J.Maier Structuring effects of Community Research › research › evaluations › pdf › ... · 2...

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Structuring Effects of Community Research

The Impact of the Framework Programme on Research and Technology Development (RTD) Network Formation

Brussels, 18th June 2009

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The three main objectives of the Netpact project

1. Ensure QA of database with data on EC funded projects and to establish thereto a closely linked dataset on publications to determine the impact

2. Conduct in-depth quantitative and qualitative network analyses of the RTD collaborations in 5 identified RTD fields

3. Develop policy recommendations for RTD network-driven policies to improve innovation and competitiveness in Europe

… for FP6: Citizens, Food, IST, LSH and NMP… for FP6: Citizens, Food, IST, LSH and NMP

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Conduct in-depth quantitative and qualitative network analyses…

… there are some 2.500 projects with in average 15 participants. In total there are some 10.000 unique organisations involved.

… this can be easily transformed into a two-mode network

BUT: Not all participants interact equally with each other.

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Network analysis: Calculation of interaction volume

Coordinator(English)

Participant A(French)

Participant B(English)

Coordinator

Participant A

Participant B

Project (members, Funding, duration)

• Interaction Volume is higher between coordinators and participants; between actors with many common projects and with same language as well as in long-term and higher funded projects.

• Interaction Volume is lower in projects with many participants.• Creating a valued graph assigning intensity values to the connections.

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The five major findings: Summary

1. Structure of FP6 RTD network- FP6 has a core-periphery structure dominated by a small number of

closely-knit organisations

2. Role of industry in FP6 RTD networks- Industry plays less of a coordinating role and is weakly embedded in FP6

networks, with exception of IST

3. Research outcomes of FP6 funding- FP6 has resulted in more upstream than downstream outputs –

providing the basis for more long-term positive impact on European competitiveness

4. Efficiency of FP6 RTD projects- Performance decreases as project size increases

5. New member state integration in FP6- New member state integration is dominated by a few actors in Poland,

Hungary, and the Czech Republic

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In-depth look at the five major findings

1. Structure of FP6 RTD network2. Role of industry in FP6 RTD networks3. Outcomes of FP6 funding4. Efficiency of FP6 RTD projects5. New member state integration in FP6

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The more funding, the higher the clustering

and centralization

1. Finding: FP6 networks have a core-periphery structure dominated by a small number of closely-knit organisations

CentralizationDegree to which networks are dominated by a few participants

Low

High

HighLow

ClusteringDegree to which

networks are dominated by

participants cooperating in two or

more EU funded projects

Circle size = funding

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1. Finding: Core-periphery structure

• Small number of closely-knit organisations in core that dominate FP6 RTD network- Core organisations highly connected to one another through several projects

- Remaining organisations in network periphery and only connected to core and not with one another

• Central actors primarily large national research associations- E.g., Fraunhofer Gesellschaft, CNRS, INSERM and universities

- Except IST where industry actors also central

• More funding thematic area receives, the stronger the pattern ofcore-periphery and heavy centralization- To point of oligopoly structures at instrument level

- IP strongest core-periphery and highest centralization structure followed by STREP and then NOE

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1. Explanation: Core-periphery structure

• High degree of path dependency and capability trajectory

- Organisations develop coordinating capability over time and thus reputation

as coordinators

- Efficiency gained through economies of scale by coordinating multiple

projects

- Periphery organisations seek more prestigious organisations for collaboration

to improve own reputation (“preferential attachment”)

• Reduced transaction costs between partners over time

- Shared understanding and common way of working developed

- Trust built as collaborate over time

- Relationships then built and strengthened over time

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1. Implication: Core-periphery structure

• Stable social structure leads to efficiency in research

- Lowered communication costs due to shared understanding and trust

- Critical mass and sharing of resources

- Specialization on research topics

But

• Stable social structure can have drawbacks

- “The rich get richer” – centralization leads to more centalization and“in-crowd”

- Knowledge becomes redundant

- Reduced creativity and radical innovation

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2. Finding: Industry plays less of a coordinating role and is weakly embedded in FP6 networks with exception of IST

Organizational Roles•Circle size indicates total number of projects coordinating•Arrow size indicates number of projects coordinated •Arrow direction indicates who coordinates whom

Organizational Roles•Circle size indicates total number of projects coordinating•Arrow size indicates number of projects coordinated •Arrow direction indicates who coordinates whom

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2. Finding: Role of industry

• Industry accounts for 52.8% of all FP6 organisations - Industry is very active in NMP and IST, somewhat active in LSH and FOOD,

while CITIZENS is university domain

• Industry tends to be on periphery of FP6 networks - Industry connected to core of highly reputed, large national research

associations and universities coordinating FP6 projects

- Only in IST does industry dominate network (Majority of actors + Highest

share of coordination)

- France Telecom, Siemens, and Philips Electronics central in IST

• IP is instrument with highest industry participation and coordination- NMP-IP is instrument with highest industry participation

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2. Finding: Role of industry

Most central actors

Most central actors

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Co-publication ratio from T0 to T1

5%

4%

3%

2%

1%

0%

Co-publication Data• 2,615 co-publications, ie publication by 2 participants (of total 84,670 publications )• “Citizens” not selected due to a significantly different publication pattern

Time Period• T0 = period from 4 years before project start until 1 year after project start• T1 = period starting one year after project start until max 5 years after

+8% +52% +25% +56%

Significant increase in rate of co-publication between RTD participants

Significant increase in rate of co-publication between RTD participants

3. Finding: FP6 has resulted in more upstream than downstream outputs – providing basis for long-term impact

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3. Finding: Outcomes of FP6 funding

• FP6 funded research has made a significant impact on the international scientific field- Increased co-publication activity between FP6 project’s partners

• I.e., two partners from same FP6 project publish one or more articles

together after having participated together in FP6

- Publications from PIs either with or without other FP6 partners have 50% higher impact than world average

- Co-publications by collaborating FP6 partners

• Significantly higher impact than publications in which FP6 partners do not co- publish

• Around two times the world average

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4. Finding: Performance decreases as project size increases

Smaller teams (maximum of five partners) are more productive in publishing

Smaller teams (maximum of five partners) are more productive in publishing

0%

2%

4%

6%

8%

10%

12%

14%

16%

Larg

e

Med

ium

Smal

l

Larg

e

Med

ium

Smal

l

Larg

e

Med

ium

Smal

l

Larg

e

Med

ium

Smal

l

LSH IST Nano (NMP) Food

Shar

e Pc

P/A

llPcP share by thematic area

and project size

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5. Finding: New member states

• Poland, Hungary, and to some extent Czech Republic are most highly integrated new EU member states (NMS)

• NMS participation varies across thematic areas- In absolute terms, IST most important with over 41% of NMS interaction

- CITIZENS has highest engagement of NMS (%)

- LSH is weak point for NMS (%)

• Several NMS organisations among top ten on instrument level, except for LSH

• NMS participation dominated by a few organizations - Budapest University of Technology and Electronics has 42% of all Hungary’s

FP6 projects

- Over 75% of Budapest University of Technology and Electronics in IST and over 55% of Academy of Sciences of Czech Republic in LSH

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Issues to be addressed

•Testing and refining the concept of interaction probability.

•Refine the approaches to better link persons, organization and output information ( e.g. publications, patents, technology offer,…)?

•Are the efficient ways to identifying the role of an participant in a project and resulting from that in a network?

•Is there a need for a research information system allowing to gather the footprints and following the dynamics?

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Further information on the sample and the method

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Data cleaning of the FP6 dataset led to a significant decrease in the number of unique participants ….

1. FP6 project data (source: EC)2. Worldwide publication data (source: Thomson Scientific)

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…as well as in a significant redistribution of organizations across organizational types.

IST Organizations

Num

ber

of p

arti

cipa

nts

LSH Organizations

•The current organizational categorization scheme does not adequately represent the true organizational types

-Current categories are not sufficient to clearly select different sets of project participants, i.e., there is no category for TTOs, public agencies or consultants.

-Industry participation is systematically underestimated since many companies are tagged as “Other” when it comes to organisational type.

+ 29,41 %

+ 55,56 %

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The shift when looking at all five thematic areas

• Significant shifts in organisational types• Remaining N/A organisations mostly have their legal name

missing, making it impossible to determine their type

+ 2% +100% +39% -79% -51% -6%

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Looking at the overall funding structure, we see significant differences.

• IST is by far the largest thematic program in overall funding• FOOD has the highest amount of funding and the largest number of participants per

project• LSH has the highest funding per participant

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Data cleaning

K A R L S R U H E U N I V ( T H )

U N I V E R S I T A E T K R L S R U H E

1. Transforming the original data to a vector-based format.

2. For each sequence of n characters that occurs in an organisation name an entry in the vector representation is created and in case of multiple occurrences increased.

3. With the index of each organisational name, we can calculate the distance between two names using vector-distance measures.

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Impact analysis: We received a satisfactory response rate for the RTD case studies.

Thematic Area

Number of RTDs

RespondingResponse

Rate of RTDs

Number of Organizations Responding

Response Rate of

OrganizationsCITIZENS* 15 94% 90 36%

FOOD 27 96% 195 36%IST 31 97% 113 22%LSH 30 94% 178 34%NMP 30 94% 171 32%

TOTAL 133 95% 747 32%

• For each thematic area, the selection criteria were based on twoperformance measures

• 2 Highest and 2 Lowest ”Publishing Impact” RTDs for each instrument• (2 High +2 Low) x 4 Instruments = 16 RTDs in total

• 2 Highest and 2 Lowest ”Centrality in the RTD Network” RTDs for each instrument• (2 High +2 Low) x 4 Instruments = 16 RTDs in total

* Only High Centrality RTDs were chosen due to different publishing patterns

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Impact analysis: Breakdown of respondents by organization type and instrument

Org Type Number

Percent Total

HES 348 46,6

HSP 19 2,5

IND 108 14,5

OTH 38 5,1

REC 234 31,3

Total 747 100%

InstrumentType Number

Percent Total

CA 160 21,4

IP 241 32,3

NOE 251 33,6

STP 95 12,7

Total 747 100%

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