Jully 2008
Do firms know the scope of their R&D network?An empirical investigation of network awareness on French survey data
Stéphane Lhuillerya, Etienne Pfisterbc
a Ecole Polytechnique Fédérale de Lausanne, Collège du Management de la Technologieb Direction de l’Evaluation, de la Prospective et de la Performance c BETA-Universités de Nancy and CNRS, France
Jully 2008
Definition:
Whole set of external R&D relationships of a firm
All types of partners: firms, universities, government agencies
All types of relationships: formal/informal, close/distant, institutional/personal, etc…
Can be broadened to include indirect ties
The relationships of a firm’s partners
Definition:
Whole set of external R&D relationships of a firm
All types of partners: firms, universities, government agencies
All types of relationships: formal/informal, close/distant, institutional/personal, etc…
Can be broadened to include indirect ties
The relationships of a firm’s partners
Why consider R&D networks in economics ?
INTRODUCTION1. R&D networks in the economics of innovation
Jully 2008
Networks contribute to the innovation capabilities of a firm
Expands the pool of knowledge
Help attract and select R&D partners
How a firm benefit from its network depends on
its position within that network (periphery vs centrality)
the type of relationships within that network
the diversity of the network
The ability of the network to accept new entrants
Networks contribute to the innovation capabilities of a firm
Expands the pool of knowledge
Help attract and select R&D partners
How a firm benefit from its network depends on
its position within that network (periphery vs centrality)
the type of relationships within that network
the diversity of the network
The ability of the network to accept new entrants
Do firms really know the extent of their R&D network ?
Which ones do ?
INTRODUCTIONSome results of the empirical literature on R&D networks
Jully 2008
Two common beliefs among scholarsTwo common beliefs among scholars
R&D alliances are good for (innovative) performances
Cooperation can be detrimental for innovation
Usual results are flawed : missing non-R&D alliances in models
Network is an efficient coordination mechanism
Firms in networks ignore if they have complete information or not
Firms do not know the architecture of the network they belong to
INTRODUCTION2. Challenging the literature on S&T interactions
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The research measuring the ignorance of agents in networks has been scantyThe research measuring the ignorance of agents in networks has been scanty
Agents imagine a network they believe to belong to (Stockman and Doreian, 1997; Janicik and Larrick, 2005)
Incomplete information in theoretical network models (Galeotti et al., 2008; McBride, 2008)
INTRODUCTION2. Incomplete information in networks: very few results
Jully 2008
Sometime the indirect links between nodes are knownSometime the indirect links between nodes are known
INTRODUCTION3. Incomplete information in networks: Example 1
Jully 2008 INTRODUCTION4. Incomplete information in networks: Example 2
Key For All Figures:
CYAN NODE = DBFORANGE = PROBROWN = Gov’tYELLOW = PharmaGRAY = VCWHITE = Other
Triangle = New EntrantCircle = IncumbentNode size = standardizednetwork degree, constantwithin figures
RED TIE= R&DGREEN = FinanceBLUE = CommercialMAGENTA = Licensing
From Powell et al., American Journal of Sociology, January 2005
Sometime the indirect links between nodes are difficult to knownSometime the indirect links between nodes are difficult to known
Jully 2008 INTRODUCTION5. Incomplete information in networks: Example 2
The EPFL co-patenting network, according to different heuristics
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Jully 2008 INTRODUCTION
6. Incomplete information in networks: the F firm point of view
Jully 2008 INTRODUCTION6. Aims and Methods
This research aims at investigating:
To what extent firms know/ignore their network architecture
What are the determinants of ignorance of firms regarding their R&D network architecture
This research aims at investigating:
To what extent firms know/ignore their network architecture
What are the determinants of ignorance of firms regarding their R&D network architecture
We propose an econometric setting to analyze whether firms know the existence of indirect links between its R&D partners
We analyze the awareness of links among the three main R&D partners
Jully 2008 INTRODUCTION7. Our research differs from previous works in two major ways
Our work prose an econometric model based on a unique postal survey providing a large and representative sample of firms with R&D dyadic links
We do not assume the link between nodes are known by network participants
The R&D links considered are declarative and qualitative: they are broader than the one approximated by other databases (MERIT CATI, patent data)
Our work prose an econometric model based on a unique postal survey providing a large and representative sample of firms with R&D dyadic links
We do not assume the link between nodes are known by network participants
The R&D links considered are declarative and qualitative: they are broader than the one approximated by other databases (MERIT CATI, patent data)
Jully 2008
Outline
1. The determinant of ignorance
2. Data and Variables
3. Empirical methods
4. Results
Jully 2008 1.THE DETERMINANTS OF IGNORANCE 1.1 Categories
Determinants of awareness are difficult to identify. Four Classes can be considered
THE CARACTERISTICS OF NODES
o The firm
o The R&D partners
THE CHARACTERISTICS OF R&D LINKS
o The direct links
o The indirect links
Determinants of awareness are difficult to identify. Four Classes can be considered
THE CARACTERISTICS OF NODES
o The firm
o The R&D partners
THE CHARACTERISTICS OF R&D LINKS
o The direct links
o The indirect links
Jully 2008
• Experience: experienced firms are more aware of the existing network
• Size: small firms are depending more on external resources
• Intense and diversified R&D investors are more aware
• Affiliates rely more on their group and are less interested in indirect ties
• Uncertain and rapidly evolving technological environments can confer greater value to indirect ties
1.THE DETERMINANTS OF IGNORANCE 1.2. The firm’s characteristics
Jully 2008
• Collaborations focused on R&D and knowledge exchange aspects
• R&D collaboration more likely to supply information than R&D subcontracting.
• The length of the relationship
• Complementary competences are bridging two complementary networks.
1.THE DETERMINANTS OF IGNORANCE 1.3. The direct link characteristics
Jully 2008
• Techno Center or PROs are more likely to disclose their ties
• R&D partners are more likely to disclose their relationships when they are similar
• Geographical proximity (even international…)
• Collocation of the two R&D partners
• Reputation is a positive driver of awareness
• Access to new market
1.THE DETERMINANTS OF IGNORANCE 1.4. The R&D partners’ characteristics
Jully 2008
Several possible dimensions here:• Regularity• Formal contracts • Formal organization (RJV as in CATI)• …
A severe methodological problem is that thecharacteristics of the indirect links
o are not often known by firmso Are difficult to ask in questionnaires
1.THE DETERMINANTS OF IGNORANCE 3. The undirect link characteristics
Jully 2008 2.DATA AND VARIABLES2.1 Different data sources
2002 ERIE survey on partnerships. We use the one on R&D links
The 2000 R&D annual survey
2002 ERIE survey on partnerships. We use the one on R&D links
The 2000 R&D annual survey
Jully 2008 2.DATA AND VARIABLES2.2 Cost data
The 3 main R&D partners are detailed in ERIE The 3 main R&D partners are detailed in ERIE
P1
P2 P3
Firmi
DL1 DL2
DL3
IL12
IL23
IL13 P4
P7 P6
P5 P8
IL47
IL34
IL57
Jully 2008 2.DATA AND VARIABLES2.3 Different data sources
oMerging these two datasetsoExcluding R&D service firms (NACE=”731”) oExcluding firms with only intra-group partnerships
1461 firms involved in external R&D relationships.
oExcluding firms with less than two R&D partners declared496 firms with multiple links.
oReshaping the dataset in order to be at the indirect link level:1041 potential indirect ties whose existence may be known or not by
the responding firm.
oMerging these two datasetsoExcluding R&D service firms (NACE=”731”) oExcluding firms with only intra-group partnerships
1461 firms involved in external R&D relationships.
oExcluding firms with less than two R&D partners declared496 firms with multiple links.
oReshaping the dataset in order to be at the indirect link level:1041 potential indirect ties whose existence may be known or not by
the responding firm.
Jully 2008 2.DATA AND VARIABLES2.4 Variables : Characteristics of the responding firm
o R&D intensity (R&DI)
oFirm size (SIZE) is measured by the logarithm of the number of employees.
oIndustry dummies: high-technology industries, medium-high-technology industries, medium-low-technology industries, low-technology industries.
oBusiness groups : Domestic or foreign business groups
Jully 2008 2.DATA AND VARIABLES2.5 Variables : the type of the R&D partner
o Four types of R&D partner (PRO, TECHNO CENTRE, CONSORTIUM, ENTERPRISE)
o HOMOGAMY o BUSINESS GROUP is 1 if one of the partner belongs to the
same groupo Three geographical levels (LOCAL, NATIONAL,
INTERNATIONAL). o COLOCATIONo KNOWLEDGE as a asset providedo REPUTATION.
Jully 2008 2.DATA AND VARIABLES2.6 Variables : Characteristics of the R&D collaboration
o ASYMMETRY
o COMPLEMENTARITY
o R&D SUBCONTRACTING
o LONGTERM
o MARKET ACCESS
Jully 2008 3.METHODS3.1 A probit model with selection
1
2
( & 1)( 1)
P number of R D partners XP Awareness Z
β εβ µ> = +⎧
⎨ = = +⎩
An Heckman probit model
Jully 2008 3. METHODS3.2 Additionnal control variables
• Three dummy variables (RANK1, RANK2, RANK3)
• NUMBER OF CENSORED TIES
• HEADQUARTER
Jully 2008 4.RESULTS4.1 Descriptive stats
Variable Mean St. Dev. Variable Mean St. Dev.
AWARENESS 0.42 0.49 RD INTENSITY 2.57 1.47TECHNO CENTRE° 0.20 0.40 SIZE 5.29 1.79PROs° 0.46 0.50 FRENCH BUSINESS GROUP° 0.43 0.50
CONSORTIUM° 0.13 0.34 FOREIGN BUSINESS GROUP° 0.27 0.45BUSINESS GROUP° 0.27 0.44 HEADQUARTER° 0.07 0.25HOMOGENOUS 0.74 0.44 MEDIUM LOW TECH° 0.27 0.44
REPUTATION° 0.20 0.40 MEDIUM HIGH TECH° 0.29 0.45
COMPLEMENTARITY° 0.52 0.50 HIGH TECH INDUSTRY° 0.29 0.45
LOCAL° 0.44 0.50 RANK2° 0.70 0.46INTERNATIONAL° 0.45 0.50 RANK3° 0.60 0.49COLOCATION° 0.45 0.50 SELECTION 11.40 34.80LONG TERM° 0.54 0.50KNOWLEDGE TASKS° 0.59 0.49R&D SUBCONTRACTOR° 0.41 0.49ASYMMETRY° 0.47 0.50MARKET ACCESS° 0.37 0.48
Jully 2008 4.RESULTS4.2 Econometric results (1)
variable Marginal effects Std. Err.
TECHNO CENTER° 0.010 0.027PROs° 0.045** 0.022CONSORTIUM° 0.015 0.027BUSINESS GROUP° 0.007 0.021HOMOGENOUS 0.041* 0.023REPUTATION° -0.013 0.023COMPLEMENTARITY° -0.016 0.019LOCAL° 0.007 0.019INTERNATIONAL° 0.025 0.025COLOCATION° 0.014 0.028LONG TERM° -0.016 0.017KNOWLEDGE TASKS° 0.237*** 0.042R&D SUBCONTRACTOR° -0.025 0.025ASYMMETRY° -0.016 0.018MARKET ACCESS° 0.039** 0.020
Jully 2008 4.RESULTS4.3 Econometric results (2)
RD INTENSITY -0.024*** 0.011
SIZE -0.037*** 0.011
FRENCH BUSINESS GROUP° -0.078* 0.045
FOREIGN BUSINESS GROUP° -0.017 0.044
HEADQUARTER° 0.067 0.060
MEDIUM LOW TECH° -0.096* 0.056
MEDIUM HIGH TECH° -0.086* 0.044
HIGH TECH INDUSTRY° -0.106** 0.049
RANK2° 0.007 0.021
RANK3° -0.018 0.030
SELECTION 0.001 0.000
Jully 2008 5. Conclusion
• Firms are often blind on their R&D network architecture• Awareness decreases with size and R&D• Proximity and colocalization do not matter
= Implicit criticism of the literature on the ability of firms to manage their network and their ability to improve theirperformances thanks to this optimization.
Jully 2008 5.Conclusion
• THANK YOU