Innovation Ecosystem Transformation – Finnish Perspective

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The Transforma,on of Innova&on Ecosystems in Global Metropolitan Areas A DataDriven 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

Transcript of Innovation Ecosystem Transformation – Finnish Perspective

Page 1: Innovation Ecosystem Transformation – Finnish Perspective

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  

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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  

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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  

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•  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  

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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      

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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)  

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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    

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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  

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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  

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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  

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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      

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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  

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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  

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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  

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Boundary  specifica,on:    nodes  and  edges  

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Metrics:  for  descrip,ons  

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Visualiza,on  1:    Highligh,ng  enterprise  level  rela,onships  

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Visualiza,on  2:  Highligh,ng  growth  companies    

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Visualiza,on  3:  Highligh,ng  start-­‐up  companies  

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Visualiza,on  4:  Mul,scope  with  aggregated  data  

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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  

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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

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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

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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

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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

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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.

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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

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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

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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

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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

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Result 3: Project sphere animation

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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

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Critique

•  Method? •  Results? •  Validation? •  NAV model vs.

visual analytics

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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

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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

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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)?

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Measuring  Rela,onal  Capital  –  work  on  progress  

Dr.  Kaisa  S,ll  

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•  Sindi  2010-­‐2012  •  Reino  2013-­‐2014  •  Entegrow?  2014-­‐2015  •  SPEED  2014-­‐2015  

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Kuvalähde:  Laihonen  et  al.  (2013)  

Suhdepääoma  &    verkostot  

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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)  

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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,  Coleman  1988)  and  refers  to  the  durability  of  social  structures  as  well  as  the  social  processes  by  which  the  focal  actor’s  ac,ons  are  influenced,  directed  and  constrained  by  norms  and  ins,tu,onal  perssures  

Random  &  exogenous  

Exogenous  factors  that  can  have  an  impact    that  emanate  from  beyond  the  network  or  from  simply  random  processes,  whether  generated  inside  or  outside  (Mizruchi  1989)