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Beaudry, Schiffauerova & Moazami_The scientific and technological nanotechnology networks the comparison between canada, quebec and the united states

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Parallel session 3

Parallel session 3


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  • 1. A  Network  Perspec.ve  of  Nanotechnology  Innova.on:    A  Comparison  of  Quebec,  Canada  and  the  United  States   The  Responsible  Development  of  Nanotechnology:  Challenges  and  Perspec.ves   Ne3LS  Network  Interna.onal  Conference  November  1-­‐2,  2012,  Montreal,  Canada   Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami  
  • 2. Outline•  Introduc)on  •  Literature  review  •  Hypotheses  •  Data  and  methodology  •  Results  •  Policy  Implica)ons  •  Limita)ons  and  future  works   Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   2  
  • 3. Introduction •  Nanotechnology   –  a  general-­‐purpose  technology   –  a  concern  for  many  countries,  including   Canada   •  Innova)on   –  where  is  it  created?     –  how  is  it  transferred?  IntroductionLiterature reviewHypotheses •  Networks  analysis  Data and methodologyResults –  studying  the  structure  of  rela)onships  Policy Implications between  actors  Limitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   3  
  • 4. National Innovation System (NIS) •  A  network  of  ins)tu)ons  which   contribute  to  the  development  and   diffusion  of  new  technologies  in  a   country  (Freeman  1987,  Lundval  1992)   •  Three  main  sectors  of  NIS  and  their  oJen   focus   1.  universi)es:  fundamental  research  IntroductionLiterature review 2.  governmental  labs:  applied  research  HypothesesData and methodology 3.  industrial  sectors:  applied  research  ResultsPolicy Implications (Niosi  2000)  Limitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   4  
  • 5. NIS in Quebec, Canada, and the US •  More  important  role  of  interna)onal   linkages  in  Canada  compared  to  the  US   (OECD  1999)   •  The  US  is  leading  in  nanotechnology   publica)ons  and  patents   •  Quebec  policies  (QPSI  2002)   –  independent  ac)vi)es  IntroductionLiterature review –  financial  resources    HypothesesData and methodology –  infrastructure    ResultsPolicy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   5  
  • 6. Network Structural Properties •  Vertex  centrality   –  betweenness  centrality  (Brandes  2001)   •  number  of  shortest  paths  that  pass  through  one   vertex  over  the  total  number  of  shortest  paths   –  degree  centrality  (Arenas  et  al.  2008)   •  number  of  edges  connected  to  one  vertex   •  Fragmenta)on  (Beaudry  and  Schiffauerova  2010)  Introduction –  size  of  the  largest  component  Literature reviewHypotheses –  average  size  of  components  Data and methodologyResults –  number  of  isolated  ver)ces  Policy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   6  
  • 7. Hypotheses (I / III) •  Regional  characteris)cs  of  the  networks   –  H1  (Reg-­‐CA/Intl):  Interna)onal  collabora)on   form  a  significant  part  of  the  overall   Canadian  collabora)on  paern   –  H2  (Reg-­‐QC):  Quebec-­‐based  researchers  are   involved  in  more  internal  research   rela)onships  within  Quebec  Introduction  Literature reviewHypothesesData and methodologyResultsPolicy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   7  
  • 8. Hypotheses (II / III) •  Academia  and  industry   –  H3  (Aff-­‐metrics):  Academics  are  more   clustered,  more  centralized  and  have  a   higher  number  of  direct  )es  than  non-­‐ academics   –  H4  (Aff-­‐AC/NA  pos):  Academics,  who  co-­‐ author  ar)cles  with  industrial  scien)sts,   occupy  (a)  more  cliquish  and  (b)  more  IntroductionLiterature review central  posi)ons  compared  with  academics  HypothesesData and methodology who  do  not  collaborate  with  industrial  ResultsPolicy Implications scien)sts  Limitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   8  
  • 9. Hypotheses (III / III) •  Regional  differences  in  academia  and   industry   –  H5  (RegAff-­‐AC):  American  academics  (a)   collaborate  more  with  non-­‐academic   scien)sts,  and  occupy  (b)  more  central  and   (b)  more  cliquish  network  posi)ons     compared  to  their  Canadian  counterparts.    Introduction –  H6  (RegAff-­‐NA):  The  US  non-­‐academic  Literature review network  is  (a)  more  centralized  and  HypothesesData and methodology clustered,  and  (b)  accounts  for  a  greater   propor)on  of  the  researchers  than  Canada  ResultsPolicy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   9  
  • 10. IntroductionLiterature reviewHypothesesData and methodologyResultsPolicy Implications Methodology StepsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   10  
  • 11. IntroductionLiterature reviewHypothesesData and methodologyResultsPolicy Implications Methodology StepsLimitations and future works Database of patents patents: 240,436 Database of articles inventors: 236,784 articles: 748,251 collaborations: 688,052 authors: 1,050,676 collaborations: 3,160,795 Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   11  
  • 12. Network Building •  Time  events  networks   –  3-­‐year  intervals   –  patents  (1994-­‐2002),  ar)cles  (1994-­‐2008)   •  Regional  Networks   –  patents:  based  on  city  of  residency   –  ar)cles:  based  on  affilia)ons  Introduction •  Affilia)on  Networks    Literature reviewHypotheses –  only  for  ar)cles  and  based  on  the  Data and methodologyResults affilia)ons  of  scien)sts  Policy ImplicationsLimitations and future works –  high  share  of  industry  in  non-­‐academics   Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   12  
  • 13. Canada vs. the US •  Interna)onal  collabora)ons  (H1  Reg-­‐Ca/Intl)     Canada   The  US   Canada   Canada   30%   2%   World   World   43%   44%   The  US  Introduction 55%  Literature review The  US  Hypotheses 27%  Data and methodologyResultsPolicy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   13  
  • 14. Quebec vs. Rest of Canada •  The  tendency  to  internal  collabora)on   is  increasing  in  Quebec  (H2  Reg-­‐QC)   3   Authors    for  Quebec-­‐based  researchers     Average  Number  of  Collaborators  per   2.5   2   1.5   1  IntroductionLiterature review 0.5  HypothesesData and methodology 0  ResultsPolicy ImplicationsLimitations and future works Quebec   Rest  of  Canada   Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   14  
  • 15. Academics vs. Industry  Degree  Centrality  (Academia  vs.  Industry)   9.5   9   8.5   Average  Degree  Centrality   8   7.5   7   6.5   6  IntroductionLiterature review 5.5  HypothesesData and methodology 5  Results 94-­‐96   95-­‐97   96-­‐98   97-­‐99   98-­‐00   99-­‐01   00-­‐02   01-­‐03   02-­‐04   03-­‐05   04-­‐06   05-­‐07   06-­‐08  Policy Implications Academic  researchers   Non-­‐academic  researchers  Limitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   15  
  • 16. Academics vs. Industry  Betweenness  Centrality  (Academia  vs.  Industry)   30   Average  Betweenness  Centrality  (x10^6)   25   20   15   10  Introduction 5  Literature reviewHypotheses 0  Data and methodology 94-­‐96   95-­‐97   96-­‐98   97-­‐99   98-­‐00   99-­‐01   00-­‐02   01-­‐03   02-­‐04   03-­‐05   04-­‐06   05-­‐07   06-­‐08  ResultsPolicy Implications Academic  researchers   Non-­‐academic  researchers  Limitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   16  
  • 17. Academics vs. Industry Cliquishness  (Academia  vs.  Industry)   0.88   0.87   Average  Clustering  Coefficient     0.86   0.85   0.84   0.83   0.82  Introduction 0.81  Literature reviewHypotheses 0.8  Data and methodology 94-­‐96   95-­‐97   96-­‐98   97-­‐99   98-­‐00   99-­‐01   00-­‐02   01-­‐03   02-­‐04   03-­‐05   04-­‐06   05-­‐07   06-­‐08  ResultsPolicy Implications Academic  researchers   Non-­‐academic  researchers  Limitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   17  
  • 18. Academics vs. Industry •  Nanotechnology  researchers  from   industry  are     a)  more  clustered     b)  more  central  in  terms  of  degree   centrality   c)  slightly  less  central  in  terms  of  Introduction betweenness  centrality      than  academics  Literature reviewHypothesesData and methodologyResults  (H3  Aff-­‐metrics)  Policy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   18  
  • 19. Links of Academia and Industry Degree Centrality 12   11   Academics  with  only   academic  collabora)on   10   (AC-­‐AC)   Average  Degree  Centrality     9   Academics  with  at  least   8   one  non-­‐academic   collabora)on  (AC-­‐NA)   7   6   Non-­‐Academics  with  only   Non-­‐academic   5   collabora)on  (NA-­‐NA)   4  Introduction Non-­‐  Academics  with  at   3   least  one  academic  Literature review collabora)on  (NA-­‐AC)  Hypotheses 2   94-­‐96  95-­‐97  96-­‐98  97-­‐99  98-­‐00  99-­‐01  00-­‐02  01-­‐03  02-­‐04  03-­‐05  04-­‐06  05-­‐07  06-­‐08  Data and methodologyResultsPolicy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   19  
  • 20. Links of Academia and Industry Betweenness Centrality 40   Average  Betweenness  Centrality  (x10^6)   35   Academics  with  only   academic  collabora)on   30   (AC-­‐AC)   25   Academics  with  at  least   one  non-­‐academic   20   collabora)on  (AC-­‐NA)   15   Non-­‐Academics  with  only   Non-­‐academic   10   collabora)on  (NA-­‐NA)  Introduction 5   Non-­‐  Academics  with  at  Literature review least  one  academic  Hypotheses collabora)on  (NA-­‐AC)   0  Data and methodology 94-­‐96  95-­‐97  96-­‐98  97-­‐99  98-­‐00  99-­‐01  00-­‐02  01-­‐03  02-­‐04  03-­‐05  04-­‐06  05-­‐07  06-­‐08  ResultsPolicy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   20  
  • 21. Links of Academia and Industry Cliquishness 1   0.95   Academics  with  only   academic  collabora)on   Average  Clustering  Coefficient     (AC-­‐AC)   0.9   Academics  with  at  least   one  non-­‐academic   0.85   collabora)on  (AC-­‐NA)   Non-­‐Academics  with   0.8   only  Non-­‐academic   collabora)on  (NA-­‐NA)  Introduction 0.75  Literature review Non-­‐  Academics  with  at   least  one  academic  Hypotheses collabora)on  (NA-­‐AC)  Data and methodology 0.7   94-­‐96  95-­‐97  96-­‐98  97-­‐99  98-­‐00  99-­‐01  00-­‐02  01-­‐03  02-­‐04  03-­‐05  04-­‐06  05-­‐07  06-­‐08  ResultsPolicy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   21  
  • 22. Links of Academia and Industry •  Researchers  who  links  academic  and   industry  –  academics  who  have   collaborators  from  industry  and  vise   versa  –  are:   a)  more  central     b)  less  cliquish  Introduction  than  the  ones  who  create  collabora)ve    partnerships  only  within  their  own    Literature reviewHypothesesData and methodologyResultsPolicy Implications  subgroup  (H4  Aff-­‐AC/NA  pos)  Limitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   22  
  • 23. Academics: Canada vs. the US  Academia  /  Non-­‐Academia  (Canada  vs.  the  US)   70.00%   60.00%   Percentage  of  Collabora.ons   50.00%   40.00%   30.00%   20.00%   10.00%  IntroductionLiterature review 0.00%  HypothesesData and methodologyResults Canada   The  US  Policy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   23  
  • 24. Academics: Canada vs. the US Betweenness  Centrality  of  Academics  (Canada  vs.  the  US)   60   Average  Betweenness  Centrality     50   40   (x  106)     30   20   10  Introduction 0  Literature reviewHypothesesData and methodology Canada  Academics   The  US  Academics  ResultsPolicy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   24  
  • 25. Academics: Canada vs. the US Degree  Centrality  of  Academics  (Canada  vs.  the  US)   10   9   Average  Degree  Centrality   8   7   6   5  Introduction 4  Literature reviewHypothesesData and methodology Canada  Academics   The  US  Academics  ResultsPolicy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   25  
  • 26. Academics: Canada vs. the US Cliquishness  of  Academics  (Canada  vs.  the  US)   0.89   0.87   Average  Clustering  Coefficient     0.85   0.83   0.81   0.79   0.77  Introduction 0.75  Literature reviewHypothesesData and methodology Canada  Academics   The  US  Academics  ResultsPolicy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   26  
  • 27. Academics: Canada vs. the US •  American  academic  nanotechnology   scien)sts   a)  collaborate  more  with  non-­‐academic   scien)sts   b)  occupy  more  central  network   posi)ons  Introduction c)  occupy  less  cliquish  network  Literature reviewHypotheses posi)ons    than  the  Canadian  ones  (H5  RegAff-­‐AC)  Data and methodologyResultsPolicy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   27  
  • 28. Non-academics: Canada vs. the US Propor.on  of  Non-­‐academics  (Canada  vs.  the  US)   50.00%   45.00%   Percentage  of  Non-­‐Academics   40.00%   35.00%   30.00%   25.00%   20.00%   15.00%   10.00%   5.00%   0.00%  IntroductionLiterature reviewHypotheses Canada   The  US  Data and methodologyResultsPolicy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   28  
  • 29. Non-academics: Canada vs. the US Degree  Centrality  of  Non-­‐academics  (Canada  vs.  the  US)   10   9   Average  Degree  Centrality     8   7   6   5   4  IntroductionLiterature reviewHypotheses Canada  Non-­‐Academics   The  US  Non-­‐Academics  Data and methodologyResultsPolicy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   29  
  • 30. Non-academics: Canada vs. the US Betweenness  Centrality  of  Non-­‐academics  (Canada  vs.  the  US)   60   Average  Betweenness  Centrality     50   40   (x  106)     30   20   10   0  IntroductionLiterature reviewHypotheses Canada  Non-­‐Academics   The  US  Non-­‐Academics  Data and methodologyResultsPolicy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   30  
  • 31. Non-academics: Canada vs. the US Cliquishness  of  Non-­‐academics  (Canada  vs.  the  US)     Average  Clustering  Coefficient     0.89   0.87   0.85   0.83   0.81   0.79   0.77  Introduction 0.75  Literature reviewHypotheses Canada  Non-­‐Academics   The  US  Non-­‐Academics  Data and methodologyResultsPolicy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   31  
  • 32. Non-academics: Canada vs. the US •  American  non-­‐academic   nanotechnology  network   a)  accounts  for  a  greater  propor)on  of   the  researchers     b)  does  not  occupy  more  central  and   cliquish  posi)ons  Introduction  than  the  Canadian  ones  (H6  RegAff-­‐NA)  Literature reviewHypothesesData and methodologyResultsPolicy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   32  
  • 33. Policy Implications (Canada) •  Government  of  Canada  should   –  encourage  industrial  research  through   suppor)ng  small  nanotechnology   companies   –  facilitate  industry-­‐academia  collabora)on   by  providing  more  programs,  grants  and   funding  opportuni)es  IntroductionLiterature reviewHypothesesData and methodologyResultsPolicy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   33  
  • 34. Policy Implications (Quebec) •  Government  of  Quebec  should   –  support  na)onal  and  interna)onal   connec)ons  by  inves)ng  on  joint  programs   and  alloca)ng  financial  supports   –  s)mulate  collabora)on  of  Quebec-­‐based   academic  researchers  with  non-­‐academia   by  providing  more  funding  for  academia-­‐ industry  collabora)ons  IntroductionLiterature reviewHypothesesData and methodologyResultsPolicy ImplicationsLimitations and future works   Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   34  
  • 35. Thank  you  Quebec nanotechnology network ofresearchers (articles) in 2006-2008;academics and non-academics Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   35  
  • 36. Limitations and Future Work Limita.on   Future  Work   •  Quan)ty  instead  of   •  Indicators  for   quality;  equal  weight   quality  of   for  every   collabora)on;  e.g.   collabora)on   number  of  cita)ons   •  The  informal   •  Study  of  other  Introduction rela)onships  are   professional  Literature reviewHypotheses ignored   networks  like  Data and methodology LinkedIn      ResultsPolicy ImplicationsLimitations and future works Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   36  
  • 37. References (I / II)•  C.  Freeman,  Technology  and  Economic  Performance:  Lessons   from  Japan,  London:  Pinter,  1987.  •  B.  A.  Lundvall,  Na)onal  Innova)on  Systems:  Towards  a  Theory   of  Innova)on  and  Interac)ve  Learning,  London:  Pinter,  1992.  •  J.  Niosi,  Canadas  na)onal  system  of  innova)on,  Montreal:   McGill-­‐Queen’s  University,  2000.  •  OCED,  Managing  Na)onal  Innova)on  Systems,  Paris:   Organiza)on  for  Economic  Coopera)on  and  Development,   1999.  •  "Québec  Policy  on  science,  technology  and  innova)on,"  Conseil   de  la  science  et  de  la  technologie  du  Québec,  Québec,  2002.  •  U.  Brandes,  "A  Faster  Algorithm  for  Betweenness  Centrality,"   Journal  of  MathemaWcal  Sociology,  vol.  25,  p.  163–177,  2001.   Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   37  
  • 38. References (II / II)•  A.  Arenas,  A.  Diaz-­‐Guilera,  J.  Kurths,  Y.  Moreno  and  C.  Zhou,   "Synchroniza)on  in  complex  networks,"  Physics  Reports,  vol.   469,  pp.  93-­‐-­‐153,  2008.  •  C.  Beaudry  and  A.  Schiffauerova,  "Biotechnology  and   Nanotechnology  Innova)on  Networks  in  Canadian  Clusters,"  in   InnovaWon  Networks  and  Clusters,  Brussels,  P.I.E  Peter  Lang,   2010,  pp.  159-­‐197.   Catherine  Beaudry                    Andrea  Schiffauerova                    Afshin  Moazami   38