what are university-industry research links about?

What are university-industry research links about?

Structure of the Lecture

The university-industry complex: A yet poorly understood system.

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

The Governance of University-Industry Knowledge Transfer: Are Different Models Coexisting?

The university-industry complex

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What do we know?

30 years after the start of the institutionalisation (with policy support) of uni-ind relationships we know something but not yet enough to have a consolidated understanding (conflicting results):– Field/sector effect– Researcher characteristics– University characteristics– Firm characteristics

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Field/sector

Most of the evidence is based on hightech industries and especially biomedical; in most recent years also other fields (engineering) have been increasingly studied; Fields with most intense collaborations.

We still fail to recognize the importance of non hightech fields: see for example Food;

We know very little of the interactions in services (important in the UK case);

Field/sector

Across fields/sectors there are extremely important differences in: – type of knowledge, – the codification of knowledge, – incentives and reward system, – supply or demand led, etc…

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Researcher Characteristics

Recent wave of studies at the individual level:– Previous experience;– Entrepreneurial capacity in raising funding (public

and private);– Seniority and tenure ~– Male ~– Teaching ?

University characteristics

More likely to occur in some universities than in others due to differences in:– Type (disciplinary orientation, local development

focus) of the UNI;– Environment of the UNI;– Culture (more is done in the centre/department and

more is accepted and more will be done …. B. Clark entrepreneurial UNI);

University characteristics

– Quality of the centre/department +/-– Existence of formal infrastructure of KT ?– Size ?

Firm Characteristics I

Quantitative analysis based on surveys: Yale, Carnegie Mellon, PACE, CIS II-III-IV, 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

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.

Firm Characteristics III

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

Product versus process innovation:– Mixed results.

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

Firm Characteristics IV

Countries differences. Technological sector. Distance matters (but not always and not for

all).

University-industry relationships: The importance of searching, screening

and signalling

Roberto Fontana,

Aldo Geuna,

Mireille Matt

Contribution of the paper

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.

We want to test if “openess” of the firm plays a role – e.g. the role of demand

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 (internalisation of spillovers).

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

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

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.

ECONOMETRIC RESULTS

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.

Conclusion (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 signed 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.

Conclusion (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.

The Governance of University-Industry Knowledge Transfer: Are

Different Models Coexisting?

Isabel Bodas Freitas

Aldo Geuna

Federica Rossi

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Research questions

What is the relative importance of the two governance models?

Do firm differ according to the governance model they choose?

Do proximity and collaboration objective explain the importance of institutional collaborations?

None of these questions have yet been addressed by the literature in an exhaustive way

Data & Methodology

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Data

The questionnaire was circulated in October/November 2008

– 1052 valid responses (representative sample validated by the

local Chamber of the Commerce)

Survey asked about

– whether firms engaged in institutional or personal collaborations

in the last three years

– for non-collaborators: reasons for not collaborating

– for institutional collaborators: which universities they collaborated

with, objectives of the collaboration, amount of money spent

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Institutional collaborations

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Personal contractual collaborations

Overall 17.5% of the sample has had a collaboration with at least one univ

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Methodology: Models 1&2

A firm does not decide to collaborate and then select the “best” governance structure to collaborate, institutional or personal.

– A firm may not collaborate (either it has internal competences to solve the technological problem or does collaborate with other partners);

– Collaborate with a personal contract with a researcher;– Develop an institutional collaboration.

We start by running a series of Logit models (to exploit the larger number of observation) then we check our results with a Multinomial Logit model.

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Methodology: Model 3

For those firms that engaged in institutional collaborations– factors that explain the financial investment in

institutional collaborations – Tobin– on the logarithm of one plus the total amount spent in the

collaboration– Regressors as in Model 2 with the addition of:

The objective of the collaboration (R&D, testing, organisation, marketing, etc…)

The location of the university (in the region, in neighbouring regions, in Italy abroad)

Results

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Methodology: Model 1

For those firms that did not engage in institutional collaborations with universities in the last three years:– the choice of establishing personal collaborations vs. not

collaborating Logit model dependent variable: personal collaboration vs. no

collaboration at all Size, Innovative effort, Sourcing knowledge outside,

organizational characteristics (outsource, multinational, expert)

Table 5. Reasons for not collaborating with universities: distribution of answers

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Table 6. Rotated Loading factors of reasons for not having participated in institutional collaborations with universities in the previous 3 years

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Table 6. Logit Model Estimation of Probability of non-institutional collaborators to engage in personal collaborations with Universities

Methodology: Model 2

Are firms engaging in institutional collaborations with universities significantly different from those that either do not cooperate or cooperate with university researchers through personal contract?

2 Logit model Dependent variable: institutional collaboration vs. no

institutional collaboration, Dependent variable: institutional collaborations vs

personal collaboration.

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Table 7. Logit Model Institutional Collaboration with Universities

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Model 3

Total investment

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Table 9. Tobit model of the logarithm of total investment

Summary of results

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Results

In line with results from other empirical literature large firms making innovative efforts (R&D or design

activities) are generally more likely to collaborate with universities.

however, by distinguishing between institutional and personal collaborations, we find that

they are both important channels of knowledge transfer they seem to involve firms with different research

strategies

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Results

Firms that maintained only contractual personal collaboration with university researchers were found: – to invest more into the acquisition of external knowledge than firms that collaborated institutionally, – and to be more likely to rely on external sources of technological knowledge than firms that did not collaborate at all. – These firms also tend to be smaller!!

More open innovation strategies based on multiple forms of collaborations with external partners and on the integration of internal and external R&D.

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Results

Our analysis of the amount of investment in institutional

collaborations with universities suggests that R&D and

technological development activities require the highest

investment, followed by testing and organizational

problem solving. The higher the number of objectives,

which to a certain extent facilitates the absorption of

knowledge developed, the higher the level of investment

in the collaboration.

Results

The higher the number of geographical areas with which the firm maintains university collaborations, the more it invests in collaborations.

The number of links with local universities is associated with higher levels of investment, while collaboration with international universities does not significantly affect the level of investment in university collaboration.

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