of the project sphere; international collaborators have also expressed an interest in them
  33. 33. Critique •  Method? •  Results? •  Validation? •  NAV model vs. visual analytics
  34. 34. Acknowledgements & thank you Time for your comments and questions. Jukka Huhtamäki <jukka.huhtamaki @tut.fi> Ville Luotonen Ville Kairamo Kaisa Still Martha G. Russell Acknowledgements Ville Ilkkala, Meanfish Ltd, supported animation development. Heikki Ilvespakka took care of exporting the data from the Demola platform
  35. 35. Innovation ecosystem Context Data-driven visualization Process Availability of relational data about innovation activities (free, easily available public data) Can be studied as networks (SNA) Application arena Supporting insights on Highlighting Network visualization Innovation indicators Indicator ”osoitin” Network dynamics Relational capital (Ecosystemic relational capital) metrics Various levels: International National Local/regional Organizational
  36. 36. Questions What would your ecosystem look like based on the publicly available data? §  What info is there about you, your organization, your stakeholders– and the connections between all these? à Is this relevant for you? Could this have implications for some action? Would the visualization of your ecosystem be valuable for you? §  How? §  What could you do better with that? §  What could you do that you cannot do now? Would knowing about your relational capital be valuable for you? §  How? §  What could you do better with that? §  What could you do that you cannot do now? Where could we find more relational data (easily available public data, almost free)?
  37. 37. Measuring  Rela,onal  Capital  –   work  on  progress   Dr.  Kaisa  S,ll  
  38. 38. •  Sindi  2010-­‐2012   •  Reino  2013-­‐2014   •  Entegrow?  2014-­‐2015   •  SPEED  2014-­‐2015  
  39. 39. Kuvalähde:  Laihonen  et  al.  (2013) Suhdepääoma  &     verkostot  
  40. 40. Framework  of  network  dynamics  (Ahuja  et  al  2009):   Operate  via  the  mechanisms   of:   •  Homophily   •  Heterophily   •  Prominence  aorac,on   •  Brokerage   •  Closure     Microdynamics   of  networks   Network   Architecture   Dimension   Network   primi,ves     Micro-­‐ founda,ons  of   networks:   Basic  factors  that  drive  or   shape  the  forma,on  and   content  of  ,es  in  the  network:   •  Agency   •  Opportunity   •  Iner,a   •  Random  &  Exogenous     Causing  changes  in  network   membership    (through   dissolu,on  or  forma,on  of   ,es,  changes  in  ,e  content,   strength  and  mul,plexity)   Structure   -­‐  Ego  network   •  Centrality   •  Contraint   -­‐  Whole  network   •  Degree  distribu,on   •  Connec,vity   •  Clustering   •  Density   •  Degree    assorta,vity   Content   •  Types  of  flows   •  Number  of  dis,nct  flows   (mul,plexity)     Architecture  of  any  network  can  be   conceptualized  in  terms  of:     •  Nodes  (that  comprise  the  network)   •  Ties  (that  connect  the  nodes)   •  Structure  (the  paoerns  of  structure  that   result  from  these  connec,ons)  
  41. 41. Dimensions  of   dynamics   Descrip&on   Meaning   Network  Architecture  Dimension  for  structure   Ego  network   Centrality   has  been  associated  with  a  wide  variety  of  poten,al  benefits  such  as  access  to  diverse  informa,on  and  higher  status  or  pres,ge    (Brass   1985)     Constraint   The  presence  of  structural  hole  is  commonly  related  to  brokerage  possibili,es  (Burt  1992,  Zaheer  and  Soda  2009)   Whole  network   Degree   Distribu,on   reflects  the  rela,ve  frequency  of  the  occurrence  of   ,es  across  nodes  or  the  variance  in  the  distribu,on   of  ,es  (Jackson  2008   has  been  used  to  signify  the  dis,bu,on  of  status,  power  or  pres,ge  across  organiza,ons  (Gula,  and  Caguilo,  1999;  Ahuja,  Polidoro  and   Mitchell  2009);  may    be  reflec,ve  of  changes  in  the  status  hierarchy  of  the  observed  system  (Ahuja  et  al  2009)   Connec,vity   Is  captured  in  the  diameter  of  a  network  which  in   turn  reflects  the  largest  path-­‐distance  between  any   two  nodes  of  the  network  (Jackson  2008)   The  average  path  length  connec,ng  any  two  nodes  in  the  ntework  is  an  indicator  of  the  connec,vity  or  ”small-­‐wordness”  of  the   network;  as  network  becomes  more  ”small-­‐wordly”  informa,on  can  diffuse  more  quickly  fostering  outcomes  such  as  inova,on  or   crea,vity  (Schilling  2005,  Schilling  and  Phelps  2007);  as  the  path  length  between  any  two  nodes  of  a  network  diminishes,  it  is  possible   that  informa,on  can  become  more  decomra,zed  and  result  in  a  reduc,on  in  the  informa,onal  advantage  of  any  single  player  (Ahuja   et  al  2009)     Clustering   The  degree  to  which  the  network  is  formed  of  ,ghtly   interconnected  cliques  (Ahuja  et  al  2009)   The  emergence  of  inter-­‐connected  subgroups  or  cliques  suggests  that  the  network  is  being  differen,ated  into  a  variety  of  dis,nct  sub-­‐ networks  or  communi,es  (Ahuja  et  al  2009);  at  inter-­‐organiza,onal  level  this  may  represent  the  reclustering  of  clusters  or   constella,ons  of  firms  that  may  be  compe,ng  against  each  other  as  ’alliance  network’  (Gomes-­‐Cassares  1994);  clique  instability  maybe   a  precursor  of  a  significant  technological  discon,nuity    if  the  network  is  an  interorganiza,onal  technology  network,  or  perhaps  portend   an  imminent  change  in  the  power  structure  of  an  organiza,on  in  an  intraorganiza,onal  employee  network  (Ahuja  et  al  2009)   Density   The  propor,on  of  ,es  that  are  realized  in  the   network  rela,ve  to  the  hypothe,cal  maximum   possible  (Ahuja  et  al  2009)   In  organiza,onal  segngs,  higher  network  density  may  be  reflec,ve  of  network  closure,  a  condi,on  that  in  turn  may  be  associated  with   the  development  of  norms;  increasing  density  could  be  reflec,ng  in  a  reduc,on  of  diversity  of  perspec,ves  and  choice  within  the   network  as  the  high  propor,on  of  realized  ,es  provide  a  hologenizing  influnce  across  actors  ,  and  thus  results  in  increasing  reifica,on   of  ideas  (Ahuja  et  al  2009)   Degree   Assorta,vity   The  degree  to  which  nodes  with  similar  degrees   connect  to  each  other  (Waos,  2004)   Posi,ve  assorta,vity  implies  that  high-­‐degree  nodes  connect  to  other  high  degree  nodes  etc.  ;  in  an  intra-­‐organiza,onal  segng,   assorta,vity  could  be  driven  by  homophily  processes  and  disassorta,vy  by  complimentary  needs  (Ahuja  et  al  2009;    assorta,vity  can   be  associated  with  the  emergence  of  a  core-­‐periphery  structure  (Borgag  and  Evereo  1999)  where  a  set  of  densely  connected  actors   cons,tute  a  core  of  an  industry  while  many  of  other  low  degree  actors  cons,tute  a  periphery.  Changes  might  signal  a  shik  in  the   resource  requirements  for  success  in  the  industry    (Powell,  Packalen  and  Whigngton  ????)   Microfounda&ons–  d   Agency   Agency  behavior,  choosing  or  not  choosing  to   establish  connec,ons;  The  focal  actor’s  mo,va,on   and  ability  to  shape  rela,ons,  and  create  a  beneficial   link  or  dissolve  an  unprofitable  one  or  shape  an   advantageous  structure  (Sewell  1992;  Emirbayer  and   Goodwin  1994;  Emirbayer  and  Mische  1998)   As  actors  deliberately  seek  to  create  social  structures,  which  is  in  line  iwth  Burt’s  idea  of  structural  holes  as  socfial  capital,  highligh,ng   the  entrepreneurial  role  in  the  crea,on  of  this  valuable  form  os  social  structure  (Burt  1992)   à  Network  structures  emerbe  as  a  result  of  self-­‐seeking  ac,ons  by  focal  nodes  and  their  connec,ons,  no,ng  that  actors  can  devise   unique  responses  to  imporve  their  own  situa,ons  in  the  network  (Ahuja  et  al  2009)   Opportunity   Reflects  the  structural  context  of  ac,on  (Blau  1994)   and  includes    the  argument  that  actors  tend  to   prefer  linking  within  groups  rather  than  across  them   (Li  and  Rowley  2002)   Iner,a   Includes  the  pressures  for  persistence  and  change   (Giddens  1984,  Portes  and  Sensenbrenner  1993,  

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