Network analysis of civil society organisations’ participation in the EU framework programmes.
Date: 2017-08-02
Institutions: Directorate-General for Research and Innovation
Subjects: EU research policy, Framework Programme for Research and Development, civil society, information network, innovation, report, research and development, sustainable development

Network Analysis of Civil  
Society Organisations’  
participation in the  
EU Framework Programmes 

December 2016 

 
 
 
 
 
 
 
 
EUROPEAN COMMISSION 

Directorate-General for Research and Innovation 
Directorate B —Open Innovation and Open Science 
Unit  B.7 - —Science with and for society 

Contact: Constantin-Ovidiu Dumitrescu 

E-mail:  RTD-SIS7-CSO-SIMULATION@ec.europa.eu   

constantin-ovidiu.dumitrescu@ec.europa.eu 
RTD-PUBLICATIONS@ec.europa.eu 

European Commission 
B-1049 Brussels 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EUROPEAN COMMISSION 

Network Analysis of Civil  
Society Organisations’  
participation in the  
EU Framework Programmes 

edited by 

WU Vienna  

FAS.research  

 De Montfort University  

2017 

Directorate-General for Research and Innovation 
Science in Society 

 
 
 
 
 
 
 
 
 
 
 
 
       
 
 
                                
 
 
 
 
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Luxembourg: Publications Office of the European Union, 2017. 

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ISBN 978-92-79-69743-2 

doi: 10.2777/71466  KI-04-17-578-EN-N 

© European Union, 2017. 
Reproduction is authorised provided the source is acknowledged. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
This  report  represents  Deliverable  D7  “Final  report”  of  the  project  “Study  on  network 
analysis  of  Civil  Society  Organisations’  participation  in  EU  framework  programmes” 
commissioned  by  DG  Research  &  Innovation  of  the  European  Commission  (contract  No. 
RTD-B6-2014-SI2.687781).  It  has  been  prepared  by  WU  Vienna  (Austria)  together  with 
FAS.research (Vienna, Austria) and De Montfort University (Leicester, UK). 

Authors: 

Project leader: André Martinuzzi (WU) 

Coordination of work: Markus Hametner (WU) 

Team leaders: Harald Katzmair (FAS), Bernd Stahl (DMU) 

Contributing  authors:  Asya  Dimitrova,  Wolfgang  Lorenz,  Eva  More-Hollerweger,  Gabriel 
Wurzer (WU); Christine Chung, Christian Gulas, Georg Schroll, Andrea Werdenigg (FAS); 
Stephen Rainey, Kutoma Wakunuma (DMU). 

Suggested citation: 

Martinuzzi,  A.,  Hametner,  M.,  Katzmair,  H.,  Stahl,  B.,  Dimitrova,  A.,  Lorenz,  W.,  More-
Hollerweger, E., Wurzer, G., Chung, C., Gulas, C., Schroll, G., Werdenigg, A., Rainey, S., 
Wakunuma,  K.,  Network  Analysis  of  Civil  Society  Organisations’  participation  in  EU 
Framework Programmes, Vienna & Leicester, 2016. 

Vienna & Leicester, December 2016 

Disclaimer: The information and views set out in this report are those of the authors 
and do not necessarily reflect the official opinion of the Commission. The Commission 
does not guarantee the accuracy of the data included in this study. Neither the 
Commission nor any person acting on the Commission’s behalf may be held responsible 
for the use which may be made of the information contained therein. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table of contents 

Table of contents ................................................................................................ 3 

List of tables ....................................................................................................... 5 

List of figures ..................................................................................................... 6 

Abstract .............................................................................................................. 7 

Executive summary ............................................................................................ 8 

Résumé ............................................................................................................ 15 

Résumé Analytique ........................................................................................... 16 

Kurzfassung ..................................................................................................... 23 

1 

Introduction ............................................................................................... 31 

2  Literature review of network analysis approaches ..................................... 33 

2.1  Critical analysis of previous SNA approaches and studies in R&I evaluation and 

measurement of impact in general ...............................................................33 

2.2  Critical analysis of previous SNA studies in FP1-7 in regard of effects on R&I, CSO 
participation and impact on European society ................................................36 

2.3  Critical analysis of R&I related SNA Literature in the context agent based 

simulation modelling, p* modelling and systems dynamics .............................38 

2.4  Development of Multi Theory Multi Level SNA methodology (MTML) .................39 

3  Literature review on CSOs in EU and in R&I ............................................... 41 

3.1  Background ..............................................................................................41 

3.2  Methodology .............................................................................................42 

3.3  Summary of findings ..................................................................................43 
3.3.1  CSO definition in different bodies of literature ....................................... 43 
3.3.2  Action Logic of CSOs .......................................................................... 44 

3.4  Refinement of proposed CSO typology ..........................................................45 

4  Compilation of project dataset ................................................................... 49 

4.1  Survey with organisations categorised as “OTH” ............................................49 

4.2  Refined CSO typology and additional screening of “OTH” organisations .............49 

4.3  Finalisation of project dataset .....................................................................50 

5  Case studies of networks involving CSOs ................................................... 52 

5.1  Guiding Questions .....................................................................................52 

5.2  Methodology .............................................................................................52 
5.2.1  CONSIDER re-analysis ........................................................................ 52 
5.2.2  Additional case studies ....................................................................... 53 
5.2.3  Selection of cases .............................................................................. 53 

5.3  Findings ...................................................................................................53 
5.3.1  Summary of findings – additional cases ................................................ 53 
5.3.2  Summary of findings – CONSIDER cases ............................................... 54 

5.4  Conclusions ..............................................................................................55 

3 

 
 
 
6  Role and position of CSOs in FP networks .................................................. 56 

6.1  Network Analysis .......................................................................................56 
6.1.1  Data Base ......................................................................................... 56 
6.1.2  Data Management and Network Generation .......................................... 56 
6.1.3  CSO Participation – General Statistics ................................................... 59 
6.1.4  Network Parameters – Key Network Analysis ......................................... 64 
6.1.5 
The Correlation of Network Parameters and  CSO Participation ................ 65 
6.1.6  Key Actor Analysis ............................................................................. 67 
6.1.7  Key Link Analysis – Connections of CSOs .............................................. 71 

6.2  Publication Analysis ...................................................................................73 
6.2.1  Data Base ......................................................................................... 73 
6.2.2  Core Areas of Scientific Output ............................................................ 76 
6.2.3  Scientific Output and CSO Participation ................................................. 76 

6.3  Media Analysis ..........................................................................................82 
6.3.1  Background and Data Base ................................................................. 82 
Types of institutions mentioned in media reports ................................... 83 
6.3.2 
Topics of Media Reports ...................................................................... 84 
6.3.3 
Types of Institutions and Topics ........................................................... 84 
6.3.4 

7  Simulation of policy interventions .............................................................. 87 

7.1  Overview of the simulation model ................................................................87 
7.1.1  Scope of the model ............................................................................ 87 
7.1.2  Organisations .................................................................................... 87 
7.1.3  Skill vectors ...................................................................................... 88 
7.1.4  Call design ........................................................................................ 89 
7.1.5 
Participation ...................................................................................... 90 
7.1.6  Simulation ........................................................................................ 90 
7.1.7  Output ............................................................................................. 91 
7.1.8  Calibration and validation of the model ................................................. 91 

7.2  Simulated scenarios ...................................................................................93 

7.3  Analysis of scenarios ..................................................................................95 
7.3.1  Overview across different scenarios ..................................................... 95 
7.3.2  Summary of findings per scenario ........................................................ 97 
7.3.3 
Effects of the main scenarios on network parameters ............................. 99 
7.3.4  Differences between the main scenarios in terms of concentration effects 100 
7.3.5  Differences between the main scenarios in terms of capacity building 

(“learning”) ...................................................................................... 102 

8  Conclusions & recommendations .............................................................. 105 

8.1  CSO participation in the FPs ...................................................................... 105 

8.2  Benefits related to CSO participation in the FPs ........................................... 107 

8.3  How to increase CSO involvement in the FPs ............................................... 109 

8.4  Recommendations for the upcoming FP9 .................................................... 111 

8.5  Research outlook ..................................................................................... 113 

9  Bibliography ............................................................................................. 115 

4 

 
 
List of tables 

Table 1:  

Key search strings used .......................................................................42 

Table 2:   Working CSO definitions ......................................................................44 

Table 3:  

Preoccupations in literature reviews ......................................................45 

Table 4:  

Preliminary CSO typology from the literature review ...............................46 

Table 5:  

Numbers of CSOs included in the final project dataset .............................51 

Table 6:   Main findings from additional cases .......................................................54 

Table 7:   Main findings from re-analysis of CONSIDER cases ..................................54 

Table 8:  

FP6, FP7, and H2020* types of institutions ............................................59 

Table 9:  

Funding distribution among organisation types .......................................59 

Table 10:   Percentages of funding by CSO type, programme, and thematic area  (1) ..61 

Table 11:   Percentages of funding by CSO type, programme, and thematic area  (2) ..62 

Table 12:   FP7 participation of institution types in funding schemes ..........................63 

Table 13:   Percentages of organisation types within the core, semi-periphery,  

and periphery of the FP networks (FP6 and FP7) .....................................69 

Table 14:   Selected network parameters: FP7 without universities and without CSOs .70 

Table 15:   FP7 coordination and participation of institution types (absolute numbers) .72 

Table 16:   Scientific disciplines and types of institutions by number of publications 

(absolute numbers). ............................................................................77 

Table 17:   Scientific disciplines and types of institutions by number of publications  

(row percentages). .............................................................................78 

Table 18:   Scientific disciplines and types of institutions by number of publications 

(column percentages). ........................................................................78 

Table 19:   Numbers of projects with/without CSO participation (all types)  

and with/without publications ...............................................................81 

Table 20:   Numbers of institutions mentioned in media reports about  

EU funded projects (FP related or not) 2007 - 2015 ................................83 

Table 21:   Parameters for scenarios .....................................................................90 

Table 22:   Comparison of baseline scenario with FP7 data .......................................92 

Table 23:   Effects of main scenarios on the overall network .....................................99 

Table 24:   Summary of the four main scenarios and their effects on the FP network 

(relative to the baseline scenario) ....................................................... 104 

Table 25:   Summary of findings in relation to the study's research questions  

on CSO benefits ................................................................................ 108 

Table 26:   Summary of findings in relation to the study's research questions  

on how to increase CSO involvement ................................................... 109 

Table 27:   Characteristics of the Horizon 2020 programme structure ...................... 112 

5 

 
 
List of figures 

Figure 1:   MLMT approach ..................................................................................40 

Figure 2:  

Levels of "CSO-ness" ...........................................................................46 

Figure 3:   Operationalised typology ("decision tree") for categorising CSOs ..............48 

Figure 4:   Transforming CORDIS project data into network data. .............................57 

Figure 5:   Core of FP7 cooperation network ..........................................................58 

Figure 6:  

“Survival rate” of institution types from FP6 to FP7 in % of organisations ..60 

Figure 7:   Correlations between network parameters (FP6) .....................................66 

Figure 8:   FP7 average duration of projects and relative amount of CSO  

participations (all types) ......................................................................71 

Figure 9:   FP7 thematic areas by CSO participation and percentages  

of publications within OpenAIRE data. ...................................................74 

Figure 10:   Combining different data sources to generate FP7 publication networks .....75 

Figure 11:   Network of FP7 thematic areas and ASJC disciplines ................................76 

Figure 12:  Network of ASJC-disciplines (All Journal Science Classification) which are 

connected through EU funded publications .............................................80 

Figure 13:   Topics of projects with CSO participation in media reports –  

comparison inside/outside FPs ..............................................................85 

Figure 14:   Top 5 topics of each institution type in media reports about EU funded 

projects (FP and non FP related) ...........................................................86 

Figure 15:   Budget distribution and evaluation weights for main scenarios .................94 

Figure 16:   Budget shares of different organisation types across the main scenarios ....95 

Figure 17:   Budget to the different organisation types across the main scenarios ........96 

Figure 18:   Shares of different organisation types across the main scenarios ..............96 

Figure 19:   Numbers of different organisation types across the main scenarios ...........97 

Figure 20:   Differences in shares of organisations by size (big, medium, small) across 

the main scenarios relative to the baseline scenario .............................. 100 

Figure 21:   Budget share of the top-10 organisations across the main scenarios ....... 101 

Figure 22:   Differences in regional budget distribution across the main scenarios relative 
to the baseline scenario ..................................................................... 101 

Figure 23:   "Learning" effects of organisations across the main scenarios  

relative to the baseline scenario ......................................................... 103 

6 

 
 
Abstract 

This  study  examines  the  participation  of  Civil  Society  Organisations  (CSO)  in  the  EU 

research  framework  programmes  (FP)  through  literature  reviews,  a  series  of  case 

studies, social network analyses and agent-based modelling. It revealed that CSOs have 

played  a  marginal  role  in  FP6  and  FP7,  and  had  very  limited  effects  on  network 

performance  and  research  output.  This  can  be  explained  by  different  logics:  up  to  now, 

the  FPs  have  been  mainly  shaped  by  researchers,  focusing  on  scientific  excellence,  and 

the  business  sector,  focusing  on  profits  and  competitiveness,  when  CSOs  follow  a 

different logic, focusing on societal impacts. 

Therefore, in addition to involving CSOs as partners, future FPs could optimise the use of 

CSOs  potential  by  (1)  involving  them  more  significantly  in  other  roles  (e.g.  agenda 

setting,  proposal  evaluation),  (2)  putting  stronger  emphasis  on  the  societal  impacts  of 

the  whole  programme,  and  (3)  funding  more  and  smaller  projects  to  counter 

concentration  effects  and  reduce  entry  barriers.  Our  simulation  showed  that  such  a 

scenario would substantially increase the FPs contributions to high level policy objectives 

(such as the UN Sustainable Development  Goals),  enhance the  respective competencies 

of different types of organisations (universities, businesses and CSOs) and bring science 

and citizens closer without diluting scientific excellence. 

7 

 
 
Executive summary 

Purpose 

The  purpose  of  this  report  is  to  analyse  the  participation  of  Civil  Society  Organisations 

(CSOs)  in  the  European  research  framework  programmes  (FPs),  in  particular  FP6  and 

FP7, and to derive policy recommendations on how to strengthen their role. In doing so, 

the study pursued the following objectives: (1) developing a practical classification of 

CSOs  that  can  help  differentiate  and  understand  the  roles  CSOs  can  play  in  European 

research framework programmes in the context of Responsible Research and Innovation 

(RRI), as well as their specific knowledge, properties and latent preferences determining 

their  participation;  (2) analysing  the  extent  of  CSO  involvement  in  the  FPs  and 

determining  their  specific  contribution  to  research  and  network  performance  through 

Social  Network  Analysis;  and  (3) simulating  different  policy  scenarios and  their 

effects  on  CSO  participation,  by  complementing  knowledge-based  agent  behaviour  (as 

used  in  existing  modelling  approaches)  with  an  orientation  towards  solving  societal 

challenges based on the UN Sustainable Development Goals (SDGs).  

Methodological approach 

Several  methodological  approaches  were  combined  for  this  study.  This  included 

literature reviews on Social Network Analysis (SNA) and Civil Society Organisations in 

research  and  innovation  (R&I),  desk  research  and  a  survey  for  developing  a  suitable 

definition  and  typology  of  CSOs  in  the  FPs,  and,  on  this  basis,  identifying  and 

categorising CSOs in the FP6 and FP7 project datasets. The main methods applied in the 

empirical  parts  of  the  study  were  case  studies  involving  in-depth  interviews  with 

participating  CSOs,  social  network  analysis  of  FP6  and  FP7  research  networks  with  a 

specific  focus  on  CSOs,  an  analysis  of  publication  output  and  media  coverage  of 

research projects with/without CSOs, as well as agent-based modelling to simulate the 

effect of policy interventions on the FP networks and the role and position of CSOs. The 

developed  simulation  approach  expands  previous  models  by  focusing  not  only  on 

knowledge  flows  in  research  networks,  but  also  by  additionally  taking  into  account 

research impacts on societal challenges derived from the UN SDGs. 

Key findings and recommendations 

CSO participation in the FPs 

CSOs  constitute  a  very  heterogeneous  group  of  different  kinds  of  organisations 

(regarding  size,  strategic  orientation,  business  model,  funding  streams,  target  groups, 

etc.),  which  makes  it  challenging  to  clearly  distinguish,  characterise  and  identify  them 

among  FP  participants.  Consequently,  the  study  developed  an  approach  that 

distinguishes  between  four  types  of  CSOs:  citizen-oriented  (“core”)  CSOs  (CSO1), 

8 

 
society-oriented/public-funded  CSOs  (CSO2),  society-oriented/business-funded  CSOs 

(CSO3),  and  business-oriented  CSOs  (CSO4).  The  study’s  results  show  that  this  is  a 

feasible way for assessing CSO participation patterns in the FPs. 

  We  strongly  suggest  better  categorising  CSOs  in  the  CORDA  dataset  in  the  future, 

following the typology applied in this study. 

CSOs  are  marginal  players  in  the  FP  network.  Although  on  average  one  out  of 

thirteen  projects  involves  a  CSO,  they  only  account  for  about  6%  of  all  participating 

organisations in FP7, and receive only about 1% of the funding provided by the European 

Commission. Only about 10% of  the CSOs in FP7 coordinated a project, and only about 

1%  of  all  FP7  projects  were  coordinated  by  a  CSO.  Furthermore,  they  usually  do  not 

occupy  a  central  role  when  participating  in  a  project,  which  may  be  related  to  several 

issues  they  are  facing:  low  status  in  consortium  hierarchies,  lack  of  clarity  in  task 

division, and concerns  among academic researchers that CSO participation may weaken 

scientific legitimacy. 

  Having  in  mind  their  marginal  role  in  the  FP  networks,  CSOs  cannot  be  expected  to 

have a significant impact when participating in research projects. 

CSOs do not show a systematic pattern of participation, although there are themes 

in which they appear more frequently and prominently than in others: Science in Society 

(SiS) is the only theme in which citizen-oriented (CSO1) and society-oriented (CSO2 and 

CSO3) CSOs play a more than marginal role. Regions of Knowledge (RoK) and Research 

for  the  benefits  of  SMEs  (SME)  show  above-average  shares  of  CSOs,  which  are  mostly 

gained  by  business-oriented  CSOs  (CSO4).  Activities  of  International  Cooperation 

(INCO),  Research  policies  (COH),  and  Socio-economic  sciences  and  Humanities  (SSH) 

show  small  but  above-average  budget  shares  with  a  substantial  involvement  of  citizen- 

and  society-oriented  CSOs  (CSO1-3).  In  absolute  terms,  Health  and  Information  and 

Communication  Technologies  (ICT)  are  important  funding  sources  for  all types  of  CSOs, 

because  of  the  size  of  the  allocated  EU  funding  budgets.  Important  parts  of  the  FP,  in 

which CSOs virtually do not participate at all, are the European Research Council (ERC), 

the Marie Skłodowska-Curie actions, as well as Nanotechnologies (NMP) and Transport. 

  Increasing the budget for themes that already have higher levels of CSO participation 

would  help  strengthen  the  role  of  civil  society  in  the  FPs  and  bring  science  closer  to 

citizens.  This  budget  shift  needs  to  be  accompanied  by  additional  interventions  such 

as smaller projects and adjusting evaluation weights. 

Most  of  the  CSOs  are  not  able  to  build  up  long-standing  relationships  with  the 

FPs.  Their  drop-out  rate  of  85%  from  FP6  to  FP7  is  the  highest  of  all  types  of 

participating  organisations.  About  70%  of  the  CSOs  in  FP7  participated  only  once;  for 

FP6, this figure is even higher. This lack of consistency does not allow cross-fertilization 

through  CSO  involvement  (which  would  be  possible  if  e.g.  the  same  CSOs  were  to 

participate  in  both  SiS  and  in  other  themes).  High  turnover  of  staff  and  a  lack  of 

organisational memory in CSOs constitute further hampering factors. 

9 

 
  Institutional  capacity-building  and  continuous  development  of  skills  is  required  to 

make  CSOs  “fit”  for  FP  participation;  this  could  involve  staff  exchange,  launching 

specific initiatives and institutional capacity-building. 

CSOs find it difficult to link up with the highly competitive and excellence-driven 

logic  of  research  and  innovation.  Mission-driven  CSOs  focus  much  more  on  policy 

impacts  and  the  needs  of  their  stakeholders  than  on  scientific  publications  and  building 

up  academic  track  records.  Their  mission  is  to  influence  current  national  or  regional 

policy-making;  consequently,  they  can  rarely  gain  benefits  from  being  partners  in 

projects lasting several years. On the other hand, many of the service oriented CSOs do 

not see much added value in research and innovation for their core business, which often 

is not highly innovative. As a consequence, both types of CSOs participate slightly more 

in coordination and support actions rather than in research and/or innovation projects. 

  Apart  from  participating  in  coordination  and  support  actions,  other  options  for 

involving CSOs  could be in agenda-setting, proposal evaluation and dissemination  of 

results. 

CSOs often find it difficult to gain a European added-value from participating as 

partners in EU-wide projects, as they often target national or regional policies. This is 

particularly valid for social issues, where no common EU policies exist. Compared to the 

FPs,  funding  opportunities  in  the  European  Structural  and  Investment  Funds  seem  to 

better address the regional/local geographical scope of many CSOs.  

  Better  aligning  the  FPs  with  the  European  Structural  and  Investment  Funds,  as 

suggested  by  the  FP7  ex-post  evaluation,  could 

indirectly  strengthen  CSO 

involvement in transnational research and innovation activities. 

Benefits related to CSO participation in the FPs 

CSOs  neither  follow  the  logic  of  academia  (characterised  by  excellence  in  scientific 

disciplines)  nor  the  logic  of  business  (shaped  by  competitiveness  and  profit).  Instead, 

their  logic  focuses  on  solving  societal  problems  –  either  as  mission-driven  CSOs,  by 

influencing  policy  making,  or  as  service-oriented  CSOs,  by  improving  the  situation  of 

their primary target group. As a consequence of this different logic, CSO contribution to 

research  performance  is  most  likely  to  materialise  where  research  promotes  the  CSO 

aims.  

Due  to  their  marginal  role  in  the  FPs,  CSOs  also  do  not  contribute  to  network 

performance.  As  shown  through  social  network  analysis,  CSO  participation  hardly 

depends  on  network  morphology;  in  FP7  there  are  virtually  no  correlations  between 

network  measures  and  CSO  participation.  In  the  FP  networks,  CSOs  are  “hangers  on”, 

they  do  not  build  sub-clusters  or  bridge  gaps,  and  they  do  not  have  an  important 

brokerage  function  (and  consequently  do  not  plug  structural  holes).  Notably,  removing 

CSOs would have virtually no effect on network parameters and morphology. 

10 

 
 
  The  broad  diversity  of  benefits  related  to  CSO  participation  in  the  FPs  cannot  be 

adequately  assessed  by  applying  success  criteria  such  as  “network  performance”  or 

“research  performance”.  This  is  due  to  the  fundamentally  different  logic  (or 

“currency”)  of  CSOs:  while  the  FPs  are  shaped  by  research,  which  focuses  on 

scientific  excellence,  and  the  business  sector,  which  focuses  on  profits  and 

competitiveness,  CSOs  follow  the  logic  of  societal  impacts.  Assessing  the  benefits  of 

involving mission-driven CSOs needs to take into account policy impacts; the benefits 

of involving service-oriented CSOs can be measured through positive impacts on the 

stakeholders  they  represent.  We  therefore  advocate  a  new  view  on  the  benefits 

related to CSO participation in the FPs, by assessing the (potential) societal benefits 

of the FPs as a whole, and by identifying the roles in which CSOs can best contribute 

to maximising these societal benefits. 

How to increase CSO involvement in the FPs 

A  series  of  case  studies  and  the  preliminary  scenarios  of  the  agent-based  model  have 

shown that micro-management of the programme results in only marginal effects on the 

network as well as on the involvement of CSOs and their impacts. 

  Micro-management of individual programme features results in only marginal effects. 

We consequently suggest considering a broader range of roles that CSOs could play in 

future FPs: (1) in proposal evaluation boards (at least for the “impact” section), (2) in 

agenda setting and work programme development (beyond online-based consultation 

processes),  and  (3)  in  ethics  boards  and  steering  committees  (on  project  and 

programme  level).  Many  of  these  roles  would  better  fit  to  the  mission-driven  core 

logic  of  CSOs  and  could,  therefore,  be  more  attractive  than  being  involved  as 

marginalised project partners. 

Four scenarios were developed, tested by an agent-based model and analysed regarding 

network  morphology,  budget  shares  of  different  types  of  organizations,  scientific 

excellence  and  positive  impacts  on  the  Sustainable  Development  Goals.  These  results 

were  compared  to  a  well-calibrated  baseline  scenario  that  mirrored  budget  shares  and 

project sizes of FP7. 

  Scenario  “Competitiveness  &  high-tech”  aimed  at  strengthening  competitiveness 

through 

promoting 

technology-oriented 

research 

fields 

(such 

as 

ICT, 

Nanotechnologies (NMP) or Transport) and funding fewer, but bigger projects than in 

the past. The results showed a higher share of the business sector, comparable high 

concentration effects, but also higher shares of business-oriented CSOs (CSO4) than 

in the baseline scenario, while the share of citizen- and society-oriented CSOs (CSO1-

3)  remained  marginal.  This  scenario  also  showed  the  strongest  learning  effects  in 

terms of scientific disciplines and positive effects on capacity building. 

11 

 
 
  Scenario  “Social  cohesion  &  well-being”  focused  on  societal  challenges  and  pro-

cess-oriented  types  of  research  (such  as  Health,  SSH,  Science  in  Society  (SiS)  and 

Regions  of  Knowledge)  and  funding  more,  but  smaller  projects  than  in  the  past.  It 

showed the strongest increase of CSOs’ involvement to 5% of organisations and 1.3% 

of  the  budget,  allowing  in  particular  citizen-  and  society-oriented  CSOs  (CSO1-3)  to 

double  their  numbers  and  budget.  This  scenario  also  showed  a  more  stable  network 

(higher average degree, higher clustering) with collaborations being more equally dis-

tributed  among  organisations  (less  centralisation).  Furthermore,  it  showed  positive 

effects  on  capacity  building  and  the  highest  learning  effects  regarding  societal  chal-

lenges, but without diluting scientific excellence. 

  Scenario  “Fortress  Europe”  focused  research  and  innovation  primarily  on  security- 

and  health  related  topics  in  view  of  keeping  Europe  safe  and  making  it  more  self-

sufficient.  Out  of  the  four  scenarios,  it  shows  the  least  differences  compared  to  the 

baseline  scenario  in  terms  of  promoting  specific  organisations  or  creating  strong 

network or learning effects. When looking at CSOs, however, this scenario stands out 

with respect to showing the smallest budget share for CSOs (about 0.9% only). 

  Scenario “FP rollback” simulates a dramatic budget cut of the collaborative, mission-

driven parts of the FP – either reflecting an overall cut in the FPs budget, or reflecting 

budget  shifts  towards  the  ERC.  This  budget  cut  results  in  a  dramatic  drop-out  of 

smaller  organisations  (including  CSOs),  while  big  players  (such  as  universities) 

manage  to  remain  core  players.  This  leads  to  a  densely  connected  network  that  is 

dominated by a smaller number of actors. Furthermore, the capacity of organisations 

in  terms  of  both  knowledge  and  impact  are  significantly  smaller  than  in  the  other 

three scenarios. 

  As the results of the agent-based modelling have shown, the four scenarios are quite 

distinct in terms of their effects  on the  overall  research and innovation network, the 

involvement of CSOs and the learning effects of all types of organisations involved. If 

addressing  societal  challenges,  bringing  science  closer  to  citizens  and  increasing  the 

diversity  and  resilience  of  the  European  research  and  innovation  network  are 

perceived as important policy goals, the scenario “Social cohesion & well-being” is the 

most  beneficial:  It  significantly  increases  the  capacities  of  all  actors  to  contribute  to 

solving societal problems without diluting scientific excellence and without disrupting 

the existing network or completely altering the nature of the programme, and, thus, 

still  being  capable  to  adequately  address  the  logic  of  the  main  players  (universities, 

research organisations and companies). As a side effect, it doubles the involvement of 

CSOs in FPs. 

12 

 
 
 
Recommendations for the upcoming FP9 

As concerns the upcoming FP9, decisions on the strategic orientation of the programme 

and  its  thematic  foci  are  required.  They  will  have  to  consider  the  different  logics  of  the 
key  actors  involved,  as  well  as  the  “currencies”1  and  the  governance  principles  they 
apply:  

  The  first  pillar  of  Horizon  2020,  “Excellent  Science”,  focuses  on  frontier  research, 

which  aims  at  new  discoveries,  targets  individual  scientists,  addresses  their 

“currency” excellence as measured by publications and academic careers, and awards 

grants  in  a  highly  selective  process  based  on  evaluation  carried  out  by  other 

researchers. Through its high ambition and focus on excellence, this process is highly 

selective,  but  free  from  any  influence  beyond  scientific  communities.  Governance  is, 

therefore,  self-referential,  and  considering  societal  impacts  is  often  perceived  as  a 

potential dilution of excellence. 

  The second pillar of Horizon 2020, “Industrial Leadership”, focusses on innovation, 

targets  highly 

innovative  business  sectors,  and  aims  at  supporting 

their 

competitiveness,  growth  and  jobs.  The  “currency”  of  this  pillar  is,  therefore,  profit, 

and patents are used to safeguard competitive advantages. This leads to an exclusion 

(of potential competitors) and joint agenda setting (in an inner circle of participants) 

as governance principles. 

  The  third  pillar  of  Horizon  2020,  “Societal  Challenges”,  focuses  on  impacts  of 

societal  practices,  targets  citizens  and  civil  society,  and  follows  a  key  logic  of 

(political)  responsibility.  Increasing  positive  (and  reducing  negative)  impacts  are, 

therefore,  success  criteria  for  projects.  While  the  other  two  pillars  run  the  risk  of  a 

“lock-in” (through selection on the one hand,  and exclusion on the other), this pillar 

can  balance  these  tendencies  and  serve  as  an  integrative  element.  Moreover,  while 

the other two pillars follow insider-oriented governance principles, the third pillar has 

the potential to address major societal challenges that are set on a global scale. 

  If European research and innovation policy wants to safeguard its legitimacy vis-à-vis 

European  citizens,  an  independent  third  pillar,  focusing  on  the  grand  societal 

challenges, is vital (no merging of the second and the third pillar under the headline 

of  “mission  oriented  research”).  The  globally  agreed  Sustainable  Development  Goals 

could  serve  as  an  orientation  and  avoid  that  agenda  setting  is  driven  by  interest 

groups.  In  order  to  bring  people  closer  to  research  and  research  closer  to  people, 

arenas  for  knowledge  co-creation  and  innovation  need  to  be  designed  into  which  a 

broad variety of stakeholders should be involved. Funding instruments should reflect 

1  The  notion  of  “currencies”  stems  from  the  understanding  that  in  different  social  fields  different 
units exist for measuring status within this field (e.g. publication track record for people working 
at universities). The metaphor of “currency” highlights that while each currency values something 
in a specific field, there should also be a way to convert a currency into another one (similar like 
the Euro that can be converted to US Dollar), thus allowing exchange across different fields. 

13 

 
                                           
that  societal  challenges  are  “wicked”  problems,  and  that  innovations  are  not  final 

deliverables  but  continuous  processes.  Therefore,  interventions  should  focus  on 

governing  landscapes  (and  not  only  projects),  develop  institutional  capacities  and 

structures,  and  allow  for  more  continuous  processes  (overcoming  the  limited 

perspective of individual projects). Responsible research and innovation (RRI) should 

become a guiding principle in all three pillars and themes of the FPs. This will ensure 

that  future  FPs  do  not  only  promote  frontier  research  at  universities  and  research 

organisations  or  support  competitiveness  of  businesses,  but  that  they  also  consider 

the needs of citizens and address relevant societal challenges. 

14 

 
 
Résumé 

Cette  étude  examine  l’implication  des  organisations  de  la  société  civile  (OSC)  dans  les 

programmes-cadres  de  recherche  de  l’Union  Européenne  (PC)  sur  base  de  revues 

documentaires,  d’une  série  d’études  de  cas,  d’analyses  des  réseaux  sociaux  et  de  la 

modélisation  basée  sur  des  agents.  Elle  révèle  que  les  OSCs  ont  joué  un  rôle  marginal 

dans le PC6 et le PC7 et n’ont eu que des effets limités sur la performance des réseaux et 

sur les résultats de la recherche. Ceci peut être expliqué suivant différentes logiques : les 

PCs sont façonnés par les  chercheurs, mettant l’accent sur l’excellence scientifique et le 

secteur  des  entreprises  mettant  l’accent  sur  les  profits  et  la  compétitivité,  alors  que  les 

OSCs mettent l’accent sur les impacts sociétaux.  

Par conséquent, en sus d’essayer de faire intervenir les OSCs en tant que partenaires, les 

futurs  PCs  pourraient  optimiser  le  potentiel  des  OSCs  plus  efficacement  en  (1)  les 

impliquant  dans  d’autres  rôles  (p.ex.  définition  des  programmes,  évaluation  des 

propositions),  (2)  mettant  plus  particulièrement  l’accent  sur  les  impacts  sociétaux  de 

l’ensemble  du  programme,  et  (3)  finançant  davantage  de  plus  petits  projets  afin  de 

contrer les effets de concentration et de réduire les barrières à l’entrée. Notre simulation 

a montré qu’un tel scénario augmenterait considérablement les contributions des PCs aux 

grands  objectifs  politiques  (tels  que  les  ODD  des  Nations  Unies),  valoriserait  les 

compétences  respectives  de  différents  types  d’organisations  (universités,  entreprises  et 

OSCs) et rapprocherait la recherche et les citoyens sans affecter l’excellence scientifique.  

15 

 
Résumé Analytique 

Objectif 

L’objectif  du  rapport  consiste  à  analyser  la  participation  des  organisations  de  la  société 

civile (OSC) aux programmes-cadres de recherche européens (PC), notamment aux PC6 

et PC7, et à élaborer des recommandations politiques sur la façon de renforcer leur rôle.  

Ce  faisant,  l’étude  cherche  à  atteindre  les  objectifs  suivants:  (1)  développer  une 

classification  pratique  des  OSCs  qui  pourra  aider  à  distinguer  et  à  comprendre  les  rôles 

que les OSCs peuvent jouer dans les programmes-cadres de recherche européens dans le 

cadre  de  Recherche  et  Innovation  Responsable  (RIR),  ainsi  que  leurs  connaissances 

spécifiques, leurs propriétés et leurs préférences latentes déterminant leur participation; 

(2)  analyser  l’ampleur  de  la  participation  des  OSCs  aux  PCs  et  déterminer  leur 

contribution spécifique à la recherche et à la performance des réseaux par l’analyse des 

réseaux  sociaux  ;  et  (3)  simuler  différents  scénarios  politiques  et  leurs  effets  sur  la 

participation  des  OSCs  en  complétant  le  comportement  des  agents  de  la  connaissance 

(tels que ceux utilisés dans les approches de modélisation existantes) par une orientation 

vers la résolution d’enjeux sociétaux  fondés  sur les  Objectifs de développement durable 

des Nations Unies (ODD).  

Approche Méthodologique  

Pour  cette  étude,  différentes  approches  méthodologiques  ont  été  combinées.  Celles-ci 

incluent  l’examen  documentaire  sur  l’analyse  des  réseaux  sociaux  (ARS)  et  sur  les 

organisations  de  la  société  civile  (OSC)  dans  la  recherche  et  innovation  (R&I),  une 

recherche  documentaire  ainsi  qu’une  enquête  visant  à  l’élaboration  d’une  définition  et 

d’une typologie appropriée des OSCs dans les PCs, et, sur cette base, l’identification et la 

catégorisation des OSCs dans l'ensemble des données se rapportant aux projets des PC6 

et PC7.  

Les  principales  méthodes  qui  ont  été  appliquées  dans  les  parties  analytiques  de  l’étude 

sont  des  études  de  cas  impliquant  des  entrevues  en  profondeur  avec  les  OSCs 

participantes, une analyse des réseaux sociaux des réseaux de recherches du PC6 et PC7 

axée  spécifiquement  sur  les  OSCs,  une  analyse  des  publications  et  de  la  couverture 

médiatique des projets de recherche avec ou sans OSCs ainsi qu’une modélisation basée 

sur  des  agents  destinée  à  simuler  les  effets  des  interventions  politiques  sur  les  réseaux 

du PC et le rôle et la position des OSCs.  

Le  procédé  de  simulation  mis  au  point  a  permis  d’élargir  l’horizon  des  modèles 

précédents  en  mettant  non  seulement  l’accent  sur  le  flux  de  connaissances  au  sein  des 

réseaux  de  recherche,  mais  aussi  en  prenant  en  considération  les  impacts  de  la 

recherche sur les défis sociétaux provenant des UN ODD. 

16 

 
Résultats clés et Recommandations  

Participation des OSC aux PCs  

Les OSCs constituent un groupe très hétérogène de différents types d’organisations 
(taille,  orientation  stratégique,  modèle  commercial,  flux  de  financement,  groupe  cible, 
etc.),  rendant  relativement  ardues  leur  nette  distinction,  leur  caractérisation  et  leur 
identification parmi les participants du PC.  

Par conséquent, l’étude a débouché sur une approche qui distingue quatre types d’OSCs: 
les  OSCs  orientés  vers  les  citoyens  («  le  noyau  »)  (OSC1),  les  OSCs  orientés  vers  la 
société/financés publiquement (OSC2), les OSCs orientés vers la société/financés par les 
entreprises (OSC3) et enfin, les OSCs orientés vers les entreprises (OSC4). Les résultats 
de  l’étude  montrent  que  cette  approche  rend  possible  l’évaluation  des  tendances  en 
matière de participation des OSCs aux PCs.  

  À  l’avenir,  nous  recommandons  fortement  une  meilleure  catégorisation  des  OSCs 

dans les données CORDA suivant la typologie appliquée dans cette étude. 

Les OSCs  sont  des  acteurs tout  à  fait marginaux  dans le  réseau  PC.  Bien  qu’une 
OSC soit, en moyenne, impliqué dans un projet sur treize, elles représentent seulement 6 
%  de  l’ensemble  des  organisations  participantes  au  PC7  et  ne  reçoivent  que  1%  des 
fonds fournis par la Commission Européenne.  

Environ seulement 10% des OSCs du PC7 ont coordonné un projet et seulement environ 
1%  des  projets  PC7  ont  été  coordonnés  par  un  OSC.  De  plus,  ils  ne  prennent 
généralement  pas  un  rôle  central  lorsqu’ils  participent  à  un  projet  pouvant  être  lié  aux 
différents  enjeux  auxquels  ils  font  face  :  le  statut  inférieur  dans  les  hiérarchies  de 
consortium,  le  manque  de  clarté  dans  le  partage  des  tâches  et  l’inquiétude  parmi  les 
chercheurs  universitaires  par  rapport  à  la  participation  des  OSCs  peuvent  affaiblir  la 
légitimité scientifique.  

  Connaissant  leur  rôle  marginal  joué  par  les  OSCs  dans  les  réseaux  de  PC,  ceux-ci 
n’auront vraisemblablement pas un impact considérable lors de leur participation aux 
projets de recherche. 

Les OSCs  ne montrent pas un modèle de participation  systématique, bien qu’il y 
ait des sujets où ils apparaissent plus fréquemment et éminemment que dans d’autres : 
Science  dans  la  Société  (SdS)  est  le  seul  sujet  dans  lequel  des  OSCs  orientés  vers  les 
citoyens (OSC1) et les OSCs orientés vers la société (OSC2 et OSC3) jouent un rôle plus 
que  marginal.  Régions  de  la  Connaissance  (RdC)  et  Recherche  visant  l'intérêt  de  SMEs 
(SME)  affichent  une  part  supérieure  à  la  moyenne  des  OSCs  généralement  obtenus  par 
des OSCs orientés vers les entreprises (OSC4).  

Activités  des  Coopérations  internationales  (INCO),  Politiques  de  la  recherche  (COH),  et 
Sciences socioéconomiques et humaines (SSH) font apparaître des parts budgétaires qui 
sont certes petites, mais supérieure à la moyenne, avec une implication importante des 
OSCs orientés vers les citoyens et vers la société (OSC1-3).  

En  termes  absolus,  la  santé  ainsi  que  les  technologies  de  l'information  et  des 
communications  (ICT)  sont  des  sources  financières  importantes  pour  tous  les  types 
d’OSCs  confondus  et  ce,  dû  à  la  taille  du  budget  de  financement  délivré  par  l’UE.  Le 

17 

 
Conseil  européen  de  la  recherche,  les  actions  de  Marie  Skłodowska-Curie  ainsi  que  les 
nanotechnologies  et  le  transport  forment  des  parts  importantes  du  PC  auxquelles  les 
OSCs ne participent pratiquement pas.  

  Une augmentation du budget pour les sujets qui ont déjà une plus haute participation 
des OSCs sera utile pour le renforcement du rôle de la société civile  dans les PCs et 
du rapprochement de la science et des citoyens. Ce revirement du budget devra être 
accompagné par des interventions supplémentaires. 

La plupart des  OSCs ne  sont  pas capables d’entretenir  une  relation  durable  avec 
les PCs. Leur 85% de  taux de décrochage de FP6 à P7 représente le taux le plus élevé 
de  tous  les  types  d’organisations  participantes.  Environ  70%  des  OSCs  du  PC7  n’ont 
participé qu’une seule fois: pour le PC6, ce chiffre est encore plus élevé. Ce manque de 
cohérence  ne  permet  pas  la  fertilisation  croisée  au  travers  l’implication  d’OSCs  (cela 
aurait  été  possible  si,  par  exemple,  ces  OSCs  avaient  participé  aux  deux  SiS  ainsi  qu’à 
d’autres  sujets).  La  rotation  fréquente  du  personnel  et  un  manque  de  mémoire 
organisationnelle des OSCs représentent d’autres types d’obstacles.  

  Le  renforcement  des  capacités  institutionnelles  et  le  développement  permanent  des 
compétences  est  requis  pour  que  les  OSCs  puissent  être  aptes  à  participer  au  PC  ; 
cela pourrait impliquer un échange de personnel, le lancement d’initiatives spécifiques 
et le renforcement des capacités institutionnelles.  

Les OSCs peinent à rejoindre la logique de recherche et d’innovation hautement 
compétitive et axée sur l’excellence.  Les OSCs axés sur leur mission se concentrent 
bien  plus  sur  les  impacts  politiques  et  les  besoins  des  parties  prenantes  que  sur  les 
publications scientifiques et sur la construction d'un palmarès académique. Leur mission 
consiste à influencer la politique actuelle, qu’elle soit nationale ou régionale. Ainsi, ils ne 
retirent aucun avantage à être partenaires dans un projet d’une durée supérieure à trois 
ans.  Cela  dit,  beaucoup  d’OSCs  orientés  vers  le  service  ne  bénéficient  pas  vraiment  de 
valeur  ajoutée  à  la  recherche  et  à  l’innovation  pour  leur  activité  de  base  étant,  par 
ailleurs, souvent peu innovatrice.  

Par  conséquent  les  divers  types  d’OSCs  participent  un  peu  plus  aux  actions  de 
coordination et de soutien qu’aux projets de recherche et/ou aux projets d’innovation.  

  Hormis  la  participation  aux  actions  de  coordination  et  de  soutien,  d’autres  options 
impliquant  les  OSCs  pourraient  être  l’élaboration  de  programmes,  l’évaluation  de 
propositions et la diffusion des résultats.  

Pour  les  OSCs,  il  est  souvent  fort  peu  aisé  d’acquérir  une  valeur  ajoutée 
européenne suite  à  la participation  à  des  projets menés  à  l’échelle  européenne 
car ceux-ci visent des politiques nationales ou régionales. Ceci vaut en particulier 
pour  les  sujets  sociétaux  pour  lesquels  il  n’existe  aucune  politique  commune  de  l’Union 
Européenne.  Comparés  aux  PCs,  les  possibilités  de  financement  des  Fonds  structurels 
européen  et  du  Fonds  européen  d'investissement  semblent  mieux  cibler  la  portée 
régionale/locale de nombreux OSCs.  

  Un  meilleur  alignement  des  PCs  entre  le  Fonds  structurel  européen  et  le  Fonds 
européen d'investissement, comme proposé par l'évaluation ex-post du PC7 , pourrait 
indirectement  renforcer  l’implication  des  OSCs  dans  des  activités  d’innovation  et  de 
recherches transnationales. 

18 

 
Avantages liés à la participation des OSCs aux PCs  

Les  OSCs  ne  suivent  ni  la  logique  du  monde  universitaire  (caractérisée  par  l’excellence 
dans  des  disciplines  scientifiques)  ni  la  logique  des  entreprises  (façonnée  par  la 
compétitivité  et  le  profit).  En  revanche,  leur  logique  met  en  exergue  la  solution  des 
problèmes  sociétaux  –  soit  comme  OSCs  motivés  par  leur  mission,  par  l’influence  des 
actions  politiques,  soit  comme  OSCs  orientés  vers  le  service  par  l’amélioration  de  la 
situation de leur principal groupe cible. Par conséquent, suivant cette logique différente, 
nous n’avons pas trouvé de preuve mettant en évidence le fait que des OSCs contribuent 
à la performance des recherches.  

En  raison  de  leur  rôle  marginal  dans  les  PCs,  les  OSCs  ne  contribuent  pas  à  la 
performance  des  réseaux.  Comme  indiqué  dans  les  analyses  des  réseaux  sociaux,  la 
participation  d’une  OSC  n’est  pas  dépendante  de  la  morphologie  du  réseau  ;  il  n’y  a 
pratiquement  pas  de  corrélation  entre  les  mesures  des  réseaux  et  la participation  d’une 
OSC  dans  le  PC7.  Dans  les  réseaux  des  PCs,  les  OSCs  sont  des  «  suiveurs  »,  ils  ne 
constituent  pas  de  sous-groupe  ni  ne  remplissent  des  vides  et  ils  n’assurent  pas  de 
fonction  d'intermédiaire  significative  (par  conséquent,  ceux-ci  ne  bouchent  pas  de  trous 
structurels).  En  outre,  la  suppression  des  OSCs  n’aurait  pas  eu  de  véritable  impact  sur 
les paramètres des réseaux et de leur morphologie.  

  La  grande  diversité  des  avantages  liés  à  la  participation  des  OSCs  aux  PCs  ne  peut 
pas être évalué de façon adéquate par l’application des critères de succès tels que « 
performance  des  réseau  »  ou  «  performance  de  la  recherche  ».  Ceci  est  dû  à  la 
logique foncièrement différente (ou “monnaie”) des OSCs: les PCs sont façonnés par 
les  chercheurs,  mettant  l’accent  sur  l’excellence  scientifique,  et  par  le  secteur  des 
entreprises,  mettant  l’accent  sur  le  profit  et  la  compétitivité,  alors  que  les  OSCs 
suivent la logique des impacts sociétaux. L’évaluation des avantages de l’inclusion des 
OSCs motivées par leur mission doit considérer les impacts politiques ; Les avantages 
de  l’implication  des  OSCs  orientées  vers  le  service  peuvent  être  mesurés  à  l’aide  de 
l’impact  positif  pour  les  parties  prenantes  qu’ils  représentent.  Ainsi,  nous  favorisons 
une nouvelle  façon d’appréhender les avantages liés à la participation  des  OSCs aux 
PCs par l’évaluation des avantages sociétaux (potentiels) des PCs dans leur ensemble 
et par l’identification des rôles dans lesquels les OSCs peuvent au mieux contribuer à 
maximiser ces avantages.  

Comment augmenter l’implication des OSCs dans les PCs  

Une  série  d’études  de  cas  et  de  scénarios  préliminaires  du  modèle  basé  sur  des  agents 
ont  montré  que  la  microgestion  du  programme  n’entraînent  seulement  que  des  effets 
marginaux sur le réseau ainsi que sur l’implication des OSCs et leurs impacts.  

  La  microgestion  de  caractéristiques  individuelles  de  programmes  n’a  que  des  effets 
marginaux. Par conséquent, nous proposons de considérer une gamme plus ample de 
rôles  que  les  OSCs  pourraient  jouer  dans  de  futurs  PCs  :  (1)  dans  les  groupes 
d’évaluation  des  propositions  (au  moins  pour  la  section  «  impact  »),  (2)  dans  la 
création  d’ordres  du  jour  et  dans  le  développement  de  programmes  de  travail  (au-
delà des processus de consultation en ligne) et (3) dans les conseils d’éthique et les 
comités  directeur  (au  niveau  des  projets  et  des  programmes).  Nombre  de  ces  rôles 

19 

 
 
rentreraient mieux dans la logique fondamentale des OSC (axés sur les missions)  et 
pourraient,  par  conséquent,  être  plus  intéressants  que  le  rôle  de  participant  comme 
partenaires marginalisés dans les projets. 

Quatre  scénarios  ont  été  développés,  examinés  par  un  modèle  basé  sur  les  agents,  et 
analysés  par  rapport  à  la  morphologie  des  réseaux,  les  parts  de  budget  des  différents 
types  d’organismes,  l’excellence  scientifique  et  les  impacts  positifs  sur  les  objectifs  de 
développement  durable.  Ces  résultats  ont  été  comparés  avec  un  scénario  de  référence 
bien calibré qui reflétait les parts de budget et les dimensions des projets du PC7.  

  Le scénario « Compétitivité & haute technologie » avait pour objectif d’accroître la 
compétitivité par la promotion de secteurs de recherche axés sur la technologie (tels 
que  les  TIC,  les  nanotechnologies  (NMP)  ou  le  transport)  et  de  financer  des  projets 
moins nombreux, mais plus amples que dans le passé. Les résultats ont montré une 
plus  grande  part  du  secteur  des  affaires,  des  effets  de  haute  concentration 
comparables,  mais  aussi  de  plus  grandes  parts  d’OSC  axées  sur  les  affaires  (OSC4) 
que dans le scénario de référence, alors que la part des OSC axées sur les citoyens et 
les  sociétés  (OSC1-3)  est  resté  marginale.  Ce  scénario  a  aussi  démontré  de  forts 
effets d’apprentissage en matière de disciplines scientifiques et des effets positifs sur 
le renforcement des capacités. 

  Le scénario « Cohésion sociale & bien-être » se concentrait sur les défis sociétaux 
et  sur  les  types  de  recherche  axés  sur  les  processus  (tels  que  la  santé,  les  SHS,  la 
science dans la société (SiS) et les régions de la connaissance) en finançant des pro-
jets plus nombreux, mais de moindre ampleur que dans le passé. Il a montré la plus 
forte augmentation de l’implication d’OSC à 5% des organismes et à 1,3% du budget, 
permettant  ainsi,  particulièrement  aux  OSCs  visant  les  citoyens  et  les  sociétés 
(OSC1-3), de doubler leur nombre et leur budget. Ce scénario a aussi montré un ré-
seau plus stable (plus haut degrès moyen, plus de regroupement) avec des collabora-
tions qui étaient distribuées plus équitablement entre les organismes (moins de  cen-
tralisation). En outre, il a montré des effets positifs sur le renforcement de capacités 
et  le  plus  grand  effet  d’apprentissage  en  ce  qui  concerne  les  défis  sociétaux,  mains 
sans affaiblir pour autant l’excellence scientifique. 

  Le  scénario  «  Europe-forteresse  »  a  surtout  concentré  sa  recherche  et  son 
innovation  sur  des  sujets  tournant  autour  de  la  sécurité  et  de  la  santé  en  vue  de 
maintenir  la  sécurité  en  Europe  et  de  la  rendre  plus  autonome.  Parmi  les  quatre 
scénarios, c’est celui-ci qui montre le moins de différences par rapport au scénario de 
référence  en  matière  de  promotion  d’organismes  particuliers  ou  de  création  de  forts 
réseaux ou d’effets d’apprentissage. Cependant, si l’on regarde les OSCs, ce scénario 
se démarque en raison de la plus petite part de budget pour les OSCs (environ 0,9% 
uniquement). 

  Le  scénario  «  PC  démantèlement  »  simule  une  coupe  budgétaire  dramatique  des 
parties collaboratives et axées sur les missions des PCs, soit par le reflet d’une coupe 
générale dans le budget des PCs, soit par le reflet de redistribution de budget vers le 
CER.  Cette  coupe  budgétaire  a  pour  résultat  un  abandon  dramatique  d’organismes 
plus  petits  (y  compris  des  OSCs),  alors  que  les  grands  acteurs  (tels  que  les 
universités) parviennent à rester des acteurs fondamentaux. Cela mène à un réseau 
étroitement connecté qui est dominé par un nombre diminué de parties prenantes. En 

20 

 
outre, la capacité des organismes, autant en termes de connaissances que d’impacts, 
est considérablement plus petite que dans les trois autres scénarios. 

  À  l’instar  des  résultats  du  modelage  axé  sur  les  agents,  les  quatre  scénarios  se 
distinguent les uns des autres  en fonction de leurs effets sur le réseau de recherche 
et d’innovation en général, de l’implication des OSCs et des effets d’apprentissage de 
tout  type  d’organisme  impliqué.  Lorsqu’il  s’agit  de  relever  les  défis  sociétaux,  le 
rapprochement de la science et des citoyens et l’augmentation de la diversité et de la 
résilience des réseaux européen de recherche et d’innovation sont considérés comme 
des  objectifs  stratégiques  importants,  ce  qui  a  pour  conséquence  que  le  scénario  « 
cohésion sociale & bien-être » est le plus propice : il accroît de manière considérable 
les  capacités  de  toutes  les  parties  prenantes  pour  contribuer  à  résoudre  des 
problèmes  sociétaux  sans  affaiblir  pour  autant  l’excellence  scientifique  et  sans 
perturber les réseaux existants ou changer complètement la nature du programme, et 
est  donc  encore  en  mesure  de  s’occuper  de  manière  adéquate  de  la  logique  des 
acteurs principaux (universités, organismes de recherche et entreprises). Cela a pour 
effet de, indirectement, doubler l’implication des OSCs dans les PCs. 

Recommandations pour le PC9 à venir  

En  ce  qui  concerne  le  PC9  à  venir,  des  décisions  sur  l’orientation  stratégique  du 
programme  et  ses  axes  thématiques  doivent  être  prises.  Elles  devront  prendre  en 
considération  les  différentes  logiques  des  parties  prenantes  les  plus  importantes 
impliquées ainsi que les « monnaies » 2 et les principes de gouvernance qui sont utilisés 
par les parties :  

  Le  premier  pilier  d’Horizon  2020,  la  «  Science  excellente  »,  met  l’accent  sur  la 
recherche  exploratoire  qui  a  pour  objectif  de  nouvelles  découvertes,  vise  des 
chercheurs  individuels,  s’occupe  de  leur  excellence  en  termes  de  «  monnaie  »  telle 
qu’elle  est  mesurée  par  les  publications  et  les  parcours  universitaires,  et  remet  des 
bourses  dans  un  processus  hautement  sélectif  qui  se  base  sur  l’évaluation  effectuée 
par  d’autres  chercheurs.  À  travers  ses  hautes  ambitions  et  son  accent  sur 
l’excellence,  ce  processus  est  hautement  sélectif,  mais  libre  de  toute  influence  au-
delà de la communauté scientifique. Par conséquent, la gouvernance ne fait référence 
qu’à elle-même et considérer les impacts sociétaux est souvent considérée comme un 
affaiblissement potentiel de l’excellence. 

  Le  deuxième  pilier  d’Horizon  2020,  le  «  primauté  industrielle  »,  se  concentre  sur 
l’innovation, vise des secteurs d’affaires hautement innovateurs et a pour objectif de 
stimuler leur compétitivité, leur croissance et leur création d’emplois. La « monnaie » 
de ce pilier est donc le profit et les brevets sont utilisés pour assurer des avantages 
compétitifs.  Cela  mène  à  l’exclusion  (de  possibles  concurrents)  et  un  établissement 

2 L’idée des « monnaies » provient du point de vue que différentes unités existent dans différents 
champs sociaux pour mesurer le statut à l’intérieur de ce champ (par ex. le bilan des publications 
pour  des  personnes  travaillant  dans  une  université).  La  métaphore  de  «  monnaie  »  souligne  le 
fait  que,  tandis  que  chaque  monnaie  a  une  valeur  particulière  dans  un  champ  particulier,  la 
conversion d'une monnaie en une autre devrait également être possible (à l’instar de l’euro qui 
peut  être  converti  en  dollars  américains)  afin  de  permettre  ainsi  l’échange  à  travers  différents 
champs. 

21 

 
 
                                           
conjoint  de  l’ordre  du  jour  (dans  un  cercle  interne  de  participants)  comme  principes 
de gouvernance. 

  Le  troisième  pilier  d’Horizon  2020,  les  «  Défis  sociétaux  »,  met  l’accent  sur  les 
impacts  de  pratiques  sociétales,  vise  les  citoyens  et  la  société  civile  et  suit  une 
logique  fondamentale  de  responsabilité  (politique).  L’augmentation  d’impacts  positifs 
(et  la  réduction  des  négatifs)  constitue,  ainsi,  le  critère  de  succès  pour  les  projets. 
Alors  que  les  deux  autres  piliers  courent  le  risque  d’un  «  verrouillage  »  (par  la 
sélection  d’un  côté  et  l’exclusion  de  l’autre),  ce  pilier  peut  mettre  en  équilibre  ces 
tendances  et  servir  d’élément  intégrant.  En  outre,  alors  que  les  deux  autres  piliers 
suivent  des  principes  de  gouvernance  axés  sur  les  initiés,  le  troisième  pilier  a  le 
potentiel de relever des défis sociétaux majeurs d’une pertinence globale. 

  Si la politique européenne de recherche et d’innovation souhaite assurer sa légitimité 
face aux citoyens européens, un troisième pilier qui se concentre sur de grands défis 
sociétaux  est  d’une  importance  vitale  (et  non  la  combinaison  du  deuxième  et  du 
troisième pilier sous le titre de « recherche axée sur des missions »). Les objectifs de 
développement  durable  fixés  au  niveau  global  pourraient  fournir  une  orientation  et 
empêcher  que  l’établissement  de  l’ordre  du  jour  soit  motivé  par  des  groupes 
d’intérêts.  Afin  d’ouvrir  davantage  le  monde  de  la  recherche  à  la  société  et 
inversement, d’ouvrir la société au monde de la recherche, des espaces de cocréation 
de  connaissances  et  d’innovation  devraient  être  fondés  en  impliquant  une  grande 
variété  de  parties  prenantes.  Les  mécanismes  de  financement  devraient  refléter  le 
fait  que  les  défis  sociétaux  soulèvent  des  problèmes  «  pernicieux  »  et  que  les 
innovations  ne  sont  pas  des  produits  finaux,  mais  des  processus  continus.  Par 
conséquent, 
la  gouvernance 
interventions  devraient  se  concentrer  sur 
d'écosystèmes  (et  pas  seulement  de  projets),  développer  des  capacités  et  des 
structures  institutionnelles  et  permettre  davantage  de  processus  continus  (en 
surmontant la perspective limitée de projets individuels). La recherche et l’innovation 
responsable  (RIR)  devraient  alors  devenir  un  principe  de  guidance  dans  les  trois 
piliers  et  thèmes  des  PCs.  Cela  constituera  une  garantie  pour  que  de  futurs  PCs 
mettent  en  avant  non  seulement  la  recherche  exploratoire  au  sein  d’universités  et 
d’organismes  de  recherche  ou  soutiennent  la  compétitivité  des  entreprises,  mais 
aussi, qu’ils prennent en considération les besoins des citoyens et qu’ils relèvent des 
défis sociétaux. 

les 

22 

 
 
Kurzfassung 

Ziel 

Ziel dieses Berichts ist es, die Teilnahme von zivilgesellschaftlichen Organisationen (Civil 

Society  Organisations  –  CSOs)  in  den  Europäischen  Forschungsrahmenprogrammen 

(Framework  Programmes  –  FPs),  insbesondere  in  FP6  und  FP7,  zu  untersuchen  und 

daraus  Politikempfehlungen  zur  Stärkung  der  Rolle  von  CSOs  abzuleiten.  Die  Studie 

verfolgte  dabei  die 

folgenden  Ziele:  (1)  die  Entwicklung  einer  praktikablen 

Klassifizierung  von  CSOs,  welche  helfen  soll,  die  Rollen  der  CSOs  innerhalb  der 

Europäischen  Forschungsrahmenprogramme  im  Rahmen  von  „Responsible  Research  and 

Innovation“  (RRI)  zu  unterscheiden,  sowie  deren  spezifisches  Wissen,  Verhalten  und 

latente  Präferenzen  die  ihre  Teilnahme  bestimmen  zu  verstehen;  (2)  die  Untersuchung 

des  Ausmaßes  der  CSO  Beteiligung  in  den  FPs  und  die  Bestimmung  ihres 

spezifischen  Beitrag  zum  Forschungsnetzwerk  mittels  sozialer  Netzwerkanalyse;  und 

(3) die  Simulation  verschiedener  Politikszenarien  und  deren  Effekte  auf  die 

Teilnahme von CSOs mittels agentenbasierter Modellierung, wobei das Agentenverhalten 

nicht  –  wie  in  bestehenden  Modellierungsansätzen  üblich  –  rein  durch  wissenschaftliche 

Exzellenz  determiniert  ist,  sondern  mit  einer  Orientierung  zur  Lösung  gesellschaftlicher 

Herausforderungen,  die  auf  den  Nachhaltigkeitszielen  der  UN  (Sustainable  Development 

Goals – SDGs) basieren, ergänzt wird.  

Methodischer Ansatz 

In  dieser  Studie  wurden  mehrere  methodische  Ansätze  verknüpft.  Dies  umfasste 

Literaturrecherchen  über  Soziale  Netzwerkanalyse  (SNA)  sowie  zivilgesellschaftliche 

Organisationen  in  Forschung  und  Innovation  (F&I),  Recherchen  und  eine  Umfrage  zur 

Entwicklung  einer  geeigneten  Definition  und  Typologie  von  CSOs 

in  den 

Rahmenprogrammen,  und  basierend  darauf  die  Identifizierung  und  Kategorisierung  von 

CSOs  in  FP6  und  FP7  Projektdatensätzen.  Die  zentralen  Methoden  für  den  empirischen 

Teil  dieser  Studie  waren  Fallstudien  mit  Tiefeninterviews  mit  teilnehmenden  CSOs, 

soziale Netzwerkanalysen der Forschungsnetzwerke in FP6 und FP7, mit spezifischem 

Fokus  auf  CSOs,  Publikations-  und  Medienanalysen  von  Forschungsprojekten  mit 

CSO  Beteiligung,  sowie  agentenbasierte  Modellierung  zur  Simulation  der  Effekte 

politischer Interventionen auf die FP-Netzwerke und insbesondere auf CSO-Beteiligungen. 

Der  für  diesen  Zweck  entwickelte  Simulationsansatz  erweitert  existierende  Modelle, 

indem  nicht  nur  Wissensflüsse 

in  Forschungsnetzwerken  sondern  auch  die 

Problemlösungskompetenzen  der 

teilnehmenden  Akteure 

in  Bezug  auf  soziale 

Herausforderungen wie z.B. den UN SDGs berücksichtigt werden. 

23 

 
 
Zentrale Erkenntnisse und Empfehlungen 

CSO-Teilnahme in den FPs 

CSOs  stellen  eine  sehr  heterogene  Gruppe  verschiedener  Organisationstypen 

(hinsichtlich  Größe,  strategischer  Orientierung,  Geschäftsmodell,  Finanzierungsquellen, 

Zielgruppen,  usw.)  dar,  wodurch  eine  klare  Trennung,  Charakterisierung  und 

Identifizierung  von  CSOs  in  den  Rahmenprogrammen  erschwert  wird.  Vor  diesem 

Hintergrund  entwickelte  die  Studie  einen  Zugang,  der  zwischen  vier  Typen  von  CSOs 

unterscheidet: 

bürgerorientierte 

(„kern“) 

CSOs 

(CSO1), 

gesellschafts-

orientierte/öffentlich-finanzierte  CSOs  (CSO2),  gesellschaftsorientierte/unternehmens-

finanzierte  CSOs  (CSO3),  und  unternehmensorientierte  CSOs  (CSO4).  Die  Resultate  der 

Studie zeigen, dass die Herangehensweise geeignet ist, die Teilnahmegewohnheiten von 

CSOs in FPs zu beurteilen. 

  Wir raten dringend zu einer zukünftig besseren Kategorisierung von CSOs im CORDA-

Datensatz, entsprechend der in dieser Studie angewandten Typologie. 

CSOs sind  vergleichsweise unbedeutende Akteure im FP-Netzwerk. CSOs sind im 

Durchschnitt  in  einem  von  dreizehn  Projekten  eingebunden;  sie  machen  ungefähr  6% 

aller  teilnehmenden  Organisationen  in  FP7  aus,  erhalten  aber  nur  etwa  1%  der  von  der 

Europäischen  Kommission  zur  Verfügung  gestellten  Förderungen.  Nur  etwa  10%  aller  in 

FP7  teilnehmenden  CSOs  koordinierten  ein  Projekt,  und  nur  etwa  1%  aller  FP7  Projekte 

wurden  von  CSOs  koordiniert.  CSOs  nehmen  in  der  Regel  keine  zentrale  Rolle  in  den 

Projekten  ein.  Dies  liegt  unter  anderem  an  einer  niedrigeren  Stellung  innerhalb  des 

Projektkonsortiums,  Unklarheiten  bei  der  Aufgabenteilung,  und  an  der  Befürchtung  von 

akademischen Forschungspartnern,  die Teilnahme von CSOs  könnte sich negativ auf die 

wissenschaftliche Reputation auswirken. 

  Aufgrund  ihrer  unbedeutenden  Rolle  in  FP-Netzwerken  können  von  CSOs  keine 

signifikanten  Wirkungen  bei  einer  Teilnahme  an  Forschungsprojekten  erwartet 

werden.  

CSOs weisen kein systematisches Verhaltensmuster in Bezug auf FP-Beteiligung 

auf, in machen Themen treten sie aber öfter und stärker in Erscheinung als in anderen: 

Science 

in  Society 

(SiS) 

ist  das  einzige  Thema 

in  dem  bürger-  und 

gesellschaftsorientierte  CSOs  (CSO1-3)  eine  bedeutsame  Rolle  spielen.  Auch  Regions  of 

Knowledge 

(RoK)  und  Research  of 

the  benefits  of  SMEs 

(SME) 

zeigen 

überdurchschnittlich  hohe  Anteile  von  CSOs,  was  aber  vor  allem  an  unternehmens-

orientierten CSOs (CSO4) liegt. Activities  of International Cooperation (INCO), Research 

policies  (COH),  und  Socio-economic  Sciences  and  Humanities  (SSH)  zeigen  kleine  aber 

dennoch  über  dem  Durchschnitt  liegende  Budgetanteile  für  bürger-  und  gesellschafts-

orientierte  CSOs  (CSO1-3).  Absolut  gesehen  stellen  auch  die  Themen  Health  und 

Information  and  Communication  Technologies  (ICT)  –  aufgrund  der  Höhe  des  zur 

Verfügung gestellten EU Förderungsbudgets – eine wichtige Finanzierungsquelle für CSOs 

dar. In anderen wichtigen Teilen des FPs, wie dem European Research Council (ERC), den 

24 

 
Marie  Skłodowska-Curie  Actions  sowie  in  den  Themen  Nanotechnologies  (NMP)  und 

Transport nehmen CSOs praktisch nicht teil.  

  Eine  Erhöhung  des  Budgets  für  Themen  die  eine  höhere  CSO-Beteiligung  aufweisen 

würde  zu  einer  Stärkung  der  Rolle  der  Zivilgesellschaft  in  den  FPs  führen  und 

Forschung den BürgerInnen näher bringen. Derartige Budgetverschiebungen bedürfen 

aber  noch  weiterer  flankierender  Maßnahmen,  wie  kleinere  Projekte  und  eine 

Veränderung der Gewichtungen bei der Antragsevaluierung. 

Die  meisten  CSOs  schaffen  es  nicht,  mehrmalig  bzw.  längerfristig  an  den  FPs 

teilzunehmen. 85% der CSOs aus FP6 nahmen nicht mehr in FP7 teil, was der höchsten 

Ausstiegsrate aller teilnehmenden Organisationstypen entspricht. Rund 70% der CSOs in 

FP7  nahmen  an  nur  einem  einzigen  Projekt  teil,  für  FP6  ist  dieser  Anteil  sogar  noch 

geringfügig höher. Diese fehlende Kontinuität verhindert die Verbreitung positiver Effekte 

der CSO-Teilnahme (dies wäre möglich wenn dieselben CSOs in mehreren Projekten und 

in  unterschiedlichen  Themen  teilnehmen  würden).  Gründe  dafür  sind  eine  hohe 

Personalfluktuation  in  CSOs  und,  daraus  resultierend,  ein  mangelndes  „organisationales 

Gedächtnis“. 

  Der Aufbau institutioneller Kapazitäten und die kontinuierliche Kompetenzentwicklung 

in  CSOs  sind  Voraussetzungen  zur  Teilnahme  an  den  FPs.  Dies  könnte  durch 

Mitarbeiteraustausch  und  mittels  gezielter  Maßnahmen  zum  Aufbau  institutioneller 

Kapazitäten erreicht werden.  

Die  Logik  von  CSOs  steht  diametral  zur  kompetitiven  und  exzellenzorientierten 

Forschungs-  und  Innovationslogik.  Missionsgetriebene  CSOs  orientieren  sich  an 

politischen  Herausforderungen  und  den  Bedürfnissen  der  von  ihnen  vertretenen  Akteure 

und legen wenig Wert auf wissenschaftliche Publikationen oder akademische Laufbahnen. 

Ihr  Ziel 

ist  die  Beeinflussung  aktueller  politischer  nationaler  und 

regionaler 

Entscheidungen,  daher  nutzt  ihnen  die  Teilnahme  an  mehrjährigen  Projekten  kaum. 

Andererseits  ziehen  viele  dienstleistungsorientierte  CSOs,  deren  Kerngeschäft  meist 

wenig innovativ ist, nur sehr geringen Zusatznutzen aus Forschung und Innovation. CSOs 

nehmen  in  den  FPs  daher  eher  in  Koordinierungs-  und  Unterstützungsmaßnahmen 

(CSAs) als in echten Forschungs- und/oder Innovationsprojekten teil. 

  Die  Einbindung  von  CSOs  in  Agenda-Setting,  in  die  Antragsevaluierung  und  in  die 

Verbreitung von Ergebnissen würde eine substanziellere Involvierung von CSOs in die 

FPs  ermöglich,  welche  über  die  Teilnahme  an  Koordinierungs-  und  Unterstützungs-

maßnahmen hinausgeht. 

CSOs ziehen oft keinen Europäischen Zusatznutzen durch die Teilnahme an EU-

weiten  Projekten,  da  sie  sich  zumeist  an  nationalen  und  regionalen  Politiken 

orientieren.  Das  betrifft  insbesondere  sozialpolitische  Fragestellungen  für  welche  keine 

gemeinsame EU-Politik existiert. Im Vergleich zu den FPs wird der Europäische Struktur- 

und  Investitionsfonds  der  regionalen/lokalen  geografischen  Ausrichtung  vieler  CSOs 

besser gerecht. 

25 

 
  Eine  bessere  Abstimmung  der  FPs  und  des  Europäischen  Struktur-  und 

Investitionsfonds, wie in der FP7 ex-post Evaluierung vorgeschlagen, könnte indirekt 

zu  einer  stärkeren  Einbindung  von  CSOs  in  transnationale  Forschungs-  und 

Innovationsaktivitäten führen. 

Der Nutzen aus der Teilnahme von CSOs in den FPs 

CSOs verfolgen weder eine akademische (auf Exzellenz in wissenschaftlichen Disziplinen) 

noch  eine  wirtschaftliche  (geprägt  von  Wettbewerbs-  und  Gewinnstreben)  Logik. 

Stattdessen  liegt  ihr  Fokus  auf  der  Lösung  gesellschaftlicher  Probleme  –  entweder  als 

missionsgetriebene  CSOs  durch  die  Beeinflussung  politischer  Entscheidungen,  oder  als 

dienstleistungsorientierte  CSOs,  durch  die  Verbesserung  der  Situation  ihrer  primären 

Zielgruppe. Aufgrund dieser Ausrichtung ist ein wissenschaftlicher Beitrag von CSOs  vor 

allem dort zu erwarten wo Forschung die Ziele von CSOs fördert.  

Wegen  ihrer  unbedeutenden  Rolle  in  den  FPs  tragen  CSOs  zudem  nicht  zur  Netzwerk-

Performance  bei.  Eine  soziale  Netzwerkanalyse  zeigte,  dass  die  Teilnahme  von  CSOs 

praktisch  nicht  von  der  Netzwerkmorphologie  abhängt:  in  FP7  existiert  nahezu  keine 

Korrelation  zwischen  Netzwerkparametern  und  der  Teilnahme  von  CSOs.  CSOs  sind 

„hangers-on“  in  FP-Netzwerken,  das  heißt  sie  spielen  keine  bedeutende  vernetzende 

Rolle,  sind  keine  Broker,  konstituieren  nicht  oder  nur  in  geringem  Maße  Communities, 

und verbinden nicht unterschiedliche Netzwerkbereiche (d.h. sie überbrücken auch keine 

strukturellen  Löcher).  Sie  sind  daher  auch  nicht  entscheidend  für  den  Netzwerkaufbau 

auf  Makroebene:  das  Entfernen  von  CSOs  hätte  praktisch  keinen  Effekt  auf 

Netzwerkmaße und -morphologie. 

  Der  Nutzen  der  Teilnahme  von  CSOs  in  den  FPs  kann  nicht  hinreichend  durch  die 

Verwendung gängiger Erfolgskriterien wie “Netzwerk-Performance” oder „Forschungs-

Performance“  bestimmt  werden.  Dies  liegt  an  der  grundsätzlich  unterschiedlichen 

Logik  (oder  „Währung“)  von  CSOs:  während  die  FPs  der  Logik  von  Forschung  –

ausgerichtet  auf  wissenschaftliche  Exzellenz  –  und  Unternehmen  –  ausgelegt  auf 

Gewinn  und  Wettbewerb  –  folgen,  denken  CSOs  in  der  Logik  gesellschaftlicher 

Auswirkungen. Zur Beurteilung des Nutzens der Teilnahme missionsgetriebener CSOs 

müssen politische Auswirkungen berücksichtigt werden, während die Vorteile aus der 

Beteiligung dienstleistungsorientierter CSOs durch positive Auswirkungen auf die von 

ihnen  vertretenen  Akteure  gemessen  werden  können.  Wir  empfehlen  daher,  den 

Nutzen  der  Teilnahme  von  CSOs  in  den  FPs  aus  einem  neuen  Blickwinkel  zu 

betrachten,  nämlich  durch  die  Beurteilung  des  gesellschaftlichen  Gesamtnutzens  der 

FPs, und durch  die Identifizierung von  (neuen) Rollen mittels derer  CSOs am besten 

zur Erhöhung dieses gesellschaftlichen Nutzens beitragen können. 

26 

 
 
Wie die CSO-Beteiligung in den FPs erhöht werden kann 

Eine  Reihe  von  Fallbeispielen  und  die  Szenarien  der  agentenbasierten  Simulation  haben 

gezeigt, dass ein „Mikro-Management“ einzelner Teile des FPs nur geringe Auswirkungen 

auf das Netzwerk sowie die Beteiligung von CSOs hat.  

  Mikromanagement  individueller  Programmteile  hat  nur  marginale  Auswirkungen  auf 

die  Beteiligung  von  CSOs  als  Projektpartner.  Wir  empfehlen  daher,  eine  größere 

Bandbreite  an  Rollen 

für  CSOs 

in  zukünftigen  FPs  anzudenken:  (1) 

in 

Evaluierungsgremien  für  FP-Anträge  (zumindest  für  den  „Impact“  Teil),  (2)  beim 

Agenda-Setting  und  der  Entwicklung  von  Arbeitsprogrammen  (in  substantiellerer 

Weise  als  online-basierte  Konsultationen),  und  (3) 

in  Ethik-Gremien  und 

Lenkungsausschüssen (auf Projekt- und Programmebene). Viele dieser Rollen würden 

besser  zur  missionsorientierten  Logik  von  CSOs  passen  und  könnten  daher  für  diese 

attraktiver sein, als eine untergeordnete Rolle als Projektpartner zu spielen.  

Im  Rahmen  der  Studie  wurden  vier  Szenarien  mittels  eines  agentenbasierten  Modells 

simuliert.  Diese  wurden  dann  hinsichtlich  ihrer  Effekte  auf  Netzwerkmorphologie, 

Budgetanteilen  verschiedener  Organisationstypen,  Wissenschaftsexzellenz  und  deren 

positiven Auswirkungen auf die UN Nachhaltigkeitsziele (SDGs) analysiert. Die Ergebnisse 

wurden  mit  einem  Basisszenario,  das  Budgetanteile  und  Projektgrößen  von  FP7 

widerspiegelt, verglichen.  

  Das  Szenario  „Wettbewerbsfähigkeit  &  High-Tech”  strebt  die  Stärkung  der 

Wettbewerbsfähigkeit 

durch 

die 

Förderung 

von 

technologieorientierten 

Forschungsbereichen (wie z.B. ICT, Nanotechnologies (NMP) oder Transport) und die 

Finanzierung  von  wenigen,  aber  größeren  Projekten  als  früher  an.  Gemäß  den 

Simulationsergebnissen 

führt  dieses  Szenario  zu  einem  höheren  Anteil  des 

Unternehmenssektors,  vergleichsweise  hohen  Konzentrationseffekten,  aber  auch 

einem  im  Vergleich  zum  Basisszenario  höheren  Anteil  von  unternehmensorientierten 

CSOs  (CSO4).  Der  Anteil  von  bürger-  und  gesellschaftsorientierten  CSOs  (CSO1-3) 

bleibt hingegen unbedeutend. Dieses Szenario weist die größten Lerneffekte in Bezug 

auf wissenschaftliche Disziplinen auf. 

  Das Szenario „Soziale Kohäsion & Wohlbefinden” legt den Fokus auf die Adressie-

rung  gesellschaftlicher  Herausforderungen  durch  mehr  prozessorientierte  Forschung 

(wie z.B. Health, Socio-economic Sciences and Humanities (SSH), Science in Society 

(SiS) und Regions of Knowledge (RoK)) und die Förderung von mehr, aber kleineren 

Projekten. Gemäß den Simulationsergebnissen weist dieses Szenario mit 5% der Or-

ganisationen  und  1,3%  des  Budgets  die  höchsten  Anteile  an  CSO-Beteiligung  auf. 

Insbesondere  bürger-  und  gesellschaftsorientierte  CSOs  (CSO1-3)  profitieren  über-

durchschnittlich stark. Dieses Szenario weist auch ein stabileres Netzwerk auf (höhe-

rer average degree, stärkeres clustering) mit gleichmäßiger verteilten Kooperationen 

zwischen Organisationen (weniger Zentralisierung). Außerdem zeigen sich die größten 

Lerneffekte hinsichtlich gesellschaftlicher Herausforderungen, wobei die wissenschaft-

liche Exzellenz nicht beeinträchtigt wird. 

27 

 
  Das Szenario „Festung Europa” simuliert eine „Abschottung“ Europas und fokussiert 

Ausgaben  für  Forschung  und  Innovation  daher  in  erster  Linie  auf  sicherheits-  und 

gesundheitsrelevante Themen bzw. Versorgungssicherheit mit Energie. Unter den vier 

Szenarien weist dieses die geringsten Unterschiede zum Basisszenario hinsichtlich der 

Förderung spezifischer Organisationen, der Bildung starker Netzwerke, oder bezüglich 

Lerneffekten  auf.  Es  sticht  jedoch  heraus  in  Bezug  auf  CSOs,  welche  hier  mit  ca. 

0,9% den geringsten Budgetanteil aller Szenarien erhalten. 

  Das  Szenario  “FP-Rollback”  simuliert  eine  dramatische  Budgetkürzung  der 

kollaborativen  bzw.  missionsorientierten  Teile  des  FP.  Dies  spiegelt  entweder  eine 

grundsätzliche  Kürzung  des  EU-Budgets  für  Forschung  und  Innovation  oder  eine 

signifikante  Budgetverschiebung 

in  Richtung  des  ERC  wider.  Gemäß  den 

Simulationsergebnissen  resultiert  diese  Budgetkürzung  in  einem  dramatischen 

Rückgang  kleinerer  Organisationen  (inklusive  CSOs),  während  große  Akteure  (wie 

Universitäten)  Schlüsselakteure  bleiben.  Das  führt  zu  einem  deutlich  dichteren 

Netzwerk, welches von wenigen zentralen Akteuren dominiert wird. Außerdem zeigen 

sich  deutlich  niedrigere  Lerneffekte  der  teilnehmenden  Organisationen  als  in  den 

anderen drei Szenarien. 

  Die  Ergebnisse  der  agentenbasierten  Modellierung  zeigen  deutliche  Unterschiede  der 

vier  Szenarien  hinsichtlich 

ihrer  Effekte  auf  das  gesamte  Forschungs-  und 

Innovationsnetzwerk, der Beteiligung von CSOs, und den Lerneffekten der beteiligten 

Organisationstypen.  Wenn  die  Lösung  gesellschaftlicher  Herausforderungen,  eine 

bessere  Verankerung  von  Wissenschaft  in  der  Gesellschaft,  sowie  die  Erhöhung  von 

Diversität und Resilienz des  Europäischen Forschungs- und Innovationsnetzwerks als 

wichtige  politische  Ziele  angesehen  werden,  zeigt  das  Szenario  „Soziale  Kohäsion  & 

Wohlbefinden“  den  größten  Nutzen:  Es  steigert  die  Kapazitäten  aller  Akteure  zur 

Lösung  gesellschaftlicher  Probleme  deutlich,  ohne  dabei  die  wissenschaftliche 

Exzellenz und bestehende Netzwerke zu schwächen oder das Programm komplett zu 

verändern.  Noch  dazu  wird  der  Logik  der  wichtigsten  Akteure  (Universitäten, 

Forschungsorganisationen  und  Unternehmen)  genügend  Rechnung  getragen.  Als 

Nebeneffekt wird die Beteiligung von CSOs in den FPs verdoppelt. 

Empfehlungen für das 9. Forschungsrahmenprogramm (FP9) 

Im  Hinblick  auf  das  nächste  Forschungsrahmenprogramm  –  dem  FP9  –  stehen 

strategische  Entscheidungen  betreffend  der  Ausrichtung  und  der 

thematischen 

Schwerpunkte  an.  Dabei  müssen  die  verschiedenen  Logiken  der  involvierten  Akteure 
sowie die „Währungen“3 und deren Governance-Grundsätze berücksichtigt werden: 

3 Der Begriff “Währungen” basiert auf dem Verständnis, dass in verschiedenen sozialen Kontexten 
unterschiedliche Einheiten zur Messung des sozialen Status existieren (z.B. Publikationsleistungen 
von ForscherInnen an Universitäten). Die Metapher „Währung“ soll darauf aufmerksam machen, 

28 

 
 
                                           
 
  Die erste Säule von Horizon 2020, „Wissenschaftsexzellenz“, konzentriert sich auf 

Pionierforschung  zur  Gewinnung  neuer  Erkenntnisse,  durchgeführt  von  einzelnen 

(exzellenten)  ForscherInnen.  Exzellenz  als  „Währung“  dieser  Säule  bemisst  sich  in 

Form  von  Publikationen  und  akademischen  Laufbahnen.  Förderungen  werden  in 

einem  stark  selektiven  Prozess,  basierend  auf  Begutachtungen  von  anderen 

WissenschaftlerInnen  vergeben.  Die  hohen  Ambitionen  und  der  Fokus  auf  Exzellenz 

basieren  auf  einem  Prozess,  der  stark  selektiv,  jedoch  frei  von  Einflüssen  außerhalb 

der Wissenschaft ist. Die Governance dieser Säule ist somit selbstreferenziell, und die 

Berücksichtigung  gesellschaftlicher  Auswirkungen  wird  oft  als  potentielle 

Verwässerung von Exzellenz erachtet.  

  Die zweite Säule von Horizon 2020,  „Führende Rolle der  Industrie“, richtet ihren 

Fokus auf Innovation, zielt auf hoch innovative Wirtschaftssektoren ab, und strebt die 

Förderung  von  Wettbewerbsfähigkeit,  Wirtschaftswachstum  und  die  Schaffung  von 

Arbeitsplätzen  an.  Als  „Währung“  dieser  Säule  gelten  Profite  sowie  Patente  zur 

Sicherung von Wettbewerbsvorteilen. Dies führt zu Governance-Grundsätzen, die von 

Exklusion  (potentieller  Mitbewerber)  und  gemeinsamen  Agenda-Setting  (innerhalb 

eines kleinen, geschlossenen Kreises) geprägt sind. 

  Die  dritte  Säule  von  Horizon  2020,  „Gesellschaftliche  Herausforderungen“, 

richtet  ihren  Fokus  auf  die  Lösung  gesellschaftlicher  Herausforderungen,  zielt  auf 

BürgerInnen  und  die  Zivilgesellschaft  ab,  und  folgt  der  Logik  von  (politischer) 

Verantwortung.  Zu  den  Erfolgskriterien  dieser  Projekte  zählen  deshalb  die  Erhöhung 

positiver  (und  die  Verminderung  negativer)  Auswirkungen.  Während  für  die  anderen 

zwei Säulen die Gefahr eines „Lock-ins“ (einerseits durch Selektion, und andererseits 

durch  Exklusion)  besteht,  kann  diese  Säule  entsprechende  Tendenzen  ausgleichen 

und als integratives Element dienen. Zudem verfolgen die beiden die anderen Säulen 

Insider-orientierten  Governance-Grundsätzen,  während  die  dritte  Säule  das  Potential 

hat,  die  wichtigsten  gesellschaftlichen  Herausforderungen  auf  globaler  Ebene 

aufzugreifen. 

  Wenn  Europäische  Forschungs-  und  Innovationspolitik  ihre  Legitimität  gegenüber 

Europäischen BürgerInnen bewahren will, ist eine unabhängige dritte Säule mit einem 

Fokus  auf  die  großen  gesellschaftlichen  Herausforderungen  entscheidend  (d.h.  kein 

Zusammenlegen  der  zweiten  und  dritten  Säule  unter  dem  Titel  „missionsgetriebene 

Forschung“).  Die  globalen  UN  Nachhaltigkeitsziele 

(SDGs) 

könnten  als 

Orientierungshilfe  zur  Schwerpunktsetzung  dienen  und  vermeiden,  dass  Agenda-

Setting von Interessensgruppen gelenkt wird. Um Forschung näher an Menschen und 

Menschen  näher  an  Forschung  zu  bringen  braucht  es  Plätze  zur  gemeinsamen 

Wissensfindung  und  Innovation,  in  die  eine  Vielfalt  von  Stakeholdern  eingebunden 

werden.  Die  Förderinstrumente  sollten  die  Tatsache  widerspiegeln,  dass  es  sich  bei 

dass  jede  einzelne  Währung  eines  bestimmten  Bereiches  auch  in  eine  andere  Währung 
konvertiert  werden  können  sollte  (ähnlich  dem  Umwechseln  von  Euro  Banknoten  in  US  Dollar), 
um einen Austausch zwischen verschiedenen Kontexten zu ermöglichen. 

29 

 
                                                                                                                                    
gesellschaftlichen  Herausforderungen  um  komplexe  Probleme  („wicked  problems“) 

handelt, und dass Innovationen nicht endgültige Ergebnisse sondern vielmehr stetige 

Prozesse  sind.  Daher  sollte  der  Fokus  von  Interventionen  auf  der  Gestaltung  von 

offenen  Prozessen  (und  nicht  nur  Projekten)  und  dem  Aufbaue  institutioneller 

Kapazitäten  liegen  und  stetige  Prozesse  erlauben  welche  die  limitierte  Perspektive 

individueller  Projekte  aufheben.  Verantwortungsvolle  Forschung  und  Innovation 

(Responsible  Research  and  Innovation  -  RRI)  sollte  ein  Leitprinzip  aller  drei  Säulen 

und  Themen  der  FPs  werden.  Dadurch  kann  gewährleistet  werden,  dass  zukünftige 

FPs  nicht  nur  Pionierforschung  an  Universitäten  und  Forschungsorganisationen 

fördern oder die Wettbewerbsfähigkeit von Unternehmen unterstützen, sondern auch 

die  Bedürfnisse  der  BürgerInnen  berücksichtigen  und  sich  den  relevanten 

gesellschaftlichen Herausforderungen annehmen. 

30 

 
 
 
 
1  Introduction 

Responsible Research and Innovation (RRI) relies on the assumption that, in order to find 
sustainable  research  and  innovation  (R&I)  solutions  for  societal  challenges,  all  societal 
actors (researchers, citizens, policy makers, business, civil society organisations) should 
work  together  during  the  whole  R&I  process,  aligning  priorities  of  research  and 
innovation  to  the  values,  needs  and  expectations  of  society.  Horizon  2020,  the  EU’s 
current  research  framework  programme,  goes  conform  with  this  understanding  of 
involving civil society into the overall research landscape: “With the aim of deepening the 
relationship  between  science  and  society  and  reinforcing  public  confidence  in  science, 
Horizon  2020  should  favour  an  informed  engagement  of  citizens  and  civil  society  on 
research  and  innovation  matters  by  promoting  science  education,  by  making  scientific 
knowledge more accessible, by developing  responsible  research and innovation agendas 
that  meet  citizens'  and  civil  society's  concerns  and  expectations  and  by  facilitating  their 
participation in Horizon 2020 activities.” 

Previous monitoring and evaluation activities related to the FPs have mainly investigated 
participation  patterns  of  the  “classical”  organisation  types  defined  in  the  framework 
programmes: universities (HES), private research organisations (REC), companies (PRC), 
public  bodies  (PUB),  and  other  organisations  not  fitting  to  any  of  the  other  four 
categories  (OTH).  These  analyses  continue  to  show  that  European  R&I  activities  are 
dominated  by  universities  and  companies.  The  currency  of  universities  is  “excellence”, 
measured in the form of scientific publications. The currency of companies is “profit”, in 
terms  of  filed  patents.  FP7  specifically  addressed  these  two  currencies  by  creating  two 
new vehicles in 2007: On the one hand the European Research Council (ERC) was set up, 
encouraging  the  highest  quality  research  in  Europe  through  competitive  funding  and 
supporting investigator-driven frontier research across all fields, on the basis of scientific 
excellence. On the other hand, Joint Technology Initiatives (JTIs) were created to support 
large-scale  multinational  research  activities  in  areas  of  major  interest  to  European 
industrial competitiveness. 

The  concept  of  RRI  shifts  the  focus  on  finding  sustainable  R&I  solutions  for  societal 
challenges; it seeks to achieve this by better engaging society in R&I activities. RRI thus 
introduces  a  third  “currency”  to  the  R&I  landscape  -  the  currency  of  “responsibility”.  A 
number  of  recent  research  activities  have  consequently  focused  on  the  question  of  how 
to better involve civil society organisations (CSOs) – which have  so far been a marginal 
player  in  the  EU’s  framework  programmes  –  in  R&I  activities.  These  include  FP-funded 
research projects such as CONSIDER (“Civil Society Organisations in Designing Research 
Governance”;  www.consider-project.eu),  SecurePART  (“Increasing  the  Engagement  of 
Civil  Society  in  Security  Research”;  www.securepart.eu)  and  Responsible-Industry 
(www.responsible-industry.eu). 

Against this background, the DG Research & Innovation has commissioned a study aimed 
at identifying CSOs in the FPs, analysing their participation patterns in FP6 and FP7, and 
simulating policy interventions for strengthening CSOs’ role in FP-funded research.  

31 

 
 
 
This  report  summarises  the  findings  of  this  study,  which  pursued  the  following 
objectives: 

1.  Developing  a  practical typology  of  CSOs that  can  help  differentiate  and  understand 
the roles CSOs can play in European research Framework programmes in the context 
of  RRI,  as  well  as  their  specific  knowledge,  properties  and  latent  preferences 
determining their participation. 

2.  Analysing  the  extent  of  CSO 

framework 
programmes  and  determining  their  specific  contribution  to  research  and  collaborative 
research network performance through Social Network Analysis. 

in  European  research 

involvement 

3.  Simulating  different  policy  scenarios and  their  effects  on  CSO  participation, 
through  expanding  already  existing  modelling  approaches  by  complementing 
knowledge-based  agent  behaviour  with  an  orientation  towards  solving  societal 
challenges based on the Sustainable Development Goals (SDGs). 

This report is structured according to the main tasks carried out in the project. Chapters 
2 and 3 summarise the findings from initial literature reviews on social network analysis 
(SNA)  approaches  and  on  CSO  definitions  and  logics.  Chapter  4  presents  the  work  on 
identifying  and  categorising  CSOs  participating  in  the  FPs.  Chapter  5  summarises  the 
findings from case studies with different kinds of CSOs. Chapter 6 describes the results of 
the  social  network  analysis  (SNA),  focusing  particularly  on  the  role  of  CSOs  in  the 
European research networks. More detailed SNA results are included in the Annex to this 
report. 

Chapter  7  is  dedicated  to  the  agent-based  simulation  model  that  was developed  for  the 
purpose  of  this  project.  It  briefly  explains  the  model  and  looks  in  more  detail  into  the 
results  of  four  main  scenarios  that  were  simulated  in  view  of  deriving  policy 
recommendations  on  CSO  involvement  in  the  FPs.  A  discussion  with  stakeholders  at  a 
workshop held at the EuroScience Open Forum (ESOF) 2016 in Manchester in July 2016 
helped to refine the model and to develop the scenarios. The summary of the workshop 
as  well  as  the  detailed  description  of  the  model  itself  is  included  in  the  Annex  to  this 
report. 

Chapter  8  finally  presents  conclusions  and  policy  recommendations  regarding  the 
involvement  of  CSOs  in  the  European  research  framework  programmes  as  well  as  an 
outlook on future research topics. 

32 

 
 
2  Literature review of network analysis 

approaches 

The main objective of this task was to review SNA (Social Network Analysis) literature in 
terms  of  R&I  (research  and  innovation)  performance  and  cooperation  structures  in 
general and in EU Framework Programs and to review of R&I related SNA literature in the 
context of agents based modelling. The task also aimed at developing a Multi-Level Multi-
Theory  approach  composed  of  existing  theories  combined  in  a  new  way  that  provides  a 
deeper  analysis  and  understanding  of  social  network  structures  in  the  context  of  R&I 
performance  and  the  integration  of  CSOs  in  the  cooperative  processes.  Finally,  a 
terminological guideline was made to define a common understanding of the used terms.  

The  use  of  SNA  for  evaluating  European  cooperation  structures  is  not  a  radically  new 
approach;  however,  the  discipline  itself  is  constantly  developing  and  unfolds  into  more 
and more specialized subfields ranging from social sciences, computer science, physics to 
life  sciences.  Since  increasing  IT  capacities  led  to  vast  possibilities  of  data  mining  and 
collection, SNA methods and tools were  forced to constantly improve  in order to handle 
big data. Due to the complexity and dynamics of the discipline it is crucial to establish an 
overview of the state of the art. 

The literature research was structured as follows:  

  Stocktaking  and  systematically  reviewing  existing  literature  on  Social  Network 

Analysis (SNA), 

  Building  a  conceptual  and  analytical  SNA  framework  in  the  context  of  the  tendered 

project, 

  Determining strengths and weaknesses of different academic fields of SNA, 
  Detail the most accepted terminology describing the terms to be used in the study, 
  Review of Commission SNA studies and projects in this area. 

All in all around 120 relevant journals articles, book chapters and studies were reviewed 
for  Task  1.  The  output  comprises  a  report  which  describes  the  crucial  results  in  detail. 
Additionally an annex was created containing an overview of all reviewed sources in form 
of  a  table  following  the  categories:  Author  and  Title;  Analyzed  Dataset;  Main  Variables; 
(Impact)  Indicators;  Methods  and  Types  of  Calculation;  Main  Findings;  Differences, 
Strengths and Weaknesses; Findings relevant for the project. The following pages give a 
brief overview of the main results.  

2.1 Critical analysis of previous SNA approaches and 

studies in R&I evaluation and measurement of impact 
in general 

This  sub-task  focused  on  a  critical  analysis  of  SNA  approaches  in  studies  related  to  R&I 
evaluation,  impact  measurement  and  scientific  team  performance  in  general  apart  from 
the  EU  Research  Program.  SNA  based  evaluation  of  scientific  networks  and  R&I  related 
networks  since  the  1960s  and  1970s  focused  on  the  study  of  scientific  collaboration 
networks such as citation, bibliometric and patent networks (e.g. Ko et al 2014, Newman 

33 

 
2002/2004, Barabasi et al. 2002/2008, Hicks and Katz 1996, Price 1965)4, the evaluation 
of  collaboration  on  the  basis  of  CVs  and  home  universities  (e.g.  Bozeman  et  al.  2009, 
Gaughan  et  al  2008,  Tuire  et  al  2001),  the  exploration  of  the  invisible  college5  (Crane 
1965/1972,  Tuire  2001)  and  the  examination  of  knowledge  and  communication  flows 
(e.g. Allen/Cohen 1969; cf. Mote et al, 2007: 191/Rogers et al 2001: 162). The data for 
these  studies  are  mostly  gained  from  scientific  databases  and  journals  of  different 
disciplines6, patent databases (e.g. Ko et al., 2014) and CVs (e.g. Gaughan et al., 2008).  
Some  common  relational  indicators  in  this  field  are  for  example  the  Erdös  number, 
degree 
network 
interconnectedness and average separation, average degree and centrality measures (cf. 
e.g. Newman 2001/2004, Barabasi 2001/2008, Chinchilla-Rodriguez 2012).  

distribution, 

coefficient, 

clustering, 

clustering 

degree 

of 

Another  important  topic  is  the  evaluation  and  comparison  of  formal  and  informal 
networks  and  the  impact  of  the  team  composition  and  team  structure  on  the 
performance (e.g. Allen et al. 2007, Borgatti et al. 2002; Mehra et al. 2006, Mote et al. 
2007).  Data  were  collected  using  questionnaires  in  different  organizations,  R&I  teams 
and  projects.  Important  variables  to  measure  the  team  performance  are  for  example 
productivity, key attributes of organizations and co-membership in projects. Besides the 
most common statistical approach, regression analysis, different SNA centrality measures 
(degree, closeness, betweenness, eigenvector) were applied in the mentioned studies. 

The following overview briefly describes the main results from this sub-task:  

  Co-authorship  networks  have  features  of  small  world  networks  like  a  short  node-to-
node-distance  and  a  large  clustering  coefficient  (e.g.  Newman  2000&2004,  Barabasi 
2001&2008). 

  Referring  to  the  scientific  world,  the  participation  and  integration  of  CSOs  in  R&I 
collaborations might be difficult, because the prestige-factor of being quoted often or 
working together with central scientists seems  to be very dominant in this field. The 
so called “Matthew-Effect” (people who are quoted often are going to be quoted more 
often  than  people  who  are  quoted  less  often7)  leads  to  a  strong  center  and  the 
exclusion of other players and peripheral actors who have less prestige (e.g. Tuire et 
al,  2001).  Following  this  hypothesis,  the  participation  of  CSOs  in  the  scientific 
field/academic  research  is  rather  unlikely,  as  long  as  success  is  only  measured  by 
publications and other values (e.g. mainstreaming research results to the benefit of a 
non-academic public) are not systematically considered.  

4   All sources are listed in the Annex of Deliverable D2, arranged according to the Sub-tasks in 

alphabetical order.  

5   Community of intercommunicating scientists working within a specific paradigm/field 

organized in an unofficial networks and contributing to the growth of knowledge due to the 
knowledge transfer, the connection to each other and the ability to link scientists (Social 
Research Glossary: 
http://www.qualityresearchinternational.com/socialresearch/invisiblecollege.htm 
[10.03.2015]) 

6  

e.g. Medline, (biomedical research), Los Angeles e-print Archive (theoretical physics), Spires 
(high energy physics) and Networked Computer Science Technical Reference Library 
(computer science) (cf. Newman, 2000). 

7   R.K. Merton: http://www.garfield.library.upenn.edu/merton/matthew1.pdf [10.03.2015] 

34 

 
                                           
 

Informal networks of R&I teams should not be underestimated for example when it is 
about  problem-solving  strategies  within  teams.  In  these  cases  informal  networks 
have  often  more  potential  besides  the  formal  structures.  SNA  enables  the 
identification  of  important  persons  and  institutions  within informal  networks  and  can 
help to further investigate the role of CSOs within informal and formal networks and 
the  mechanisms  to  enhance  the  capacities  of  a  better  team  performance  (e.g. 
Borgatti et al, 2002; Mehra et al., 2006; Mote et al. 2007; Allen et al., 2007). CSOs 
often work through activating informal networks. In this sense CSOs might have the 
professional  competence  to  solve  problems  informally  and  thus  increase  the  R&I 
team’s resilience and productivity.  

  The  heterogeneity  of  a  team  can  increase  the  team  performance,  if  the  team  is 
coordinated properly and the members are well connected to each other (e.g. Reagan 
and Zuckerman, 2001). The Input of CSOs, which are close to (certain groups of) the 
civil  society  could  make  an  enriching  contribution  to  often  more  homogenous  R&I 
teams.  

  National  boundaries  seem  to  restrict  the  collaboration  and  flow  of  information  and  a 
shared physical location and organizational identity support the effective collaboration 
and  knowledge  exchange  in  R&I  teams.  Related  to  international  R&I  teams,  finding 
strategies  to  overcome  the  restriction  of  national  borders  seems  to  be  important. 
According to Sell et al. (2004) NGOs are often structured as “transnational advocacy 
networks”  (p.  149),  which  could  lead  to  a  high  team  performance  when  CSOs  are 
involved  because  they  are  used  to  collaborate  across  borders.  In  this  sense  trans-
nationality could be an enabling factor for CSOs to participate in  R&I teams, because 
they are well connected on an international level.  

  CSOs are  often well integrated and involved in political processes, which leads other 
countries to do the same. There is something like a domino-effect and the impact of 
good examples. It seems that one driving factor for the participation of actors on the 
fringes such as CSOs are legitimacy gains for  political actions. Programs, addressing 
and taking up topics from society are more likely to integrate CSOs as justification in 
terms  of  the  common  goods.  Best  practice  examples  of  other  countries  including 
CSOs  in  terms  of  a  “soft”  social  pressure  might  be  utilized  to  motivate  other  groups 
(such  as  scientists  or  politicians)  to  follow  the  bottom-up  approach.  But  the 
integration  of  CSOs  just  for  the  sake  of  legitimacy  should  be  regarded  carefully  to 
avoid CSOs being misused as token-partners to justify political actions in the name of 
the common good (e.g. Böhmelt 2014). 

All  in  all  the  results  of  the  analysed  SNA  studies  are  rather  descriptive.  The  network 
structures  are  described  in  a  superficial  way.  It  seems  that  there  are  few  far-reaching 
analyses done  of the network structures, which generate a deeper understanding of the 
network  morphology.  Further,  the  studies  rest  on  a  limited  theoretical  basis.  The 
theoretical  approach  is  in  most  cases  narrowed  down  to  the  small  world  theory 
(Watts/Strogatz 1998) and the strength of weak ties (Granovetter, 1973).  

35 

 
2.2 Critical analysis of previous SNA studies in FP1-7 in 
regard of effects on R&I, CSO participation and 
impact on European society 

This  sub-task  critically reviewed  papers  and  studies  about  SNA  and  R&I  collaboration  in 
the  context  of  the  European  Framework  Programmes.  The  European  Framework 
Programmes  for  research  and  innovation  are  unique  and  distinguish  themselves  from 
other R&D instruments by some common structural key elements. First, only projects of 
limited  duration  that  mobilize  private  and  public  funds  at  the  national  level  are  funded. 
Second,  the  focus  of  funding is  on  multinational  and  multi-actor  collaborations  that  add 
value by operating at the European level. Third, project proposals are to be submitted by 
self-organized  consortia  and  the  selection  for  funding  is  based  on  specific  scientific 
excellence and socio-economic relevance criteria (Barber et al, 2013). The methods and 
approaches  used  for  FP's  evaluation  show  a  trend  over  time  away  from  panels  and 
towards  (larger-scale)  professionally  conducted  evaluations  and  studies.  They  are 
strongly focused on participant surveys, interviews and analysis of EC-internal databases 
(notably of FP participants) and documents. These are sometimes supplemented by case 
studies.  Seldom  there  are  FP  databases  matched  to  external  databases  and  surveys, 
such  as  the  Community  Innovation  Survey  or  data  about  regional  production  or  value 
added.  There  has  been  some  broadening  of  techniques  from  about  2005,  when  Social 
Network Analysis (SNA) and bibliometrics began to be used (EPEC 2011).  

Evaluations  focused  on  four  types  of  outcomes:  Knowledge  generation  &  learning, 
knowledge  networks,  knowledge  spillovers  and  outcomes  enabling  commercial 
exploitation.  The  ultimate  aim  was  to  search  for  impacts  of  FPs  on  knowledge 
exploitation,  collaboration  in  R&D,  technology  exploitation,  innovation  in  industry, 
innovation in market structures, knowledge spill-overs to the education system and other 
R&D  policies,  improved  policy  development  &  regulations  and  finally  innovation 
acceptance  among  end  users  (EPEC  2011).  Some  common  SNA  indicators  used  in 
reviewed  studies  are  for  example:  evaluation  of  strong/weak  ties,  number  of 
vertices/edges, 
and 
degree/eigenvector/closeness/betweenness  centrality.  The  following  overview  briefly 
describes the main results from this sub-task:  

coefficient,  mean 

fragmentation, 

clustering 

degree 

  Participants  are  highly  networked  and  partnerships  are  based  on  previous 
collaborations. Large organizations dominate the FP's networks, the key organizations 
are  taking  leading  roles  within  research  teams  and  projects  (Wagner  et  al  2005, 
Malerba  et  al  2006,  Wagner  et  al  2005,  Malerba  et  al  2006,  Avedas  et  al  2009, 
Malerba  et  al  2013).  Therefore,  the  connectivity  crucially  depends  on  few 
organizations. Cooperation with CSOs might be of benefit for others  in buffering and 
catalysing  the  overall  capacity  of  networks  along  the  stages  of  technology  cycles 
(Ulanowicz  2000,  Vedres  &  Stark  2010,  Katzmair  2012,  Walker  &  Salt,  2006  and 
2012).  Partners  may  directly  gain  from  the  diverse  expertise  and  unconventional 
approach  of  civil  society  groups in  carrying  out  a  certain  research  project  or 
in meeting legal commitments (Raustiala 1997; see also Betsill & Corell 2001; 2008; 
Biermann & Pattberg 2008).  

  The  research  networks  within  the  FP's  1-7  are  dense  and  hierarchical:  A  highly 
connected  core  of  frequent  participants,  taking  leading  roles  within  consortia,  linked 
to  a  large  number  of  peripheral  actors,  forming  a  giant  component  that  exhibits  the 

36 

 
characteristics  of  a  small  world.  Participation  by  type  of  organization  is  skewed 
towards  HES  and  REC  (EC,  2013,  FP7  Monitoring  Report).  The  FP  network  is  built 
around  five  core  Western  European  countries,  i.e.  Germany,  the  United  Kingdom, 
France, Italy and the Netherlands (EC 2014 EPIRIA, Ortega et al 2010). SNA studies 
attest  the  centrality  of  HES  and  REC  as  hubs  and  their  role  as  gatekeepers.  Hence, 
their preferences towards CSO involvement play an important role in facilitating their 
participation. 

  Networks  of  Excellence  are  centred  around  academic  institutions,  while  Specific 
Targeted Research Projects combine academic and central corporate members. SMEs 
and OTHERS, including CSOs participate in FP6 research networks, but they are likely 
to be on the fringes rather than in the centre. Consultancy and organisations of type 
OTHER had no or only negligible representation among core organisations. After FP3, 
100 per cent of core organisations have been classified as ‘old boys’: the pool of core 
nodes  appears  to  exhibit  remarkable  stability  and  has  not  been  renewed  in  almost 
two decades (Heller-Schuh et al, 2011).  

  The FP is a scale-free network with an accelerated growth (Heller-Schuh et al, 2011). 
The  most  important  consequence  of  the  presence  of  such  network  hubs  is  that  the 
overall  connectivity  of  the  network  as  well  as  its  topological  properties  crucially 
depends  on  few  organizations.  Scale-free  networks  are  vulnerable  to  the  targeted 
removal of the most important nodes thereby decreasing the ability of the remaining 
nodes  to  interact  with  each  other  (Vonortas  2013).  CSOs  could  act  as  brokers 
between  the  excellence-oriented  research,  profit-oriented  companies  and  societal 
challenges.   

  Collaborations between partners are established by means of preferential attachment. 
In other words, the participants with more  connections establish new ones at higher 
rate  than  participants  with  few  connections.  As  a  consequence,  the  so-called  ‘rich-
get-richer’’  phenomenon  arises,  in  which  the  most  connected  participants  increase 
their  collaborations  at  the  expense  of  the  latecomers  (Almendral  et  al  2007,  Avedas 
et al 2009).  

  Network analysis found that the main variables driving project efficiency and research 
success  in  terms  of  measurable  outputs  (such  as  publications  or  patents)  are: 
national  culture,  working  together  over  time,  project  and  funding  duration, project 
size, common goals, project management and stability of project members. They also 
found  that  performance  decreases  as  project  size  increases  (Avedas  2009,  Arnold 
2012). 

In  summary,  it  can  be  suggested  that  research  program  design  ought  to  acknowledge 
the value-adding function of CSOs in representing the general public by: 

a) opening  up  agenda  setting  to  bottom  up  initiatives  by  CSOs  in  line  with  principles  of 

responsible research and innovation RRI; 

b) mandating  a  more  formal  inclusion,  also  in  coordination  roles,  of  CSOs,  recognizing 
in  brokering  between  excellence  oriented  research,  profit-oriented 

their  role 
companies and societal challenges;  

c)  broadening  the  indicators  for  performance  assessments  to  capture  disruptive 
innovations and more long-term effects, recognizing the function of CSOs in buffering 
and catalysing the overall capacity of networks along the stages of technology cycles.  

37 

 
2.3 Critical analysis of R&I related SNA Literature in the 

context agent based simulation modelling, p* 
modelling and systems dynamics 

This  sub-task  focused  on  critical  analysis  of  R&I  related  SNA  Literature  in  the  context 
agent based simulation modelling, p* modelling and systems dynamics. The literature on 
simulation  and  networks  has  grown  extensively  over  the  past  10  years.  Some  of  the 
simulation, 
primary  methods  are  agent-based  modelling,  multi-agent-based 
mathematical equation-based models, system dynamics (Maier, Frank H. 1998; Sterman, 
2000),  statistical  models,  cellular  automata  worlds[1]  (Epstein,  2008),  exponential 
random graph models (ERGMs) (Borgatti et al., 2013), p*modelling (SIENA) (Snijders et 
al.,  2006)  and  hybrid  models.  Hybrid  models  are  a  combination  of  any  of  the  above-
mentioned  techniques  and  have  become  popular  recently  (Axelrod,  2006).   There  has 
been also advances in computation power in this field, which have made it more tractable 
to compute models with large numbers of variables. Over the past 15 years, agent-based 
modelling  has  become  widely  used  in  various  research  fields.  There  is  proliferation  of 
agent-based modelling in different fields.  

The following overview briefly describes the main results from this sub-task: 

  The  main  focus  of  current  studies  has  shifted  towards  studying  the  interactions  of 
agents.  For  example,  in  various  studies  (Moss  et  al.  1998;  Korber,  et  al.  2011; 
Kiesling, Elmar et al. 2012), agents have more complex characteristics like being able 
to  adapt  to  different  changes  in  the  environment  or  being  influenced  or  not  by 
another  agent.  Also  agents  can  take  into  consideration  the  communities  opinions 
(Sarker, and Tapabrata,2010; Li, Yongli et al 2013).  

  Our  model  is  similar  to  Axelrod’s  approach,  which  combines  the  discrete  and  highly 
interactive  manner  of  actor  decisions  and  the  continuous  process  of  the  technology 
life-cycle.  The  interaction  between  these  two  processes  will  create  a  more  realistic 
scenario in terms of applicability of the simulation results. 

  Our model draws on previous research by Gilbert et al. (2001 and 2007), in particular 
the  SKIN  model  (Simulating  knowledge  dynamics  in innovation  networks)  developed 
by Gilbert et al. (2007) that focuses on market interaction and knowledge  exchange 
among  firms.  The  SKIN  model  outperforms  previous  theoretical  attempts  in 
economics to analyse the innovation process. The novelty of the model is in its use of 
empirical  evidence  from  case  studies  to  model  the  decision  procedures  instead  of 
integrating strategic alliances and cooperative R&D in a standard equilibrium model of 
competition.  

  Korber, et al. (2014) is also of interest due to the way they assign agents’ attributes. 
They  categorised  their  agents  based  on  their  research  fields  (e.g.  dermatology, 
oncology,  proteomics,  etc.)  or  on  their  scientific,  technological  field  or  business 
domain. Then, they assign core competencies like R&D, sales, service, etc. based on 
particular competencies within the specific research field.    

  The  main  simulation  approaches  and  software  environments  used  currently  are 

NetLogo based MASON, Repast, and Swarm.    

38 

 
  The main variables used in R&I studies were the kenes, agent's capability to interact 
with  the  market,  an  agent's  ability  to  adapt  or  innovate  as  well  as  innovation  itself, 
and  the  ability  of  an  agent  to  be  conservative  or  progressive  when  entering  the 
market. The studies that focused on agents examined the environments under which 
they operate i.e. their social standing and tried to mimic  the real world conditions in 
the simulations.  

  The  weaknesses  of  agent-based  simulation  experiments  are  related  to  the  approach 
of  agent  based  simulation  in  contrast  to  system  dynamics  (“continuous  modelling”). 
Agent  based  models  provide  a  setup  to  learn  and  formulate  hypothesis  (later  tested 
with real data) rather than to predict.  

  Our simulation model will interact with the network structure, which is also created by 
a dynamic process. The network model is defined as a recursive system, where each 
actor within a consortium is an input and an output at the same time, allowing for a 
mutual exchange of information and resources. 

  We  integrate  models  from  other  disciplines  such  as  Game  Theory  (Axelrod,  1984, 
1997; Von Neumann et al., 2007), Systems Ecology (Odum, 2000, 2007), Resilience 
Theory  (Gunderson  and  Holling,  2002)  and  Cultural  Theory  (Thompson,  2003)  to 
extend standard SNA and standard agent based modelling. 

2.4 Development of Multi Theory Multi Level SNA 

methodology (MTML) 

A  large  number  of  SNA  studies  reviewed  in  the  former  Sub-tasks  have  been  made  and 
look at R&I collaboration from different angles, but often limited to a descriptive analysis 
and  one-dimensional  interpretation.  They  describe  networks  based  on  common  SNA 
indicators  such  as  weak  and  strong  links,  density,  centrality  and  clusters  of  actors,  but 
fail to provide answers to more fundamental questions pertaining to the normative level, 
i.e.  how  should  networks  be  designed  to  deliver  better  R&I  outcomes  and  what  is  the 
impact  of  particular  collaboration  structures  on  inclusiveness,  European  value  add, 
resilience  of  the  ERA  and  the  European  innovation  system.  The  results  are  often 
restricted  to  a  specific  case  under  study  without  providing  an  epistemological  frame  to 
interpret the results in its  wider  environment  or cultural context and in relation to rules 
governing the functioning of the scientific environment. For that account, we use a more 
sophisticated  multi-level  multi-theory  idea  rather  than  the  typical  one-dimensional 
approaches.  

A multi-level multi-theory approach could serve as a tool to study network development, 
morphology, constellations, structures and actions from different perspectives and hence, 
offer  opportunities  to  provide  substantive  insights  for  policy intervention  and  the  design 
of  future  research  programmes.  The  present  research  design  undertakes  to  study 
European  Research  Framework  Programmes  project  collaboration  with  state-of-the  art 
SNA  and  ABM  methodology  and  constructs  a  MTML  model  for  hypothesis  building  and 
interpretation  of  results  that  rests  on  four  pillars:  System  Ecology  (e.g.  Lietar  2010; 
Fath,  2914),  Resilience  Theory  (e.g.  Gunderson  and  Holling,  2002,  Fath/Dean/Katzmair 
2014; Vedres/Stark 2010) Agent Based Modelling/Game Theory (e.g. Axelrod/Dion 1988; 
Axelrod 2003), and Cultural Theory (e.g. Thompson, 2003; Douglas 2007).  

39 

 
The  variables  being  used  in  this  study  are 
inspired  by  Michael  Thompson’s  Cultural 
Theory.  These  variables  describe  the  value 
system  and  norms  and  the  types  of  action 
and  behaviour  of  the  different  actors.  We 
assimilate  in  this  case  the  ‘Egalitarians’  with 
CSOs  and  other  agents  on  the  fringes.  By 
analysing  the  collaboration-structure  of  the 
framework  programs,  using  SNA, 
the 
variables  extracted  from  case  studies  and 
from  the  Cultural  Theory  are  tested.  The 
importance  of  different 
meaning  and 
‘currencies’ 
(e.g. 
publications,  patents,  common  good,  profit, 
etc.)  and  the  culture  of  collaboration  arising 
from the logic of the ‘currencies’ are going to 
be incorporated in the model.  

different 

fields 

in 

Against  the  background  of  the  particular 
mindset  and  rationality  of  each  group 
(according to the groups’ logic), the relational 

Figure 1: MLMT approach 

influence  of  the  players  (referring  to  the 
concept  of  Game  Theory)  and  the  system 
referring  to  decision-making-processes  and  actions  in  general,  is  going  to  be  simulated 
by defining rules in ABM. ABM allows finding the combination of variables, including the 
players  who  stabilize  the  system  between  efficiency  and  diversity  or  who  respectively 
destabilize it (according to System Ecology and Resilience Theory). Further, the different 
phases  of  the  collaboration  should  be  considered  in  more  detail  in  the  frame  of  the 
conceptualized model to find out in which stage of the adaptive cycle the R&I teams are 
resilient  and  which  SNA  factors  and  network  constellations  lead  to  more  (or  less) 
resilience.  

In this sense, SNA will not only be used to analyze emerging network morphologies but 
also casual understanding of what are the underlying drivers of change. This MTML model 
can  provide  an  explanation  for  the  factors  underlying  current  participation  patterns  of 
CSOs 
including  different  theoretical 
perspectives.   

in  the  European  Framework  programs  by 

40 

 
3  Literature review on CSOs in EU and in R&I 

Task  2  explored  academic,  policy  and  research  project  literature  in  order  to  come  to  a 
view on the nature and perception of civil society organisations (CSOs) in research. This 
is  because  despite  widespread  discussion  on  this  issue,  there  is  little  established 
agreement upon the meaning and scope of ‘CSOs’.  

3.1 Background 

There  is  a  general  trend  in  research  policy  and  practice  toward  broader  stakeholder 
engagement  in  technical  and  scientific  projects.8  Civil  Society  Organisations  (CSOs)  are 
apparently  thought  of  as  important  actors  who  can  realise  the  promise  of  participative 
research, responsive to the real world.9 As CSOs often represent specific interest groups 
that they have specific knowledge of given areas their input into research may lead to a 
broader knowledge base and thus more robust knowledge. It can increase the legitimacy 
of  findings,  offer  transparency  and  heighten  public  awareness  and  discourse.  As  a 
consequence  there  are  numerous  attempts  to  stimulate  participation  in  research  and 
embed  participative  processes  in  research  governance.10  There  is,  however,  no 
particularly fixed consensus on what counts as participation, on the role and definition of 
CSOs, on the ways in which research can or ought to use participative methods, and so 
on. The theoretical benefits and disadvantages of participation are disputed.  

A  standard  definition  for  the  term  ‘Civil  Society  Organisation’  is  absent  from  present 
literature. This raises a number of theoretical and empirical problems. If the definition is 
too narrow, then the set of candidate examples will be too small. On the other hand, very 
broad definitions comprise a too large a variety of CSOs which are unlikely to be highly 
compatible  with  one  another.  Given  its  prominence  in  European  policy  debates,  the 
popularity  and  history  of  the  subject  implies  a  broad  reaching  application  of  the  term 
‘CSOs’ as seen in the EU’s own broad definition of a CSO as being ‘Any legal entity that is 
non-governmental,  non-profit,  not  representing  commercial  interests  and  pursuing  a 
common  purpose  in  the  public  interest’.11  Considering  the  various  interests  of 
organisations  is  crucial  for  defining  CSOs.  Some  definitions  exclude  organisations  that 
represent  mainly  commercial  interests  such  as  industry  and  business  associations. 
Hence, the need to determine this project’s working definition. 

In order to allow a useful analysis and simulation, the definition of the term CSO used in 
the  project  allows  for  varying  degrees  of  correspondence  between  the  organisation  and 
the  definition.  Therefore  a  simple  binary  definition  of  the  term  –  x  is/is  not  a  CSO  –  is 
here  rejected  and  instead  a  typology  of  CSOs,  accounting  for  the  diversity  of 
organisations participating in European framework programmes is suggested. This comes 
from an extensive literature review. 

8  Smismans, S, Reviewing Normative Theories on Civil Society Participation, 2006  
http://www.eu-newgov.org/datalists/deliverables_detail.asp?Project_ID=11    

9  see, for instance, the influential Latour, B, Science in Action: How to Follow Scientists and 

Engineers through Society 

10  see EU (2014) Horizon 2020 'Science with and for Society’ Providing advice on potential 

priorities for research and innovation in the work programme 2016-2017 consultation paper. 
http://ec.europa.eu/research/consultations/swafs/background.pdf  

11  https://ec.europa.eu/research/participants/portal/desktop/en/support/reference_terms.html  

41 

 
                                           
For the purposes of this research, it is vital to address the lack of evidence of the effect 
of CSO participation within research. This will require at least two steps, one  descriptive 
and  one  explanatory.  The  descriptive  step  will  begin  with  a  literature  review  from 
research projects that have participated with CSOs  or that have  otherwise explored this 
aspect  of  research.  This  serves  to  give  a  picture  of  how  CSO  participation  has  been 
carried  out  or  how  it  has  been  approached  in  research  projects.  The  explanatory  step 
comes from an analysis of what we discovered in order to explain why discussion of CSO 
participation has been realised in the way it has. 

Together,  these  two  steps  provide  a  basis  for  suggesting  revisions  to  enable  more 
optimal  participatory  practices,  bearing 
in  mind  the  perspectives  of  academic 
researchers, project participants and policy discourses. 

3.2 Methodology 

The review aimed to clarify the nature of CSOs by reviewing several streams of literature 
that  are  relevant  to  CSOs  in  research.  To  achieve  maximum  complete  coverage  of  the 
literature, the project therefore reviewed academic, policy and project (e.g. deliverables, 
reports) documents. Searches for evidence were precisely targeted for information tightly 
linked to the study aims. Inclusion and exclusion criteria were determined with the aim of 
generating  search  results  with  the  maximum  relevance  and  impact  in  understanding 
perspectives on CSOs in research. The review and analysis made use of NVivo10, a text-
mining resource, in order to permit qualitative analysis of large bodies of literature. 

The  present  study  sought  to  put  forward  a  working  definition  of  CSOs,  effects  of 
participation  and  the  empirical  support  for  the  main  hypotheses.  Therefore,  data  was 
searched  focusing  on  the  key  term,  “Civil  Society  Organisation.”  Given  that  the 
application  of  the  term  CSO  may  vary,  alternative  terms  of  CSOs  such  as  non-
governmental  organisations  (NGOs), 
third  sector  organisations,  and  non-profit 
organisations  were  used.  These  terms  were  used  as  the  initial  search  terms  since  they 
represented  the  topic  name  and  its  alternative  names.  More  search  terms  were 
developed as the researchers increased their familiarity with terminology used within this 
field through reading articles. 

To  get  more  specific  results  which  produce  useful  evidence  within  this  framework  of 
study, some terms were added to augment the key search terms. These were effectively 
contextualising  terms.  As  shown  in  the  table  below,  terms  and  alternative  terms  were 
used to ensure that as much data as possible within each context could be located.  

Table 1: Key search strings used 

Civil Society 
Organisation 

Civil Society 
Organisation AND 
European Union 

Civil Society AND 
European policy 

Civil Society 
Organisation AND 
European Union 
AND Responsible 
Research and 
Innovation 

Civil Society 
Organisation AND 
European Union 
AND Research and 
Innovation 
Non-governmental 
Organisation 

Civil Society 
Organisation AND 
European Union 
AND Responsible 
Innovation 
Non-governmental 
AND European 
Union AND 
Responsible 
Research Innovation 

42 

 
Non-governmental 
Organisation AND 
European Union 

Third sector 
organisation AND 
European Union 
AND Research and 
Innovation 

Non-profit 
Organisation AND 
European Union 
AND Research and 
Innovation  

Non-governmental 
Organisation AND  
European Union 
AND Research and 
Innovation 
Third sector 
organisation AND 
European Union 
AND Responsible 
Research and 
Innovation 
Non-profit 
Organisation AND  
European Union 
AND Responsible 
Research and 
Innovation 

Third sector 
Organisation  

Third sector 
organisation AND 
European Union 

Third sector 
organisation AND 
Research and 
Innovation 

Non-profit 
organisation AND 
European Union 

Non-profit 
Organisation AND 
Responsible 
innovation  

Non-profit 
organisation AND 
Research and 
Innovation 

Since the project was interested in critically reviewing definition and terminology used in 
relation to CSO participation in R&I,  academic literature was one domain in which the 
search-terms  were  deployed.  Given  that  a  critical  review  on  relevant  EU  rules  and 
initiatives  for  engaging  CSOs  in  research  and  innovation  activities  across  different  DGs 
and  for  involving  them  in  policy  development,  in  particular  research  policy,  was  also 
intended, policy discourse was examined. Finally, a critical analysis of the role of CSOs 
in  R&I  and  a  review  of  CSOs  relations  with  other  R&I  stakeholders  and  partners  was 
required, and so  project outputs formed a final domain of interest.  Thus  the literature 
review covered the following bodies of literature: 

  Academic Literature 
  European policy-relevant literature 
  Projects outputs of public engagement projects 

In  order  to  ensure  as  complete  coverage  of  the  literature  as  possible,  articles  were 
searched  for  in  different  databases  and  sources  based  on  the  inclusion  criteria  listed 
above. 

3.3 Summary of findings 

3.3.1  CSO definition in different bodies of literature 

What  is  immediately  striking  in  the  academic  literature  is  the  diversity  of  the 
discussion  of  CSOs,  which,  at  the  very  least,  is  a  verification  of  the  dynamic  interest  in 
this  field  of  study.  In  the  academic  literature,  non-profit,  non-governmental,  self-
constituting  voluntary  associations  are  centrally  discussed  characteristics  of  CSOs,  and 
therefore  constitute  a  core  definition  of  CSOs  in  academic  circles.  However,  benefits  in 
terms of participation, empowerment and capacity-building, feature much more widely in 
the academic literature than CSO definitions do. Since such terms appear so prominently 
in academic discussion of CSOs, the benefit-bringing nature of CSOs could be considered 
as indicative of a defining characteristic of CSOs in the academic literature. 

43 

 
 
The  discussions  of  CSOs  in  policy  literature  are  cognate  with  standard  EC  definitions, 
but  what  is  of  interest  is  the  limited  scope  and  emphasis  suggested  in  the  policy 
discourse.  Common cause NGOs, non-governmental and non-profit definitions dominate 
the  discussion.  The  discourse  of  CSOs  in  policy  literature  is  also  centred  on  matters  of 
current  CSO  involvement  in  research  and  the  benefits  of  CSO  involvement  in  research. 
Within the discussions of CSO involvement it can be seen that reasons for including CSOs 
are  widely  discussed,  alongside  CSO  roles,  constraints  and  enablers,  and  impact  on 
research.  The  most  prominently  discussed  benefits  are  the  enabling  of  citizen 
participation in research and the quality of research itself. 

In contrast to the academic and policy discourse, project outputs discuss CSOs’ current 
involvement  in  research  about  as  much  as  they  discuss  definitions  of  CSOs,  benefits  of 
CSO  inclusion,  downsides  of  inclusion  and  empirical  support  combined.  Although 
discussions on “current involvement” seem to be very vibrant and unsettled, a few focal 
points could be identified, namely constraints on CSO participation, activities and impacts 
of CSOs in research, and the motivations of CSOs in research. Beyond this, the discourse 
is highly fragmented, suggesting very detailed discussion and, perhaps, open questions. 
This  doesn’t  mean,  however,  that  the  discourse  is  incoherent  –  the  many  references 
outside of the apparent focal points could certainly be related back to broader themes  – 
but it does suggest the need for close scrutiny. 

The following table compares the EC/EU view, the academic view, the policy view and the 
emerging  project  view,  based  on  the  most  frequently  discussed  themes  from  project 
outputs. 

Table 2: Working CSO definitions 

EC/EU View 

non-State 
not-for-profit 

Working Academic 
View 
non-governmental 
non-profit 

Working Policy 
View 
non-governmental 
non-profit 

non-partisan 
nonviolent 
through which 
people organise to 
pursue shared 
objectives and 
ideals 
political, cultural, 
social or economic 
promotes the public 
good 

--- 
--- 
self-constituting, 
voluntary 
associations 

Common cause 
NGOs 

--- 

--- 

Working Project 
View 
non-governmental 
non-profit / no 
commercial interest 
--- 
--- 
faith-based 
association / 
alternative legal 
status 

no single definition 
(?) / think tanks 
public interest / 
public well-being 

Consensus, it seems, emerges on the non-state, not-for-profit basis of CSOs, but beyond 
that, more is up for grabs. 

3.3.2  Action Logic of CSOs 

It  is  possible  to  reconstruct  from  the  analyses  above  a  kind  of  ‘action  logic’  for  CSOs, 
from the perspectives of the three literature reviews undertaken. Firstly, it was useful to 
compile the general preoccupations indicated by the three literature reviews: 

44 

 
 
 
 
 
 
Table 3: Preoccupations in literature reviews 

Academic Literature 

Policy Literature 

Project Literature 

Benefits 

Current involvement in 
research 

Downsides 

Capacity building 

Empower 

Increase transparency 

current CSO involvement in 
research 

constraints on CSO 
participation 

benefits of CSO 
involvement in research 

activities and impacts of 
CSOs in research 

reasons for including CSOs 

motivations of CSOs 

CSO roles 

constraints and enablers 

impact on research 

enabling citizen 
participation 

quality of research 

These themes show various focal points of project literature. This can be instrumental in 
generating  criteria  for  case-study  selection  and  in  setting  up  key  interests  for  an  agent 
model, from a project literature point of view. For case-study selection this is interesting 
because  it  throws  up  ‘hot  topics’  in  project  literature  and  so  provides  a  basis  for  either 
pursuing themes that preoccupy current projects, or grounding a gap analysis and finding 
interestingly  understudied  cases.  The  same  approach  can  be  applied  to  the  literature 
reviewed from the academic and policy perspectives. Essentially, we can develop a basis 
for an action logic for CSOs from the perspectives of the literature reviewed. 

3.4 Refinement of proposed CSO typology 

Drawing upon all of the research and analysis above we can propose a streamlined table 
of  CSO  typology,  including  examples  and  hints  at  preoccupations.  This  provided  insight 
into both the nature and the action-logic of the different CSOs. 

With  the  views  developed  here,  it  became  increasingly  fruitful  to  look  at  the  various 
‘levels of CSO-ness’ in order to come to a detailed view of CSO identity and action across 
a range of dimensions. 

Using  the  methods  from  above,  these  various  levels  were  modelled  by  screening  out 
various  nodes  in  order  to  constitute  the  different  levels,  narrow,  medium  and  broad 
definition.  This  showed  us,  in  the  various  domains  of  academic,  policy  and  project 
discourses, how each level of definition is discussed. At the very least, from each domain 
of  discourse  it  was  possible  to  visualise  the  extent  to  which  each level  of  definition  was 
discussed. This was possible by viewing the distribution of nodes across the domains. An 
immediate  result  from  this  was  a  clear  view  of  the  ways  in  which  different  domains  of 

45 

 
 
 
discourse typically discuss CSOs. The preliminary typology emerging from this exercise is 
shown in the following table. 

Figure 2: Levels of "CSO-ness" 

Table 4: Preliminary CSO typology from the literature review 

Name 

Common 
cause 

Definition   Their focus is 

on contributing 
to the public 
good. 

EU-based / 
policy literature 
AGE Platform 
Europe 
http://www.age-
platform.eu/  
CSO, not for 
profit, NGO 
Environment  
Sustainable 
development 
Research and 
innovation 
Non-
governmental 
Not-for profit 
Non-partisan 
Non-violent 
Shared 
objectives 

Source 

Example 

Terminolo
gy  
Area of 
activity 

Prosperitie
s / 
characteri
stics 

Shared voice  

Academic literature 
concentrates on how 
CSOs are 
constituted to 
express or support a 
particular position 
Academic literature 

Research-
oriented 
Participate in 
research. 

Project outputs 

Greenpeace 
http://www.greenpeace.org.
uk/  

IARS 
http://www.iars.org.uk/  

Industry 
oriented 
Promote 
interest of 
companies but 
are not profit-
making 
organisations  
Not in the 
literature 
ACEA 
http://www.acea.be/  
CSR Europe 

Faith-based / 
values 
Research 
Ethics 
Oversight  

Non-government 
Non-profit / no 
commercial 
interests 
Faith-based 
associations 
Alternative legal 

Promote the 
interests of a 
organisations 
with a profit 
interest  
Non-
government 
Not for profit 
Have 
commercial 
interests  
Sectoral 

NGOs, Voluntary 
associations 
Environment 
Sustainable 
development 
Economic 
development 
Non-government 
Non-profit 
Self-constituting 
Voluntary 
associations 

46 

 
 
 
 
 
Name 

How they 
can be 
identified 
by their 
mission 
Legal 
status 
Areas of 
interest 

Action 
logic in 
research  

Common 
cause 
Political, social, 
cultural, 
economic 
groups 
Promote the 
public good 
Public good 
orientation  

Shared voice  

Specific cause 

Industry 
oriented 
wellbeing 
Lobbying  

Promote a 
sector 

Research-
oriented 
status 
No single 
definition 
Public interest / 
public wellbeing 

Adding value to 
research 
practices 
Being responsible 
in research 

Legal status is likely to be relevant but difficult for us to determine 

Promotion of 
public 
engagement, 
deliberative 
democracy  

Benefits that CSOs 
bring: 
  Capacity building 
  Enable citizen 
participation 

Interested in 
constraints, 
activities, impact 
and motivations 
of CSOs in 
research 

  Interested in 

specific topics 
and / or CSO 
implications in 
research itself 

  Interested in 

open research 
opportunities  
  Interested in 
and aware of 
research 
landscape 

  Raise visibility 
of research 
and CSO 
involvement 

  Increased 

transparency  
  Mitigate effects 
of business 
interests  
  Strengthen 

representation 
  Research needs 
to benefit from 
the inclusion of 
this voice. Or: 
  The voice needs 
to benefit from 
the research  

  Are interested in 
specific research 
topics relevant to 
their mission 

  Raise visibility of 
their mission 

  Steer research 

  Promote 

towards specific 
questions and 
outcomes. 

engagement 
as means of 
improving 
research, 
increasing 
legitimacy of 
findings 

  Interested in 
how CSOs 
research can 
influence 
policy.  

  Will engage 
in research 
that clearly 
promotes 
the public 
good. 
  Promote 

transparenc
y in research  

  Promote 
public 
engagement 
as 
expression 
of common 
interest 

  Interested in 
research that 
further the 
interest of 
the sector 
  Demonstrate 
the social 
responsibility 
of the sector 

  Seeking to 
extend 
specific 
networks 

  Raise 

visibility of 
the sector 
  Research to 
support or 
achieve 
specific 
(policy) goals 

This  table,  based  in  the  synthesis  of  the  above  literature  reviews’  outcomes,  provides  a 
grounding  for  a  further  step.  Essentially,  this  material  can  be  operationalised  in  such  a 
way that it provides a methodology for classifying CSOs. Rather than a list of criteria, as 
the table is presented, this operational version of the typology gives a means of actively 

47 

 
 
 
 
 
 
 
 
using  the  literature  review  material  to  select  and  to  sort  CSOs.  This  ‘decision  tree’  is 
grounded  in  the  insights  from  the  literature  review  and  so  it  proceeds  in  a  principled 
rather than an ad hoc way. 

Figure 3: Operationalised typology ("decision tree") for categorising CSOs 

Rather  than  a  direct  mapping  of  the  typology  to  the  decision  tree,  this  represents  an 
iteration.  This  is  owing  to  the  fact  that  an  additional  step  of  analysis  takes  place  in 
producing  the  decision  tree  from  the  typology.  Mainly,  this  involves  non-duplication  of 
headings or themes, and general streamlining. For instance, in the typology headings for 
common  cause,  shared  voice,  research-oriented,  profit  oriented  CSOs.  In  the 
operationalised  decision-tree  image  this  shrinks  to  a  tripartite  functional  split  between 
service,  advocacy  and  research.  This  is  because  service  can  be  seen  to  cover  common 
cause  and  some  advocacy  CSOs,  as  well  as  profit-oriented  CSOs  (e.g.  the  business 
advocacy CSO, ACEA mentioned in the typology). 

In  short,  in  creating  the  CSO  classificatory  tool  in  the  above  image  streamlining  and 
conceptual  clarity  was  facilitated  further,  without  sacrificing  the  strong  foundation 
provided by the literature review. Moreover, owing to the conceptual coherence between 
the typology and the decision-tree tool, each can be used in conjunction with the other in 
a  useful,  productive  tension  in  order  to  provide  robust  insights  regarding  CSO 
identification and evaluation. 

48 

 
 
 
4  Compilation of project dataset 

The  purpose  of  this  task  was  to  prepare  the  project’s  database  of  organisations 
participating in FP6, FP7 and H2020 projects, and identifying the different types of CSOs 
therein.  After  carrying  out  the  required  cleaning  and  harmonisation  across  the  three 
different  sources,  the  project  dataset  was  comprised  of  220,454  entries,  consisting  of 
45,031  organisations  and  37,662  projects  (from  FP6,  FP7  and  Horizon  2020).  This 
dataset  consequently  served  as  a  basis  for  identifying  and  categorising  the  CSOs 
participating  in  the  FPs.  This  was  done  though  an  online  survey  with  organisations 
categorised  as  “OTH”  in  the  project  dataset  and  a  subsequent  manual  screening  of 
organisations. 

4.1 Survey with organisations categorised as “OTH” 

A survey questionnaire was developed on the basis of the “decision tree”, which included 
some additional questions that may help categorise the CSO. About 8,000 contact details 
for  “OTH”  organisations  were  retrieved  (for  a  number  of  organisations,  more  than  one 
person  was  approached  in  view  of  ensuring  a  high  return  rate).  The  full  survey  was 
launched  in  October  2015  and  remained  open  for  about  two  months.  From  the  around 
8,000 contacts that were approached, about 3,200 turned out to be non-existent (i.e. the 
respective  email  addresses  included  in  E-CORDA  were  not  active  anymore).  During  the 
two months in which the survey was open, about 1,500 replies were received. 

4.2 Refined CSO typology and additional screening of 

“OTH” organisations 

The  analysis  of  the  1,500  replies  from  the  online  survey  allowed  reflecting  on  the 
proposed CSO typology from the literature review (see above), also in view of coming up 
with  a  manageable  procedure  for  manually  screening  more  than  5,700  organisations 
concerning their CSO status. The analysis revealed that the distinction of CSOs according 
to  the  two  dimensions  “income”  and  “target  group”  was  feasible,  while  the  results 
regarding the potential third dimension (“function”) were not homogenous. For instance, 
the results showed that the function “carrying out research” does not describe a specific 
type of  research-oriented CSO (as was suggested in our proposal), but rather research-
oriented CSOs can be of very different types (e.g. big, industry-oriented CSOs as well as 
smaller “core” CSOs). The CSO typology used for this study was consequently refined in 
view  of  focusing  on  the  two  dimensions  “income”  and  “target  group”,  distinguishing 
between the following four types of CSOs: 

1)  CSO  1:  „core“  CSOs  (oriented  towards  individuals/citizens):  this  category 
includes organisations funded by individuals or other non-profit organisations (NPOs), 
whereby  this  funding  source  is  clearly  specified  in  financial  reports  or  other  official 
documents  of  the  organisation,  and  with  individuals  as  the  main  beneficiaries. 
Examples of CSO 1 organisations include: European Patients Forum, Red Cross, WWF, 
European Brain Council. 

2)  CSO  2:  oriented  towards  society/public,  financed  by  public:  this  category 
includes  organisations  funded  by  the  state  (government)  and  having  individuals  or 
public authorities as the main beneficiaries. This category also includes organisations 

49 

 
potentially  being  CSO  1,  but  where  the  funding  source  is  not  clearly  specified  in 
official  documents.  Examples  of  CSO  2  organisations  include:  Transparency 
International, European Platform of Women Scientists, Alzheimer Europe. 

3)  CSO 3: oriented towards society, financed by businesses: this category includes 
organisations  funded  by  businesses  but  having  a  clear  social/environmental  focus 
concerning  target  groups.  Examples  of  CSO  3  organisations  include:  European  Food 
Information Council, European Aquaculture Society. 

organisations 

4)  CSO 4: oriented towards businesses (e.g. business associations): this category 
includes 
or 
government/state/public  funds  with  the  aim  of  supporting/enhancing  business, 
industries or professional groups and their interests. Examples of CSO 4 organisations 
include:  Association  of  European  Railway  Industries,  European  Business  and 
Innovation Centre Network, European Business Register. 

profit-oriented 

companies 

financed 

by 

The  refined  CSO  typology  described  above  was  used  as  the  basis  for  the  manual 
screening  of  the  remaining  5,700  organisations  categorised  as  “OTH”.  Additionally,  a 
check of other  organisations (e.g. categorised  as “REC” etc.) was carried out by  looking 
at  CSO-umbrella  organisations  and  their  members  and  cross-checking  this  with  the 
organisations  in  the  project  dataset  (i.e.  organisations  participating  in  the  framework 
programmes). For a number of European CSO umbrella organisations (such as European 
Citizen Action Service (ECAS), ECRE – European Council on Refugees and Exile, European 
Union Fundamental Rights Platform, Europe+, EU Civil Society Contact Groups), the lists 
of  member  organisations  were  retrieved  and  cross-checked  with  the  organisations 
included in the project dataset to identify additional CSOs listed under other categories. 

4.3 Finalisation of project dataset 

The CSO categorisation described above was eventually merged with the project dataset. 
Organisations  that  could  not  be  allocated  to  any  of  the  four  CSO  types  remained  in  the 
project dataset under the category “OTH”. By doing so, the category “OTH” was split into 
five  categories  (CSO1,  CSO2,  CSO3,  CSO4,  OTH).  Moreover,  organisations  that  were 
obviously wrongly categorised as “OTH”, but were in fact public bodies or profit-oriented, 
were  re-categorised  into  the  correct  category  (“PRC”  or  “PUB”)12.  The  outcome  of  this 
sub-task  was  a  final  project  dataset  (by  the  end  of  January  2016)  with  the  following 
organisation types to be applied in the remainder of the study: 

  HES: Higher or secondary education 
  REC: Research organisations 
  PRC: Private for profit (excluding education) 
  PUB: Public body (excluding research and education) 
  CSO1: “core” (citizen-oriented) CSOs 
  CSO2: society-oriented/public-funded CSOs 
  CSO3: society-oriented/business-funded CSOs 
  CSO4: business-oriented CSOs 

  OTH: Other (non-CSOs) 

12  Due to this re-categorisation of “OTH” organisations, the numbers for “PRC” and “PUB” in the 

present study will therefore differ from other analyses on FP6 and FP7 participations. 

50 

 
                                           
The main novelty of this study (and its dataset) is the identification of CSOs according to 
the four different types among all the organisations participating in FP6, FP7 and H2020. 
The following table provides an overview of the numbers of CSOs included in the project 
dataset, including the remaining “OTH” organisations. 

Table 5: Numbers of CSOs included in the final project dataset 

Type 

FP6 

FP7 

H2020* 

Total FP7 & 
H2020* (no 
duplicates) 

Total FP6, FP7, 
H2020* (no 
duplicates) 

CSO1 

CSO2 

CSO3 

CSO4 

OTH 

124 

364 

214 

387 

188 

208 

48 

93 

58 

748 

895 

215 

624 

192 

58 

Total CSO1-3 

Total CSO1-4 

Total "OTH" 

676 

1424 

2048 

809 

1704 

1896 

199 

414 

472 

* Data from June 2015 

247 

445 

248 

1007 

235 

940 

1947 

2182 

352 

748 

409 

1594 

804 

1509 

3103 

3907 

51 

 
5  Case studies of networks involving CSOs 

CSOs  and  other  potential  research  partners  operate  on  different  logics,  such  that 
Framework Programme funding does not appear in the same way to each. Tweaks within 
the existing system can be used to incentivise CSOs to participate more; however, where 
the  goal  is  to  bring  science  closer  to  society,  a  different  kind  of  change  is  probably 
necessary.  The  work  carried  out  in  this  task  aimed  at  identifying  and  implementing 
further case studies based on the case studies carried out in the course of the CONSIDER 
project (“Civil Society Organisations in Designing Research Governance”; www.consider-
project.eu). The idea was to go beyond existing cases and detect gaps in the knowledge 
created by CONSIDER and to fill these gaps using additional cases.  

5.1 Guiding Questions 

The  following  questions  structure  the  overall  approach  beyond  CONSIDER  case  studies. 
They were informed by the results of the network analysis and the discussions related to 
the  agent-based  modelling.  They  were  designed  to  capture  interesting  cases  that  can 
inform  simulation  tasks.  This  is  based  on  insights  about,  for  example,  the  resilience  of 
CSOs that appear to fail, yet keep trying. The overall areas structuring the approach are 
as follows: 

  CSOs that tried participating several times and did not succeed, but keep trying 

  Important "pure" CSOs (type 1) that never tried participating 

  CSOs that (tried to) coordinate(d) projects in FP6/FP7 

  Highly-active CSOs (e.g. coordinators and/or active in many projects) in FP6 that got 

lost in FP7 

5.2 Methodology 

5.2.1 CONSIDER re-analysis 

The data collection and analysis of the CONSIDER project have been described in detail in 
the  project  deliverables  (see:  www.consider-project.eu).  For  the  purposes  of  this  task, 
the data were revisited with the aim of finding additional insights concerning the research 
questions. This was done via the coded data of the CONSIDER project and a re-analysis 
of the case study summaries. 

It is important to underline, however, that the CONSIDER project, by nature of its chosen 
cases,  could  not  provide  responses  to  some  of  the  questions.  All  CONSIDER  cases  were 
of  active  research  projects  that  included  CSOs.  This  included  some  cases  where  CSOs 
were  coordinators.  What  the  project  data  does  not  provide,  however,  are  insights  into 
any sort of research in which CSOs are not part of consortia, e.g. answers to the question 
why successful CSOs have dropped out of participation.  

In  total,  there  were  slightly  over  30  CONSIDER  case  studies  that  were  analysed.  These 
case studies were all successfully involved in research. However, the level of involvement 
differed. As such, looking at the 4 CSO type categories that have been elected for use in 
our  study,  we  can  discount  CONSIDER  case  studies  as  falling  under  the  first  two 
categories of: 

52 

 
i. 

ii. 

CSOs that tried participating several times and did not succeed, but keep trying. 

Important "pure" CSOs (type 1) that never tried participating. 

We can however expect CONSIDER case study CSOs to fall under the last two categories: 

iii. 

CSOs that (tried to) coordinate or coordinated projects in FP6/FP7. 

iv. 

Highly-active CSOs (e.g. coordinators and/or active in many projects) in FP6 that 
got lost in FP7. 

To gather responses to these questions, additional data had to be collected. 

5.2.2  Additional case studies 

The  use  of  blanket,  fully-fledged  case  studies  was  revised,  in  view  of  providing  insights 
through the targeting of specific types of CSOs, in particular circumstances, with specific 
experiences. Although the CONSIDER cases were fully-fledged case studies, the cases for 
the  CSO  tender  were  more  “explanatory/inspirational  interviews”.  The  interviews  were 
semi-structured  and  open  in  order  to  ensure  that  participants  were  not  restricted,  but 
had  the  opportunity  to  reflect  and  expand  on  the  questions  if  need  be.  This  allowed  for 
the collection of detailed data. 

Given the difference in focus, it was not possible to follow the CONSIDER methodology, 
which used as its unit of analysis the case of projects including CSOs. This logic could not 
be  transferred  to  unsuccessful  CSOs  and,  therefore,  needed  to  be  revised.  For  the  sake 
of  consistency with the proposal, we nevertheless continue to use the term ‘case study’ 
when talking about the empirical work. The ‘cases’ in this case are the collection of data 
used to answer the individual guiding questions listed above.  

5.2.3  Selection of cases 

In order to make a justified selection of interviewees, the guiding questions were used to 
frame  areas  of  enquiry.  The  CORDIS  database  was  then  used  to  find  groups  of  CSOs 
relevant  to  each  framing  question.  The  typology  developed  in  the  project  was  used  to 
further refine the relevance of targets to the aims of the study. Primarily, those CSOs of 
type  1  and  2  were  given  priority,  owing  to  their  being  more  like  the  sorts  of  CSOs  that 
governance  structures  tend  to  seek,  in  order  to  include  in  research  (i.e.  without 
commercial interests etc.).  

5.3 Findings 

5.3.1  Summary of findings – additional cases 

The  table  below  summarises  the  outcomes  of  the  work  beyond  CONSIDER  based  on 
interviews  with  key  players  in  organisations  identified  as  relevant.  The  organisations 
were identified using information from prior tasks. 

53 

 
Table 6: Main findings from additional cases 

Question 

Response 

CSOs that tried participating several 
times and did not succeed, but keep 
trying – Why do they keep trying? 

Important "pure" CSOs (type 1) that 
never tried participating – Why are 
they not trying? 

Under what conditions would they 
be interested in participating? 

CSOs that (tried to) coordinate(d) 
projects in FP6/FP7 – why did they 
(seek to) coordinate? 

Highly active CSOs (e.g. 
coordinators and/or active in many 
projects) in FP6 that got lost in FP7 
– Why did they drop out? 

  Funding 
  Commitment to Mission 
  Steering research toward valuable ends 

 

In a sense, some are – sometimes by 
proxy 

  Capturing participation requires scrutiny 

(e.g. a researcher applying for funds from 
a lab funded by another agency is an 
arguably participation) 

  See FP as peer (or rival?) funding scheme 

– agenda clash or complementarity 

  Constitutional and legal factors may make 
it impossible (e.g. rules for endowment 
disbursement) 

  Where possible, co-determining research 
agendas at high levels would be incentive 

  New challenges 
  Capacity-building internally 
  Elevate role beyond minor partner 
  Create impact in an area of interest 
  Steer research, non-trivially 

  Lack of capacity to keep up with calls 
  Administrative burdens/hurdles (e.g. visas 

 

for non EU members) 
“Bad luck” (entropy – ‘dropping out’ was 
not a decision) 

  Participation lacked the expected impact 
on research (e.g. through low esteem) 

5.3.2  Summary of findings – CONSIDER cases 

The table below summarises the re-analysis of the CONSIDER cases. 

Table 7: Main findings from re-analysis of CONSIDER cases 

Question 

Response 

What was the motivation for 
CSO participation? 

  Desire to contribute to research 
 

Improve lives of CSO interest groups as they 
have easy access to research outcomes 
  Keep abreast of scientific developments 
  Grow reputation in research field 
  Act as intermediaries between researchers, 

industry and CSO interest groups 

54 

 
 
Why were some CSOs involved 
several times? 

  WP leadership and coordinator 
  CSO power 

  Existing networks and collaboration 
  Prior research experience 
  Funding purposes 

What were CSOs’ experiences? 

  Prior research experience 
  Experience of working with vulnerable groups 

How FP participation changed 
the organisation? 

  Raising company profile 
 
  CSO power 

Improving CSO standing in research 

5.4 Conclusions 

As can be concluded from the findings above, CSOs are clearly motivated to participate in 
research due to a  number of reasons. Such motivations may potentially be extended to 
other  CSOs  when  research  is  more  conducive  for  CSOs  to  have  the  opportunity  to 
contribute,  grow  their  reputation  and  keep  abreast  of  scientific  developments.  This  may 
be  realised  if  CSOs  are  given  roles  that  are  deemed  as  important  in  the  consortium 
structure.  These  roles  should  enable  the  CSOs  to  have  the  ability  to  influence  the 
research  agenda  and  decision-making  in  the  research  process.  Such  roles  may  include 
WP leadership or coordination roles. However, there is need for caution when it comes to 
the  possible  assigning  of  coordination  roles  to  CSOs,  especially  within  the  current  EU 
research  frameworks,  as  CSOs  consider  EU  research  regulations  as  bureaucratic, 
particularly for CSOs who often do not have research capacity.  

The  analysis  has  also  shown  that  some  CSOs  are  likely  to  be  involved  several  times  in 
research.  This  has  usually  happened  when  there  had  been  long-term  networks  and 
collaborations, prior research experience and for funding purposes.  It can, therefore, be 
concluded  that  it  is  essential  to  nurture  and  ensure  that  CSOs  continue  to  be  involved 
and  collaborate  in  research.  This  can  be  done  through  the  creation  of  networking 
platforms  by  funders  and  policy  makers,  so  that  they  are  the  norm  and  not  the 
exception.  

Moreover, the inclusion of CSOs in agenda-setting at the highest levels of FP formulating 
is likely to be the most efficient way to include their values, norms, insights, knowledge, 
and  influence.  This  kind  of  change  could  alter  the  character  of  FPs,  such  that 
collaborative research, centred on socially relevant areas, would be incentivised. 

Including  CSOs  in  project  evaluations  would  likely  serve  to  alter  the  character  of  a) 
proposals  and  b)  consortium  makeup  by  incentivising  other  researchers  to  take  CSO 
involvement in research seriously. 

55 

 
6  Role and position of CSOs in FP networks 

6.1 Network Analysis 

6.1.1  Data Base 

In  order  to  determine  the  role  and  functional  impact  of  CSOs  in  the  EU  research 
framework  programmes  and  their  networks,  we  use  CORDIS  data  of  FP6,  FP7,  and 
Horizon 202013 projects. Based on the common definition that a network is a set of nodes 
connected through a set of links, we treat organisations as actors that are connected to 
each  other  through  EU  funded  projects.  These  networks  are  called  two-mode,  because 
they consist of two classes of nodes (organisations and projects) and can be transformed 
into  a  one-mode  network  of  organisations  (two  organisations  are  connected  if  they 
cooperate  in  at least  one  project)  or  into  a  one-mode  network  of  projects  (two  projects 
are linked if at least one common organisation participates in them). Our analysis mainly 
refers to the one-mode organisation networks.14 

6.1.2  Data Management and Network Generation 

Relationships  between  FP  participants  are  derived  from  project  data  by  linking 
organisations that cooperate in one or more EU funded projects. Imagine a project with 
four participants; it leads to a fully connected graph, i.e. a network of four organisations 
in  which  each  organisation  is  linked  to  every  other  organisation.  Each  of  the  six 
relationships  would  be  treated  equally  because  it  is  derived  from  cooperation  in  exactly 
one  project.  In  cases  where  organisations  cooperate  in  more  than  one  project,  the  link 
between  them  would  receive  a  strength  that  correlates  with  the  number  of  common 
projects. 

This,  however,  tends  to  generate  artificial  relational  data:  We  have  to  assume  that  the 
intensities of relationships are not equal, and that strength differs between different pairs 
of actors within one project depending on certain criteria. For example, we would expect 
that  there  is  more  communication  between  the  coordinator  of  the  project  and  the 
participants  than  among  the  participants  themselves.  Furthermore,  interaction  happens 
more frequently between participants within short geographical distances and that share 
the same language. Additionally, if we compare different projects with each other, we will 
assume  that  there  is  more  communication  and  more  transaction  of  resources  in  long-
term  projects.  For  these  reasons,  we  developed  a  model  for  the  estimation  of  the 
variance  of  interaction  and  computed  ‘encounter  probabilities’,  which  estimate  the 
amount of communication and resource transaction among participants according to the 
following criteria: 

1.  Number  of  common  projects:  encounter  probability  rises  with  the  number  of 

projects in which two organisations cooperate. 

2.  Position  within  project:  encounter  probability  is  higher  between  coordinators  and 

participants than among participants. 

3.  Duration: encounter probability is higher in long-term projects (duration in months). 

4.  Funding:  encounter  probability  is  higher  in  highly  funded  research  projects  (EU 

contribution). 

13  End of period for H2020 data: April 2015. 
14  See the annex for the numbers of projects, institutions, and funding in detail. 

56 

 
                                           
5.  Geographical distance: encounter probability  is higher among organisations of the 

same country (same language). 

6.  Project  size:  encounter  probability  decreasing  with  the  number  of  participating 
organisations  (the  higher  the  number  of  participants,  the  lower  the  interaction 
intensity). 

According  to  these  influence  factors,  we  computed  the  strength  of  relationships  of  all 
pairs  of  participants  within  each  thematic  area  of  FP6  and  FP7.  In  doing  so,  we 
transformed the CORDIS project data into network data  of organisations that cooperate 
with each other in EU funded projects, in which the amount of estimated interaction (i.e. 
strength  of  relationships)  corresponds  to  encounter  probability  (Figure  4).  The 
computation of encounter probabilities is our approach to come to a better understanding 
of the meaning of relationships and networks. 

Figure 4: Transforming CORDIS project data into network data. Line strength 
corresponds to encounter probability 

Graphic: FASresearch. 

Figure  5  represents  the  core  of  the  FP7  cooperation  network.  In  order  to  represent  the 
overall structure of the network, it is necessary to reduce the number of relations and to 
select  the  most  important  organisations.  An  arc  from  A  to  B  indicates  that  A  was  the 
coordinator  of  B  in  at  least  one  FP7  project.  For  each  organisation,  the  five  most 
important  collaboration  partners  in  terms  of  the  number  of  common  projects  are 
depicted.  The  strength  of  the  arcs  refers  to  the  robustness,  which  is  measured  by  the 
number of common neighbours of each collaboration relation. The size and the colours of 
the organisations indicate aggregated encounter probability. The thematic core areas are 
clearly visible: On the left side we find organisations in the fields of engineering, ICT, and 
energy (also most  of the  companies are located in this sector).  On the  right side, there 
are  the  institutions  doing  research  in  health  and  life  sciences.  This  duality  is  typical  for 
the EU funded research and science. Around these two centres, additional clusters can be 
found  –  notably  agriculture/food  and  aerospace.  Of  course  these  are  not  all  thematic 
areas and this is not the complete FP7 network – it is the absolute core, the key drivers 
of  the  network  that  dominate  it,  and  around  which  the  structure  of  the  collaboration 
network emerges. 

57 

 
 
 
 
Figure 5: Core of FP7 cooperation network (694 out of 28.817 organisations) 

Data source: CORDIS, WU. 
Analysis and visualisation: FASresearch. 

58 

 
 
 
 
 
6.1.3  CSO Participation – General Statistics 

As already described above within the participant data  – among the OTHERS category – 
different  types  of  CSOs  have  been  identified.  Table  8  lists  the  organisation  types  by 
numbers and percentages. 

Table 8: FP6, FP7, and H2020* types of institutions 

Type of Institution 

Number 

Percent 

Number 

Percent 

Number 

Percent 

FP6 

FP7 

H2020 

HES 

REC 

PRC 

PUB 

OTH 

CSO1 

CSO2 

CSO3 

CSO4 

Total 

2.296 

3.590 

11.965 

1.924 

624 

124 

364 

188 

751 

10,5 

16,4 

54,8 

8,8 

2,9 

0,6 

1,7 

0,9 

3,4 

2.296 

3.602 

18.997 

2.026 

192 

214 

387 

208 

895 

8,0 

12,5 

65,9 

7,0 

0,7 

0,7 

1,3 

0,7 

3,1 

780 

793 

2.646 

447 

58 

48 

93 

58 

215 

15,2 

15,4 

51,5 

8,7 

1,1 

0,9 

1,8 

1,1 

4,2 

21.826 

100,0 

28.817 

100,0 

5.138 

100,0 

Data source: CORDIS, WU. Table: FASresearch. *April 2015 

Altogether, 1.424 (FP6) and 1.704 (FP7) CSOs have been found, that is 6,6% and 5,8% 
of all institutions respectively. Without the business oriented type 4, only 3,2% (FP6) and 
2,7%  (FP7)  of  all  organisations  are  CSOs.  We  can  see  that  the  amount  of  CSOs 
decreases  from  FP6  to  FP7,  but  increases  when  we  include  H2020  data  until  April  2015 
(8,1% CSOs, 3,9% CSOS without business oriented types 4). 

Finding  1:  All  in  all,  we  found  fewer  CSOs  than  expected.  There  are  only  1.424  (FP6) 
and  1.704  (FP7)  CSOs  in  the  database,  that  is  6,6%  and  5,8%  of  all  institutions 
respectively.  Without  the  business  oriented  type  4,  only  3,2%  (FP6)  and  2,7%  (FP7)  of 
all organisations are CSOs. 

Table 9: Funding distribution among organisation types (in 1,000 Euro and %) 

Types 

Thousand € 

Percent 

Thousand € 

Percent 

Thousand € 

Percent 

FP6 

FP7 

H2020* 

HES 

REC 

PRC  

PUB 

OTH 

CSO1 

CSO2 

CSO3 

CSO4 

Total 

6.430.936,8 € 

38,7 

19.424.028,1 € 

43,3 

1.315.398,6 € 

35,8 

4.914.048,6 € 

29,6 

12.206.798,1 € 

27,2 

1.175.139,3 € 

31,9 

4.206.080,6 € 

25,3 

11.064.961,2 € 

24,6 

930.606,8 € 

25,3 

575.057,5 € 

258.771,2 € 

15.732,7 € 

51.315,2 € 

32.339,4 € 

133.988,1 € 

3,5 

1,6 

0,1 

0,3 

0,2 

0,8 

1.293.869,0 € 

311.267,3 € 

42.886,3 € 

77.617,8 € 

81.282,3 € 

387.249,7 € 

2,9 

0,7 

0,1 

0,2 

0,2 

0,9 

164.415,2 € 

6.582,5 € 

8.084,0 € 

19.300,0 € 

12.125,1 € 

46.525,5 € 

4,5 

0,2 

0,2 

0,5 

0,3 

1,3 

16.618.270,0 € 

100,0 

44.889.959,7 € 

100,0 

3.678.177,1 € 

100,0 

Data source: CORDIS, WU. Table: FASresearch. *April 2015 

When we look at the funding distributions among organisation types, proportions become 
even clearer. All in all, in FP7, CSOs received 1,4% of funding (FP6: 1,3%); disregarding 
business  oriented  CSOs  type  4  it  is  only  0,6 %  (FP6)  and  0,4%  (FP7).  H2020  numbers 

59 

 
  
 
until April 2015 exhibit increases  on a low level (2,3%; 1,1%  without business oriented 
CSOs).  Funding  percentages  indicate  that  the  FPs  are  more  customized  for  universities 
and research organisations (plus specific programmes for enterprises), and this fact has 
a  strong  impact  on  the  currencies  that  are  regarded  as  valuable  within  these  systems: 
scientific excellence and applicability. 

Finding  2:  The  share  in  funding  of  CSOs  is  even  lower  than  the  share  in  institution 
numbers  and  in  project  participations.  In  FP7,  CSOs  received  1,4%  of  funding  (FP6: 
1,3%);  disregarding  business  oriented  CSOs  type  4  it  is  only  0,6  %  (FP6)  and  0,4% 
(FP7). 

Another  indicator  that  there  are  obstacles  for  CSOs  to  take  part  in  Framework 
Programmes  is  their  low  “survival  rate”  (Figure  6).  Only  15.3%  (19  out  of  124)  of  the 
CSOs  type  1  (“core”  CSOs)  in  FP6  can  be  found  in  FP7  again.  The  rates  for  business 
oriented CSOs are a little bit higher (20,6% or 154 out of 748 for CSOs type 4). 

Figure 6: “Survival rate” of institution types from FP6 to FP7 in % of organisations 

Data source: CORDIS, WU. Chart: FASresearch. 

Finding 3:  CSOs exhibit a high “dropout rate”  from FP6 to FP7.  Only 15.3% (19 out of 
124) of the CSOs type 1 (“core” CSOs) in FP6 can be found again in FP7. For CSOs type 
2, the rate is 16.3%, and for CSOs 3, 13,8%. The rates for business oriented CSOs are a 
little bit higher (20,6% or 154 out of 748 for CSOs type 4). 

There  are  specific  “playgrounds”  of  CSOs  –  thematic  areas  in  which  CSOs  participate 
more  than  in  others.  It  is  obvious  that  universities  and  non-university  research 
institutions  dominate  the  arena.  There  are  thematic  areas  specific  to  companies 
(Information  and  Communication  Technologies,  Research  for  the  benefit  of  SMEs).  Here 
we  also  find  a  number  of  business  oriented  CSOs  (type  4).  However,  when  it  comes  to 
more  “pure”  CSOs  (types  1  and  2),  numbers  are  small.  Regarding  FP7,  there  are  48 
CSO1s in Health (which is 0.6% of all organisations in Health), 36 in Science and Society, 
and  33 in  ICT.  CSOs  type  2  were  mainly  found  in  Science  in  Society  (76),  Environment 
(46), and Health 46). 

These  patterns  become  even  clearer  when  we  look  at  the  funding  distribution  among 
types  of  CSOs  across  thematic  areas.  The  share  in  funding  of  CSOs  within  the  thematic 
areas  (Table  10)  is  very  small,  even  if  thematic  areas  that  are  typical  for  CSOs  are 
considered. The core CSOs 1 received 3.7% of the funds of Science in Society (FP7), for 
CSOs of type 2 the percentage is 6.1. All in all, CSOs types 1, 2, and 3 received 12.8% of 

60 

 
 
SiS  funding  in  FP7.  Business  oriented  CSOs  received  at  least  11.8%  in  Regions  of 
Knowledge. 

Table 10: Percentages of funding by CSO type, programme, and thematic area  
(row sum = 100) 

Read: Within FP6 in Activities of International Cooperation CSOs type 1 get 0,9% of funding. Data source: CORDIS. Table: 
FASresearch. 

By looking at the column percentages (Table 10) we can determine how important each 
thematic area is for each type of institution in terms of funding. In FP7, CSOs of type 1 
receive  22.4%  of  their  funding  through  Health,  16.1%  through  ICT,  13%  through 
Security, and 12.7% through Science in Society. 

61 

Thematic AreaFP6FP7FP6FP7FP6FP7FP6FP7FP6FP7Activities of International Cooperation0,90,51,71,20,20,80,62,32,82,6Regions of Knowledge0,00,20,60,91,30,84,811,81,91,9Research for the benefit of SMEs0,10,10,20,30,60,68,26,40,90,9Research Infrastructures0,00,10,50,80,70,71,41,81,21,6Research Potential0,00,00,00,00,00,00,01,40,00,0Science in Society3,13,78,16,13,33,05,83,814,412,8Energy0,00,10,00,10,10,30,51,80,10,5Environment0,20,20,90,60,40,21,00,51,41,0Food0,20,20,40,30,60,71,21,21,21,2Health0,30,40,40,30,00,30,40,30,71,1ICT0,10,20,40,20,20,20,70,90,70,6Nanosciences0,00,00,20,20,20,21,01,10,40,4Security0,00,70,00,30,00,50,01,50,01,5Socio-economic sciences and Humanities0,20,80,91,00,40,22,70,51,41,9Space0,30,20,20,10,40,00,70,30,90,3Transport (including Aeronautics)0,00,00,10,20,10,11,01,60,20,3European Research Council0,00,00,00,00,00,00,00,00,00,0Nuclear Research0,00,10,10,00,00,00,50,50,10,1General Activities0,00,00,80,30,00,00,71,00,80,3Joint Technology Initiatives0,00,10,00,20,00,40,00,50,00,7Research policies0,20,20,81,70,61,71,32,41,53,5Marie-Curie Actions0,10,00,10,10,10,10,20,20,30,2Total0,10,20,40,30,20,31,01,10,80,9CSO1CSO2CSO3CSO4CSO1-CSO3 
 
Table 11: Percentages of funding by CSO type, programme, and thematic area  
(column sum = 100) 

Read: Within FP6 CSOs type 1 get 5.9% of their funding through Activities of International Cooperation. Data source: 
CORDIS. Table: FASresearch. 

Participation  of  CSOs  in  areas  like  Health,  ICT  or  Environment  is  significantly  below 
average.  However,  these  are  big  thematic  areas  (much  funding,  many  projects). 
Therefore,  in  absolute  terms,  these  thematic  areas  are  important  funding  sources  for 
CSOs. 

Finding  4:  There  are  specific  “playgrounds”  for  CSOs  –  thematic  areas  in  which  CSOs 
participate  more  than  in  others.  In  relative  terms  (CSO  share  in  each  thematic  area), 
Science  in  Society,  Activities  of  International  Cooperation,  Research  policies,  and  Socio-
economic sciences and Humanities are most significant. In absolute numbers, Health and 
ICT are also important funding sources for CSOs because of their sheer size. 

Furthermore, there is a clear correlation between the participation of different institution 
types  and  funding  schemes,  as  Table  12  shows.  The  last  of  the  three  tables,  which 
includes  the  column  percentages,  indicates  that  the  amount  of  participation  in 
Coordination and support actions (CSA) is higher among CSOs than among universities, 
research  institutes  and  enterprises.  For  example,  43.5%  of  CSO  type  1  participation 
takes  place  in  CSA  projects;  this  number  for  universities  is  only  10.7%,  for  research 
institutes  16.8%,  and  for  enterprises  9.3%.  This  means  that  CSOs  (like  public  bodies) 
are  rather  engaged  in  coordination,  networking  and  dissemination  activities  than  the 
research-oriented  participants  in  framework  programmes.  Participation  of  CSOs  only 
takes place in CP or in CSA projects; we hardly find them in other funding schemes (ERC, 
JTI, MC, or NOE). 

62 

Thematic AreaFP6FP7FP6FP7FP6FP7FP6FP7FP6FP7Activities of International Cooperation5,91,13,61,60,81,10,50,93,11,3Regions of Knowledge0,00,40,00,80,10,80,13,30,10,7Research for the benefit of SMEs0,71,01,01,84,73,815,612,52,12,3Research Infrastructures0,02,77,016,116,213,88,010,58,612,0Research Potential0,00,00,00,00,00,00,00,10,00,0Science in Society4,712,74,013,32,86,91,22,53,810,8Energy0,01,60,21,81,33,91,47,00,52,6Environment16,35,324,79,317,23,811,52,621,06,3Food9,56,67,35,816,912,48,86,310,68,4Health33,022,412,012,01,512,55,13,012,414,7ICT15,716,124,312,917,911,615,716,420,913,2Nanosciences1,31,24,34,26,86,89,79,04,54,4Security0,013,00,04,10,06,00,05,50,07,0Socio-economic sciences and Humanities2,95,14,04,33,10,75,50,63,63,2Space2,62,30,70,42,10,31,00,71,40,8Transport (including Aeronautics)4,22,01,54,43,44,112,013,32,53,7European Research Council0,00,30,00,00,00,00,00,00,00,1Nuclear Research0,00,30,20,10,10,10,80,50,10,1General Activities0,00,01,90,10,00,00,70,11,00,0Joint Technology Initiatives0,05,60,04,90,010,20,04,20,07,1Research policies1,90,02,70,13,50,12,00,02,80,1Marie-Curie Actions1,20,10,52,01,31,20,40,90,91,3Total100100100100100100100100100100CSO3CSO4CSO1-CSO3CSO1CSO2 
 
Table 12: FP7 participation of institution types in funding schemes:  
numbers (first table), row % (second table), column % (third table) 

Data source: CORDIS. Table: FASresearch. 

Finding  5:  Funding  schemes  have  a  significant  impact  on  CSO  participation  as  well. 
CSOs are engaged in coordination, networking and dissemination activities (CSAs) rather 
than in research activities (CPs). 

Our conclusion is that we found less “real” CSOs in the FP project data than we expected. 
With  regard  to  project  participation  and  funding,  the  importance  of  CSOs  within  the 
European  Framework  Programmes  is  rather  small.  Nevertheless,  there  are  specific 
thematic areas in which we identified a bigger number of CSOs (“playgrounds” of CSOs). 
In  relative  terms,  Science  in  Society  (a  small  thematic  area  relative  to  the  others)  is 
most  important.  In  absolute  terms,  the  big  areas  Health  (for  “core”  CSOs)  and  ICT  (for 
the business oriented CSOs) contain a notable number of projects with CSO participation. 
When it comes to funding schemes, CSO participation is much higher in coordination and 
support  actions  than  in  genuine  research  oriented  projects.  This  indicates  that  CSO 
participation  in  EU  funded  projects  does  not,  first  and  foremost,  target  advancing 
scientific excellence, research, and the production of knowledge. 

63 

SchemeHESRECPRCPUBCSO1CSO2CSO3CSO4OTHTotalCP24.63218.29330.8482.46416322519789812577.845CP-CSA1.8592.1464233885341872134.958CSA5.3045.5253.7243.46814132117472520419.586ERC3.9541.3636614200105.400JTI1.3481.1163.1667498153575.778MC11.7294.0401.8731511126151217.839NOE580347121293031011.094Total49.40632.83040.2216.5883246004131.756362132.500SchemeHESRECPRCPUBCSO1CSO2CSO3CSO4OTHTotalCP31,623,539,63,20,210,290,251,150,2100,0CP-CSA37,543,38,57,80,100,690,361,450,3100,0CSA27,128,219,017,70,721,640,893,701,0100,0ERC73,225,21,20,30,040,000,000,020,0100,0JTI23,319,354,81,30,160,140,260,610,1100,0MC65,722,610,50,80,010,070,030,080,1100,0NOE53,031,711,12,70,270,000,270,910,1100,0Total37,324,830,45,00,20,50,31,30,3100,0SchemeHESRECPRCPUBCSO1CSO2CSO3CSO4OTHTotalCP49,955,776,737,450,3137,5047,7051,1434,558,8CP-CSA3,86,51,15,91,545,674,364,103,63,7CSA10,716,89,352,643,5253,5042,1341,2956,414,8ERC8,04,20,20,20,620,000,000,060,04,1JTI2,73,47,91,12,781,333,631,991,94,4MC23,712,34,72,30,312,001,450,853,313,5NOE1,21,10,30,40,930,000,730,570,30,8Total100,0100,0100,0100,0100,0100,0100,0100,0100,0100,0 
 
6.1.4  Network Parameters – Key Network Analysis 

We use the following parameters to describe the overall network structures. 

  Funding:  Percentage  of  funding  of  each  thematic  area  within  the  whole  Framework 

Programme; it is an indicator for the foci of the Framework Programmes. 

  Organisations:  Number  of  institutions  within  the  network  (universities,  non-
university  research,  companies,  public  institutions,  four  types  of  CSOs,  and  others). 
The number of organisations is an indicator for the volume of a thematic area and the 
size of a scientific and technological field. 

  Lines: This number shows the amount of cooperation relations within the networks. 

  Density:  Percentage  of  existing  relations  compared  to  possible  relations.  Because  of 
the  limited  number  of contacts  each social actor is possible to maintain (limited time 
and  energy),  density  usually  depends  on  network  size:  The  bigger  a  network,  the 
lower its density. Density indicates the amount of connectedness of a thematic area. 

  Average  degree:  Average  number  of  project  partners  each  organisation  has  within 
the  thematic  area.  It  shows  whether  an  area  is  dominated  by  big  research  projects 
with many participants. 

  Fragmentation:  Amount  of  actors  within  the  network  that  cannot  be  reached  by 
direct and/or indirect contacts. If a network consists of different components  that are 
not  connected  to  each  other,  the  scientific  or  technological  field  is  still  small  or 
emerging.  We  also  find  high  fragmentation  in  thematic  areas  in  which  cooperation 
does not belong to the goal functions of the calls. 

  Centralisation:  Network  analysis  calls  “degree”  the  number  of  partners  an 
organisation is connected to. Degree centralization of a network is the variation in the 
degrees  of vertices divided by the maximum degree which is possible in the network 
of the same size. The higher the centralisation, the more dominated the network is by 
a  single  organisation  or  by  a  few  actors  (monopoly-like  distribution  of  linkages). 
Centralisation is also an indicator for the maturity of a technological field (established 
organisations dominate the arena). 

  Modularity:  Indicator  for  the  existence  of  subnetworks  (modules).  Networks  with 
high modularity have dense connections within the sub-networks, but sparse connec-
tions between them. Networks with high/low modularity are called multi peaked/single 
peaked networks. 

  Average Clustering Coefficient: First measure for the amount of clustering. In gen-
eral, clustering refers to the existence of triadic relationships. Triadic means if A coop-
erates with B and A cooperates with C, then A and C are also cooperating (transitive 
relationships).  The  clustering  coefficient  of  an  organisation  indicates  to  which  degree 
the cooperation partners of each actor are connected to each other as well. The table 
lists the average values for the area networks.  

  3-Rings: Second measure for the amount of clustering. It also refers to the existence 
of triadic relationships but takes the number of common partners into account as well. 
A  value  of  20,  for  example,  indicates  that,  on  average,  each  connected  pair  of 
organisations has 20 cooperation partners in common.  

  CSO  percentages:  Percentages  of  CSOs  types  1  –  4  within  the  network  of  each 

thematic area. 

The results for all thematic area networks in detail can be found in the Annex. 

64 

 
6.1.5  The Correlation of Network Parameters and  

CSO Participation 

The  objective  is  to  find  out if  there  are  any  statistical  correlations  between  the  network 
parameters  described  in  chapter  6.1.4  and  the  participation  of  CSOs  in  EU  funded 
projects. Do networks with a higher amount of CSOs have specific characteristics? – The 
analysis takes the following parameters into account: 

(1)  Thematic  area:  How  does  the  content  of  a  thematic  area  influence  the 
participation of CSOs? Are there specific (research) questions and topics that foster 
the  participation  of  CSOs?  And  is  there  a  correlation  between  the  currency  of  an 
area (scientific excellence, benefit, mission) and CSO participation? 

(2)  Share  of  funding:  Is  there  a  relation  between  the  size  of  an  area  and  CSO 

participation? Do we find CSOs in big or in small programmes? 

(3)  Organisations: Is there a connection between network size and CSO participation? 

Do we find CSOs in small (or in big) networks? 

(4)  Lines  and  density:  Is  there  a  correlation  between  network  density  and  CSO 

participation? Is it easier for CSOs to enter less connected networks? 

(5)  Average  degree:  Does  the  mean  number  of  project  partners  have  an  impact  on 

CSO participation? Do we find CSOs in big or in small consortia? 

(6)  Fragmentation: Does the degree of fragmentation influence CSO’s attendance? Is 

it easier for CSOs to join fragmented and less hierarchical networks? 

(7)  Centralisation:  Is  there  a  correlation  between  the  degree  of  link  monopolisation 
and  CSO  frequency?  Is  there  a  higher  probability  to  find  CSOs  in  less  centralised 
networks? 

(8)  Modularity:  Do  we  find  CSOs  in  networks  with  high  modularity, i.e.  multi  peaked 
networks  that  consist  of  densely  connected  sub-networks  that  are  only  loosely 
connected to each other? 

(9)  Average  clustering  coefficient:  Is  there  a  correlation  between  the  amount  of 
triadic  relationships  around  each  organisation  and  CSO  participation?  Do  we  find 
CSOs in less clustered networks? 

(10) Clustering:  Is  clustering  measured  by  3-rings,  the  absolute  amount  of  triadic 

relationships within each area network, an obstacle for the participation of CSOs? 

We correlate each of these parameters with the relative amount of CSOs for FP6, as well 
as for FP7 (see Annex for details and tables). To get a better overview, we transform this 
matrix  into  a  network  graph  (Figure  7).  The  nodes  represent  the  network  parameters, 
lines  indicate  significant  positive  (solid)  and  negative  (dotted)  Pearson  correlation 
coefficients. 

The node for the percentages of CSOs 1 (and for the number of organisations as well) is 
isolated.  This  means  that  there  is  no  significant  correlation  between  the 
participation  of  CSOs  type  1  and  specific  network  parameters  in  FP6.  The 
network structure itself has no impact on the amount of CSOs type 1. 

There  are  two  groups  of  parameters  with  positive  correlations  within  the  groups  and 
negative  correlations  between  the  groups.  The  group  in  the  centre  of  the  network 
consists  of  parameters  that  refer  to  network  size  (funding,  number  of  lines)  and 

65 

 
clustering (average degree, 3-rings, centralization) respectively.  In particular, there is a 
strong correlation between funding and number of links, between funding and clustering 
(measured  by  the  amount  of  triadic  relationships  within  the  network:  the  higher  the 
funding,  the  higher  the  network  clustering),  and  between  funding  and  the  average 
degree  (the  average  number  of  project  partners  each  organisation  has  within  the  area 
network).  More  money  means  more  cooperation,  more  cliques  and  a  higher  number  of 
project partners for each organisation within the network. 

Figure 7: Correlations between network parameters (FP6) 

Continuous lines indicate positive correlations, dotted lines refer to negative correlations. Chart: FASresearch 

The parameters on the right indicate if the networks are single peaked or multi peaked. 
Single  peaked  means  there  is  only  one  core  or  one  centre  within  the  network;  multi 
peaked means that there is more than one core and more than one centre (the network 
of  Figure  7  is  multi  peaked,  as  it  contains  two  clusters  of  parameters).  High  modularity 
and high fragmentation characterise multi peaked networks. A high clustering coefficient 
(the  degree  to  which  project  partners  cooperate  with  each  other)  also  indicates 
modularity because the “modules” emerge through triadic relationships. We can see that 
there  is  a  tension  between  the  clustering  parameters,  on  the  one  hand,  and  the 
parameters  of  multi  peaked  networks,  on  the  other.  If  the  networks  of  the  framework 
programmes  grow  (higher  funding,  more  organisations,  more  linkages),  clustering  also 
increases  (triadic  relationships,  cliques);  new  links  connect  different  sub-networks  and 
close the gaps between them; a single peaked network emerges. 

The  analysis  identifies  the  following  correlations  between  network  parameters 
and CSO participation for the FP6 thematic area networks: 

  Centralisation:  There  is  a  negative  correlation  between  the  participation  of  CSOs 
(types  2,  3,  and  4)  and  network  centralisation.  We  rather  find  these  CSOs  in  less 
centralised networks that are less dominated by a few organisations. 

  Modularity:  There  is  a  positive  correlation  between  modularity  and  the  participation 
of CSOs type 3 and CSOs type 4. We rather find these CSOs in multi peaked networks. 

  Fragmentation:  There  is  a  positive  correlation  between  fragmentation  and  the 
participation  of  CSOs  type  3  and  4.  We  rather  find  these  CSOs  in  networks  with  a 

66 

 
 
higher  degree  of  fragmentation  (networks  with  sub-networks  that  are  not  connected 
to each other: Fragmentation leads to modularity). 

  Average clustering coefficient: There is a positive correlation between the average 
clustering coefficient of the thematic area networks and the percentages of CSOs type 
4. The average clustering coefficient also indicates modularity, the existence of several 
sub-networks with a high amount of internal transitive relationships. 

All  in  all,  there  are  just  a  few  significant  correlations  between  the  network  parameters 
and CSO participation in FP6. For CSOs type 1, we do not find any correlations. For CSOs 
types 2, 3, and 4 we can say that they rather appear in less centralised and multi peaked 
networks.  Diagrams,  which  show  the  correlations  described  above,  can  be  found  in  the 
Annex. 

For  the  7th  framework  programme,  it  is  even  more  difficult  to  detect  significant 
correlations between network parameters and CSO participation. There is only a relation 
between  the  relative  amount  of  CSOs  type  4,  on  the  one  hand,  and  modularity  and 
fragmentation,  on  the  other.  The  two  thematic  areas,  Regions  of  Knowledge  and 
Research for the benefit of SMEs, are leading in terms of CSO type 4 participation (type 4 
represents business oriented CSOs), and they consist of networks with a high amount of 
modularity. 

In  the  case  of  FP7,  we  additionally  ran  a  regression  analysis  (for  FP6  this  procedure 
brought  no  significant  results),  which  indicated  that  there  is  a  significant  co-variance  of 
network  centralisation  (domination  of  a  few  actors  in  terms  of  the  number  of  project 
partners) and CSO participation. In the case of FP7, we find the highest amount of CSOs 
type 1 (“core” CSOs) within Science in Society and in SSH, and both of them consist of 
less centralised networks. These correlations are significant, but weak. 

Our  conclusion is that there are just a  few correlations of network parameters and CSO 
participation.  The  absolute  CSO  participation  is  low  even  in  networks  which  fulfil  the 
criteria  of  these  parameters.  Changes  of  these  parameters  would  change  CSO 
participation to a minor degree. We take it for granted that the network parameters that 
describe  network  size  and  structure,  as  well  as  CSO  participation  itself,  are  not  causes, 
but  effects  of  other  factors,  which  influence  both  network  structure  and  CSO 
participation. 

Finding  6:  CSO  participation  hardly  depends  on  network  morphology.  If  anything,  CSO 
participation  is  –  like  network  morphology  –  an  effect  of  other  influencing  factors, 
especially  of  funding  distribution  and  cyclicity.  We  find  CSOs  involved  more  in  small 
thematic  areas  with  less  centralised  networks  that  have  a  higher  degree  of  modularity 
and specific thematic foci. 

6.1.6  Key Actor Analysis 

6.1.6.1  Network Position of Institution Types 

The  first  task  of  the  key  actor  analysis  is  to  define  the  criteria  for  determining  the  key 
actors.  In  order  to  describe  the  position  of  the  institution  types  (universities,  research 
institutions, companies, CSOs  etc.)  within the overall network, we use the social capital 
concept of brokerage and closure. 

67 

 
 
In terms of network analysis, actors or key players are organisations  that are somehow 
well  embedded  in  the  network  of  organisations  connected  to  each  other  through  EU 
funded  projects.  Network  embeddedness,  in  particular,  refers  to  centrality  measures. 
There  are  different  ways  to  be  central  in  a  network15.  Degree  centrality,  for  example, 
indicates  the  number  of  cooperation  partners  of  each  organisation  (an  indicator  for 
activity).  Closeness  centrality  refers  to  the  distance  each  actor  has  to  cover  to  reach 
every other actor within the network. The smaller this distance, the higher the closeness 
centrality and the better the accessibility of the actor. There are a lot of further centrality 
measures,  but  in  the  end  two  dimensions  of  network  centrality,  or  in  other  words,  of 
social capital, are decisive: brokerage and closure16. 

  Brokerage  means  that  an  organisation  is  connected  to  many  other  organisations 
that  are  not  connected  to  one  another.  Thus,  broker  organisations  bridge  structural 
holes between other actors. This is an advantageous position within a network. The 
hypothesis  is  that  network  actors  that  are  not  connected  to  each  other  differ  in the 
resources they own. If an organisation is connected to disconnected organisations, it 
has access to a more diverse resource portfolio. 

  Closure,  on  the  contrary,  indicates  that  an  organisation  is  connected  to  many 
organisations  that  are  also  connected  to  each  other  (triadic  relationships).  Closure 
capital means that an actor belongs to  one  or  more groups.  The assumption is that 
the  members  of  a  group  are  similar  regarding  the  resources  they  own.  Therefore, 
closure  capital  rather  enables  to  accumulate  and  to  save  resources  than  to  get 
access to new ones. 

Being well embedded means to have both closure and brokerage capital – stabilisation of 
existing  resources  and  efficient  access  to  new  resources.  Organisations  with  a  high 
amount  of  brokerage  and  closure  belong  to  the  cores  of  the  FP  cooperation  networks. 
These  cores  consist  of  structural  folds,  i.e.  of  many  cohesive  groups  whose 
relationships  overlap  with  one  another17.  Structural  folds  reduce  tension  and  close  the 
gap between robust relationships to similar organisations, on the one hand, and efficient 
but vulnerable contacts to different project partners, on the other. 

Social capital is unequally distributed among the different types of institutions within the 
FP networks. We computed brokerage and closure for every organisation18 and added up 
these  two  dimensions  in  a  general  index  for  network  embeddedness.  If  we  sort  the 
organisations  by  this  index  in  descending  order  and  divide  them  into  three  groups,  we 
get  the  organisations  of  the  core  (top  30%),  the  semi-periphery  (40%  in  the  middle), 
and  the  periphery  of  the  networks  (30%  at  the  end  of  the  list).  Table  13  shows  the 
distribution  of  organisation  types  over  the  network  core,  the  semi-periphery,  and  the 
periphery  separately  for  FP6  and  FP7.  The  numbers  are  column  percentages  and  row 
percentages respectively. 

15  Wasserman, S./Faust, K. (1994), pp. 167ff. 
16  Burt, R.S. (2007). 
17  Vedres, B./Stark, D. (2010). 
18  Brokerage is measured by betweenness centrality (Freeman, L.S. [1979]) and closure by the 

two-step Eigenvector Centrality (Bonacich, P. [1972]) within the two-mode networks of 
organisations and projects. 

68 

 
 
                                           
Table 13: Percentages of organisation types within the core, semi-periphery, and 
periphery of the FP networks (FP6 and FP7) 

Deciles based on brokerage and closure capital of organisations in FP6 and FP7. Data source: CORDIS, WU. Analysis and 
table: FASresearch. 

An  example  from  the  first  table:  In  FP6,  54.8%  of  the  universities  are  located  in  the 
network  core,  22.7%  belong  to  the  semi-periphery,  and  22.5%  to  the  periphery.  The 
second  table  tells  us  that  in  FP6,  10.5%  percent  of  all  institutions  are  universities,  but 
within the network core, they account for 19.2%. In FP7, we find almost half (49.2%) of 
the  non-university  research  institutions  in  the  network  core,  29.7%  in  the  semi-
periphery,  and  21.2%  in  the  periphery.  12.5%  of  all  organisations  in  FP7  refer  to  non-
university research, but within the core they add up to 20.5%. 

This  shows  once  more  that  universities  and  non-university  research  institutions  are  the 
driving  forces  behind  the  FP  networks.  The  concentration  increased  from  FP6  to  FP7 
(from 54.6% to 60.3% of the universities in the core, from 45.7% to 49.2% of the non-
university research in the core). 

In  FP6,  more  than  53.2%  of  all  CSOs  of  type 1  (66  out  of  124)  are  located  in  the 
periphery  of  the  network;  only  13.7%  (17  out  of  124)  are  located  in  the  centre,  and 
33.1% (41 out of 124) in the semi-periphery of the FP6 network. All in all, CSOs seem to 
improve  their  network  position  slightly  from  FP6  to  FP7:  We  find  a  higher  amount  of 
them in the semi-periphery (52.8%), the percentage in the centre is stable (15.9%), and 
there is a smaller amount in the periphery (31.3%). 

Furthermore, the numbers indicate that the more business oriented CSOs (types 3 and 4) 
are slightly better embedded: Within FP7 more than 26% of CSOs type 4 are located in 

69 

FP6 column percentagesNetwork areaHESRECPRCPUBCSO1CSO2CSO3CSO4OTHTotalCore54,845,722,229,713,718,119,725,916,330,0Semi-Periphery22,730,544,643,433,145,345,748,944,940,0Periphery22,523,833,226,953,236,534,625,138,830,0Total100100100100100100100100100100FP6 row percentagesDecileHESRECPRCPUBCSO1CSO2CSO3CSO4OTHTotalCore19,225,040,78,70,31,00,63,01,6100,0Semi-Periphery6,012,561,29,60,51,91,04,23,2100,0Periphery7,913,060,67,91,02,01,02,93,7100,0Total10,516,454,88,80,61,70,93,42,9100,0FP7 column percentagesNetwork areaHESRECPRCPUBCSO1CSO2CSO3CSO4OTHTotalCore60,349,223,035,015,915,824,526,118,830,0Semi-Periphery23,129,742,743,552,851,250,547,651,040,0Periphery16,621,234,421,531,333,125,026,330,230,0Total100100100100100100100100100100FP7 row percentagesDecileHESRECPRCPUBCSO1CSO2CSO3CSO4OTHTotalCore16,020,550,58,20,40,70,62,70,4100,0Semi-Periphery4,69,370,47,61,01,70,93,70,9100,0Periphery4,48,875,55,00,81,50,62,70,7100,0Total8,012,566,07,00,71,30,73,10,7100,0 
 
the centre (for CSOs type 1 this number is 15.9%); we find only 25% of CSOs 4 in the 
periphery (compared to 53.2% in case of CSOs 3). 

Finding  7:  Universities  and  non-university  research  institutions  are  the  key  drivers  of 
the  FP  networks.  We  mainly  found  CSOs  in  the  periphery  and  in  the  semi-periphery  of 
the networks. 

Finding 8: The more business oriented CSOs (lobbying groups, type 4) exhibit a better 
network embeddedness than the “core” CSOs (type 1). 

6.1.6.2  Networks with and without CSOs 

To  illustrate  the  position  of  CSOs,  we  compare  the  values  of  different  network 
parameters – how will they change if we remove CSOs from the FP7 network? And how 
will they change if we remove the universities (Table 14). 

Table 14: Selected network parameters: FP7 without universities and without CSOs 

Data source: CORDIS, WU. Analysis and table: FASresearch. 

Removing  the  CSOs  would  mean  removing  2.1%  of  the  organisations  and  1.8%  of  the 
cooperation  relations.  Removing  universities  means  7.8%  fewer  actors,  but  46.8%  less 
linkages,  which  further  underlines  the  central  position  of  universities.  Network  density 
(which is the proportion of existing and possible links) would increase by removing CSOs. 
Without  universities,  we  would  have  37.3%  less  density.  The  average  distance  between 
all  actors  in  the  network  would  not  change  if  there  were  no  CSOs,  but  would  be  9.2% 
larger  without  universities.  The  average  number  of  cooperation  partners  of  each 
organisation  within  the  total  network  is  43.3,  without  CSOs  it  would  be  43.4,  without 
universities  only  25.  Without  CSOs,  the  network  would  be  a  little  bit  more  centralised, 
meaning  they  are  at  the  periphery  of  the  network.  Without  universities,  it  is  less 
centralised,  as  they  are  in  the  core  of  the  network.  Finally,  removing  CSOs  would  not 
change  the  average  number  of  3-rings,  which  indicates  the  amount  of  clustering  (the 
existence  of  cliques).  Without  universities,  there  would  be  52%  less  clustering.  These 
numbers  indicate  that  CSOs  do  not  play  an  important  role  for  the  emergence,  for  the 
connectedness, nor for the stability of the FP7 collaboration network. 

Finding  9:  Removing  CSOs  would  not  change  network  parameters  and  morphology 
(assuming  all  other  things  remaining  the  same).  CSOs  were  “hangers  on”,  they  did  not 
build  sub-clusters  or  bridge  gaps,  and  they  did  not  have  an  important  brokerage 
function. 

70 

 
 
 
 
6.1.7  Key Link Analysis – Connections of CSOs 

6.1.7.1  Duration of Cooperation 

Which kinds of relations are typical for CSOs? At first, we assume that CSOs, in general, 
are  more  involved  in  short-term  projects.  They  participate  in  coordination  and  support 
actions (CSAs) more often than universities, research institutes, or  enterprises; and the 
average  duration  of  CSAs  is  shorter  than  of  collaborative  projects  (28.2  months  vs.  39 
months). They do not participate in ERC projects (with three exceptions) whose average 
duration is 58.2 months. 

Figure 8: FP7 average duration of projects and relative amount of CSO participations (all 
types) 

X-axis: percentages of CSO participations within FP7 thematic areas (all CSO types). y-axis: average duration of projects in 
months. Data source: CORDIS. Chart: FASresearch. 

Figure  8  represents  average  project  duration  and  CSO  participation  in  FP7  thematic 
areas.  We  know  that  CSO  participation is  highest  within  Science  in  Society  and  Regions 
of Knowledge. Regions of Knowledge is about coordination and support actions, and only 
25% of all projects within Science in Society are collaborative research projects. Both of 
them  rather  consist  of  short-term  projects  (average  duration  about  32  months).  Also 
within Research policies (25 months), International cooperation (37,6), and Research for 
the  benefit  of  SMEs  (27  months),  project  duration  is  shorter  and  CSO  participation  is 
higher  than  within  the  research-oriented  thematic  areas.  The  chart  indicates  that  CSOs 
appear more frequently in thematic areas with lower project duration. This could be one 
reason  that  it  is  more  difficult  for  CSOs  to  build  up  long-term  relationships  to  other 
organisations through EU funded projects (high drop-out rate). 

Finding  10:  CSO  participation  was  higher  in  thematic  areas  and  funding  schemes  with 
projects  with  shorter  average  duration.  Thus,  it  was  more  difficult  for  CSOs  to  build  up 
more stable and more continuous relationships. 

71 

 
 
6.1.7.2  Coordination and Participation 

What are the roles and positions of institution types and of CSOs, in particular, within EU 
funded  projects?  Who  is  coordinator  and  who  is  participant?  Who  has  the  lead  if  CSOs 
take part in FP projects? 

In  general,  CSOs  rarely  function  as  coordinators.  Only  6.8%  of  all  CSO  type  1 
participations (22 out of 302) are in a coordination position. This value for universities is 
28.8% (14.220 out of 49.455) and 21.5% (7.081 of 32.875) for non-university research 
institutes. At least 11.2% of all CSO type 4 project participations (196 out of 1.756) are 
in a coordination position. CSOs 2 and CSOs 3 are coordinators only in 9.7% and 8.9% of 
their  project  participations.  Enterprises,  in  general,  exhibit  a  high  number  of  project 
participations, but a low proportion of coordination positions (7.6%). 

Even within their “specific playgrounds”, CSOs rarely act as coordinators. Within Science 
in  Society,  only  2  out  of  183  projects  are  coordinated  by  CSOs  of  type  1  (1,1%),  4.9% 
(=  9  projects)  by  CSOs  type  2,  7  projects  by  CSOs  type  3,  and  6  projects  by  business 
oriented  CSOs.  This  type  of  CSO  coordinates  16.7%  of  all  projects  within  Regions  of 
Knowledge (14 out of 84) and 5.5% (57 out of 1.028) within Research for the benefit of 
SMEs. Within Socio-economic sciences and Humanities, CSOs do not act as coordinators 
at all. 19 

We  can  assume  that  there  is  a  correlation  of  the  coordination  and  the  participation  of 
certain  institution  types.  Depending  on  the  types  of  participants,  certain  types  of 
coordinators  appear.  Table  15  represents  the  relationship  of  coordination  and 
participation  for  the  overall  FP7.  Note  that  the  numbers  indicate  relationships,  rows 
represent  the  institution  types  as  coordinators,  and  columns  represent  institution  types 
as  participants.  For  example,  (first  table),  universities  are  embedded  in  37.646 
relationships,  in  which  they  act  as  coordinators,  and  in  which  the  institutions  they  are 
connected  to  act  as  participants.  There  are  16.956  relations  in  which  universities  are 
coordinators  and  participants.  Within  the  FP7  network,  there  are  132  relations  in  which 
universities are coordinators and CSOs type 1 are participants. To compare the numbers 
for different types, we have to look at the column percentages (second table). 

Table 15: FP7 coordination and participation of institution types (absolute numbers) 

19 See the Annex for a detailed listing of roles and positions of institution types across the thematic 

areas. 

72 

TypeHESRECPRCPUBCSO1CSO2CSO3CSO4OTHTotalHES16.9568.7449.8761.2851321921132826637.646REC10.7749.99311.3382.303731431044539835.279PRC5.9655.40014.0631.0864693995029827.352PUB9461.1297851.111234922110334.208CSO135159581173194CSO280576032923644275CSO361365215210266190CSO436837795520031723193182.154OTH5050453164176200Total35.23525.80137.1836.0683025423771.560330107.398ParticipationCoordination 
 
                                           
Data source: CORDIS. Table: FASresearch. 

If universities are participants, in 48.1% of the relations universities are coordinators as 
well.  If  CSOs  type  1  are  participants,  then  in  43.7%  of  the  cases  universities  are 
coordinators.  Thus,  CSOs  type  1  are  the  institution  type  for  which  universities  as 
coordinators are most important (with the exception of universities themselves). In other 
words, CSOs of type 1 are rather invited to participate by universities. This is because we 
find them in thematic areas in which mainly universities are coordinators. The pattern for 
CSOs  type  2  is  similar  (research  institutes,  enterprises,  public  bodies  and  CSOs  type  4 
are  slightly  more  important  as  coordinators  for  them  than  universities).  For  the  more 
business oriented CSOs and lobbying groups (CSOs types 3 and especially type 4), which 
we  rather  find  in  thematic  areas  specific  to  companies,  it  is  the  companies  that  are 
important  as  coordinators.  If  a  CSO  type  4  is  a  participant,  the  probability  that  an 
enterprise  is  coordinator  is  32.2%  (research  institutes:  29%,  universities:  18.1%,  and 
CSO type 4 themselves 12.4%). 

Finding  11:  CSOs  rarely  acted  as  coordinators.  It  hardly  depended  on  the  type  of 
coordinator  if  a  CSO  was  invited  to  participate  in  a  project  or  not.  If  a  thematic  area 
addressed  excellent  research,  the  number  of  universities  and  research  institutes  was 
above  average,  and  these  types  of  institutions  then  coordinated  the  projects.  If  a 
thematic  area  was  designed  for  the  private  sector,  companies  more  often  acted  as 
coordinators.  In  the  first  case,  CSOs  were  rather  “invited”  by  universities  or  research 
institutes. In the second case, it was the enterprises that asked for CSOs to take part in 
EU funded projects. 

6.2 Publication Analysis 

The first part of the network analysis of our study described the network indicators of the 
European  FP  networks  and  their  correlation  with  CSO  participation.  The  following  part 
refers to the analysis of the research indicators, i.e. of the scientific output of EU funded 
projects and the involvement  of CSOs in this output activities. For this purpose,  we use 
OpenAIRE publication data. 

6.2.1  Data Base 

The OpenAIRE team provided publication data in October 2015. All in all, they included a 
list of 166.250 unique publications. 166.110 of them are connected to 11.796 unique IDs 
of  EU  funded  FP7  projects.  Our  project  network  data  set  includes  25.238  FP7  projects. 
Thus, we have publication data for 46,7% of the projects in our network data set. Certain 
funding schemes produce more publications than others. Above all, the amount of  ERC-
projects  and  of  Collaborative  projects  (CPs)  is  higher  within  the  publication  data  than 

73 

TypeHESRECPRCPUBCSO1CSO2CSO3CSO4OTHTotalHES48,133,926,621,243,735,430,018,120,035,1REC30,638,730,538,024,226,427,629,029,732,8PRC16,920,937,817,915,217,226,332,229,725,5PUB2,74,42,118,37,69,05,87,110,03,9CSO10,10,10,00,12,62,01,90,20,30,1CSO20,20,20,20,53,04,21,60,31,20,3CSO30,20,10,10,20,71,80,50,41,80,2CSO41,01,52,63,31,03,16,112,45,52,0OTH0,10,20,10,52,00,70,30,41,80,2Total100100100100100100100100100100ParticipationCoordination 
 
within the overall project data (28% vs. 18% and 34.5% vs. 27.5%). On the other hand, 
just  5%  of  projects  with  publications  are  CSAs  (Coordination  and  support  actions), 
whereas  their  total  amount in  CORDIS  is  10%.  This is important  because  we  know  that 
CSO participation is higher among CSAs than among CPs or ERC projects.  

The  European  Research  Council  projects,  Information  and  Communication  Technologies, 
and  Health  are  the  thematic  areas  with  the  highest  number  of  publications,  followed  by 
the  Marie-Curie  Actions,  Environment,  Research  Infrastructures,  and  Environment.  Our 
first  impression  is  that  there  are  fewer  publications  in  areas  with  a  (relatively)  high 
amount  of  CSO  participation.  Figure  9  illustrates  this  correlation:  Within  Science  in 
Society, CSOs (types 1, 2, and 3) are involved in 42.1% of the projects, but only 0,1% of 
the  publications  in  our  OpenAIRE  data  set  originate  from  Science  in  Society.  Within  the 
European  Research  Council,  on  the  other  hand,  only  2  of  4.523  projects  (=  0,04%) 
happen with CSO participation, but 36.4% of the OpenAIRE publications derive from ERC. 

Figure 9: FP7 thematic areas by CSO participation and percentages of publications within 
OpenAIRE data. 

Data sources: OpenAIRE, CORDIS, WU. Chart: FASresearch. 

Looking at the diagram we can propose that it is the Health area that exhibits a number 
of projects with CSO participation and a publication output above average. 

Finding  12:  With  regard  to  publication  output,  Health  and  ICT  are  by  far  the  most 
important thematic areas (they dominate the ERC funded projects as well). 

Finding 13: The publication output is dominated by thematic areas, in which the amount 
of projects with CSO participation is rather low. 

74 

 
 
 
 
 
 
The main questions of the analysis of the publication data are: How can we describe the 
contribution of CSOs to the publication output of EU funded projects? In which areas do 
we find the highest amount of scientific output? And how important is the participation of 
CSOs for these areas? 

We  use  the  All  Science  Journal  Classification  (ASJC)  to  describe  the  scientific  fields  in 
which  the  publication  output  takes  place.  ASJC  is  provided  by  the  SCImago  Journal  & 
Country  Rank  portal20.  There  is  an  important  limitation  regarding  the  OpenAIRE  data: 
They do not include the information about the organisations of the authors (universities, 
institutes,  companies  etc.).  Therefore,  we  cannot  estimate  the  “real”  contribution  of 
CSOs to publishing results of EU funded projects. However, we know which FP7 projects 
led to publications, and we know  whether CSOs participated in these projects. All in all, 
the ASJC Journal Classification contains 307 different classes of scientific disciplines in 24 
different  fields  (for  example,  Agricultural  and  Biological  Sciences,  Arts  and  Humanities, 
Biochemistry,  Genetics  and  Molecular  Biology,  Business,  Management  and  Accounting, 
etc.).  These  classes  are  added  to  the  Journals  to  describe  the  main  content  of  the 
publications that are published within them. This is the second limitation of the OpenAIRE 
data: The ASJC classes do not refer to the content of the specific publication, but rather 
to  the  Journal in  which  the  article  was  published.  Thus,  we  have  to  combine  three  data 
sets to determine the research performance of CSOs in EU funded projects: CORDIS data 
with  FP7  projects  and  participants,  OpenAIRE  data  with  publications  (including  the 
CORDIS  project-IDs  of  the  FP7  projects,  to  which  the  articles  belong,  and  the  ISSN 
International Standard Serial Number of the journal in which the article was published), 
and  the  SCImago  data  which  add  the  ASJC  scientific  disciplines  to  the  journals  (Figure 
10). 

Figure 10: Combining different data sources to generate FP7 publication networks 

Chart: FASresearch. 

The  CORDIS  data  include  the  information  about  projects  (thematic  area,  funding, 
duration etc.) and about the participating organisations (universities, research institutes, 
public  bodies,  CSOs…).  The  OpenAIRE  data  contain  the  articles  (tiltle,  publication  date, 
name  of  the  author,  journal,  ISSN  number),  including  the  CORDIS  project  IDs  through 
which  we  can  connect  them  to  the  CORDIS  database.  Additionally,  we  have  the  list  of 
journals and the ASJC classes, which we add to the other data sets via the ISSN number 
of the journals. Journal data (SCImago) were not available for all of the publications. In 
the  end,  we  have  a  comprehensive  data  set  containing  83.677  unique  articles  with 

20  SCImago. (2007). SJR — SCImago Journal & Country Rank. Retrieved July , 2016, from 

http://www.scimagojr.com  

75 

 
 
                                           
information  about  the  FP7  projects  and  about  the  scientific  disciplines  of  the  journals  in 
which they were published. 

6.2.2  Core Areas of Scientific Output 

Figure  11  shows  in  which  scientific  disciplines  the  articles  appear  and  through  which 
thematic area they were funded. 

Figure 11: Network of FP7 thematic areas and ASJC disciplines 

Data source: CORDIS, OpenAIRE. Analysis and visualisation: FASresearch. Arcs lead from thematic areas to scientific 
disciplines and indicate that both are connected through at least 500 publications which derived from EU funded projects 
within the particular thematic area. 

For  example,  in  the  field  of  Genetics  2.238  articles  were  published  that  were  funded 
through projects in the FP7 Health area. Only links which represent at least 500 articles 
are depicted. We see that ERC, Health, Marie-Curie Actions, and ICT produce the highest 
number  of  articles.  It  is  also  visible  (at  this  highly  aggregated  level)  that,  on  the  one 
hand,  there  are  area-specific  disciplines  that  are  only  connected  to  one  thematic  area 
(e.g., articles in the field of Cellular and Molecular Neuroscience only arise in the Health 
theme),  and,  on  the  other  hand,  we  have  disciplines  that  connect  different  areas. 
However,  it  is  remarkable  that  the  areas  are  highly  disconnected.  Articles  that  were 
funded by Health projects are linked to different disciplines than articles that were funded 
within  the  ICT  theme.  We  can  also  see  the  disciplines  that  dominate  the  publications  of 
the ERC and the Marie Curie-Action projects. 

6.2.3  Scientific Output and CSO Participation 

Now  we  combine  the  three  data  sets  (publications,  scientific  disciplines,  and  projects) 
once more and determine the amount of CSO participation in the different scientific fields 
in  which  the  articles  were  published.  Note  that  the  numbers  in  Table  16  indicate 
publications (for example, all in all we identified 21 different publications in the discipline 
Physics  and  Astronomy  with  the  participation  of  at  least  one  CSO1),  but  multiple 

76 

 
 
mentions  are  possible  (one  article  can  be  assigned  to  more  than  one  discipline).  And 
there  is  another  limitation:  We  do  not  know  if  the  CSOs  themselves  participated  in 
writing the article, we only know that the article was funded by an FP7 project in which at 
least one CSO 1 participated. 

Table 16: Scientific disciplines and types of institutions by number of publications 
(absolute numbers). 

Multiple mentions possible. Data source: OpenAIRE, CORDIS, WU. Computation and table: FASresearch. 

We find the highest numbers of publications with (at least indirect) CSO1 participation in 
disciplines which belong to the Health area: Medicine (461); Biochemistry, Genetics, and 
Molecular Biology (177), and Neuroscience (119). Delving deeper into the CORDIS data, 
we  see  that  in  many  cases  these  are  charities,  patient  organisations,  and  self-help 
groups in the fields of human medicine and diseases. The organisations with the highest 
number  of  projects  that  lead  to  publications  are:  Age  Platform  Europe,  Foundation  for 
Innovative  New  Diagnostics,  Autism  Europe,  The  European  Foundation  for  Clinical 
Nanomedicine, AbilityNet, and Skalbes (a Crisis Intervention Centre in Latvia). For CSOs 
type 2 (besides Medicine) disciplines show up which refer to Decision Sciences (238) and 
Environmental  Sciences  (235).  The  most  important  CSOs  here  are  the  Stichting 
Nederlands  Instituut  Voor  Beeld  En  Geluid,  ICLEI  European  Secretariat  GmbH,  the 
Smithsonian  Institution,  Centro  de  Referencia  em  Informacao  Ambiental,  Species  2000, 
Fondazione  Per  Adroterapia  Oncologica  –  Tera,  Fundacion  Phantoms,  and  the  Stichting 
Wetlands International. Publication numbers are again higher for business oriented CSOs 
(types 3 and 4). They take part in projects in the thematic areas  that refer to economy 
and business and address companies and enterprises (Research for the benefit of SMEs, 
ICT,  etc.),  and  the  articles  are  published  in  the  fields  of  Physics  and  Astronomy; 
Engineering;  Medicine;  Biochemistry,  Genetics  and  Molecular  Biology;  and  Agricultural 
and Biological Sciences. 

In determining the amount of CSO participation within each scientific field (Table 17), we 
see that in the Medicine category only 1.2% of the articles were funded by FP7 projects 
with CSO type 1 participation. The relative number is highest within the Nursing category 

77 

ASJC scientific areaHESRECPRCPUBCSO1CSO2CSO3CSO4OTHTotalPhysics and Astronomy22.77717.3306.9831.3192112919960226649.626Biochemistry, Genetics and Molecular Biology17.02114.05910.1373.05317717719445310145.372Medicine13.11011.0638.5164.23946136527846712438.623Materials Science14.27110.0196.26798516836041010132.212Engineering11.0838.3006.8561.15331667550810128.173Decision Sciences9.5717.2182.8082.41853238761784822.608Computer Science8.1065.9544.89087342861142488220.395Agricultural and Biological Sciences6.1565.1653.3101.844621511813577817.304Chemistry6.7014.7013.1968251830602176315.811Environmental Science5.0534.3813.0491.84396235912685215.068Immunology and Microbiology4.3924.0042.9141.4499646632132513.202Chemical Engineering3.8972.7022.06629562510136369.173Pharmacology, Toxicology and Pharmaceutics2.6472.2081.942912692153213158.080Neuroscience3.2162.4281.571462119324610197.984Social Sciences2.0821.15573833156643259164.533Energy1.1701.0239051663171659163.375Psychology87648324076767911.705Nursing501442395161268394061.618Health Professions4083322657703221231.131Economics, Econometrics and Finance482226111605710174922Arts and Humanities534172502493010793Business, Management and Accounting24917414571663396699Veterinary5452471600020171Dentistry15121410000042Total134.372103.60367.41522.6531.3791.8271.6094.6091.153338.620 
 
 
(1.6%),  but  this  concerns  only  26  articles.  Nevertheless,  the  relative  numbers  show  us 
that  if  there  are  any  publications  in  which  CSOs  participate,  then  this  happens  in  the 
fields  of  human  medicine  and  diseases  in  many  cases.  For  the  business  oriented  CSOs 
and lobbying organisations, Business, Management and Accounting is the most important 
category. 

Table 17: Scientific disciplines and types of institutions by number of publications (row 
percentages). 

Multiple mentions possible. Data source: OpenAIRE, CORDIS, WU. Computation and table: FASresearch. 

Table 18: Scientific disciplines and types of institutions by number of publications 
(column percentages). 

Multiple mentions possible. Data source: OpenAIRE, CORDIS, WU. Computation and table: FASresearch. 

78 

ASJC scientific areaHESRECPRCPUBCSO1CSO2CSO3CSO4OTHTotalPhysics and Astronomy45,934,914,12,70,00,30,41,20,5100,0Biochemistry, Genetics and Molecular Biology37,531,022,36,70,40,40,41,00,2100,0Medicine33,928,622,011,01,20,90,71,20,3100,0Materials Science44,331,119,53,10,00,30,21,30,3100,0Engineering39,329,524,34,10,10,20,31,80,4100,0Decision Sciences42,331,912,410,70,21,10,30,80,2100,0Computer Science39,729,224,04,30,20,40,61,20,4100,0Agricultural and Biological Sciences35,629,819,110,70,40,91,02,10,5100,0Chemistry42,429,720,25,20,10,20,41,40,4100,0Environmental Science33,529,120,212,20,61,60,61,80,3100,0Immunology and Microbiology33,330,322,111,00,70,30,51,60,2100,0Chemical Engineering42,529,522,53,20,10,30,11,50,4100,0Pharmacology, Toxicology and Pharmaceutics32,827,324,011,30,90,30,72,60,2100,0Neuroscience40,330,419,75,81,50,40,61,30,1100,0Social Sciences45,925,516,37,31,21,40,71,30,4100,0Energy34,730,326,84,90,10,50,51,70,5100,0Psychology51,428,314,14,50,40,40,40,50,1100,0Nursing31,027,324,410,01,60,52,42,50,4100,0Health Professions36,129,423,46,80,02,80,21,10,3100,0Economics, Econometrics and Finance52,324,512,06,50,50,81,11,80,4100,0Arts and Humanities67,321,76,33,01,10,40,00,10,0100,0Business, Management and Accounting35,624,920,710,20,90,90,45,60,9100,0Veterinary31,630,427,59,40,00,00,01,20,0100,0Dentistry35,728,633,32,40,00,00,00,00,0100,0Total39,730,619,96,70,40,50,51,40,3100,0ASJC scientific areaHESRECPRCPUBCSO1CSO2CSO3CSO4OTHTotalPhysics and Astronomy17,016,710,45,81,57,112,413,123,114,7Biochemistry, Genetics and Molecular Biology12,713,615,013,512,89,712,19,88,813,4Medicine9,810,712,618,733,420,017,310,110,811,4Materials Science10,69,79,34,31,24,53,78,98,89,5Engineering8,28,010,25,12,23,64,711,08,88,3Decision Sciences7,17,04,210,73,813,04,73,94,26,7Computer Science6,05,77,33,93,04,77,15,47,16,0Agricultural and Biological Sciences4,65,04,98,14,58,311,27,76,85,1Chemistry5,04,54,73,61,31,63,74,75,54,7Environmental Science3,84,24,58,17,012,95,75,84,54,4Immunology and Microbiology3,33,94,36,47,02,53,94,62,23,9Chemical Engineering2,92,63,11,30,41,40,63,03,12,7Pharmacology, Toxicology and Pharmaceutics2,02,12,94,05,01,13,34,61,32,4Neuroscience2,42,32,32,08,61,82,92,20,82,4Social Sciences1,51,11,11,54,13,52,01,31,41,3Energy0,91,01,30,70,20,91,01,31,41,0Psychology0,70,50,40,30,50,30,40,20,10,5Nursing0,40,40,60,71,90,42,40,90,50,5Health Professions0,30,30,40,30,01,80,10,30,30,3Economics, Econometrics and Finance0,40,20,20,30,40,40,60,40,30,3Arts and Humanities0,40,20,10,10,70,20,00,00,00,2Business, Management and Accounting0,20,20,20,30,40,30,20,80,50,2Veterinary0,00,10,10,10,00,00,00,00,00,1Dentistry0,00,00,00,00,00,00,00,00,00,0Total100,0100,0100,0100,0100,0100,0100,0100,0100,0100,0 
 
 
 
Table  18  indicates  which  scientific  fields  are  most  important  for  each  category  of  CSO. 
One  third  of  all  publications  with  (at  least  indirect)  CSO  1  participation  are  assigned  to 
the  Medicine  category.  For  CSOs  type  2,  this  number  is  20%,  followed  by  Decision 
Sciences  (13%),  and  Environmental  Science  (12.9%).  Articles  of  projects  with  the 
participation  of  CSOs  type  3  are  related  to  Medicine  (17.3%),  Physics  and  Astronomy 
(12.4%),  Biochemistry,  Genetics,  and  Molecular  Biology  (12.1%).  For  CSOs  of  type  4 
Physics  and  Astronomy  (13.1%),  Engineering  (11%),  and  again  Medicine  (10.1%)  are 
most important. 

Although  these  are  interesting  cases,  organisations  and  fields,  the  numbers  of 
publications that were funded by FP7 projects with the participation of CSOs are marginal 
when  we  compare  it  to  the  publication  numbers  of  universities,  research  institutes,  and  
companies. Furthermore, the scientific fields in our publication data with a higher relative 
amount of CSO participation refer to smaller thematic areas (less funding, less projects, 
less  organisations).  Figure  12  shows  this  correlation.  It  represents  the  network  of 
scientific  disciplines  (ASJC  classes  at  the  4-digit  level)  and  their  connections  through 
publications  that  are  derived  from  EU  funded  projects.  Disciplines  can  be  connected 
because  articles  were  published  in  journals  with  more  than  one  class  or  in  different 
journals.  We  can  treat  disciplines  that  are  linked  to  each  other  as  similar  regarding  the 
knowledge  they  produce.  The  size  of  the  classes  refers  to  the  number  of  articles  in  the 
years  2007  –  2014  that  were  assigned  to  them.  The  colours  indicate  the  amount  of 
project  participations  of  CSOs  types  1,  2,  and  3  (i.e.  without  the  genuine  business 
oriented  CSOs)  within  the  projects  that  produced  publications  in  the  particular  scientific 
field. The maximum value for CSO participation is 4.3% in the category “Tourism, Leisure 
and Hospitality Management” with 11 articles. 

We can easily identify two big clusters: technical and engineering sciences on the left and 
life sciences on the right. The classes with the biggest numbers of articles are Condensed 
Matter  Physics;  Astronomy  and  Astrophysics;  and  Electronic,  Optical  and  Magnetic 
Materials on the right and Molecular Biology, Biochemistry, and Genetics on the left. It is 
typical that the scientific discipline, which mainly connects technical and life sciences, is 
Computer  science  applications.  Both  technical  and  life  sciences  need  the  mathematical 
and computer sciences for methods and techniques of analysis. 

All  in  all,  the  percentages  of  CSO  participations  are  low  –  the  average  value  for  the 
disciplines within the network is 0.46%. But we can see that CSO participation is a little 
bit higher among the classes within the life science sector than among the classes within 
the  technical  sciences  sector.  This  corresponds  to  the  result  that  the  Health  thematic 
area is the only theme with publication output and CSO participation above average. Life 
science  categories  with  a  relatively  high  CSO  participation  are  Health  Policy, 
Developmental  Neuroscience,  Complementary  and  alternative  medicine,  and 
Anaesthesiology and Pain Medicine. Note that these are “small” categories with respect to 
the number of articles. 

79 

 
 
Figure 12: Network of ASJC-disciplines (All Journal Science Classification) which are connected through EU funded publications 

Data source: OpenAIRE. Analysis and graphic: FASresearch. 

80 

 
 
 
Beyond  the  two  clusters  of  technical  and  life  sciences,  there  are  disciplines  at  the 
periphery  of  the  network  which  exhibit  the  highest  amount  of  CSO  participation.  They 
belong  to  the  economical  sciences,  to  the  social  sciences,  and  to  the  humanities.  The 
ones with the highest amount of CSO participations are Tourism, Leisure and Hospitality 
Management  (11  articles/4.3%  CSO  participation);  Business,  Management  and 
Accounting (7/3.8%); History (215/1.9%), and Cultural Sciences (95/1.8%). 

Finding  14:  The  network  of  scientific  disciplines  derived  from  publications  is  driven  by 
two  thematic  cores:  engineering  and  computer  sciences,  on  the  one  hand,  and  human 
medicine and life sciences, on the other. In both of them, CSO participation is rather low. 

The  results  so  far  seem  to  demonstrate  that  CSOs  do  not  participate  in  EU  funded 
projects  with  a  high  publication  output.  Research  and  publishing  research  results  does 
not  seem  to  be  the  main  goal  of  CSOs  when  they  take  part  in  EU  funded  projects.  Our 
next step is to show that this correlation  – more projects with higher publication output 
and lower CSO participation – is significant. 

Table 19: Numbers of projects with/without CSO participation (all types) and 
with/without publications 

Data sources: CORDIS, WU, OpenAIRE. Table: FASresearch. 

Table 19 represents a cross tabulation with the number of projects  that were conducted 
with or without the participation of CSOs (all types) and which led to publications or not. 
The  first  table  includes  the  observed  numbers  of  projects.  All  in  all,  we  have  25.238 
projects  in  our  FP7  data  set.  There  are  12.186  projects  (48,3%)  without  any  CSO 
participation and without publication output. We have identified 11.135 projects (44.1%) 
with  publications  but  without  CSOs.  In  1.256  projects  (5%)  CSOs  took  part  but  no 
publications  were  written.  And  finally,  there  are  661  projects  (that  is  2.6%  of  all  FP7 
projects) with CSOs and with publications. 

The second table contains the values we would expect if there was a random distribution 
of projects. Based on the given distribution of projects with and without publications and 
CSOs  respectively,  we  would  expect  896  projects  with  CSO  participation  and  with 
publication  output.  But  we  only  have  observed  661,  that  is  35.6%  less  than  expected. 
And the chi-square test shows that this divergence is significant. There is a significant 
negative  correlation  of  CSO  participation  and  publication  output.  One  important 
reason might be that CSOs are more engaged in coordination activities than in research 
projects (see Table 12), and coordination activities have a lower publication output than 
research  projects.  The  average  number  of  publications  per  project  among  CSAs  is  8.2; 
among CPs the number is 20.8. 

81 

NoYesTotalObservedNo12.18611.13523.321valuesYes1.2566611.917Total13.44211.79625.238NoYesTotalExpectedNo12.42110.90023.321valuesYes1.0218961.917Total13.44211.79625.238Chi square0,00CSOparticipationCSOparticipationPublicationsPublications 
 
Finding  15:  There  is  a  significant  (statistical,  not  causal)  negative  correlation  of  CSO 
participation and publication output across EU funded projects. We rather found CSOs in 
thematic areas with a smaller number of  publications. Scientific excellence  – and thus a 
high publication output – is not the objective of CSOs when they take part in EU funded 
projects. 

Finding 16: Our conclusion is that CSOs do not have an impact on research performance 
as measured by publication output. The participation of CSOs in EU funded projects does 
not  increase  the  number  of  scientific  articles.  On  the  contrary,  we  rather  find  them  in 
projects that do not generate, or generate fewer, publications. One reason is that CSOs 
take part in more coordination projects than in research projects. Research and scientific 
excellence  are  not  the  main  goal  functions  of  CSOs  when  they  take  part  in  EU  funded 
projects. They play a different role in the innovation process. 

6.3 Media Analysis 

6.3.1  Background and Data Base 

We  consider  publicity  and  media  exposure  as  important  currencies  of  civil  society 
organisations.  If  it  is  true  that  the  main  goal  of  CSOs  in  FPs  is  not  scientific  excellence 
(as it is for universities and research  institutions) and not applicability, sales, and profit 
(as it is for companies), but finding solutions for societal problems, they need to be well-
known and raise awareness for the problems they deal with. For this reason, we tried to 
determine the presence of CSOs in media reports of newspapers and magazines (print as 
well  as  online)  about  EU  funded  projects.  We  used  international  media  data  bases  (e.g. 
Factiva) as data sources. Within the content they provide, we identified articles about EU 
funded  projects  with  specific  search  terms.21  The  period  of  investigation  was  from 
2007/01/01  until  2015/12/31.  For  this  period,  we  detected  a  total  number  of  7.456 
articles  that  contained  the  search  terms  mentioned  above.  From  these,  we  took  a 
random  sample  of  2.570  articles  for  an  in-depth  qualitative  analysis.  We  went  through 
each article and identified the EU funded project it pertained to, recorded the institutions 
that  were  mentioned  and  classified  them  (universities,  research  institutes,  companies, 
public  institutions,  CSOs,  and  others),  and,  finally,  we  determined  the  topic  of  the  EU 
funded  project  that  was  reported  in  the  article.  Regarding  these  topics,  we  took  the 
target  classification  of  the  EU  Sustainable  Development  Strategy  (EU  SDS)  and  added 
additional  categories  when  necessary.  A  certain  number  of  articles  in  our  data  set  deal 
with  EU  funded  projects  outside  of  the  Framework  Programmes;  nevertheless,  they  are 
important for our analysis, and the SDS target classification, plus additional categories, is 
more suitable to describe the content of the articles and projects – that is why we did not 
use the thematic areas of the CORDIS database. 

21  "eu project" or "eu funded project" or "project funded by the eu" or "european union funded project" or "eu 
research project" or "european union research project" or "h2020 project" or "fp7 project" or "horizon2020 
project" or "horizon 2020 project" or "seventh framework programme project" or "7. framework programme 
project". 

82 

 
 
 
                                           
6.3.2  Types of institutions mentioned in media reports  

After data cleaning, we could refer to a data set with 864 articles about FP projects and 
1.475  articles  about  EU  funded  projects  outside  the  FPs  (INTERREG,  European 
Neighbourhood  and  Partnership  Instrument,  European  Development  Fund,  European 
Regional  Development  Fund,  etc.).  Within  these  articles  we  found  5.004  unique 
institutions  mentioned by  name.  The  type  with  the  highest  number  is  public institutions 
(41,3%),  followed  by  companies  (25,8%),  universities  (14%),  CSOs  (12,9%),  and 
research institutes (4,7%); the rest is others (1,2%).  

Table 20: Numbers of institutions mentioned in media reports about EU funded projects 
(FP related or not) 2007 - 2015 

Table: FASresearch 

Within  the  articles  reporting  on  FP  projects,  companies  dominate  the  arena  (42,4%), 
followed by universities (26,9%), public institutions (15,3%), research institutes (8,7%), 
and  CSOs  (6,1%).  We  can  compare  these  numbers  with  the  percentages  of  institutions 
within  our  FP7  CORDIS  data  set  (not  listed  in  the  table).  Within  the  media  reports 
universities  and  public  institutions  are  overrepresented  (universities:  26,9%  vs.  8%; 
public institutions: 15,3% vs. 7%). There is a similar amount of CSOs in both of the data 
sets (media reports: 6,1%; FP7: 5,8%).  There are fewer companies (42,4% vs. 65,9%) 
and fewer non-university research institutes (8,7% vs. 12,5%) in the media reports than 
in  the  CORDIS  project  data.  Therefore,  the  articles  about  FP  projects  –  like  the 
projects  themselves  –  are  dominated  by  universities  and  companies  (by  the 
currencies of scientific excellence and profit). 

When  it  comes  to  media  reports  about  EU  funded  projects  outside  the  Framework 
Programmes, media exposure of CSOs (and of public institutions as well) is much higher. 
These  projects  rather  belong  to  the  spheres  of  regional  cooperation  or  development  aid 
than  to  R&I.  More  than  16%  of  all  institutions  mentioned  in  the  articles  about  projects 
outside the FPs are CSOs, compared to 6% in FP related articles. The projects outside 
of  FPs  seem  to  address  the  interests,  needs,  and  skills  of  CSOs  rather  than 
projects that are essentially scientific. 

Finding 17: Within the articles about FP projects the presence of CSOs is as marginal as 
in the FP projects themselves. 

Finding 18: Media exposure of CSOs is much higher in articles about EU funded projects 
outside  of  the  FPs.  If  we  take  media  exposure  as  an  indicator  for  CSO  participation, 
programmes  and  funds  like  the  European  Development  Fund,  the  European  Regional 
Development  Fund  or  INTERREG  seem  to  address  the  needs  and  capabilities  of  CSOs 
more than the research or business oriented Framework Programmes. 

83 

TypeNumber%Number%TotalUniversities53226,92918,9823Non-university research1728,7852,6257Companies83742,449315,11.330Public institutions30215,31.80555,52.107CSOs1216,153016,3651Others110,6511,662Total1.9751003.2551005.230FP relatedNot FP related 
 
 
6.3.3  Topics of Media Reports 

The  topics  of  projects  are  different  when  we  differentiate  between  FP  projects  and 
projects outside of FPs. 28.7% of articles about FP projects refer to Health. Health is the 
most  important  topic  of  articles  about  FP  funded  projects  in  our  data  set,  followed  by 
Science,  innovation,  and  technology  in  general  (21.3%),  and  Climate  change  and  clean 
energy (17.7%). 

Finding  19:  The  most  important  topics  of  media  reports  about  EU  funded  projects  are 
Health,  Innovation  and  Science 
in  general,  Climate  Change,  Information  and 
Communication Technologies, and Natural Resources. 

Topics  of  articles  about  EU  funded  projects  outside  of  FPs  are  different.  The  most 
important topic is Promoting sustainable regional development (26.4%), Social inclusion, 
demography,  and  migration  (20.8%),  and  Promoting  and  strengthening  SD  governance 
(18.5%).  Within  these  fields  we  will  find  a  higher  amount  of  CSOs  mentioned  in  the 
articles. 

Finding  20:  Media  reports  about  EU  funded  projects  outside  of  the  FPs  mainly  refer  to 
Promoting  sustainable  regional  development;  Social  inclusion,  demography,  and 
migration;  Promoting  and  strengthening  SD  governance;  Climate  Change,  and  Natural 
Resources. 

6.3.4  Types of Institutions and Topics 

Our next step is to combine these dimensions of information – kinds of projects, types of 
institutions,  and  topics.  We  have  seen  that  universities  and  research  institutes  appear 
more often in articles about FP projects, whereas CSOs and public institutions dominate 
the media reports about EU funded projects outside of the Framework Programmes. We 
can  assume  that  this  correlates  with  the  specific  topics  of  FP  projects  and  non-FP 
projects. 

  We  know  that  –  regarding  media  reports  –  Health  is  the  most  important  topic  of  FP 
projects  for  universities.  26.5%  of  all  cases,  in  which  universities  show  up,  refer  to 
Health  projects.  Health  is  followed  by  Science/Innovation/Technology  (14.2%),  ICT 
(12.4%),  Conservation  and  management  of  natural  resources  (11.8%),  and 
Sustainable consumption and production (9.3%). When it comes to articles about non-
FP  projects  with  university  participation,  the  most  important  topic  is  Promoting 
sustainable 
inclusion, 
regional  development  (13%),  Health  (12.3%),  Social 
demography,  and  migration  (11.3%),  Conservation  and  management  of  natural 
resources (9.9%), and Climate change and clean energy (8.2%).  

  Articles  about  FP  projects  with  company  participation  mostly  refer  to  Science, 
Innovation  &  Technology  (16.5%),  Climate  change  and  clean  energy  (15%),  Health 
(14.2%),  ICT  (13.9%),  and  Sustainable  Transport  (11.7%).  Media  reports  about  EU 
funded  projects  outside  of  the  FPs,  in  which  companies  are  namely  mentioned,  deal 
with  Climate  change  and  clean  energy  (17.3%),  Promoting  sustainable  regional 
development  (12.9%),  Conservation  and  management  of  natural  resources  (8.5%), 
Science/Innovation/Technology (7.8%), and Sustainable Transport (7.2%). 

  Let  us  see  in  which  media  reports  the  CSOs  appear.  The  most  important  topic  of 
articles  about  EU  funded  projects,  in  which  CSOs  are  mentioned,  is  Social  inclusion, 
demography,  and  migration.  28.2%  of  all  articles  with  CSOs  belong  to  this  thematic 

84 

 
area. But the importance of this topic only results from its importance within the non-
FP projects (only 5.2% of the FP related articles refer to Social inclusion). The pattern 
is similar for the next two topics: Promoting sustainable regional development (26.6% 
of  all  articles  in  which  CSOs  are  mentioned)  and  Promoting  and  strengthening  SD 
governance  (17.9%)  get  their  importance  from  non-FP  projects.  The  fourth  topic 
Conservation and management of natural resources, however, is equally important in 
both  FP  and  non-FP  projects  with  CSO  participation  (16,5%  and  16%  respectively). 
And  the  fifth  topic  is  Health  (15%),  which  is  the  most  important  topic  within  the 
articles  about  FP  projects  with  CSO  participation.  If  there  are  CSOs  mentioned  in 
media reports about FP projects, the topics are similar to the topics of FP projects in 
general  –  Health,  Science/Innovation/Technology  in  general,  ICT,  Conservation  and 
management of natural resources, and Climate change and clean energy. 

If  we  compute  the  differences  of  the  percentages  for  articles  about  projects  inside  and 
outside  the  FPs,  we  can  identify  the  topics  that  are  overrepresented  within  the  articles 
about FP projects and the ones that are overrepresented within the articles about non-FP 
projects respectively – with CSO participation in both cases (Figure 13). 

Figure 13: Topics of projects with CSO participation in media reports – comparison 
inside/outside FPs 

Data base: FP and non FP related articles in which CSOs are mentioned (N = 523). Diagram: FASresearch 

In general, media exposure of CSOs is higher in articles about EU funded projects outside 
of  FPs.  In  these  articles,  the  following  topics  are  important:  Sustainability,  Regional 
development,  Social  inclusion,  Global  poverty,  and  Preserving  of  historic  environment 
and national heritage. In articles about FP funded projects with CSO participation, health, 
science and innovation, in general, and ICT are topics with an above average frequency. 

85 

 
 
Figure  14  shows  the  five  most  important  topics  of  each  institution  type  as  a  network 
graph.  Grey  nodes  indicate  institution  types;  arcs  lead  to  the  topics  (grey  nodes),  and 
the  strength  and  the  darkness  of  the  arcs  depend  on  the  percentages  of  articles  that 
refer  to  the  institution  type  and  the  topic.  We  can  easily  identify  two  clusters,  meaning 
that  there  are  institution  types  with  typical  topics.  On  the  left  there  is  the  cluster  with 
companies, universities, and research institutes. It represents the currencies of scientific 
excellence  and  profit.  Typical  topics  of  this  cluster  are  Health,  ICT,  and  Science  and 
innovation  in  general.  On  the  right  we  find  a  cluster  that  connects  politics  and  civil 
society through their topics Sustainable regional development, Sustainable development 
governance,  and  Social  inclusion.  There  are  at  least  two  topics  that  connect  these  two 
clusters: Health and Conservation and Management of natural resources. The structure of 
this  network  indicates  that  the  currencies  of  the  scientific  field  (excellence),  and  of  the 
economic  field  (profit),  differ  from  the  currency  of  the  civil  society  (problem  solving, 
awareness). 

Figure 14: Top 5 topics of each institution type in media reports about EU funded 
projects (FP and non FP related) 

Data base: FP and non FP related articles (N = 2.339). Visualisation: FASresearch. 

Finding  21:  Within  the  media  reports  about  EU  funded  projects,  different  types  of 
organisations  typically appear  together  with  specific  topics.  Both  organisation  types  and 
topics  represent  the  three  currencies  of  innovation:  Science  (universities  and  research 
institutions/basic research in general and health); economy (companies/ICT, energy and 
transport),  and  civil  society  (CSOs/social  inclusion,  sustainable  regional  development, 
and SD governance). 

Finding 22: There are typical topics of articles about EU funded projects in which CSOs 
participate.  Topics  which  support  the  participation  of  CSOs  are  most  notably 
sustainability,  regional  development,  international  development  and  cooperation,  social 
inclusion, demography, migration, and poverty. 

86 

 
 
  
 
 
7  Simulation of policy interventions 

This  section  describes  the  agent-based  simulation  model  that  was  developed  in  the 
course  of  the  project  and  presents  the  results  from  several  scenarios  that  were 
simulated.  A  detailed  technical  description  of  the  simulation  model  is  included  in  the 
Annex to this report. 

7.1 Overview of the simulation model 

An agent-based model (ABM) was developed for the purpose of this study, simulating the 
Framework  Programmes  (FPs)  and  their  participating  organisations  in  terms  of  call 
design, consortium formation, proposal evaluation, and project implementation. The core 
of  the  simulation  model  lies  in  consortium  formation,  i.e.  the  process  by  which  a 
coordinator searches for project partners with respect to a call for proposals that requires 
specific (combinations of) skills.  

As  a  product  of  this  “formation”  phase,  the  consortium  produces  a  proposal  that  is 
evaluated based on its expertise levels in the required skills, its management aspects, as 
well  as  other  eligibility  conditions  (e.g.  requested  budget,  number  of  partners  and 
country distribution). The following sub-sections briefly summarise the main elements of 
the  model;  a  more  comprehensive  (technical)  description  of  the  model  explaining  each 
element in detail is included in the Annex to this report. 

7.1.1  Scope of the model 

The  ultimate  aim  of  the  model  was  to  simulate  the  participation  of  Civil  Society 
Organisations  (CSOs)  in  the  FPs.  Since  “CSO”  is  not  a  pre-defined  category  for 
organisations  participating  in  the  FPs,  it  was  necessary  to  first  define,  characterise  and 
identify  them  in  the  previous  FPs  (FP6  and  FP7)  and  then  to  analyse  their  participation 
patterns;  the  results  of  these  tasks  are  documented  in  the  previous  chapters  of  this 
report.  Based  on  these  analyses,  the  scope  of  the  model  was  defined  so  as  to  simulate 
the top-down defined, collaborative parts (“themes”) of the FPs. With respect to FP7, 
this  includes  the  “Cooperation”  programme  and  some  parts  of  the  “Capacities” 
programme,  but  excludes  the  European  Research  Council  (ERC;  referred  to  as  “Ideas” 
programme  in  FP7)  and  the  Marie  Skłodowska-Curie  actions  (MCSA;  referred  to  as 
“People”  programme  in  FP7).  With  respect  to  Horizon  2020  (H2020),  this  includes  the 
“societal challenges” pillar, a major part of the “industrial leadership” pillar, as well as a 
small part of the “excellent science” pillar.  

7.1.2  Organisations 

All organisations are modelled as nodes of a social network graph. They are categorised 
according  to  the  organisation  typology  developed  for  the  purpose  of  this  study, 
distinguishing  between  universities  (HES),  research  organisations  (REC),  companies 
(PRC), public bodies (PUB), other (OTH), as well as the four CSO-categories (see section 
4.3 above for an overview of the organisation types used in this study). 

Each organisation also has a network position attribute – big, medium or small – that is 
calculated  based  on  this  organisation’s  encounter  probability  (a  measure  derived  from 
the social network analysis; see section 6.1.2 above for an explanation). 

87 

 
7.1.3  Skill vectors 

A  central  element  of  the  simulation  model  is  the  different  skills  of  the  participating 
organisations,  upon  which  they  form  consortia  and  upon  which  their  proposals  are  then 
evaluated.  Skills  have  names  combined  with  respective  levels  of  expertise.  In  order  to 
simulate consortium formation and proposal evaluation, skills need to be defined for both 
organisations (so that they can contribute their expertise to a consortium) and  topics 
(so that proposals can be evaluated according to the skills required by the topic). 

Two  different  domains  of  skills  are  used  in  the  model:  knowledge  and  impact. 
Knowledge  refers  to  scientific  disciplines  according  to  the  Scopus  classification  of 
subject categories (also called minor subject areas in Scopus22). Impact refers to the 169 
targets  of  the  Sustainable  Development  Goals  adopted  by  the  United  Nations  in 
201523.  

There are several reasons why a distinction between knowledge and impact makes sense 
for simulating CSO participation in the FPs. Most importantly, knowledge in the sense of 
scientific excellence is not a “currency” according to which CSOs act (as has been shown 
in the previous chapters); instead, their motivation stems from creating societal impact, 
i.e.  solving  societal  problems.  Moreover,  excellence  and  impact  are  two  separate 
evaluation categories for FP proposals; an analysis of evaluation scores in FP7 proposals 
has  revealed  that  there  exists  no  correlation  between  the  scores  received  for  the  two 
criteria  (r  =  0.12).  We  can  expect  CSOs  to  mainly  contribute  to  a  consortium’s  impact 
score, whereas their contribution to excellence would be negligible. 

Skills  also  represent  resources  that  an  organisation  can  contribute,  thus  representing 
staff capacity. Consequently, in the model, each skill has a certain capacity, which can be 
reserved,  resulting in a  utilization of that skill. In other words, an  organisation can only 
work on so many proposals and projects at the same time as given by its staff capacity. 
Big  organisations  –  as  defined  by  their  size  –  can  consequently  participate  in  several 
proposals  and  projects  at  the  same  time,  whereas  small  organisations  are  only  able  to 
participate in one or few activities. 

Skill  vectors  for  topics  and  organisations  were  generated  using  two  sources.  The 
knowledge-vector  was  generated  based  on  actual  publication  output  of  FP7  projects,  as 
documented  in  the  OpenAIRE  database  (see  section  6.2.1  above).  The  impact-vector 
was generated building on previous work of the contractor for DG Research & Innovation 
in the course of the FP7-4-SD.eu project24, by linking FP7 topics with the 169 targets of 
the  UN  Sustainable  Development  Goals  (SDGs).  This  “linking”  was  done  based  on  the 
same  cross-referencing  methodology  developed  for,  and  applied  in,  the  FP7-4-SD.eu 
project25 (see www.fp7-4-sd.eu; also see Dimitrova et al., 2015). 

22  Scopus, ‘Content Coverage Guide’, January 2016, available at: 

https://www.elsevier.com/__data/assets/pdf_file/0007/69451/scopus_content_coverage_guide.
pdf, section 4.4, p.21, accessed 7 July 2016. 

23  See https://sustainabledevelopment.un.org/sdgs; also see United Nations (2015), ‘Transforming 

our world: the 2030 Agenda for Sustainable Development’, available at: 
https://sustainabledevelopment.un.org/post2015/transformingourworld, accessed 7 July 2016. 

24  Project “Monitoring system for FP7 contribution to sustainable development”, contract no. 30-

CE-0249018/00-13. 

25  See https://www.fp7-4-sd.eu/index.php?request=public:page:default&page=about#context  

88 

 
                                           
While  the  two  data  sources  give  a  realistic  picture  in  terms  of  knowledge  and  impact 
skills,  it  needs  to  be  acknowledged  that  due  to  data  gaps  not  all  organisations  actually 
participating  in  FP7  can  be  covered  by  this  approach:  knowledge-vectors  can  only  be 
assigned to organisations participating in topics with publication output (this is the case 
for  slightly  more  than  three  quarters  of  organisations);  impact-vectors  can  only  be 
assigned to organisations participating in topics with SDG-relevance (this is the case for 
about two-thirds of organisations). 

Organisations participating more frequently in FP7 are more likely to participate in a topic 
related to publication output and/or SDG relevance; this tends to be the case for bigger, 
more central actors, and, vice versa, organisations participating only once (or few times) 
are more likely to not be equipped with skill vectors that would allow them to participate 
in  the  simulation  game.  This  tends  to  be  the  case  for  smaller,  more  peripheral  actors, 
such as public bodies (PUC), CSOs and other organisations (OTH), which consequently do 
not participate in the simulation.  

Looking  at  total  numbers,  FP7  data  contain  about  23,000  unique  organisations 
participating  in  the  themes  simulated  in  the  model  (see  section  7.1.1  above).  In 
comparison,  the  dataset  used  for  the  simulation  contains  about  20,000  organisations 
having knowledge- and/or impact-vectors, i.e. about 3,000 – mainly small and peripheral 
–  organisations  less.  However,  as  shown  in  section  7.1.8  below,  this  has  no  major 
consequences for the simulation results. The skill vectors used in the present model are 
quite  similar  to  what  others  call  kene  vectors  (see  e.g.  Ahrweiler,  Pyka  and  Gilbert, 
2004),  albeit  with  the  addition  of  an  explicit  resource  aspect  (i.e.  capacity,  utilization). 
Another difference lies in the fact that we do not only model knowledge, but also impact, 
hence, the terminus skill vector to differentiate. 

7.1.4  Call design 

The  topics  fed  into  the  model  in  order  to  stimulate  consortium  formation  are  based  on 
actual FP7 topics published between 2007 and 2013. These have been transformed into 
skill  vectors  for  each  topic  (as  described  in  section  7.1.3  above),  representing 
knowledge- and impact-requirements of this topic.  

To “steer” the model in view of simulating different scenarios, the “steering  parameters” 
listed in Table 21 below can be used to operate the list of topics fed into the simulation. 
This can be done on the theme-level (for influencing the theme’s budget, topic size, and 
number  of  projects  per  topic)  and  on  the  overall  programme  level  (for  evaluation 
weights).  

89 

 
 
 
 
Table 21: Parameters for scenarios 

Parameter 

Level 

Characteristics 

No. of topics 

Theme  Multiplier, influencing the number of topics called for per 

theme (and thus also the budget allocated to the theme, as 
each topic already has its budget “attached” to it) 

Topic size 

Theme  Multiplier, influencing the size of the topics in terms of 

budget (increasing topic size without correspondingly 
reducing the number of topics leads to an increase in the 
theme’s budget; increasing topic size without adjusting the 
number of projects per topic leads to larger projects) 

No. of projects 
per topic 

Theme  Multiplier, influencing the number of projects funded per 

topic (increasing the no. of projects without adjusting topic 
size will lead to smaller projects) 

Evaluation 
weights 

Overall  Factor specifying the weights of the evaluation criteria 

excellence, impact and management; the weighting can be 
set separately for each criterion 

7.1.5  Participation 

Participation in a proposal is represented by an edge (i.e. a link) between an organisation 
and a consortium. In that, a participant is either a participant or coordinator, receiving a 
certain  budget.  Furthermore,  organisations  contribute  a  certain  skill  (knowledge  and/or 
impact).  All  these  contributions  are  aggregated  in  the  consortium‘s  knowledge  and 
impact vectors, as is the requested budget. 

Consortia  are  linked  to  their  respective  topic  by  an  edge  of  type  evaluation.  For  the 
latter, the score of the proposal (as outcome of the evaluation) and the funding decision 
(accept  or  reject)  of  the  evaluation  phase  is  stored.  A  successful  consortium  has  the 
linked  evaluation  status  accepted  and  an  additional  attribute  duration  that  turns  the 
consortium into a project. After the project ends (duration), organisations of the project 
are  linked  by  a  relationship  edge,  which  carries  an  attribute  weight,  reflecting  the  total 
number of joint project participations. 

7.1.6  Simulation 

The simulation uses the social network graph as input and simulates its evolution over a 
specified time interval in discrete steps of one month. For the purpose of this study, the 
timing  observed  for  FP7  was  followed,  resulting  in  topics  being  generated  over  a 
simulated  period  of  7.5  years  (i.e.  90  months).  The  simulation  stops  after  all  projects 
have  been  terminated.  The  input  of  the  simulation  model  consists  of  the  basic  graph,  a 
list of topics to be generated, and evaluation weights (see section 7.1.4 above). In case 
that  no  basic  graph  exists,  the  data  of  project  participations  from  FP6  and  FP7  is 
imported and the calibration is initiated. Thereafter, the base graph and the list of topics 
to be generated are loaded and the supplied evaluation weights are read. 

In  every  simulation  step  (i.e.  month),  consortia  are  formed  for  all  new  topics, 
establishing proposals. Each proposal is evaluated and the consortium is either disbanded 
or  a  project  is  funded.  Finally,  projects  that  have  achieved  their  specified  duration  are 
terminated, knowledge is transferred (“learning”) and project partners are linked. 

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7.1.7  Output 

The output of the simulation comprises a set of key performance indicators, including: 

  Number of consortia (created, eligible, rejected, shortlisted, funded) 

  Numbers  and  shares  of  organisations  (unique)  and  budget  (EC  contribution),  per  Or-
gType: HES, REC, PRC, PUB, OTH, CSOs (separately and grouped into “CSO1-3” (i.e. 
excluding business-oriented CSOs) and “CSO1-4”) 

  Network measures (see section 6.1.4 above for an explanation): 

  Per theme: lines, density, average degree, centrality, 3-rings 

  Per organisation type (HES, REC, PRC, etc.): brokerage & closure 

  Skill  vectors  per  organisation  (knowledge  and  impact),  allowing  aggregation  per  or-

ganisation type and for the whole network 

  Budget  per  organisation  (aggregated  over  all  projects),  allowing  analysis  of  regional 

budget distribution 

Additionally, the complete graph at the end of a simulation run is exported, to allow for 
further, in-depth analyses on the level of the individual organisations. The results of the 
various  simulation  runs,  based  on  above-mentioned  performance  indicators  and  on 
further in-depth analyses, are presented in section 7.3 below. 

7.1.8  Calibration and validation of the model 

An important step in developing the agent-based simulation model was its calibration, in 
view  of  producing  realistic  results.  This  comprised  two  main  aspects:  calibrating  the 
proposal  evaluation  approach  in  the  model  based  on  the  generated  skill  vectors,  and 
calibrating the consortium formation process. 

Calibration of the model with respect to the proposal evaluation process was based on in-
depth  analyses  of  the  generated  skill  vectors  for  individual  topics  and  organisations. 
Additionally,  a  “simulation”  of  the  evaluation  phase  was  performed  using  the  software 
package R, by aggregating the generated skill vectors for actually formed  FP7 proposals 
(for  which  data  were  available)  and  then  calculating  the  proposal  score  and  the 
respective  decision  of whether  a  proposal  would  be  approved  or  rejected.  A  comparison 
with  actual  funding  decisions  for  submitted  FP7  proposals  (as  documented  in  CORDA) 
showed  that  the  proposed  approach  –  based  on  the  generated  skill  vectors  and  the 
evaluation  method  included  in  the  model  –  was  able  to  reproduce  72%  of  the  actual 
funding decisions in FP7. 

Calibration of the model with respect to consortium formation was based on an in-depth 
analysis  of  the  structural  characteristics  of  consortia  at  the  social  network  level.  This 
referred  to  the  decision  of  coordinators  to  coordinate  a  project,  the  choice  of  partners 
(which and how many partners  to involve), the choice  of budget distribution among the 
partners, the project duration,  etc. Based on these analyses,  respective probabilities  for 
each component were derived and implemented in the model. 

Validation  of  the  model  was  based  on  comparing  a  “baseline  scenario”,  referring  to  a 
“business-as-usual”  continuation  of  FP7  (with  the  same  parameter  settings),  against 
actual FP7 statistics. Table 22 below shows an overall good “fit” of the simulation results 
with  actual  FP7  figures,  in  particular  concerning  projects,  budget,  and  budget  shares  of 

91 

 
the  different  organisation  types.  Slight  discrepancies  are  visible  with  regards  to  the 
(unique) numbers of organisations, which are related to the fact that the simulation over-
emphasizes bigger organisations (as explained in section 7.1.3 above). These differences 
do  not  influence  the  reliability  of  the  model  as  concerns  the  other  indicators;  however, 
they need to be kept in mind when interpreting the shares of organisation types (based 
on unique numbers) from the simulation runs in relation to actual FP7 figures. 

Table 22: Comparison of baseline scenario with FP7 data 

Parameter 

FP7 data* 

Simulation (baseline scenario) 

Projects 

Budget 

Shares of 
OrgTypes 

Budget shares 
of OrgTypes 

7,680 

28.2 billion 

HES: 9% 

REC: 14% 

PRC: 63% 

PUB: 8% 

CSOs: 6% 

OTH: 1% 

HES: 36% 

REC: 29% 

PRC: 30% 

PUB: 4% 

CSOs: 1% 

OTH: 0% 

7,613 

28.1 billion 

HES: 11% 

REC: 17% 

PRC: 62% 

PUB: 6% 

CSOs: 3% 

OTH: 0% 

HES: 35% 

REC: 31% 

PRC: 29% 

PUB: 4% 

CSOs: 1% 

OTH: 0% 

Note: * “FP7 data” refers to the themes simulated in the agent-based model only  
(to allow comparison with the simulation results). 

Another validity check  of the model involved sensitivity tests by simulating a number of 
preliminary  test  scenarios  featuring  different  parameter  settings  regarding  project  size, 
evaluation  weights  and  budget  distribution  between  themes.  For  the  latter,  both  more 
moderate  and  more  radical  budget  shifts  were  assumed.  About  20  preliminary  test 
scenarios were simulated, and their  results were compared with  the “business as usual” 
(baseline) scenario. An analysis of these 20 simulation test runs is included in the Annex 
to this report.  

These  simulation runs  did not only lead to refinements of the  model, but also helped in 
developing four main scenarios (described below) that were simulated in view of deriving 
policy recommendations for the study. 

92 

 
 
 
 
7.2 Simulated scenarios 

Four  main  scenarios  were  developed  referring  to  four  different  strategic  orientations  of 
the FP. They reflect significant but realistic changes in the FP’s structure and budget and 
feature  distinct  combinations  of  the  model’s  steering  parameters  in  terms  of  budget 
distribution to themes, project size and evaluation weights. 

The four main scenarios are: 

  M.1:  “Competitiveness  and  high-tech”,  strongly  promoting  the  H2020  pillar  of 
industrial  leadership  and  the  more  technological  themes  of  the  societal  challenges 
pillar; 

  M.2:  “Social  cohesion  and  well-being”,  strongly  promoting 

the  socio-
environmental  parts  of  the  societal  challenges  pillar  and  the  cohesion  aspects  of 
H2020 by enhancing the parts Science with and for Society and Regions of Knowledge; 

  M.3:  “Fortress  Europe”,  assuming  an  encapsulation  of  Europe  from  other  parts  of 
the  world  and  thus  focusing  on  security-related  aspects,  such  as  the  themes  Health, 
Energy and Security; and 

  M.4:  “FP  rollback”,  simulating  a  dramatic  cut  of  the  FP  budget  for  collaborative 
research  by  about  two  thirds  in  favour  of  other  spending  (such  as  significantly 
increasing  the  European  Research  Council’s  (ERC)  budget)  or  due  to  an  overall 
reduction in the EU budget. 

Figure 15 below shows the budget distribution across themes for the four main scenarios. 
The FP7 baseline scenario is included to allow comparison of the simulated budget shifts 
in relation to FP7.  

In addition to the budget shifts, the scenarios also apply different settings  regarding the 
project  size  (larger  projects  for  “Competitiveness  and  high-tech”  and  “Fortress  Europe” 
vs.  smaller  projects  for  “Social  cohesion  and  well-being”)  and  evaluation  weights 
(stronger  weight  of  criterion  “excellence”  for  “Competitiveness  and  high-tech”  vs. 
stronger weight of criterion “impact” for “Social cohesion and well-being”). The exception 
is “FP rollback”, where project size and evaluation weights were not changed (see bottom 
part of Figure 15 below).  

Additionally, both “Competitiveness and high-tech” and “Social cohesion and well-being” 
feature an increase in their total budget by about 20%. In this sense they can be seen as 
reflecting opposite strategic orientations of the FP – in the one case putting the focus on 
technological  advancement  with  the  aim  of  increasing  the  competitiveness  of  European 
industry,  and  in  the  other  case  strongly  promoting  processes  through  which  European 
(civil)  society  would  have  a  much  stronger  role  in  the  FPs,  thus  contributing  to  social 
cohesion aspects. 

93 

 
 
Figure 15: Budget distribution (EUR billion) and evaluation weights for main scenarios 

94 

evaluation weightsevaluation weightsevaluation weightsevaluation weightsevaluation weights- excellence1- excellence3- excellence1- excellence2- excellence1- impact1- impact1- impact3- impact2- impact1- management1- management2- management2- management1- management1project sizeproject sizeproject sizeproject sizeproject size- same as FP7- larger projects- smaller projects- larger projects- same as FP7 
 
 
7.3 Analysis of scenarios 

The simulation runs of the scenarios described above were analysed  in terms of the key 
performance indicators listed in section 7.1.7 above.  

7.3.1  Overview across different scenarios 

The  figures  below  provide  an  overview  of  the  results  from  simulating  the  four  main 
scenarios  in  terms  of  budget  shares  of  the  different  organisation  types  (Figure  16),  the 
total budget received by those organisations (Figure 17), the shares of organisation types 
based  on  unique  numbers  (Figure  18),  as  well  as  the  actual  (unique)  numbers  of 
organisations per type (Figure 19). 

In  terms  of  budget  shares  of  the  different  organisation  types,  the  scenarios  M.1 
“Competitiveness & high-tech” and M.2 “Social cohesion & well-being” stand out, showing 
considerably different budget distributions than the other scenarios. Scenario M.2 shows 
the highest budget share for CSOs, with 1.3%. Scenario M.1 also shows a higher budget 
share for CSOs, with 1.2%. However, the two scenarios differ in terms of budget shares 
for  business-oriented  CSOs  (CSO4)  and  the  society-oriented  CSOs  (CSO1-3),  with  M.1 
showing a higher share for the business-oriented CSO4 group.  

Figure 16: Budget shares of different organisation types across the main scenarios 

The budget figures shown in Figure 17 reflect the total budgets set for the scenarios (see 
Figure 15) and the budget distribution by organisation type (see Figure 16).  

95 

 
 
Figure 17: Budget to the different organisation types across the main scenarios (EUR billion) 

In  terms  of  shares  of  the  different  organisation  types  based  on  counting  unique 
organisations  (see  Figure  18),  scenario  M.4  “FP  rollback”  shows  a  higher  share  of 
universities (HES) than all other scenarios; it also shows the smallest share of companies 
(PRC)  and  the  highest  share  of  private  research  organisations  (REC).  Scenario  M.2 
“Social  cohesion  &  well-being”  shows  a  considerably  higher  share  of  CSOs  (almost  5%) 
than the other scenarios, being more than 50% above the share of CSOs in the baseline 
scenario. 

Finding  23:  Scenario  “Social  cohesion  &  well-being”  shows  the  strongest  effects  on 
promoting CSO participation in the FPs. 

Figure 18: Shares of different organisation types (unique numbers) across the main scenarios 

In  terms  of  numbers  of  organisations  participating  in  projects,  Figure  19  shows  the 
expected  results  based  on  the  settings  applied  in  the  scenarios  (see  Figure  15  above), 

96 

 
 
 
i.e.  in  total  higher  numbers  of  organisations  in  scenarios  M.1  and  M.2,  and  lower 
numbers  in  scenario  M.4.  The  differences  between  M.1  “Competitiveness  &  high-tech” 
and M.2 “Social cohesion & well-being” are due to differences in project size; while in M.1 
larger  projects  were  funded,  M.2  funded  considerably  smaller  consortia  and  therefore 
also  shows  a  much  higher  number  of  projects  (almost  20,000  projects  –  this  is  almost 
three  times  as  much  projects  as  in  the  baseline  scenario).  Scenario  M.2  also  shows  the 
by far highest numbers of CSOs; in particular, numbers of society-oriented CSOs (CSO1-
3) are twice as high as in the baseline scenario. 

Figure 19: Numbers (unique) of different organisation types across the main scenarios 

Comparing  Figure  19  and  Figure  17,  it  is  interesting  to  note  that  while  in  scenario  M.2 
“Social cohesion & well-being” there are only about 20% more universities (HES) than in 
the  baseline  scenario,  their  budget  increases  by  almost  two  thirds.  Moreover,  while 
society-oriented CSOs (CSO1-3) double their numbers in scenario M.2 compared with the 
baseline  scenario,  the  increase  in  their  budget  is  even  stronger  (+125%).  As  concerns 
scenario  M.1  “Competitiveness  &  high-tech”,  a  similar  effect  can  be  observed  for 
companies  (PRC)  and  the  industry-oriented  CSOs  (CSO4):  while  their  numbers  only 
increase by about 10%, their budget grows by more than 50%. 

7.3.2  Summary of findings per scenario 

In the following the key findings from the four main scenarios (in relation to the baseline 
scenario)  are  summarised  based  on  the  figures  shown  above  and  based  on  further 
simulation results such as numbers of proposals and projects. 

Scenario M.1 “Competitiveness & high-tech” 

M.1  can  be  interpreted  as  the  industry-oriented  scenario,  with  companies  (PRC)  having 
the highest budget share (34%; slightly more than universities (HES) with 33%). Despite 
the much higher overall budget, the number of funded projects is about 10% lower than 
in  the  baseline  scenario,  which  can  be  explained  by  larger  project  sizes.  The  number  of 
submitted proposals does not differ significantly compared with the baseline scenario.  

97 

 
 
The  slight  increase  in  the  number  of  organisations  by  about  7%  is  mainly  due  to  more 
companies  (PRC)  participating.  In  relative  terms,  also  industry-oriented  CSOs  (CSO4) 
and other organisations (OTH) show increases of more than 10%. 

Scenario M.2 “Social cohesion & well-being” 

M.2 can be interpreted as the scenario promoting universities (HES) and society-oriented 
CSOs,  in  particular  CSO1  and  CSO2.  It  is  also  characterised  by  a  dramatically  higher 
number  of  projects  (almost  three  times  more)  and  proposals  (almost  two  times  more) 
than  in  the  baseline  scenario.  It  consequently  also  shows  a  much  higher  number  of 
organisations (+20%), with the highest increases (in relative terms) for CSO1 and CSO2 
of  more  than  +100%.  Looking  at  all  CSOs  (CSO1-4),  scenario  M.2  shows  the  by  far 
highest shares of CSOs in terms of numbers (almost 5%) and budget (1.3%).  

Scenario  M.2  is  also  showing  the  strongest  effects  regarding  concentration  effects  and 
capacity  building  of  the  participating  organisations  (see  analyses  on  “concentration 
effects” and “learning” further below).  

Scenario M.3 “Fortress Europe” 

Despite  the  considerable  budget  shifts  in  favour  of  the  themes  HEALTH,  ENERGY  and 
SECURITY  (see  Figure  15),  scenario  M.3  shows  the  least  differences  to  the  baseline 
scenario across all simulated scenarios in terms of budget and organisations. It features 
about  15%  fewer  projects  (and  a  similarly  lower  amount  of  proposals),  mainly  due  to 
larger project sizes as compared with the baseline scenario.  

Although the total number of organisations is virtually the same, public bodies (PUB) and 
citizen-oriented CSOs (CSO1) show increases of about 7%, which are likely related to the 
strong  promotion  of  the  themes  HEALTH  and  SECURITY.  At  the  same  time,  however, 
scenario  M.3  shows  the  smallest  budget  share  for  CSOs,  with  CSO1-4  having  a  budget 
share of less than 0.9%. 

Scenario M.4 “FP rollback” 

The budget cut applied for scenario M.4 (see Figure 15) by about two thirds results in a 
correspondingly lower number of projects and proposals. This scenario consequently also 
features  a  much  lower  number  of  participating  organisations  (-37%),  mainly  due  to 
significantly  fewer  companies  (PRC),  but  also  CSOs  (CSO1-4)  and  other  organisations 
(OTH),  all  of  which  show  reductions  of  more  than  40%  compared  with  the  baseline 
scenario.  This  can  be  explained  by  small  organisations  (SMEs,  but  also  CSOs  and  OTH) 
dropping out of the programme, while only the larger ones remain as project participants 
(see the analysis on “concentration effects” below).  

In  this  sense,  universities  (HES)  are  least  affected  by  the  budget  cut  simulated  in 
scenario  M.4,  with  their  numbers  only  falling  by  about  20%  (and  their  share  of 
organisation types consequently increasing to 15%, as shown in Figure 18 above). Under 
the assumption that the budget cut simulated in scenario M.4 is due to a correspondingly 
major  increase  of  the  budget  for  the  European  Research  Council  (ERC)  –  a  programme 
clearly  dominated  by  universities  –  this  would  lead  to  a  strong  (double)  promotion  of 
universities (HES) in the whole Framework Programme over other organisation types.  

98 

 
7.3.3  Effects of the main scenarios on network parameters 

The  four  main  scenarios  also  differ  in  terms  of  their  effects  on  key  network  parameters 
(which stem from the Social Network Analysis; see chapter 6.1.4 above). 

  Scenario  M.1  “Competitiveness  &  high-tech”  shows  a  much  lower  network 
density, as well as lower avg. degree, centralisation and clustering compared with the 
baseline  scenario.  These  effects  are  due  to  the  moderately  larger  number  of 
organisations  combined  with  a  considerably  smaller  number  of  (larger)  projects, 
resulting in less collaboration opportunities and consequently a lower average number 
of cooperation partners, as well as less triadic relationships. 

  Scenario  M.2  “Social  cohesion  &  well-being”  also  shows  a  much  lower  network 
density  and  lower  centralisation,  due  to  the  much  larger  number  of  organisations 
participating in this scenario. In contrast to M.1, however, M.2 features a  higher avg. 
degree  and  higher  clustering,  indicating  stronger  collaboration  relationships  (more 
cooperation  partners  per  organization,  more  triadic  linkages)  between  participating 
organisations  (in  particular  in  the  theme  “Regions  of  Knowledge”).  This  can  be 
explained by the significantly higher number of consortia, with almost three times as 
many projects as in the baseline scenario. 

  Scenario  M.3  “Fortress  Europe”  shows  a  similar  pattern  as  M.1,  with  all  network 
parameters being slightly lower than in the baseline scenario. Similar to M.1, this is a 
result  of  fewer  (but  larger)  projects  being  funded  in  this  scenario,  while  the  total 
number of participating organisations does not change. 

  Scenario M.4 “FP rollback” shows the effects as expected from its settings: a much 
higher density and also higher centralisation. As shown in Figure 19 above and Figure 
20  below,  this  scenario  features  a  significantly  lower  number  of  participating 
organisations, with especially small, peripheral actors dropping out of the programme. 
With  only  large  organisations  –  and  disproportionally  more  universities  (HES)  – 
remaining  in  the  network,  the  network  is  much  more  densely  connected  and  more 
dominated by a smaller number of actors. 

Table  23  below  gives  an  overview  of  the  network  effects  of  the  four  main  scenarios.  It 
shows  that  in  particular  scenario  M.2  “Social  cohesion  &  well-being”  leads  to  beneficial 
network  effects,  with  collaborations  being  more  equally  distributed  among  the 
participating  organisations  (less  centralisation),  but  at  the  same  time  leading  to  more 
and  stronger  collaboration  patterns  (higher  average  degree,  more  3-rings),  which 
increase the overall stability of the network. 

Table 23: Effects of main scenarios on the overall network  

“Competitivene
ss & high-tech” 

“Social cohesion 
& well-being” 

“Fortress 
Europe” 

“FP rollback” 

Density 

Avg. Degree 

Centralisation 

3-Rings 

-- 

- 

- 

- 

-- 

+ 

- 

+ 

- 

- 

- 

- 

++ 

+ 

Note: a single + or - means a change of between 5% and 20%; a ++ or -- means a change of more than 20%; empty cells 
mean no change or a change of less than 5%. 

99 

 
 
   
 
   
 
7.3.4  Differences between the main scenarios in terms of 

concentration effects 

Concentration  effects  can  be  analysed  in  terms  of  (a)  shares  of  organisations  by  size 
(big,  medium,  small),  (b)  budget  shares  of  the  central  players  in  the  network  (top  10 
organisations),  and  (c)  the  regional  distribution  of  budget,  in  particular  between  “old” 
and “new” Member States. 

Figure  20:  Differences  in  shares  of  organisations  by  size  (big,  medium,  small)  across  the  main 
scenarios relative to the baseline scenario (percentage points difference) 

Figure  20  shows  the  differences  in  the  shares  of  organisations  (based  on  unique 
numbers) by size (big, medium, small) across  the four main scenarios in relation to the 
baseline  scenario.  Organisation  size  is  determined  by  the  encounter  probability,  a 
network measure used in the social network analysis (see section 6.1.2 above). 

Scenarios M.1, M.2 and M.3 all feature lower shares  of big organisations compared with 
the  baseline  scenario,  in  favour  of  higher  shares  for  medium-sized  and  small 
organisations. The differences are most distinct in scenario M.2 “Social cohesion & well-
being”,  with  big  organisations  having  a  6  percentage  points  lower  share  than  in  the 
baseline  scenario,  and  in  particular  medium-sized  organisations  gaining  shares.  This 
indicates less concentration on the big players in this scenario in favour of more diversity 
of participating organisations. 

In  contrast,  scenario  M.4  “FP  rollback”  shows  strong  concentration  effects  on  the  big 
organisations,  whose  share  increases  by  more  than  10  percentage  points.  As  already 
indicated above, this is due to medium-sized and small organisations dropping out of the 
programme  due  to  the  dramatic  budget  reduction,  making  the  big  organisations  even 
more dominant in the network. 

Looking  at  the  very  centre  of  the  network,  the  top-10  organisations  are  very  stable 
across all simulation runs. Five organisations rank among the top-10 across all simulation 
runs;  the  remaining  five  places  in  the  top-10  are  shared  by  a  total  of  eight  further 
organisations across the scenarios. This indicates that the composition of the very core of 
the network is only slightly affected by the different scenarios.  The  situation is however 
different  when  looking  at  the  budget  share  of  the  top-10  organisations  (see  Figure  21 
below). 

100 

 
 
Figure 21: Budget share of the top-10 organisations across the main scenarios 

The differences in terms of shares of big organisations are to some extent also reflected 
in the budget shares of the top-10 organisations in the network (see  Figure 21). Similar 
to the real situation in FP7, the top 10 organisations (representing less than 0.1% of all 
participating  organisations)  receive  almost  10%  of  the  total  budget  in  the  baseline 
scenario.  In  line  with  the  results  above,  scenarios  M.1,  M.2  and  M.3  show  a  lower 
concentration  of  the  budget  on  the  top  10  organisations.  Again,  scenario  M.2  “Social 
cohesion & well-being” shows the most distinct difference, with the top-10 organisations 
receiving only 7.7% of the total budget. In  contrast, scenario M.4 “FP rollback” shows a 
slightly higher budget allocation to the top-10 organisations in the programme. 

Figure  22:  Differences  in  regional  budget  distribution  across  the  main  scenarios  relative  to  the 
baseline scenario (percentage points difference) 

Note: the “big-5” countries refer to Germany, France, United Kingdom, Italy and Spain; the “EU-12 + HR” refer to the “new” 
Member States that have joined the EU since 2004, including Croatia (which has joined the EU only at the end of FP7).  

Figure  22  shows  the  differences  in  regional  budget  distribution  across  the  four  main 
scenarios relative to the baseline scenario. In relation to concentration effects, we look at 
the  budget  received  by  organisations  from  the  “big-5”  countries  (Germany,  France, 
United  Kingdom,  Italy  and  Spain)  and  the  budget  received  by  organisations  from  the 

101 

 
 
 
“new” Member States that have joined the EU since 2004.  In the baseline scenario, the 
“big-5”  countries  have  a  budget  share  of  57%,  whereas  the  “EU-12  +  HR”  group  of 
countries together receive only 7% of the budget.  

While  scenario  M.1  “Competitiveness  &  high-tech”  shows  only  minor  differences  to  the 
baseline  scenario,  M.2  “Social  cohesion  &  well-being”  features  a  significantly  lower 
budget share for organisations from the “big-5” countries in favour of the “EU-12 + HR” 
group  and  other  countries.  With  a  budget  share  of  54%,  the  “big-5”  countries  however 
still receive more than half of the total budget in scenario M.2. 

Scenario M.3 also shows a slightly lower budget share for the “big-5” countries, whereas 
M.4 shows a slightly larger budget share for the “EU-12 + HR” group. With 7.3% of the 
total budget, this share is quite similar to the one shown in scenario M.2 (7.2%). Overall, 
however,  these  shares  do  not  significantly  differ  from  the  baseline  scenario  (where  the 
“EU-12 + HR” group receives 7.0% of the budget); the simulated scenarios consequently 
do not show a significant budget shift from “old” to “new” Member States. 

Looking  at  project  coordinators  and  where  they  come  from,  scenario  M.2  again  stands 
out among the main scenarios, featuring the lowest share of coordinations from the “big-
5”  countries:  while  in  the  other  scenarios  about  60%  of  all  projects  are  coordinated  by 
participants  from  these  five  countries,  this  share  is  somewhat  lower  in  scenario  M.2,  at 
57%. Together with the lower budget to the “big-5” countries this indicates that scenario 
M.2  “Social  cohesion  &  well-being”  would  result  in  less  concentration  on  so  far 
dominant actors in the FP network, but higher diversity in terms of regional distribution 
of  participants  and  coordinators,  as  well  as  in  terms  of  stronger  roles  for  smaller 
organisations (including CSOs).  

Finding  24:  Scenario  “Social  cohesion  &  well-being”  shows  the  strongest  effects  on 
reducing  concentration  on  so  far  dominant  actors  in  the  FP  network,  but  instead 
increasing diversity in terms of regional distribution and involving peripheral actors, such 
as CSOs. 

7.3.5  Differences between the main scenarios in terms of capacity 

building (“learning”) 

In  the  simulation,  learning  of  organisations  refers  to  increases  of  the  expertise  levels 
concerning scientific disciplines (knowledge) and SDGs (impact). It can be understood as 
increasing  the  potential  of  organisations  to  contribute  to  the  academic  discourse  in 
specific  fields,  and  as  increasing  the  capacity  of  organisations  towards  solving  societal 
challenges.  It  can  be  measured  and  compared  by  aggregating  the  skill  vectors 
(knowledge and impact) of the participating organisations, indicating the learning effects 
of the whole system. 

As  shown  in  Figure  23,  scenarios  M.2  and  M.4  feature  the  most  distinct  differences 
concerning  learning  in  relation  to  the  baseline  scenario.  The  improvement  in  the 
aggregated  impact-vector  in  scenario  M.2  “Social  cohesion  &  well-being”  is  striking, 
indicating  a  learning  effect  being  almost  twice  as  strong  as  in  the  baseline  scenario  in 
relation  to  addressing  the  SDGs.  With  a  minor  increase  in  the  aggregated  knowledge-
vector (referring to scientific disciplines) compared  with the baseline scenario,  M.2 does 

102 

 
 
not point to a dilution of scientific excellence even when  a much higher weight is put on 
the criterion of “impact”. 

Figure  23:  "Learning"  effects  of  organisations  across  the  main  scenarios  relative  to  the  baseline 
scenario 

Scenario M.4 “FP rollback” again stands out among the main scenarios, with significantly 
less learning taking place for both knowledge- and impact-vectors. This is not surprising 
given the dramatic budget cut simulated in this scenario.  

While scenario M.3 “Fortress Europe” does not show major differences compared with the 
baseline scenario, scenario M.1 “Competitiveness & high-tech” features stronger learning 
effects  for  both  knowledge-  and  impact-vectors.  As  expected,  M.1  shows  the  strongest 
improvements in the aggregated knowledge-vector across the four main scenarios.  

Looking at learning effects of CSOs, scenario M.2 “Social cohesion & well-being” shows a 
disproportionally  high  increase  in  CSOs’  aggregated  impact-vector  compared  with  other 
organisation  types.  In  comparison,  the  improvement  in  CSOs’  aggregated  knowledge-
vector  is  quite  similar  in  M.1  and  M.2.  Notably,  both  scenarios  M.3  and  M.4  show 
reductions  in  the  CSOs’  aggregated  knowledge-  and  impact-vectors  compared  with  the 
baseline scenario.  

Finding 25: Scenario “Social cohesion & well-being” shows the strongest learning effects 
in  terms  of  impact  (SDGs),  without  compromising  learning  in  terms  of  knowledge 
(scientific  disciplines).  In  particular  CSOs  show  a  disproportionally  high  gain  in  their 
aggregated impact-vector.  

Table 24 on the next page recapitulates the essence  of this section by summarising the 
main scenarios and their effects on the FP networks and the participating organisations. 

103 

 
 
 
 
Table 24: Summary of the four main scenarios and their effects on the FP network (relative to the baseline scenario) 

Scenario 
settings 

Effects on 
projects & 
proposals 

Effects on 
organisations 
(numbers & 
budget) 

“Competitiveness & 
high-tech” 

“Social cohesion & well-
being” 

“Fortress Europe” 

“FP rollback” 

  20% more overall budget 

  20% more overall budget 

  Similar overall budget 

  Budget cut by 66% 

  Budget shifts to 

technological themes 
(ICT, NMP, Transport) 

  Budget shifts to society-& 
cohesion-oriented themes 
(Health, SSH, SiS, RoK) 

  Budget shifts to security-
related themes (Health, 
Energy, Security) 

  Less reduction for themes 
ICT, Health and Security 
than for other themes 

  Larger projects 

  More weight on 

evaluation criterion 
“excellence” 

  Smaller projects 

  More weight on 

evaluation criterion 
“impact” 

  Larger projects 

  Equal weight of 

evaluation criteria 
“excellence” & “impact” 

  No change in project size 

  Equal weight of all three 

evaluation criteria 

  10% fewer projects 

  2 times more proposals 

  15% fewer proposals and 

  Two-thirds fewer 

  3 times more projects 

projects 

proposals and projects 

  7% more organisations 

  20% more organisations 

  No significant changes 

  Almost 40% fewer 

  Companies (PRC) receive 
highest budget share 

  Two times more society-
oriented CSOs (CSO1-2) 

compared with baseline 
scenario  

  Higher budget share for 
business-oriented CSOs 

  Highest CSO budget 
share (for CSO1-3) 

organisations 

  Smaller organisations 

(SMEs, CSOs) drop out, 
universities remain 

Effects on 
network 

  All network measures 

reduced 

  More stable but less 
centralised network 

  All network measures 

reduced 

  Much denser and more 
centralised network 

Concentration 
effects 

Effects on 
capacity 
building 
(“learning”) 

  No significant differences 

  Less concentration on top 

  Less concentration on big 

  Much higher 

to baseline scenario 

organisations and big 
countries 

countries 

  Substantial learning 

  Considerable learning 

  No significant differences 

effects for “knowledge” 

effects for “knowledge” 
and outstanding learning 
effects for "impact" 

to baseline scenario 

concentration on big 
organisations 

  Considerably less learning 
for both “knowledge” and 
“impact” 

104 

 
 
 
8  Conclusions & recommendations 

This  chapter  recapitulates  the  main  findings,  draws  conclusions,  and,  on  this  basis, 
presents policy recommendations answering the following questions: 

a)  How do CSOs participate in the FPs? 
b)  Which benefits does their participation yield? 
c)  How can CSO participation in the FPs be strengthened? 
d)  What recommendations can be derived for the upcoming FP9? 

8.1 CSO participation in the FPs 

As  presented  in  the  previous  chapters,  the  participation  of  CSOs  shows  the  following 
characteristics and patterns: 

  CSOs constitute a very heterogeneous group of different types of organisations (as 
regards  size,  strategic  orientation,  business  model,  funding  streams,  target  groups, 
etc.), which makes it challenging to clearly distinguish, characterise and identify them 
among FP participants. For this study, we developed a literature based categorization 
distinguishing between four types of CSOs: 

o  CSO1:  citizen-oriented  (“core”)  CSOs,  including  consumer  and  support  groups 
mainly  in  the  area  of  health,  and,  to  a  lesser  extent,  environment,  such  as  the 
“European  Lung  Foundation”, 
for  Europe”  or  “Birdlife 
International”; 

the  “Stroke  Alliance 

o  CSO2: society-oriented/public-funded CSOs, covering a broad variety of different 
organisations  such  as  “ICLEI  -  Local  Governments  for  Sustainability”,  “Alzheimer 
Europe” or the “Black Sea NGO Network”; 

o  CSO3:  society-oriented/business-funded  CSOs,  comprising  of  organisations 
active in the food and health sectors, such as the “European Food Information Council” 
or the “European Cancer Organisation”; and  

o  CSO4: business-oriented CSOs, consisting mainly of industry associations, such as 
the  “Association  of  European  Railway  Industries”  or  the  “European  Business  and 
Innovation Centre Network”.  

This  was  a  feasible  and  efficient  way  for  gaining  better  insights  into  CSO  participation 
patterns in the FPs.  

Conclusion & recommendation: We strongly suggest better categorising CSOs in the 
CORDA dataset in the future, following the typology applied in this study. 

  CSOs  are  marginal  players  in  the  FP  network.  Although  on  average  one  out  of 
thirteen  projects  involves  a  CSO,  they  only  account  for  about  6%  of  all  participating 
organisations  in  FP7,  and  receive  only  about  1%  of  the  funding  provided  by  the 
European  Commission.  While  business-oriented  CSOs  (CSO4)  make  up  a  large 
proportion of these shares, society-oriented CSOs (CSO1-3) only account for 2.7% of 
organisations and 0.4% of the EU funding.  

105 

 
CSOs also rarely act as coordinators; only about 10% of CSOs in FP7 coordinated 
a  project,  and  only  about  1%  of  all  FP7  projects  were  coordinated  by  a  CSO. 
Furthermore,  they  usually  do  not  occupy  a  central  role  when  participating  in  a 
project.  According  to  our  case  studies,  issues  that  CSOs  are  facing  include:  low 
status  in  consortium  hierarchies,  lack  of  clarity  in  task  division,  and  concerns 
among  academic  researchers  that  CSO  participation  may  weaken  scientific 
legitimacy of research projects and jeopardize their potential to enhance academic 
reputation. 

Conclusion  &  recommendation:  Having  in  mind  their  marginal  share,  CSOs 
cannot  be  expected  to  have  a  significant  impact  when  participating  in  research 
projects. 

  CSOs  do  not  show  a  systematic  pattern  of  participation  in  the  FPs, 
although  there  are  themes  in  which  they  appear  more  frequently  and 
prominently than in others: 

o  Science  in  Society  (SiS)  is  the  only  theme  in  which  citizen-oriented  (CSO1) 
and society-oriented (CSO2 and CSO3) CSOs play a more than marginal role. 
However,  even  in  this  theme,  which  is  explicitly  aimed  at  involving  civil 
society in research, CSOs receive only about 17% of the EU funding. 

o  Regions  of  Knowledge  (RoK)  is  the  only  other  theme  in  which  CSOs  receive 
more than 10% of the total theme’s budget. However, more than 85% of this 
share is going to business-oriented CSOs (CSO4) and only 15% to citizen- or 
society-oriented CSOs (CSO1-3). 

o  Research  for  the  benefits  of  SMEs  (SME)  also  shows  above-average 
participation  of  business-oriented  CSOs  (CSO4);  with  about  6%,  however, 
their budget share is much lower than in the theme RoK. 

o  Activities  of  International  Cooperation  (INCO),  Research  policies  (COH),  and 
Socio-economic  sciences  and  Humanities  (SSH)  show  small,  but  above-
average,  budget  shares  of  the  more  society-oriented  CSOs  (CSO1-3), 
between 2% and 4%. 

o 

o 

o 

In  absolute  terms,  Health  and  Information  and  Communication  Technologies 
(ICT)  are  important  funding  sources  for  CSOs,  because  of  the  sheer  size  of 
these themes. 

The  theme  Environment  was  an  important  funding  source  for  CSOs  in  FP6, 
providing about 20% of the total budget received by CSOs in this programme. 
Notably,  in  FP7  this  share  fell  to  about  6%,  while  other  themes  like  Health, 
SiS or Security (which didn’t exist in FP6) gained importance. 

Important parts  of the  FP (in terms of budget  share) in which CSOs virtually 
do  not  participate  at  all  are  the  European  Research  Council  (ERC),  the  Marie 
Skłodowska-Curie  actions,  as  well  as  the  themes  Nanosciences  (NMP)  and 
Transport. 

Conclusion & recommendation: Increasing the budget for themes with already 
higher CSO participation would help strengthen the role of civil society in the FPs 
and bring science closer to citizens. As shown in the agent-based simulation, this 
budget  shift  needs  to  be  accompanied  with  additional  interventions  such  as 
smaller projects and adjusting evaluation weights (also see section 8.4 below). 

106 

 
  Most of the CSOs are not able to build up long-standing relationships with 
the FPs. Their drop-out rate of 85% from FP6 to FP7 is the highest of all types of 
participating organisations; more than 70% of the CSOs participated only once in 
both  FPs.  This  lack  of  consistency  does  not  allow  cross-fertilization  through  CSO 
involvement (which would be possible if e.g. the same CSOs would participate in 
both SiS and in other themes). High turnover of staff and a lack of organisational 
memory in CSOs constitute further hampering factors. 

Conclusion  &  recommendation:  Institutional  capacity-building  and  continuous 
development  of  skills  is  required  to  make  CSOs  “fit”  for  FP  participation;  this  could 
involve  staff  exchange  (e.g.  through  Marie  Skłodowska-Curie  actions),  launching 
specific initiatives and institutional capacity-building (similar to the ERC). 

  CSOs find it difficult to link up with the highly competitive and excellence 
driven  logic  of  research  and  innovation.  Mission-driven  CSOs  focus  much 
more  on  policy  impacts  and  the  needs  of  their  stakeholders  than  on  scientific 
publications and building up academic track records. Their mission is to influence 
current  national  or  regional  policy-making;  consequently,  they  can  rarely  gain 
benefits from being partners in projects lasting several years (taking into account 
the proposal phase). On the other hand, many of the service oriented CSOs do not 
see  much  added  value  in  research  and  innovation  for  their  core  business,  which 
often is not highly innovative. Both types of CSOs consequently participate slightly 
more  in  coordination  and  support  actions  than  in  research  and/or  innovation 
projects. 

Conclusion  &  recommendation:  Apart  from  participating  in  coordination  and 
support actions, other options for involving CSOs, such as in agenda-setting, proposal 
evaluation and dissemination of research results, could be better exploited. 

  CSOs  often  find  it  difficult  to  gain  a  European  added-value  from 
participating as partners in EU wide projects, as they often target national or 
regional policies. This is particularly valid for social issues, where no common EU 
policies  exist.  Compared  to  the  FPs,  funding  opportunities  in  the  European 
Structural  and  Investment  Funds  (e.g.  European  Regional  Development  Fund 
(INTERREG)  or  European  Social  Fund)  seem  to  better  address  the  regional/local 
geographical scope of many CSOs.  

Conclusion  &  recommendation:  Better  aligning  the  FPs  with  the  European 
Structural  and  Investment  Funds,  as  suggested  by  the  FP7  ex-post  evaluation,  could 
indirectly  strengthen  CSO  involvement  in  transnational  research  and  innovation 
activities. 

8.2 Benefits related to CSO participation in the FPs 

As CSOs only play a marginal role in the FPs, the benefits gained from their participation 
are  rather  low.  Table  25  below  summarises  the  study’s  findings  with  respect  to  the 
relevant research questions. 

107 

 
 
Table 25: Summary of findings in relation to the study's research questions on CSO benefits 

Research questions from the 
tender specifications 

  To  what  extent  do  different 
types  of  interventions  bring 
in  CSOs  from  different  field 
facilitate 
of  science,  and 
new scientific disciplines? 

  To  what  extent  does  in-
creased  CSO  participation 
lead  to  wider  and  more  di-
verse networks? 

  To  what  extent  and  how 
CSOs  contribute  to  network 
performance? 

  What  types  of  policy  inter-
ventions,  if  any,  help  CSOs 
contribute  to  more  stable 
and productive networks? 

  Are 

there 

the  structural 
holes  (missing  links)  in  the 
structure of the FP network, 
and  would  including  more 
CSOs  help  benefits  plug 
them? 

  To what extent and how dif-
ferent  types  of  policy  inter-
vention  contribute  to  re-
search impacts? 

Findings & conclusions from the study 

  CSOs  do  not  follow  the  logic  of  academia  charac-
terised by scientific disciplines; instead, their logic 
focuses on solving societal problems. 

  We  have  thus  not  found  evidence  on  CSOs  con-
tributing  to  research  performance26; 
instead, 
CSOs participate slightly more in coordination and 
support  actions  (e.g.  agenda-setting  or  applying 
research results) (see sections 6.1.3 and 6.2). 

  Consequently,  CSOs  cannot  be  expected  to  have 
any  effect  on  the  development  of  new  scientific 
disciplines. 

  Due to their marginal role, we have not found any 
evidence  on  CSOs  contributing  to  network  perfor-
mance. 

  CSO  participation  hardly  depends  on  network 
morphology; in FP7 there are virtually no correla-
tions between network measures and CSO partici-
pation (see sections 6.1.4 and 6.1.5). 

  CSOs  are  “hangers  on”,  they  do  not  build  sub-
clusters  or  bridge  gaps,  and  they  do  not  have  an 
important brokerage function (i.e. plug holes) (see 
section 6.1.6). 

  Removing  CSOs  would  have  virtually  no  effect  on 
network  parameters  and  morphology  (ceteris  pa-
ribus) (see section 6.1.6). 

  Looking  at  the  FP  network  overall,  scenario  M.2 
“Social  cohesion  &  well-being”  points  to  a  more 
stable  network  (higher  average  degree,  higher 
clustering), with collaborations being more equally 
distributed  among  the  participating  organisations 
(less centralisation) (see section 7.3.3). 

  When  simulating  CSO  participation in  the  FPs,  we 
understood “impact” in a broader sense than pure 
research output indicators, by referring to capacity 
building  or  “learning”  of  the  participating  organi-
sations. This addresses both “knowledge” in terms 
of  scientific  disciplines  and  “impact”  in  terms  of 
societal  challenges  (Sustainable  Development 

26 This finding refers to the results from the social network analysis and the analysis of publication 
in  section  6  of  this  report;  other  studies  such  as  the  CONSIDER  project 
output 
(http://www.consider-project.eu) have concluded that CSO contribution to research performance 
is most likely to materialise where research promotes the CSO aims. 

108 

 
                                           
Goals). 

  Scenarios  M.1  “Competitiveness  &  high-tech”  and 
M.2 “Social cohesion & well-being” both show pos-
itive effects in terms of capacity building. Scenario 
M.1  shows  the  strongest learning  effects  in  terms 
of  scientific  disciplines,  while  scenario  M.2  shows 
significantly  stronger  learning  effects  regarding 
societal  challenges  than  M.1,  but  without  diluting 
scientific excellence (see section 7.3.5). 

 

In contrast, scenario M.4 “FP rollback”, simulating 
a dramatic budget cut, points to a significantly re-
duced  capacity  of  organisations  in  terms  of  both 
knowledge and impact (see section 7.3.5). 

Conclusions  &  recommendations:  The  broad  diversity  of  benefits  related  with  CSO 
participation in the FPs cannot be adequately assessed by applying success criteria  such 
as  “network  performance”  or  “research  performance”.  This  is  due  to  the  fundamentally 
different  logic  (or  “currency”)  of  CSOs:  while  the  FPs  are  shaped  by  research,  which 
focuses  on  scientific  excellence,  and  the  business  sector,  which  focuses  on  profits  and 
competitiveness,  CSOs  follow  the  logic  of  societal  impacts.  Assessing  the  benefits  of 
involving mission-driven CSOs needs to take into account policy impacts; the benefits of 
involving  service-oriented  CSOs  can  be  measured  through  positive  impacts  on  the 
stakeholders  they  represent.  We  therefore  promote  a  new  view  on  the  benefits  related 
with CSO participation in the FPs, by assessing the (potential) societal benefits of the FPs 
as a whole, and by identifying the roles in which CSOs can best contribute to maximising 
these societal benefits. Sections 8.3 and 8.4 take up this new view. 

8.3 How to increase CSO involvement in the FPs 

Drawing from the conclusion above, Table 26 summarises the study’s results with respect 
to the question of how to better involve CSOs in the FPs. 

Table 26: Summary of findings in relation to the study's research questions on how to increase CSO 
involvement 

Research questions from the 
tender specifications 
  To  what  extent  do  different 
types  of  intervention  help 
CSOs  participate  more  fre-
quently,  more  actively  and 
more prominently in FPs? 
  All additional research ques-
tions,  referring  to  the  ef-
fects  of  changes  in  project 
size,  evaluation  weights, 
budget  allocation  across 
themes,  funding  levels  and 
overhead  costs,  mandatory 

Findings & conclusions from the study 

  The  case  studies  (see  section  5)  and  the  prelimi-
nary  scenarios  of the agent-based model (see  re-
sults in the Annex to this report) have shown that 
micro-management  of  the  programme  (as  re-
ferred  to  in  the  additional  research  questions)  re-
sults  in  only  marginal  effects  on  the  network  as 
well as on  the involvement of CSOs and their im-
pacts.  

  Smart  combinations  of  different  steering  parame-
ters,  as  featured  in  the  main  scenarios,  show 
much stronger effects (see section 7.3). 

109 

 
 
quota,  extended  2-stage 
proposals,  more 
flexible 
administrative  and  account-
ing  rules,  and  more  PR 
measures,  information  and 
training activities. 

  Scenario M.2 “Social cohesion & well-being” shows 
the strongest impacts on CSOs. In relative terms, 
their  share  increases  to  almost  5%,  and  their 
budget share rises to 1.3%. Especially the citizen- 
and  society-oriented  CSOs  (CSO1-3)  benefit 
strongly,  doubling  both  their  numbers  and  their 
budget. 

  Scenario  M.1  “Competitiveness  &  high-tech”  too 
features  a  higher  budget  share  for  CSOs  in  gen-
eral,  but  only  promotes  business-oriented  CSOs 
(CSO4)  while  showing  slightly  negative  effects  on 
citizen- and society-oriented CSOs (CSO1-3). 

  Scenario M.3 “Fortress Europe” can be seen as the 
counterpart  of  M.2  in  terms  of  CSO  promotion, 
featuring  the  smallest  budget  share  for  CSOs 
across the main scenarios (about 0.9%). 

  Similarly,  scenario  M.4  “FP  rollback”  shows 
the  assumed 
detrimental  effects  on  CSOs: 
dramatic  budget  cuts  simulated  in  this  scenario 
result 
rate  of  smaller 
organisations in general and CSOs in particular.  

in  a  high  dropout 

 

In all four scenarios, CSOs remain in the periphery 
and  semi-periphery  and  do  not  move  to  the 
network  centre;  they  remain  players  of  lower 
types  of 
importance  compared  with  other 
organisations,  such  as  universities,  research 
organisations and companies (see section 7.3). 

Conclusions  &  recommendations:  Micro-management  of  individual  programme 
features  results  in  only  marginal  effects.  More  substantial  effects  can  be  expected  by 
putting  stronger  emphasis  on  (a)  the  societal  impacts  of  the  whole  FPs,  (b)  the  budget 
allocation to themes addressing grand societal challenges and (c) the number and size of 
projects  (determining  concentration  effects  and  entry  barriers).  Our  scenario  “Social 
cohesion  &  well-being”  shows  that  such  a  smart  combination  of  different  steering 
parameters  would  substantially  increase  the  contributions  to  policy  objectives  (such  as 
the  Sustainable  Development  Goals),  foster  the  respective  competencies  of  all  types  of 
organisations  (universities,  businesses  and  CSOs),  and  bring  science  closer  to  citizens 
without  diluting  scientific  excellence.  We  consequently  suggest  considering  a  broader 
range  of  roles  that  CSOs  could  play  in  future  FPs:  (1)  in proposal  evaluation  boards  (at 
least for the “impact” section), (2) in agenda setting and work programme development 
(beyond  online-based  consultation  processes),  and  (3)  in  ethics  boards  and  steering 
committees (on project and programme level). Many of these roles would better fit to the 
mission-driven  core  logic  of  CSOs  and  could,  therefore,  be  more  attractive  than  being 
involved as marginalised project partners. 

110 

 
 
8.4 Recommendations for the upcoming FP9 

As  the  Ex-Post  Evaluation  of  FP7  (Fresco  &  Martinuzzi,  201527)  and  conceptual  input 
papers for FP9 (such as the theses paper published by the Austrian FP9 think tank28 and 
the  statement  of  the  Austrian  Interest  Group  of  Public  Benefit  Organizations29)  have 
highlighted, it will be necessary to foster the strategic orientation of future FPs. Against 
this  background,  we  have  developed  and  simulated  four  scenarios  representing  four 
significantly different strategic orientations of the FP: 

1)  Scenario  M.1  “Competitiveness  &  high-tech”  focusing  on  strengthening 
competitiveness  of  business/industry  through  promoting  technology-oriented 
research fields and funding fewer, but bigger, projects than in the past. 

2)  Scenario  M.2  “Social  cohesion  &  well-being”  focusing  on  societal  challenges, 
promoting  more  process-oriented  types  of  research  and  funding  more,  but 
smaller, projects than in the past. 

3)  Scenario  M.3  “Fortress  Europe”  focusing  the  research  areas  primarily  on 
security-  and  health  related  topics  in  view  of  keeping  Europe  safe  and  making  it 
more self-sufficient. 

4)  Scenario  M.4  “FP  rollback”,  simulating  a  dramatic  budget  cut  of  the 
collaborative,  mission-driven  parts  of  the  FP  –  either  reflecting  an  overall  cut  in 
the FPs budget, or reflecting budget shifts towards the ERC. 

Conclusions  &  recommendations:  As  the  results  of  the  agent-based  modelling  have 
shown,  the  four  scenarios  are  quite  distinct  in  terms  of  their  effects  on  the  overall 
research and innovation network, the involvement of CSOs, and the learning effects of all 
types of organisations involved. If addressing societal challenges, bringing science closer 
to  citizens  and  increasing  the  diversity  and  resilience  of  the  European  research  and 
innovation network are perceived as important policy goals, the scenario “Social cohesion 
& well-being” is the most beneficial: It significantly increases the capacities of all actors 
to  contribute  to  solving  societal  problems  without  diluting  scientific  excellence  and 
without  disrupting  the  existing  network  or  completely  altering  the  nature  of  the 
programme, thus still being capable to adequately address the logic of the main players 
(universities,  research  organisations  and  companies).  As  a  side  effect,  it  significantly 
increases (i.e. doubles) the involvement of CSOs. 

The present study was based on the available data and the programme structure of FP6 
und  FP7,  with  the  aim  to  highlight  options  for  structuring  future  FPs.  Table  27  below 
shows  a  summary  of  our  analyses  with  regard  to  the  structure  of  the  current  FP  – 
Horizon  2020  –  and  highlights  three  different  “currencies”30  and  governance  principles 
shaping the current FP:  

27  https://ec.europa.eu/research/evaluations/pdf/fp7_final_evaluation_expert_group_report.pdf  
28  https://www.ffg.at/sites/default/files/fp9thesespaper_online.pdf  
29  http://www.gemeinnuetzig.at/images/doku/igo-statement_fp9.pdf  
30  The notion of “currencies” stems from the understanding that in different social fields different 
units exist for measuring status within this field (e.g. publication track record for people working 
at  universities).  The  metaphor  of  “currency”  highlights  that  while  each  currency  values 
something in a specific field, there should also be a way to convert a currency into another one 

111 

 
                                           
 
  The  first  pillar  of  Horizon  2020,  “Excellent  Science”,  focuses  on  frontier  research, 
aiming  at  new  discoveries,  targets  individual  scientists,  addresses  their  “currency” 
excellence as measured by publications and academic careers, and awards grants in a 
highly  selective  process  based  on  evaluation  carried  out  by  other  researchers. 
Through its high ambition and focus on excellence, this process is highly selective but 
free  from  any  influence  beyond  scientific  communities.  Governance  is  therefore  self-
referential, and considering societal impacts is often perceived as a potential dilution 
of excellence. 

innovative  business  sectors,  and  aims  at  supporting 

  The  second  pillar  of  Horizon  2020,  “Industrial  Leadership”,  focusses  on  innovation, 
targets  highly 
their 
competitiveness, growth and jobs. The “currency” of this pillar is therefore profit, and 
patents are used to safeguard competitive advantages. This leads to an exclusion (of 
potential competitors)  and joint agenda setting (in an inner circle of  participants) as 
governance principles. 

  The third pillar of Horizon 2020, “Societal Challenges”, focuses on impacts of societal 
practices,  targets  citizens  and  civil  society,  and  follows  a  key  logic  of  (political) 
responsibility.  Increasing  positive  (and  reducing  negative)  impacts  are  therefore 
success  criteria  for  projects.  While  the  other  two  pillars  run  the  risk  of  a  “lock-in” 
(through  selection  on  the  one  hand,  and  exclusion  on  the  other),  this  pillar  can 
balance  these  tendencies  and  serve  as  an  integrative  element.  Moreover,  while  the 
other two pillars follow insider-oriented governance principles, the third pillar has the 
potential to address major societal challenges which are set on a global scale. 

Table 27: Characteristics of the Horizon 2020 programme structure 

Conclusions & recommendations: If European research and innovation policy wants to 
safeguard  its  legitimacy  vis-à-vis  European  citizens,  an  independent  third  pillar  focusing 
on  the  grand  societal  challenges  is  vital  (no  merging  of  the  second  and  the  third  pillar 
under  the  headline  of  “mission  oriented  research”).  The  globally  agreed  Sustainable 
Development Goals could serve as an orientation and avoid that agenda setting is driven 
by  interest  groups.  In  order  to  bring  people  closer  to  research  and  research  closer  to 

(similar  to  real  currencies  like  the  Euro  that  can  be  converted  to  US  Dollar),  thus  allowing 
exchange across different fields. 

112 

FocusTarget groupCurrencySuccessProcessGovernanceExcellentScienceDiscoveryIndividual scientistsExcellence &FreedomPublications &CareersCreativity& SelectionSelf-referentialIndustrial LeadershipInnovationBusinessProfitCompe-titivenessExclusionJoint Agenda SettingSocietalChallengesSocietalpracticesCivilSociety / PeopleResponsi-bilityImpactIntegrationPolicy 
 
                                                                                                                                    
people, arenas for knowledge co-creation and innovation need to be designed into which 
a  broad  variety  of  stakeholders  should  be  involved.  Funding  instruments  should  reflect 
that  societal  challenges  are  “wicked”  problems,  and  that  innovations  are  not  final 
deliverables  but  continuous  processes.  Therefore,  interventions  should  rather  focus  on 
governing  landscapes  (and  not  only  projects),  develop  institutional  capacities  and 
structures,  and  allow  for  more  continuous  processes  (overcoming  the  rather  limited 
perspective  of  individual  projects).  Responsible  research  and  innovation  (RRI)  should 
become a guiding principle in all three pillars and themes of the FPs. This will ensure that 
future  FPs  do  not  only  promote  frontier  research  at  universities  and  research 
organisations  or  support  competitiveness  of  businesses,  but  that  they  also  consider  the 
needs of citizens and address the grand societal challenges of our time. 

8.5 Research outlook 

The findings, conclusions and recommendations presented in this study are based on two 
years  of  data  gathering,  analysis  and  modelling.  For  understanding  the  recent  and 
potential roles of CSOs in research and innovation networks, a big step forward has been 
taken.  However,  a  few  limitations  of  this  study  are  worth  mentioning  as  they  might 
inspire future research efforts: 

  There  are  rather  high  inconsistencies  between  the  CORDA  datasets  on  FP6,  FP7 
and  H2020  projects  as  well  as  between  the  FP7  datasets  on  projects  and 
proposals.  This  prevented  us  from  more  in-depth  analyses  regarding  CSO 
participation at the proposal stage.  

Conclusion  &  recommendation:  Creating  a  longitudinally  harmonised  dataset  across 
the  FPs  that  would  be  made  available  to  future  service  contractors  would  considerably 
decrease the  workload  and thus the budgetary volume  of each service contract while at 
the same time ensuring consistency in the results.  

  The available data only focuses on participating organisations, but does not allow 
any disaggregation on the level of departments, research teams or individuals.  

Conclusion  &  recommendation:  In  future  releases  of  CORDA,  data  gathering  on  a 
more detailed level should be considered. 

  For  this  study,  our  agent-based  model  was  used  to  simulate  a  rather  small 
number of scenarios that mainly focused on societal impacts and the involvement 
of CSOs into FPs.  

Conclusion & recommendation: The model could be applied in future projects to learn 
more  about  other  policy  interventions,  e.g.  addressing  the  share  of  EU-13  countries,  or 
effects of better integrating SSH into other areas of research. 

 

In  our  study  we  found  that  other  roles  for  CSOs,  beyond  being  project  partners, 
could be beneficial for the societal impact of the research undertaken. Such roles 
include those in agenda setting or proposal evaluation. 

Conclusion  &  recommendation:  At  present  EU  FPs  make  very  limited  use  of  CSOs  in 
such other roles. Research should be undertaken to empirically evaluate the outcomes of 
CSOs taking such roles, in order to provide a sound basis for the further development of 
policy governing CSO involvement in FPs. 

113 

 
114 

 
 
 
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116 

 
 
 
 
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This study examines the participation of Civil Society Organisations (CSO) in the 
EU research framework programmes (FP) through literature reviews, a series of 
case studies, social network analyses and agent-based modelling. It revealed 
that CSOs have played a marginal role in FP6 and FP7, and had very limited 
effects on network performance and research output. This can be explained by 
different logics: up to now, the FPs have been mainly shaped by researchers, 
focusing on scientific excellence, and the business sector, focusing on profits and 
competitiveness, when CSOs follow a different logic, focusing on societal 
impacts. 

Research & Innovation policy