Science Technology Links

Aldo GeunaSPRU-University of Sussex

&Politecnico di Torino

DIMETIC, PECS, July,2007

Structure of the Lecture

Research collaborations:The broader framework JVs/CA/STA.

University-industry relationships: Historical background.

University-industry relationships: Typologies and Motivations.

Characteristics of Univ-ind relationships (focus on firms)

University-industry relationships: The importance of searching, screening and signalling

Research collaborations

JVs & CAs

Joint Venture (JV) = new organisational entity jointly owned and controlled by the parents organisations.

Cooperative agreement (CA) = non equity based agreement, can include organisational mechanisms for oversight and management.

JVs and CAs are interorganisational linkages that enable the organisation to manage some of its environmental constraints (quasi-markets, quasi-hierarchies).

Strategic Technology Alliance

Strategic Technology Alliance (STA) = form of cooperation and agreement for which a combined innovative activity or an exchange of technology is at least part of the agreement. Research Joint Venture (RJV).

Prior to 1975 STA were or little or no importance.

Types of STAs

– Simple unilateral contracts ("technology for cash") -e.g. technology licensing,

– Multilateral contracts -e.g. Cross-licensing & Technology sharing,

– Customer-supplier (user-producer) partnership,– Joint development agreement (which often

includes organisational mechanisms for oversight and management),

– ”Pure" equity joint venture.

Theoretical approaches to STA

Mainstream IO analysis of R&D cooperation based on game-theoretic approaches: analysis of strategic behaviour of firms and societal/competitive consequences of R&D cooperation (d’Aspremont and Jacquemin, 1988).

Transaction Costs and incomplete contracts : R&D cooperation as an intermediary organisational form (Williamson, 1996)

Resource based view of the firm /organisational learning: collaboration is seen as a response by organisations to environmental changes demanding improvements in their know-how and/or technological capabilities (Hagedoorn, 1993; Mowery, Oxley & Silverman, 1996).

Main motives for STAs I

– Present rapid changes in technological development ICTs/Biotech/Nano

Acquisition of new technical skills and technological capabilities,

Necessity of monitoring a wide spectrum of technologies.

Main motives for STAs II

– Necessity of quick preemption strategies,– Complexity and uncertainty surrounding

technological development:Need of spreading costs and risks,Coordinating and formulating technical

standards (user-producers, producers in telcom),

University-industry relationships:Historical background

Historical trends

1945-1980s– industry relied on universities mainly for supply of QSEs (e.g. for own R&D

labs) – exercised some influence on curricula in e.g. engineering, chemistry

– knowledge often flowed first through public sector labs before taken up by industrial labs

– industrial support for universities often took form of endowments and gifts (rather than specific project contracts) i.e. no ‘strings’ attached

– responsibility of university was to publish results of research so that available to all (see Fig. 1 in STI Review, No. 23)

Historical trends (continued)

1980s onwards – various changes driven by– increased globalisation, competition and emphasis on

innovation, so firms need to get closer to sources of knowledge creation

– increased speed of knowledge exploitation– budgetary constraints faced by governments and

universities, so latter sought new funding sources

– government policies encouraging technology transfer, collaborative research in key areas, commercialisation of research, U-I links

Historical trends

1980s onwards (continued)– declining profits and/or increasing costs of research

encouraged many firms to outsource more basic research (outsource not only to U but to also others)

– examples/‘heroic myths’ of MIT & Route 128, Stanford

and Silicon Valley, ‘the Cambridge Phenomenon’– Industry increasingly interested in university research

as well as QSEs – seen as offering specific opportunities for cooperation

– In some fields knowledge may flow directly from U to I– Resource flow to U from I no longer limited to

endowments etc.

Historical trends (continued)

US– NSF established University-Industry Cooperative Research Centers

(UICRCs) 1975. e.g. Center for Integrated Systems (Stanford)

– Required changes to regulations on cartels to allow establishment of industrial consortia

– NSF subsequently launched Engineering Research Centers, and Science and Technology Centers

– Later, individual firms signed multi-M$ partnership deals with academic departments (e.g. Monsanto & Washington U, Hoechst and Harvard Med School) concerns re (foreign) firms ‘buying up’ U departments

Historical trends (continued)

UK– establishment of Alvey Programme in early ’80s to foster

collaboration between I and U in IT– followed by variety of other schemes to foster U-I

collaboration

Other OECD countries. Industry still only accounts for under 6% of university

research funding (up just 0.4% since 1991)

Industrial Support for HERD Some OECD Countries (log scale)

1

10

100

1000

10000

1981 1987 1990 1993 1996 1998 2000

Year

$M, 1

995

pri

ces France

Germany

Japan

UK

US

% of HERD financed by industry

1991 1998 1999 2000 2001 2002 2003

France 4.2 3.4 3.4 2.7 3.1 2.9 ..

Germany 7.0 10.5 11.3 11.6 12.2 11.8 12.1

Italy 4.0 .. .. .. .. .. ..

Japan 2.4 2.3 2.3 2.5 2.3 2.6 ..

UK 7.8 7.3 7.3 7.1 6.2 5.8 ..

US 5.3 6.1 6.1 6.0 5.5 4.9 4.5

EU-25 .. 6.4 6.5 6.5 6.7 .. ..

OECD 5.5 6.0 6.1 6.2 6.1 5.9 ..

Source : OECD, Main Science and Technology Indicators, November 2004

University-industry relationships: Typologies and Motivations

EU Foundations

FIRMS UNIVERSITIES (PRO/PRC/etc)

GOVERNMENT

University-Industry-Government Relationships

Typologies of U-I partnerships

STI Review (1999) – typology based on – relative control over outputs – spectrum from full U

control to I control– degree of I involvement – zero for endowments,

reaches peak in research consortia and cooperative centres

– industry expectations re outcomes – range from very few for endowments to much greater when I involved in technology/knowledge transfer from U

Types of U-I partnershipsType of partnership

Description Example

General research support

Monetary gifts, endowments, equipment donations, research facilities

Canada – industry helped to fund >200 NSERC Industrial Research Chairs

Informal research collaboration

Informal partnerships among individual researchers in industry and academia

United States – Center for Computational Genetics and Biological Modelling

Contract research Industry finance for specific research project under contract

Knowledge transfer & training schemes

Advisory exchange programmes & student training placements in industry

UK – Teaching Company Scheme, CASE research studentships

Govt-funded collaborative research projects

Government grants to specific research projects undertaken jointly by industry and universities

Australia – Collaborative Research Grants Scheme

Research consortia

Government-sponsored large-scale research programmes involving several parties

European Union – Framework Programmes

Co-operative research centres

Govt-supported facilities for collab U-I research (including distributed or virtual centres e.g. Canada)

Sweden – NUTEK Competence Centre Programme

Source: STI Review, No.23, p.46

Universities’ motivations for research partnerships

Obtain financial support for its missions. Broaden experience of students and faculty. Identify significant and interesting research

problems. to enhance regional economic development. to increase employment opportunities.

Industry’s motivations for research partnerships

to access research infrastructure. to access expertise. to aid renewal of company’s technology. to gain access to potential employees. to increase pre-competitive research. to be plugged in the open science network.

Conditions for U-I collab’s

More likely to occur in some universities than in others due to differences in:

– disciplines emphasised by the HEI e.g. technological universities (MIT, RPI, Chalmers etc.)

– academic culture of the HEI i.e. different weight given to the various goals of the institution (cf.

B. Clark on ‘entrepreneurial universities’)

Conditions for U-I collab’s

– development strategy of the HEI e.g. new universities in Finland where research oriented

around regional needs (Oulu, Joensuu)

– environment of the HEI e.g. a thriving industrial sector in the region, a science park

(e.g. Research Triangle Park, Cambridge)

Characteristics of successful U-I partnerships

Well-defined objectives, roles and expectations;

Identification of key personnel, duties and restrictions;

Clear funding arrangements; Stable support and flexibility provided by U for

the researcher; IP and publication issues resolved early on (or

ex-ante);

Characteristics of successful U-I partnerships

Relation based on mutual trust, respect (for other partner’s values etc.) and flexibility;

Projects run professionally – deliverables, timelines, financial management;

Continuous communication between principal players for U & I;

Inclusion of dispute resolution methods.

(Source: STI Review, No. 23)

(FIRM) CHARACTERISTICS OF UNIIVERSITY-INDUSTRY

RELATIONSHIPS

Firm Characteristics I

Quantitative analysis based on surveys: Yale, Carnegie Mellon, PACE, CIS II-III, KNOW, National surveys:– Klevorick et al., 1995 US– Meyer-Krahmer and Schmoch (1998) and Beise and

Stahl (1999) national survey Germany;– Arundel and Geuna (2004) PACE EU countries;– Mohnen and Hoareau (2002) CIS II EU countries;– Cohen, Nelson and Walsh (2002) CM USA;– Swann (2002) and Laursen and Salter (2003) CIS III UK.

Firm Characteristics II

Product versus process innovation:– Mixed results.

Openness of the firm (+):– Searching, screening and signalling– The role of demand !!!

Independent (+) versus subsidiaries:– The role of the headquarter.

Firm Characteristics III

The size of the firm affect collaboration:– The larger the more collaboration.

but– Small biotech firms and spin-offs.

The R&D investment and/or R&D intensity:– Absorptive capacity.

Technological sector.

Differences Between TechnologiesFaulkner, Senker and Velho (1995)

Biotechnology– Most formal links– PSR provides help with new experimental techniques

and new recruits

Ceramics– Other companies important source of STI– Government programmes important– PSR gives access to instrumentation/expertise

Parallel Computing– Least formal links; most links with PSR users

Differences Between Technologies(Fontana et al. 2006)

BUT not only high tech & not only manufacturing:– Food industry– Comp. Services

NO OF PROJECTS FOOD CHEMICALS COMM EQ TELECOMM SERV COMP SERV 0 51 49 45 31 60 1 21 6 13 5 10 2 17 24 9 1 10 3 11 11 4 3 10 4 6 11 4 0 4 5 3 5 7 0 4 6 0 2 2 0 0 7 0 0 0 0 1 8 2 0 1 0 0 9 0 0 0 0 2 10 3 1 0 0 3 13 0 0 1 0 1 19 0 0 0 0 1 20 0 1 0 0 1 25 0 0 0 1 0

TOTAL 114 110 86 41 107

University-industry relationships: The importance of searching, screening and

signalling

Fontana, Geuna and Matt 2006

We want to explain why certain firms do cooperate with universities while other don’t (probability of cooperation yes/no);

For the sample of firms that cooperated with university, we want to explain the number of R&D JV that firms had (intensity of cooperation – how many times.

Literature and hypotheses (1)

The degree of openness: import external knowledge and knowledge disclosure on a voluntary basis

– Search strategy: firms look for sources of knowledge (number of

external knowledge channels) (Laursen & Salter 2003)

– Screening activity: selection of a specific relevant source (journals = source of open science, but also of info about scientists)

– Signalling activity: voluntary disclosure (Pénin 2004) – trigger reciprocity, gain feedbacks, network, reputation, higher order knowledge, attract potential partners.

H1: Openness should affect positively the probability and the intensity (different effects).

Literature and hypotheses (2)

The size: – Absolute - (Arundel & Geuna 2004, Mohnen & Hoareau 2003, Cohen et

al 2002, etc.);– Relative to R&D.

H2.1 Larger firms should have a higher probability to cooperate.

H2.2. Firms with larger R&D investment should be involved in a greater # of R&D projects.

Literature and hypotheses (3)

R&D intensity – Active at the technological frontier more reliant on

science (Arundel & Geuna 2004, Schartinger et al. 2001);

– High R&D investment => high absorptive capacity (Cohen & Levinthal, 1990).

H3. The higher the R&D intensity, the higher the probability of cooperating and the greater the number of projects.

Literature and hypotheses (4)

The legal status of the firm:– R&D activities concentrated at a firm’s headquarter;– Independent firms cooperate more with PROs than

firms belonging to a large group (Mohnen & Hoareau 2003).

H4. Within multi-plan firms, headquarters mediate collaboration.

Literature and hypotheses (5)

Type of innovative activities: contrasted results:– Positive relation between radical product innovation

and cooperation with PROs (Mohnen & Hoareau, 2003);

– Companies involved in process innovation are more likely to cooperate with PRO’s than those engaged in product innovation (Swann, 2002).

Data sources

KNOW survey – 2000– 7 EU countries: Denmark, France, Germany, Greece, Italy,

Netherlands, UK

– 5 sectors: food and beverages, chemicals excluding pharma, communications equipment, telecom services and computer services

– 2 size classes: (10-249 employees, 250-999 employees)

– Average response rate: 33% (minus UK)

– 50% of innovative firms (222) signed R&D cooperation with PROs in the 3 years before the survey.

The variables (1)

Openness of the firm :– Number of external sources (fairs and conferences, searching

patent db, reverse engineering, internet) - SEARCH

– Mean % of new innovations introduced in collaboration with partners - ExtCOLL

– Screening publications – PUBLICATIONS

– Government R&D projects – SUBSIDIES

– Patents - PATENTS

SEARCHING

SCREENING

SIGNALLING

The variables (2)

Firm size:– Number of employees - Employees

– R&D employment – R&D

Firm R&D Activity:– R&D intensity – R&DINT

– Outsourcing R&D expenditures – ExtR&D

– Headquarter - HEADQ

The variables (3)

Firm innovative activity– Process innovation – PROCINN

– Product innovation – PRODINN

Country and sector fixed effects – – COUNTRY, – SECTOR.

Estimation: models & results (1)

Negative Binomial Models. Zero Inflated Negative Binomial

– Number of R&D Projects = extent of collaboration;– Propensity for firms to engage in R&D Project =

existence of a relationship (Logit Selection)

(1) (2) (3) (4) (5)

INTERCEPT -2.091 -2.065 -2.601 -2.589 -3.823 [0.51]** [0.56]** [0.62]** [0.61]** [0.91]**

RELATIVE SIZE LN(R&D) 0.375 0.354 0.231 0.187 0.195 [0.07]** [0.08]** [0.08]** [0.08]** [0.09]**

ABS CAPACITY LN(R&DINT) 0.970 1.169 1.440 1.515 1.280 [0.49]** [0.54]** [0.53]** [0.52]** [0.56]**

STATUS HEADQ (DUMMY) 0.440 0.434 0.504 0.539 0.371 [0.16]** [0.17]** [0.18]** [0.18]** [0.21]*

PROCESS (DUMMY) 0.792 0.846 0.710 0.587 0.614 [0.22]** [0.25]** [0.26]** [0.26]** [0.28]**

PRODUCT(DUMMY) 0.703 0.571 0.525 0.404 0.326

TYPE OF

INNOVATIVE

ACTIVITY [0.46] [0.50] [0.50] [0.50] [0.51]

SEARCHING EXTCOLL 0.005 0.004 0.005 0.005 [0.00] [0.00] [0.00] [0.00]

PUBLICATIONS (DUMMY) 0.786 0.777 0.928 [0.24]** [0.24]** [0.29]**

SUBSIDIES (DUMMY) 0.591 0.569 0.581 SCREENING

[0.18]** [0.18]** [0.20]**

SIGNALLING PATENT (DUMMY) 0.429 0.495 [0.17]** [0.19]**

EXT R&D 0.007 [0.00]

SECTOR (DUMMY) YES CONTROLS

COUNTRY (DUMMY) YES

LOG-LIKELIHOOD

-643.11 -550.91 -506.74 -502.64 -418.41

LR CHISQ 67.95** 58.81** 70.20** 76.93** 99.73**

PSEUDO RSQ 0.050 0.050 0.065 0.071 0.106

NO OBS 395 336 304 303 255

LR CHISQ 370.20** 324.04** 268.09** 257.60** 163.34**

ZINB

(6) (7)

Logit Selection

INTERCEPT -1.17 3.35 [0.83] [4.50]

RELATIVE SIZE LN(R&D) 0.15 [0.07]**

ABS CAPACITY LN(R&DINT) 0.83 -3.22 [0.48]* [1.89]*

ABSOLUTE SIZE LN(EMPLOYEES) -0.42 [0.22]*

STATUS HEADQ (DUMMY) 0.08 -1.16 [0.19] [0.63]*

PROCESS (DUMMY) 0.50 -0.32 [0.31] [0.70]

PRODUCT (DUMMY) 0.74 2.78

TYPE OF

INNOVATIVE

ACTIVITY [0.44]* [3.69]

SEARCHING EXTCOLL 0.00 -0.00 [0.00] [0.01]

PUBLICATIONS (DUMMY) -2.05 [0.60]**

SUBSIDIES (DUMMY) -1.58 SCREENING

[0.62]**

SIGNALLING PATENT (DUMMY) 0.44 [0.16]**

EXT R&D 0.01 [0.00]**

SECTOR (DUMMY) YES CONTROLS

COUNTRY (DUMMY) YES

LOG-LIKELIHOOD -369.92

LR CHISQ 60.90**

NO OBS 255

Estimation: models & results (2)

Propensity for firms to engage in R&D Projects with PROs:– Absolute Size (+)– Openness (+): screening (publications + subsidies)– Absorptive capacity (+)– Headquarter (+)

Estimation: models & results (3)

Number of collaborations :– Relative Size: R&D employment (+)– Openness (+): signalling (patents), outsourcing– Absorptive capacity (+)

Estimation: models & results (4)

As in previous literature, the type of innovative activity (process versus product) does not provide any definitive result may be also due to the fact that the large majority of respondents do both.

Country dummies are significant to explain the number of collaborations, not so much the selection.

Sector dummies are not significant except in the case of food and chemicals in the selection model.

Findings (1)

The role of size and R&D activity:– Larger firms have a higher probability to engage in formal

agreements with PROs but the number of R&D project singed depends on the size of the R&D department (do I have enough R&D people).

– Firms with important absorptive capacity (being near the technological frontier) have a higher chance to cooperate and conclude more R&D projects with PROs.

Findings (2)

The role of openness of firms:– Acquiring external knowledge via the screening of

publications and the involvement in public policies affects the probability to cooperate with PROs.

– Signalling competencies via patenting and R&D outsourcing affects the level of collaboration.

Policy implication:– Demand pool policies informed by the idea of firm

openness (in its various specific aspects) as a major driving force.

Conclusions

Conclusions

Increasing partnerships between U, I and research institutes transforming research systems into more collaborative form;

Role of policy makers shifting to building the infrastructure to support communication and collaboration in the NSI (network support policies);

Poses challenges to structure of research funding, management of U’s, assessment of IPR, peer-review process and basis for evaluation;

Conclusions (continued)

U-I research partnerships may bring into conflict differing norms – may require compromise over e.g. timescale (I expect results quicker than U researchers used to), future research agendas;

U’s need to become more permeable, flexible and professionally managed;

Each U needs to determine own optimum profile wrt T, R, U-I partnerships and ‘other ‘3rd mission’ activities, and to monitor effects of latter on T & R;

Conclusions (continued)

University/industry links can benefits the advancement of science, scientists and industry;

Over-emphasis by governments on industrial links may be counter-productive;

Important for industrial links not to prejudice academic independence;

Links may be especially important when new technologies emerge, and become less important as the technologies become established.