STI2014 UICs UPV

INSTITUTE OF INNOVATION AND KNOWLEDGE MANAGEMENT

Do university-industry co-publication volumes

correspond with university funding from

business firms?Joaquín M. Azagra-Caro | Leiden, 4 September 2014

Co-authors: Alfredo Yegros-Yegros, Mayte López-Ferrer, Robert J.W. Tijssen

Introduction

• University-industry interactions are a slightly

controversial source of potential benefits, among other,

to partially contribute to economic development

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• Different indicators to monitor and foster university-

industry interactions (contract research, R&D projects,

patent licenses, creation of start-up companies…)

Major drawback Not freely available

University-industry co-publications

(UICs)

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• One of the very few sources for gathering aggregate-

level proxy measures of university-industry interaction

patterns and trends (Tijssen et al., 2009; Tijssen, 2011)

• Some studies rely entirely on UICs to capture

university-industry interactions

– Analyses of UICs (Calvert and Patel, 2003; Ponds et al 2007)

– Effect of UICs on university commercialization technology

(Wong and Singh, 2013)

Validity of UICs as proxy of university-

industry interactions

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• UICs are a particular type of co-authorship, only partial

indicators of research collaboration (Katz and Martin,

1997)

– One third of the companies providing funding to the university

had not UICs with the university –only 16% of the companies

publishing UICs also provided funding (Lundberg et al. 2006)

• Previous attempts of validation are scarce

– Collaborations might not produce UICs

– Some UICs might not necessarily entail collaboration

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Research question

• Our objective is to contribute to prove whether or not UICs are

good proxies of university-industry interactions

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Do UIC volumes correspond with university funding from

business firms?

• Research question

Business funding is associated with some of the most

frequently occurring university-industry interactions

(e.g. contract research or joint research agreements)

(Roessner, 1993)

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• Need of a conceptual framework

Four types of theoretical relationships between

funding and UIC – an interactive model

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University funding from

business firmst-τ

UICt


business firmst+φ


business firmst

Industry financing University signalling

Industry pull Science push


Data

• Polytechnic University of Valencia (UPV)

• 247 UPV authors of UICs in 2008-2011 (Source: WoS)

• UPV researchers of projects with firms in three periods

(Source: UPV technology transfer office)

– 1,224 in 2000-2007

– 1,004 in 2008-2011

– 482 in 2012-2013

• Name matching of both databases

• Project data includes amount of funding


Types of funding and UIC relationships

at UPV

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Industry financing

University signalling

Industry pull Science push

94%

6%

UPV participants in projects with firms 2000-2007

Non-UICauthors

UIC authors

93%

7%

UPV participants in projects with firms 2008-2011

Non-UICauthors

UIC authors

83%

17%

UPV UIC authors

Non-participantsin projects withfirms 2012-2013

Participants inprojects withfirms 2012-2013

72%

28%

UPV UIC authors

Non-participantsin projects withfirms 2008-2011

Participants inprojects withfirms 2008-2011


business firms2000-2007

UIC2008-2011






Pairwise correlation coefficients (R)

9

• Non-significant statistical relationships

Mean business project

funding(2000-2007)

Mean business project

funding(2008-2011)

# UICs 2008-2011 R = 0.01 R = -0.01

# UICs 2008-2011 (funding 2008-2011)

# UICs 2008-2011 (funding 2012-2013)

Mean business

project fundingR = -0.01 0.15

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Regression analysis – control variables

• Individual characteristics (#projects, %PI, age,

gender, visibility)

• Project characteristics (R&D type, year,

duration, #firms, %foreign)

• UIC characteristics (#authors, %international,

%third affiliations, #citations, year, journal,

area)

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Let’s start with the left half of the picture

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Industry financing

Industry pull



UIC2008-2011




Econometric models of financing and

industry pull

• UIC = f(university funding from business firms)

• But UIC=0 may mean two things:

– Authors wanted to produce UIC and could not, e.g. for

confidentiality or lack of scientific novelty

– Authors did not want to produce UICs, e.g. they used funding

for other purposes or lack of environmental culture

• Correct verification of impact of business funding in two

steps:

– Step 1 UIC(yes/no) = f (university funding from business)

– Step 2 For UIC>0, #UIC = f (university funding from business)


Heckman selection models of financing and

industry pull at the UPV

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• No sample selection bias

• No sign of financing or industry pull effects at the UPV

Step Dependent variable

Coefficient of mean

business project funding

(2000-2007)

Coefficient of mean

business project funding

(2008-2011)

1 UIC (yes/no) 2008-2011 -0.94 -0.18

(1.58) (0.48)

2 # UIC 2008-2011 2.14 -2.01

(4.24) (1.25)

# observations 1,224 1,004


Let’s move to the right half of the picture

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University signalling

Science push

UIC2008-2011






Econometric models of signalling and

science push

• University funding from business firms = f(UIC)

• But business funding=0 may mean two things:

– Researchers wanted business funding but did not get it, e.g.

need of minimum scientific visibility

– Researchers did not want business funding, e.g. to preserve

academic freedom

• Correct verification of impact of UICs on business

funding in two steps:

– Step 1 Business funding(yes/no)=f(UIC)

– Step 2 For funding>0, amount of university funding from

business firms=f(UIC)


Heckman selection models of signalling

and science push at the UPV

• Sample selection bias in science push

• Positive association between UIC and funding: high

(science push) or borderline (signalling) – both in Step

2

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Coefficient of # UICs

2008-2011

(funding 2008-2011)


2008-2011

(funding 2012-2013)

1 Business funding (yes/no) -0.02 -0.05

(0.09) (0.11)

2 Mean business funding 0.01*** 0.12*

(0.00) (0.01)

# observations 247 247

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Heckman selection models of signalling

and science push at the UPV

• Sample selection bias in science push

• Positive association between UIC and funding: high

(science push) or borderline (signalling) – both in Step

2




2008-2011

(funding 2008-2011)


2008-2011

(funding 2012-2013)

1 Business funding (yes/no) -0.02 -0.05

(0.09) (0.11)

2 Mean business funding 0.01*** 0.12*

(0.00) (0.01)

# observations 247 247

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Do UIC output volumes correspond with

university funding from business firms?

• In general, UICs can occur without business

funding, and business funding without UICs –

Answer: ‘no’

• For a minority of authors (those who participate

in business funded projects), there is a positive

association of current UICs and business

funding – Answer: ‘yes’ (partial evidence of a

science push)


Conclusions

• Convenience of an interactive model to capture the

complexity in the relationship between university

funding from business firms and UICs


• UICs as predicting factor: Wong and Singh (2013)

also found a positive effect of UICs on university

technology commercialization

• Scarce overlap between researchers participating in

projects and publication of UICs (consistent with

Lundberg et al, 2006)

• Studies based exclusively on UICs to analyse

university-industry interactions do not fully capture

business funded research – more evidence is needed

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Future research

• Analysis of the relationship of several project-related

features and the generation of UICs (e.g. type of

agreement, duration, gender…)


• Broader approach, including any type of R&D activity

and not only when the source of funding are business

companies

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