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Cooperation University–Industry: A Systematic
Literature Review
Natalia Figueiredo
*
Instituto Polit
ecnico de Viseu & CISeD - Center
of Investigation in Digital Services, Viseu, Portugal
University of Beira Interior
NECE Research Unit in Business Sciences Covilh~
a, Portugal
nafi@estv.ipv.pt
Cristina Fernandes
University of Beira Interior & NECE Research Unit in Business Sciences
Covilh~
a, Portugal
Centre for Corporate Entrepreneurship
and Innovation at Loughborough University, UK
cristina.isabel.fernandes@ubi.pt
Received 13 June 2020
Accepted 12 January 2021
Published 5 May 2021
Knowledge and its transference are increasingly viewed as key factors of companies' competi-
tiveness. In this sense, our research aims to analyze how the knowledge transfer takes place
between the higher education sector and the companies. Although there has been an increase in
research related to University–Industry (U–I) cooperation, the existing literature is still rela-
tively fragmented and lacks a comprehensive view. In this way, this study aims to ¯ll this gap by
reducing the existing gap in the literature. Thus, this study aims at identifying the di®erent
trends and themes prevailing in the literature on U–I cooperation. Through a systematic lit-
erature review, using a bibliometric analysis, we identify four themes: (1) Triple Helix, (2)
Knowledge Transfer, (3) Determinants of Cooperation and (4) Strategic Alliances. This re-
search makes several important contributions: this review helps highlight not only what the
previous literature has analyzed about cooperation between U–I but also prepares the ground
for the second wave of research on this topic, synthesizing the main gaps in knowledge and the
emerging trends in studies. Another contribution is the challenge of several prevailing theo-
retical/conceptual assumptions in cooperation between U–I and o®ering new theoretical/con-
ceptual perspectives that may shape future research on this topic. Last but not the least, this
paper de¯nes a roadmap for a future research agenda by proposing multiple directions that can
open new avenues for future research and the construction of relevant and appropriate theories
for measuring the contributions of cooperation between U–I.
Keywords: Cooperation university–industry, innovation, systematic literature review.
*
Corresponding author.
International Journal of Innovation and Technology Management
Vol. 17, No. 8 (2020) 2130001 (36 pages)
#
.
cWorld Scienti¯c Publishing Company
DOI: 10.1142/S0219877021300019
2130001-1
1. Introduction
In the current scenario of globalization and knowledge-based economies, companies
are forced to establish partnerships to become more competitive, achieving, for that,
knowledge based on innovation and on a set of speci¯c skills that highlight their
potential [Fern
andez-L
opez et al. (2019)]. The knowledge generated in universities
and the °ow of trained people inserted in the productive structure of the economy is
understood as a prerequisite for technological and economic development [Bodas
Freitas et al. (2013)]. In this context, companies seek with universities the acquisi-
tion of new ideas, the development of new capacities, access to the latest academic
research, access to government funding and a reduction in costs in I&D [Perkmann
et al. (2011); Wallin et al. (2014)].
Cooperation between university and industry (U–I) is recognized as being an
important pillar of economic development contributing to social inclusion, the cre-
ation of quali¯ed jobs and the increase of companies' competitive advantages
[Bercovitz and Feldman (2006); Giuliani and Arza (2009); Rampersad (2014)]. U–I
cooperation has to do with all the interactions that occur between any institution in
the higher education system and industry whose objective is to stimulate the ex-
change of technology and knowledge [Bercovitz and Feldman (2006); Bekkers and
Bodas Freitas (2008)], and there was an increase in pressure for this cooperation
[Giuliani and Arza (2009)]. For the industry, pressures arise due to constant tech-
nological changes, shorter product life cycles and intensi¯ed global competitiveness
[Wright et al. (2008)]. For universities, the pressure is on the acquisition of new
knowledge, cost reduction and ¯nancing problems [Hagen (2002)].
There are studies that aim to understand the role of knowledge exchange and
cooperation between U–I, namely with regard to innovation and technological
changes [Schartinger et al. (2002)]. Universities contribute to industrial innovation,
not only through technological development, but because it enhances a variety of
interactions that are not restricted to some speci¯c industries and ¯elds. On the
contrary, there are a large number of scienti¯c disciplines and almost all sectors of
economic activity which exchange knowledge [Schartinger et al. (2002)]. The im-
portance of knowledge transfer between U–I is unquestionable [Aiello et al. (2019)].
According to Schartinger et al. [2002], universities and industry use a variety of
channels in the knowledge transfer process. The channels used vary according to the
intensity of the personal relationships that are established, according to the type of
knowledge that is transferred and according to the °ow of knowledge. On the other
hand, for cooperation to take place, trust between partners is a key factor. For Eom
and Lee [2010], the intellectual property right of each sector is one of the char-
acteristics that a®ects the relations established between U–I.
The transfer of knowledge between U–I takes place at various levels, namely the
generation of R&D projects, consultancy and technical assistance, di®usion of
technology, promotion of international cooperation [Badillo et al. (2017)] and
complex new ideas and innovations [Fontana et al. (2006)]. In the case of successful
innovations, cooperation with universities or research centers tends to be more as-
sociated with product innovation than with process innovation and the creation of
N. Figueiredo & C. Fernandes
2130001-2
patents [Eom and Lee (2010)]. Companies are willing to cooperate with universities
in order to bene¯t from the knowledge of teachers and students, saving costs [Eom
and Lee (2010)]. If companies give importance to cooperation and information
publicly available from universities, they will be able to increase the productivity of
their innovation activities [Badillo et al. (2017)]. Schartinger et al. [2002] emphasized
the importance of U–I cooperation, stating that companies depend heavily on the
progress of science and technology.
Cooperation between U–I is seen as a source of growth because it encourages
knowledge transfer, boosting innovation and the performance of companies. Thus,
studying and knowing the determinants that allow this cooperation is crucial [Aiello
et al. (2019)]. For example, Schartinger et al. [2002] studied the importance of the
sector of activity arguing that the U–I link is more important in industries based on
science and technology; Bellucci and Pennacchio [2016] examined the transnational
di®erences in the characteristics of innovation systems and the role of universities; Lee
[1996] explored the relationship between U–I according to several variables and where
he can conclude that seniority is one of the relevant factors of scienti¯c prestige;
another determinant that has been the object of study to understand cooperation
between U–I is the size of the company [Badillo et al. (2017); Fontana et al. (2006)].
A signi¯cant aspect of this process is the multidisciplinary nature of knowledge
production that allows interaction between science, technology and government
policies in developed and developing countries [Giuliani and Arza (2009)]. Hence, the
central need for a government structure is capable of promoting the alignment of
cooperation objectives between U–I, solving problems and establishing clear rules for
all interested parties [Alves et al. (2015;Eom and Lee (2010)].
The Triple Helix Model is structured around the relationships between three
institutional spheres: university–industry–government (U–I–G) [Etzkowitz and Ley-
desdor® (2000)]. According to this model, universities need to be directly linked to
industry in order to maximize knowledge and knowledge transfer, helping with eco-
nomic development beyond teaching and research [Etzkowitz and Leydesdor® (2000)].
For Park and Leydesdor® [2010], Triple Helix is an indicator that should be used to
examine the relationships that are e®ectively established between U–I–G, so that they
can work together. Thus, government policies play a key role in promoting collaboration
between U–I[Perkmann et al. (2011)], namely through public funds to encourage
research, enable private development [Aiello et al. (2019); Badillo et al. (2017)] and to
encourage regional technological development [Chen et al. (2016); Johnson (2008)].
Despite the increasing cooperation between U–I, this interaction has not yet
reached its maximum potential. It is then necessary to introduce resources, through
government stimuli that will help to overcome the perceived barriers in this rela-
tionship [Alves et al. (2015)].
For some authors, the low rate of collaboration with universities and research
centers results on the little awareness by Small and Medium Enterprises (SMEs)
about the real possibilities o®ered by universities [Badillo et al. (2017)]. However,
and although few, there are studies that argue the opposite, claiming that small
companies tend to be more prone to cooperation because they face a lack of internal,
¯nancial resources, R&D capacity or even facilities [Eom and Lee (2010)]. The
Cooperation University-Industry
2130001-3
adoption of modern management practices should be a priority for companies as it
can be an e®ective way of reaching high standards of collaboration with universities,
promoting innovation and taking advantage of the rapid, economic and sustainable
competitive advantages that can result from that [Aiello et al. (2019)].
The relationship between U–I has been widely described in the literature
[Perkmann et al. (2013); Mascarenhas et al. (2018); Vick and Robertson (2018); Sj€
o€
o
and Hellstr€
om(2019)] and several Systematic Literature Reviews (RSL) were found.
Sj€
o€
oand Hellstr€
om[2019] identi¯ed seven main factors that stimulate collaborative
innovation among U–I, including resources, university organization, comprehensive
functions, collaborative experience, culture, centrality of status and environmental
context. For Vick and Robertson [2018] the mechanisms and means used for U–I
cooperation depend on the motivations and barriers that are consequences of the
social and political factors that arise in the collaboration between the agents. Thus,
they identi¯ed four central measures related to U–I collaboration: motivations, ac-
tivities, barriers, and results. Rybnicek and K€
onigsgruber [2018] prepared a sys-
tematic review of the literature on the factors that a®ect U–I cooperation, presenting
the signi¯cant factors in four categories, namely institutional factors, relationship
factors, production factors and structural factors. Perkmann et al. [2013] developed a
research that aimed to analyze how academic involvement di®ers from commer-
cialization (in the sense of exploiting patented inventions). On the other hand, they
identi¯ed the individual, organizational and institutional antecedents and con-
sequences of academic involvement and then made a comparative analysis within the
antecedents and consequences of commercialization.
So far, approaches to systematic review of quantitative literature in the context of
U–I studies are limited, especially when it comes to capturing the latest develop-
ments in the ¯eld [Teixeira and Mota (2012); Perkmann et al. (2013); Meyer et al.
(2014); Davey et al. (2016); Mascarenhas et al. (2018); Skute et al. (2019)]. As
Schmidt and Hunter [2004] argue, this limitation is surprising, since narrative
reviews can include questions of sampling, measurement, and stochastic and external
validity and generally do not allow quantifying the relationships. In addition, nar-
rative reviews often incorporate several normative and cognitive biases by the re-
searcher [Rosenbusch et al. (2011)]. In turn, by employing a quantitative approach
to our systematic literature review, we unveil the scienti¯c roots of U–I research and
identify current thematic areas and emerging patterns in the ¯eld. In addition, when
conducting a qualitative content analysis, based on the articles identi¯ed, we gen-
erate di®erent perceptions about relevant future research directions.
In accordance with the objective of this study, the following research question was
formulated:
Q1 —What are the main thematic areas related to U–I cooperation?
This study brings several important contributions. This SLR study presents an SRL
on U–I cooperation using bibliometric techniques, analyzing the in°uencing factors
and determinants necessary for U–I cooperation, as well as the results that can be
obtained through it. This RSL helps to identify not only what the existing literature
N. Figueiredo & C. Fernandes
2130001-4
has analyzed regarding U–I cooperation, but also sets the stage for a second wave of
research on this topic, summarizing the main de¯ciencies in knowledge and de¯ning
directions for future studies. Second, a conceptual model was proposed that can
serve as a contribution to future analyzes of the theme. Third, we de¯ne a roadmap
for an informed research agenda, proposing multiple, but clearly de¯ned directions:
the use and development of an innovative theory capable of opening new paths for
future research and theoretical construction; a more sophisticated understanding of
the concept and its applicability; address de¯ciencies related to content at di®erent
levels of analysis; and also the implementation of relevant and appropriate meth-
odologies to measure U–I cooperation.
A rigorous research protocol was followed to prepare this SLR [Tran¯eld et al.
(2003)]. The search for articles was carried out in the Web of Science database [Gas-
paryan et al. (2013)]. The de¯ned research protocol enabled the inclusion of109 articles
in the investigation, which were subsequently submitted to bibliometric analysis
bibliographic coupling to obtain a similarity relationship between the articles grouping
them by clusters. The results allowed to identify four distinct clusters: (1) Triple Helix,
(2) Knowledge Transfer, (3) Determinants of Cooperation, and (4) Strategic Alliances.
This review of U–I cooperation will not only enrich the existing literature, but the
structure it presents will also contribute to a deeper understanding of the U–I co-
operation process. The article also presents a research agenda.
The paper is organized as follows. Section 2describes the methodology used and
the database used in the research study. Section 3presents the results. Section 4
emphasizes the discussions and future lines of research and, ¯nally, Sec. 5presents
the conclusions of the research.
2. Methodology
This study was based on an SLR of the literature where it is intended to organize,
evaluate and synthesize literature, identifying patterns, trends and gaps in future
research [Tran¯eld et al. (2003); Gough et al. (2012)], based on the topic cooperation
between universities and industry. According to Tran¯eld et al. [2003] SLR should
be developed based on a rigorous research protocol for minimizing the bias.
In order to achieve the proposed objectives, this study was based on a biblio-
metric analysis. The research conducted used the 1.6.13 version of the VOSviewer
software to draw up and present bibliometric maps and to identify clusters and their
references. The bibliographic coupling of documents was used because it presents
advantages over other methods, such as co-citation or direct citation, both in terms
of precision and in the grouping of articles [Boyack and Klavans (2010)]. The bib-
liographic coupling of documents method uses citation analysis to establish a simi-
larity relationship between documents. Thus, the more references they cite, the more
common the technical background on which they are based [Kessler (1963)].
The research was based on the collection of articles using the Web of Science
database and no time restrictions were set. This database was chosen due to its
prestige, relevance and coverage [Gasparyan et al. (2013)] that ensures the quality
and diversity of the articles used.
Cooperation University-Industry
2130001-5
The research was narrowed using the words \cooperation" AND \university–
industry" AND \Innovation" as topic. The search conducted found 280 articles. We
further limited the search to articles published within the research area of Business
Economics and written in English. The application of these ¯lters led to a reduction
to 109 articles.
Finally, these 109 articles were submitted to the VOSviewer software where we
started by \creating a map based on bibliographic data", with the articles collected
from the WoS database. After, we select \bibliographic coupling" and 10 a
\minimum number of citations of a document" and \5" as \minimum of cluster
size". The application of the software allowed the identi¯cation of four clusters,
leaving only 51 articles. After obtaining the clusters in VOSviewer, we did an in-
depth reading of each of the papers present in the respective clusters. This reading
results in the description that can be read in each cluster, as well as the name given
to each one of them. Finally and according to Paul and Criado [2020] we can say that
our research is classi¯ed as a Bibliometric Review. Bibliometric reviews analyze an
extensive amount of published research by using statistical tools, thus to ¯gure out
trends and citations and/or co-citations of a particular theme, by year, country,
author, journal, method, theory, and research problem. A graphical bibliometric
review can be developed using Viewer software programs currently available such as
VoS (Visualization of Similarities), which is widely used to carry out such a type of
Fig. 1. Research protocol.
N. Figueiredo & C. Fernandes
2130001-6
bibliometric review in diverse subject areas, including U–I cooperation [Mascarenhas
et al. (2018)]. An issue inherent in many bibliometric analyses is that out of a given
pool of articles, a relatively small number of articles represent a major part of the
total citations in the analysis [Paul and Criado (2020)].
The research was conducted on December 16, 2019.
The research protocol is presented in Fig. 1.
3. Results
Figure 2shows the evolution in the number of publications and citations of the
109 articles obtained for analysis from 1995 to 2020. The number of citations has
evolved, reaching its maximum in 2019, with 508 citations. The ¯rst article on the
theme appeared in 1996 and, to date, it was in 2019 that it reached its maximum.
From the 109 articles obtained through this research, 16 (14.68%) do not have
citations and 58 (53.21%) have less than 10 citations.
The most cited article focuses on the analysis of the determinants that allow
cooperation between ¯rms and Public research organizations based on small- and
medium-sized enterprises [Fontana et al. (2006)]. Table 1cites the 10 most cited
articles.
In order to identify trends in the literature related to U–I cooperation, the re-
search on this topic was divided into clusters, based on an analysis of bibliographic
coupling with articles with at least 10 citations, resulting in a total of 51 articles.
Thus, as a result of the analysis of the 109 articles, four clusters were obtained, as it
can be seen in Fig. 3 Vosviewer screenshot. The clusters are (1) Triple Helix, (2)
Knowledge Transfer, (3) Determining Factors for Cooperation and (4) Strategic
Alliances.
3.1. Cluster 1: Triple helix
Table 3presents the articles that are part of cluster 1 where the importance
of the government is addressed, namely the Triple Helix model, when U–I coopera
Fig. 2. Evolution of de number of publications/citations.
Cooperation University-Industry
2130001-7
Table 1. Top 10 of the most cited articles.
Article AuthorsYear Journal
Total
citations Methodology
Factors a®ecting university–industry R&D projects: The importance
of searching, screening and signaling
Fontana et al. [2006] Research Policy 307 Quantitative
Knowledge interactions between universities and industry in Austria:
sectoral patterns and determinants
Schartinger et al. [2002] Research Policy 306 Quantitative
Technology transfer' and the research university: A search for the
boundaries of university–industry collaboration
Lee [1996] Research Policy 274 Quantitative
Determinants of industry-academy linkages and, their impact on ¯rm
performance: The case of Korea as a latecomer in knowledge in-
dustrialization
Eom and Lee [2010] Research Policy 117 Quantitative
What drives the formation of `valuable' university–industry linkages?
Insights from the wine industry
Giuliani and Arza [2009] Research Policy 116 Quantitative
The role of science parks and business incubators in converging
countries: Evidence from Portugal
Ratinho and Henriques
[2010]
Technovation 108 Quantitative
Longitudinal trends in networks of university–industry–government
relations in South Korea: The role of programmatic incentives
Park and Leydesdor®
[2010]
Research Policy 106 Mixed
Determinants of university-¯rm R&D collaboration and its impact on
innovation: A perspective from a low-tech industry
Maietta [2015] Research Policy 103 Quantitative
Knowledge acquisition in university–industry alliances: An empirical
investigation from a learning theory perspective
Sherwood and Covin
[2008]
Journal of Product Innnovation
Management
98 Quantitative
Leveraging knowledge, learning, and innovation in forming strategic
government-university–industry (GUI) R&D partnerships in the
US, Germany, and France
Carayannis et al. [2000] Technovation 93 Qualitative
N. Figueiredo & C. Fernandes
2130001-8
tion through government measures and laws, as well as through cooperation
platforms.
According to Brem and Radziwon [2017], collaboration between universities and
companies can be a key factor for the growth of regional business ecosystems. The
sustainability of companies comes from innovation and this, in turn, comes from
cooperation between U–I. Since the 1970s, the interaction between U–I has become
formal, frequent and planned allowing the development of research, development,
innovation and commercialization initiatives [Farinha et al. (2016)]. The links that
are established between U–I are di®erent, with inevitably more valuable links than
others, namely those that are based on the potential for knowledge di®usion. For
Jones and Zubielqui [2017], it is the transfer of human resources (employment of
recent graduates, graduates, professional training for employees) that generates
bene¯ts that can result in innovation and in turn to the growth and productivity of
companies. So, the university that generated knowledge must be directly or indi-
rectly disseminated by the industry [Giuliani and Arza (2009)].
The acquisition of knowledge is fundamental for innovation and for that, SMEs
rely on universities and other (non-university) institutions for this purpose, for ex-
ample through spillovers from long-term R&D and growth improvements in the
value chain [Zubielqui et al. (2015)]. The universities with specialized technology
transfer o±ces promote the performance of knowledge transfer based on U–I coop-
eration. Thus, the greater the relational capital, that is to say, cooperation, the more
positive the results in research and the performance of knowledge transfer (academic
research and patent technology) to the industry [Feng et al. (2012)]. The growing
perception of the potential bene¯ts resulting from cooperation between U–I has been
Fig. 3. Clusters.
Cooperation University-Industry
2130001-9
Table 2. Presents the authors that are part of each cluster.
Cluster 1–17 articles Cluster 2–13 articles Cluster 3–11 articles Cluster 4–10 articles
Azagra-Caro et al. [2006]Bellucci and Pennacchio [2016]Arvanitis et al. [2008]Bstieler et al. [2015]
Brem and Radziwon [2017]Berbegal-Mirabent et al. [2015]Ballesteros and Rico [2001]Carayannis et al. [2000]
de Moraes Silva et al. [2018] Bonaccorsi et al. [2014]Lehmann and Menter [2018]Gubbins and Dooley [2014]
Zubielqui et al. [2015] Franco et al. [2014]Maietta [2015]Johnson and Johnston [2004]
Eom and Lee [2010]Muscio and Vallanti [2014]Nishimura and Okamuro [2011]Lee [1996]
Farinha et al. [2016]Ratinho and Henriques [2010]Okamuro and Nishimura [2013]Plewa et al. [2013]
Feng et al. [2012]V
asquez-Urriago et al. [2016]Scandura [2016]Santoro and Saparito [2006]
Giuliani and Arza [2009]Sharif and Tang [2014]Schartinger et al. [2002]Sherwood and Covin [2008]
Heitor [2015]Morandi [2013]Slavtchev [2013]Simeth and Ra®o [2013]
Johnson [2008]Park et al. [2015]Kobarg et al. [2018]Fontana et al. [2006]
Jones and Zubielqui [2017]von Raesfeld et al. [2012]Wirsich et al. [2016]
Park and Leydesdor® [2010]von Raesfeld et al. [2012]
Robin and Schubert [2013]L
opez et al. [2015]
Bellucci et al. [2019]
Frenken et al. [2010]
Lee and Kim [2016]
Ponds [2009]
N. Figueiredo & C. Fernandes
2130001-10
Table 3. Articles of cluster 1.
Authors Article Journal Contribution
Eom and Lee [2010] Determinants of industry-academy linkages
and, their impact on ¯rm performance:
The case of Korea as a latecomer in
knowledge industrialization
Research Policy Identi¯cation of the determinants for cooperation be-
tween industry university and industry
Government Research Institutes, and their impact
on company performance. It was possible to verify
that government support plays a fundamental role
mainly in R&D projects
Giuliani and Arza
[2009]
What drives the formation of `valuable' uni-
versity–industry linkages? Insights from
the wine industry
Research Policy Exploring the factors that drive the formation of links
between university and industry. The importance of
knowledge transfer was also analyzed, in business
growth and development
Park and Ley-
desdor® [2010]
Longitudinal trends in networks of universi-
ty–industry–government relations in
South Korea: The role of programmatic
incentives
Research Policy Analysis of research relations established between uni-
versity–industry–government using the triple helix
indicator. It was found that government intervention
will depend on the economic structure and culture of
each country
Robin and Schubert
[2013]
Cooperation with public research institutions
and success in innovation: Evidence from
France and Germany
Research Policy Evaluation of the impact on companies, cooperation
with public research regarding product and process
innovations. It was found that innovation and eco-
nomic development re°ect the context in which it
occurs
Azagra-Caro et al.
[2006]
Faculty support for the objectives of univer-
sity–industry relations versus degree of
R&D cooperation: The importance of re-
gional absorptive capacity
Research Policy To understand how regions analyze innovation through
pressure for cooperation with European universities,
it is important to pay attention to government sup-
port, political measures and the country's culture
Johnson [2008] Roles, resources and bene¯ts of intermediate
organizations supporting triple helix col-
laborative R&D: The case of Precarn
Technovation An intermediary organization can assist Triple H
elix
partners in the successful commercialization of new
technologies. Thus, government intervention should
be based on the culture, organizational functioning,
incentive mechanisms and objectives of each actor
Cooperation University-Industry
2130001-11
Table 3. (Continued )
Authors Article Journal Contribution
Farinha et al. [2016] Networks of Innovation and Competitiveness:
A Triple Helix Case Study
Journal of the Knowledge
Economy
The processes of transferring knowledge and technology
that occur between U–I, through an EU-funded
R&D project, should have government intervention
that will have to leverage opportunities for cooper-
ation and eliminate barriers that may exist
Feng et al. [2012] The role of intellectual capital and university
technology transfer o±ces in university-
based technology transfer
Service Industries Journal Development of a theoretical model to explain relation-
ships between intellectual capital, research results
and knowledge transfer performance
Specialized technological o±ces
Zubielqui et al.
[2015]
Knowledge transfer between actors in the in-
novation system: A study of higher edu-
cation institutions (HEIS) and SMES
Journal of Business and In-
dustrial Marketing
Small and medium-sized companies access knowledge
through external actors and higher education insti-
tutions, increasing access to knowledge and innova-
tion. In this context, government policies play a key
role
Heitor [2015] How university global partnerships may fa-
cilitate a new era of international a®airs
and foster political and economic relations
Technological Forecasting
and Social Change
Structured international relationships can act as agents
of change if associated with activities other than the
role of universities. Thus, the change in the paradigm
of universities implies a change in thinking in in-
dustry and government
Jones and Zubielqui
[2017]
Doing well by doing good: A study of uni-
versity–industry interactions, innova-
tioness and ¯rm performance in
sustainability-oriented Australian SMEs
Technological Forecasting
and Social Change
Human resources play a positive role in the innovation
capacity of small and medium-sized companies, and
the capacity for innovation is in turn related to the
performance of companies.
Thus, there is a positive e®ect on U–I interactions on the
results of innovation and the performance of small
and medium-sized enterprises
Brem and Radziwon
[2017]
E±cient Triple Helix collaboration fostering
local niche innovation projects A case
from Denmark
Technological Forecasting
and Social Change
U–I cooperation is considered an important factor in the
growth of regional ecosystems. As such, e±cient
Triple H
elix collaboration is needed to promote and
support niche innovations
N. Figueiredo & C. Fernandes
2130001-12
Table 3. (Continued )
Authors Article Journal Contribution
de Moraes Silva et al.
[2018]
University–industry R&D cooperation in
Brazil: a sectoral approach
Journal of Technology
Transfer
For sectors other than the most cooperation-intensive
outliers, the main determinants of university–indus-
try collaboration are size, extramural R&D, and
product innovativeness.
Bellucci et al. [2019] Public R&D subsidies: collaborative versus
individual place-based programs for SMEs
Small Business Economics The allocation of grant programs, aimed at SMEs'
investments in individual research projects and the
other focused on collaborative research projects be-
tween SMEs and universities, have di®erent e®ects if
we are talking about small and medium-sized com-
panies.
Frenken et al. [2010] The citation impact of research collaboration
in science-based industries: A spatial-in-
stitutional analysis
Papers in Regional Science Shows the relationship that the impact of regional, na-
tional or international collaboration has on science-
based industries
Lee and Kim [2016] Analyzing interaction in R&D networks using
the Triple Helix method: Evidence from
industrial R&D programs in Korean gov-
ernment
Technological Forecasting
and Social Change
Government intervention is seen as an engine in R&D
network interactions in national R&D programs in
Korea
Ponds [2009] The limits to internationalization of scienti¯c
research collaboration
Journal of Technology
Transfer
Despite international collaboration between academic
and non-academic organizations occurs frequently,
the collaboration between academic and non-aca-
demic organizations is less likely to occur than col-
laboration between academic organizations, at the
international level.
Cooperation University-Industry
2130001-13
notorious. So, international collaboration between academic and non-academic
organizations occurs frequently [Ponds (2009)].
de Moraes Silva et al. [2018] analyzed the determinants of cooperation between
U–I by dividing them into two groups of distinct variables, internal and external. In
terms of internal characteristics, they evaluated the size of the company, product
innovation and process innovation. In terms of external characteristics, they eval-
uated the market and government policies, such as economic risk, cost of innovation
and government ¯nancing.
The cooperation between U–I can be di±cult to create and sustain for several
reasons, including the lack of socio-economic conditions, such as the prevalence of
SMEs and the lack of tradition in cooperation with the scienti¯c base [Azagra-Caro
et al. (2006)], culture, organizational functioning, incentive mechanisms, as well as
the di®erentiation of objectives of each of them [Johnson (2008)].
A new paradigm of relationship arises between government and industry inter-
vention in association with knowledge Triple Helix model. According to Heitor
[2015], the relationships between U–I–G can act as agents of change if associated
with training, social behavior and economic appropriation of knowledge. In this
process, universities play a di®erent role from the traditional one. According to Park
and Leydesdor® [2010], di®erentiation is imposed within each of the vectors of the
Triple Helix model, i.e. U–I–G. Universities intend to publish scienti¯c articles,
the industry wants to make money from cooperation and the government represents
the public power. For these authors, government intervention will depend on each
nation's economic structure and research portfolio.
Interactions between Triple Helix players can improve countries' R&D capabil-
ities. In this context, government policies must carefully analyze the unwanted
e®ects that they may have. It will then be up to the government, in addition to
implementing the policies, to check the existing feedback from universities and in-
dustry, encouraging R&D networks [Azagra-Caro et al. (2006); Lee and Kim (2016)].
Although the award of government grants is bene¯cial, the results vary according to
the type of cooperation established. Government incentives attributed to companies
for the development of research projects have improved the performance of com-
panies. On the other hand, the government incentives that are attributed to com-
panies to cooperate with universities have not resulted in such positive results,
leading to a concern in expenditure on R&D and employment [Bellucci et al. (2019)].
According to Frenken et al. [2010] the impact of research collaboration is greater
when international collaboration is involved. However, there are industries, such as
biotechnology and organic chemistry, in which collaborations between U–I–Gona
regional scale bring more advantages. There are factors like physical proximity
[Frenken et al. (2010)], company size and intensity in R&D that are not determining
factors for cooperation between U–I–G[Eom and Lee (2010)].
According to Eom and Lee [2010] government support, namely through partici-
pation in national R&D projects, is one of the most signi¯cant and robust factors
both for cooperation between U–I and for cooperation between industry–govern-
ment. This analysis re°ects the importance that government policies play as facil-
itators of cooperation between the I–U–G. However, with regard to political
N. Figueiredo & C. Fernandes
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implications, public-private cooperation should not be encouraged at all costs, as it
may not contribute to all forms of innovation [Robin and Schubert (2013)].
3.2. Clusters 2: Knowledge transfer (KT)
Table 4presents the articles that are part of cluster 2 that addresses Knowledge
Transfer (KT) as a result of U–I cooperation, identifying some examples and some
limitations.
The markets and technology are constantly changing, so the traditional practice
of relying exclusively on internal R&D can lead companies to face a major challenge
[Park et al. (2015)]. Few studies have been developed that have focused on the
interaction between U–I, more speci¯cally on the performance of their collaboration.
According to von Raesfeld et al. [2012], there is a strong positive impact on tech-
nological diversity and on the complementarity of the value chain of partners in the
performance of public R&D projects.
Von Raesfeld et al. [2012] explained innovation based on two factors, namely the
diversity of partners and social incorporation. For these authors, the heterogeneity of
resources is a signi¯cant factor insofar as participants from di®erent sectors and
di®erent roles in the value chain can bring great advantages in the creation of radical
technology innovations. The search for knowledge from external sources has allowed
companies to improve their innovation capabilities. The entrepreneurial capacity of
universities, as well as their scienti¯c quality, are key factors when transferring
knowledge to companies [Bellucci and Pennacchio (2016)]. The universities are
under some pressure to cooperate with industry. However, there are factors (in-
equality in incentives, lack of procedures, con°ict of objectives and the nature of the
research, namely distance between university and industry) that can hinder this
cooperation, particularly regarding knowledge transfer. Companies that are associ-
ated with innovative activities, and particularly with product innovation, tend to be
more interested in collaborating with universities [L
opez et al. (2015)]. Continuous
and long-term cooperation can help to reduce con°icts and barriers, thus allowing
cooperation that bene¯ts both parties [Muscio and Vallanti (2014)]. On the other
hand, companies that have a strategy with a vision of radical innovation, high
technology companies and companies with high absorption capacity will be more
likely to capture the knowledge transmitted by universities [Bellucci and Pennacchio
(2016)]. The organizational and institutional characteristics as well as the location of
the university are considered determining factors for successful R&D partnerships
[Berbegal-Mirabent et al. (2015); L
opez et al. (2015)]. For Franco et al. [2014] the
personal and professional characteristics of the researchers a®ect the decision to
cooperate. In personal characteristics, variables such as gender, age and university
in°uence cooperation between U–I, while in professional characteristics, meetings,
conferences and publications are the basis of cooperation. Competitive advantages,
opportunities for ¯eld experiences, ¯nancing academic activities and transferring
knowledge and technology are some of the bene¯ts that can result from cooperation
between U–I[Morandi (2013)].
Cooperation University-Industry
2130001-15
Table 4. Articles of cluster 2.
Authors Article Journal Contribution
Ratinho and Henriques [2010] The role of science parks and business
incubators in converging countries:
Evidence from Portugal
Technovation Science Parks and Business Incubators are tools for eco-
nomic growth, particularly at the regional level
V
asquez-Urriago et al. [2016] Science and Technology Parks and coop-
eration for innovation: Empirical evi-
dence from Spain
Research Policy The results show that the location in an Science and
Technology Parks increases the likelihood of cooperation
for innovation and the intangible bene¯ts of cooperation
with the main innovation partner, mainly due to a more
diversi¯ed relationship
Berbegal-Mirabent et al. [2015] University–industry partnerships for the
provision of R&D services
Journal of Business
Research
Results indicate that successful R&D contracts depend on
university and TTO characteristics, and the university's
location. The are also presented a set of managerial
implications for improving the establishment of univer-
sity–industry partnerships
Bellucci and Pennacchio [2016] University knowledge and ¯rm innova-
tion: evidence from European coun-
tries
Journal of Technol-
ogy Transfer
That ¯rms oriented toward open search strategies and
radical innovations are more likely to draw knowledge
from universities. Furthermore, ¯rms belonging to high
technology sectors and ¯rms with high absorptive ca-
pacity place greater value on the various links with
universities
Muscio and Vallanti [2014] Perceived Obstacles to University–Indus-
try Collaboration: Results from a
Qualitative Survey of Italian Aca-
demic Departments
Industry and Inno-
vation
Obstacles were identi¯ed, i.e. barriers to university–indus-
try interactions that negatively a®ect the likelihood of
getting involved in collaboration with industry, namely
inequality of incentives, lack of procedures, con°ict of
objectives and nature of research, namely distance be-
tween university and industry. The estimated impact of
these perceived obstacles on the frequency of collabora-
tions requires further investigation
Franco et al. [2014] The in°uence of academic sta®'s personal
and professional characteristics on the
decision to cooperate with industry
European Journal of
International
Management
Identi¯cation of factors related to the personal and profes-
sional characteristics of the academic body that in°u-
ence the cooperation decision. Variables such as sex, age
and education in°uence the faculty's propensity to co-
operate with the business sector
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Table 4. (Continued )
Authors Article Journal Contribution
Bonaccorsi et al. [2014] Participation and commitment in third-
party research funding: Evidence from
Italian Universities
Journal of Technol-
ogy Transfer
Universities must explicitly recognize the role of dedicated
internal organizations and provide training for profes-
sionals capable of acting as value-added intermediaries.
It is important that policy makers want to improve
relations between universities and external actors, dis-
ciplinary di®erences between departments, as well as
regional inequalities in growth levels, must be carefully
considered, giving up a unique approach for all
Sharif and Tang [2014] New trends in innovation strategy at
Chinese universities in Hong Kong and
Shenzhen
International Jour-
nal of Technolo-
gy Management
Several speci¯c competitive advantages associated with
each of the universities in Hong Kong were presented to
boost their collaboration related to innovation with
institutions and companies in other sectors
L
opez et al. [2015] Are ¯rms interested in collaborating with
universities? An open-innovation per-
spective in countries of the South West
European Space
Service Business Identi¯cation of the determinants of companies interested in
cooperating with universities, which di®er according to
the technological level of the company's sector. In this
type of analysis, the importance of the culture of each
country must always be taken into account
Morandi [2013] The management of industry-university
joint research projects: How do part-
ners coordinate and control R&D ac-
tivities?
Journal of Technol-
ogy Transfer
Project management must be well de¯ned at the outset
because its characteristics and its relationship a®ect the
con¯guration of the management system. The uncer-
tainty of the task leads to decentralization of coordina-
tion and control practices, equivocity o®ers incentives
for the group coordination mode and reduces the need
for continuous informal monitoring and reciprocal in-
terdependence between partners requires the exploration
of project plans
Park et al. [2015] Exploring potential R&D collaboration
partners through patent analysis
based on bibliographic coupling and
latent semantic analysis
Technology Analysis
and Strategic
Management
Provide a systematic methodology for exploring potential
R&D collaboration partners using patent information
Cooperation University-Industry
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Table 4. (Continued )
Authors Article Journal Contribution
von Raesfeld et al. [2012] When is a network a nexus for innovation?
A study of public nanotechnology
R&D projects in the Netherlands
Industrial Marketing
Management
Analysis of combined e®ect of the heterogeneity of the R&D
partnership portfolio, interdependence and connectivity,
together with the stability of the network on innovation
performance
von Raesfeld et al. [2012] In°uence of partner diversity on collabo-
rative public R&D project outcomes:
A study of application and commer-
cialization of nanotechnologies in the
Netherlands
Technovation Investigation of the impact of technological diversity and
the complementarity of the partner value chain on the
performance of public nanotechnology R&D projects
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According to Bonaccorsi et al. [2014] universities are associated with a \third
mission" that consists of establishing connections with knowledge holders, facili-
tating the transfer of technology and knowledge. These authors analyzed coopera-
tion between U–I combining factors at the individual, departmental, university and
territorial levels. In a ¯rst phase, it is important that universities understand the
need to o®er training to professionals to add value. Another internal aspect is the
creation of a technology transfer o±ce to maintain the connection with the industrial
world. On the other hand, the di®erences between departments and regional dif-
ferences must also be considered, as departments located in wealthier regions will be
more involved in ¯nancing and cooperation with third parties.
Knowledge transfer from universities to industry is considered an important
strategy for boosting business, encouraging, and developing innovation. Knowledge
Transfer O±ces will be the main drivers of partnerships established between U–I
[Berbegal-Mirabent et al. (2015)]. The technological knowledge of companies is
obtained through research centers and governments for establishing partnerships
[Park et al. (2015)]. For Ratinho and Henriques [2010], Science Parks (SP) and
Business Incubators (BI) in°uence economic development, as well as job and wealth
creation in developed and developing countries. Although the contribution of SPs
and BI is modest, cooperation with universities can be considered an asset for the
growth of the converging economy, as is the case in Portugal. The creation of Science
and Technology Parks was one of the most important innovation policies. The
location in these parks has had a positive e®ect on innovation, cooperation, and the
intangible bene¯ts of cooperation, thanks to a diverse relationship. However, it is
di±cult to see whether the results obtained through this cooperation will be better
[V
asquez-Urriago et al. (2016)].
Sharif and Tang [2014] analyzed the cooperation between U–IandU–I–G with a
focus on knowledge transfer. Universities engage in innovative activities through
their departments and researchers, thus boosting their relationship with institutions
and companies in other sectors. The lack of a clear pattern of organization among the
elements of Triple Helix, should allow academic researchers to contribute more ro-
bustly to the innovation system.
3.3. Clusters 3: Determinants of cooperation
Table 5presents the articles that are part of cluster 3, addressing the organizational
characteristics that may be presented as more likely when U–I cooperation.
The establishment of external partnerships, especially with universities, is con-
sidered an engine of technological development. Thus, companies with an open in-
novation strategy that intend to collaborate with universities will be able to obtain
excellent resources, knowledge and technological innovations. The interdisciplinary
exchange of knowledge is essential for technological development [Wirsich et al.
(2016)]. Schartinger et al. [2002] analyzed the patterns of interaction of knowledge
between the academic and business sectors, based on di®erent ¯elds of research and
sectors of activity. For these authors, the intensity of knowledge interactions does
not follow a simple sectorial pattern and is not restricted to just a few speci¯c
Cooperation University-Industry
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Table 5. Articles of clusters 3.
Authors Article Journal Contribution
Schartinger et al. [2002] Knowledge interactions between universi-
ties and industry in Austria: sectorial
patterns and determinants
Research Policy The intensity of knowledge interactions does not follow a simple
sectorial pattern, being in°uenced by a large set of di®erent
factors that produce a complex pattern of interactions
Maietta [2015] Determinants of university-¯rm R&D
collaboration and its impact on inno-
vation: A perspective from a low-tech
industry
Research Policy Geographical proximity and training programs are the deter-
minants of collaboration between universities and industry
in R&D
Arvanitis et al. [2008] Is there any impact of university–industry
knowledge transfer on innovation and
productivity? An empirical analysis
based on swiss ¯rm data
Review of Industrial
Organization
Knowledge Technology Transfer activities with research insti-
tution and/or institutions of higher education seem to
improve considerably the innovation performance of ¯rms
both in terms of R&D intensity and sales of new products
Nishimura and Oka-
muro [2011]
R&D productivity and the organization of
cluster policy: An empirical evaluation
of the Industrial Cluster Project in
Japan
Journal of Technol-
ogy Transfer
Analysis of the e®ects of the \Industrial Cluster Project" (ICP)
on the participants' R&D productivity
Okamuro and Nishi-
mura [2013]
Impact of university intellectual property
policy on the performance of universi-
ty–industry research collaboration
Journal of Technol-
ogy Transfer
Analysis of the impact of the university's intellectual property
policy which is fair in the division of revenues and royalties
of innovative results and applied in a °exible way according
to the needs of the partner, contributing to improve the
performance of the project, increasing the commitment of
the companies
Ballesteros and Rico
[2001]
Public ¯nancing of cooperative R&D
projects in Spain: The Concerted
Projects under the National R&D Plan
Research Policy Factors such as budget and company size in°uence Spanish
public sector decision-making about ¯nancing research
projects developed in collaboration with U-I
Scandura [2016] University–industry collaboration and
¯rms' R&D e®ort
Research Policy Veri¯cation of a positive and signi¯cant impact on publicly
funded U–I collaboration in the UK companies' R&D e®ort
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2130001-20
Table 5. (Continued )
Authors Article Journal Contribution
Lehmann and Menter
[2018]
Public cluster policy and performance Journal of Technol-
ogy Transfer
Evaluation of the e®ect of the active public policy of cluster
measured by the growth of the regional GDP, but the im-
portance of robust evaluation approaches and techniques is
highlighted. On the other hand, it is important to verify the
complementary e®ects of pre-existing entrepreneurial and
innovative ecosystems to stimulate regional wealth and
make cluster policy successful at work
Slavtchev [2013] Proximity and the Transfer of Academic
Knowledge: Evidence from the Spatial
Pattern of Industry Collaborations of
East German Professors
Regional Studies Characteristics such as physical proximity and tacit knowledge
during U–I interactions allow local development
Wirsich et al. [2016] E®ects of University–Industry Collabora-
tion on Technological Newness of
Firms
Journal of Product
Innovation Man-
agement
U–I cooperation has a signi¯cant positive e®ect on technological
innovations, with a two-year time span, allowing for the
recombination of existing knowledge and the ease in
implementing new technologies
Kobarg et al. [2018] University–industry collaborations and
product innovation performance: the
moderating e®ects of absorptive ca-
pacity and innovation competencies
Journal of Technol-
ogy Transfer
Absorption capacity and innovation skills should be considered
in the context of innovation performance in U–I coopera-
tion, although these may not have an exclusively positive
in°uence on U–I cooperation
Cooperation University-Industry
2130001-21
industries or ¯elds and varies according to several determinants. Apparently, uni-
versity and industry use a di®erent set of channels in the interaction of knowledge.
For Slavtchev [2013], cooperation between U–I consists of a complex process of
correspondence between partners and factors such as individual, relational char-
acteristics, institutional factors and the speci¯c type of knowledge that play a central
role in establishing this cooperation.
Maietta [2015] analyzed the factors that drive collaboration between U–I and how
that collaboration a®ects the innovation process. Factors such as geographical
proximity and training programs in areas useful for companies positively a®ect
product innovation. Other approved studies concluded that the size of the company,
tacit knowledge [Slavtchev (2013)], general information, educational activities, re-
search activities, activities related to technical infrastructure and consultancy
[Arvanitis et al. (2008)], R&D capacity, intellectual property policies and the edu-
cational level of the managers are determining factors for cooperation between U–I
[Okamuro and Nishimura (2013)]. However, there are barriers associated with U–I
cooperation, such as divergent cultures and objectives, which must be taken into
account [Wirsich et al. (2016)]. For Okamuro and Nishimura [2013] the size of the
company has no in°uence for U–I cooperation being that the performance of coop-
eration between U–I depends on the strategies established between partners.
Knowledge and technology transfer activities with universities or research insti-
tutions improve the innovation performance of companies (both in terms of R&D
and in terms of sales of new products) positively in°uencing production [Arvanitis
et al. (2008)]. Absorption capacity and innovation skills should be considered in the
context of innovation performance in U–I cooperation, although these may not have
an exclusively positive in°uence on U–I cooperation. Everything will depend on the
type of innovation, incremental or radical that the cooperation wants to achieve
[Kobarg et al. (2018)].
There are several factors that can lead the public sector to ¯nance projects de-
veloped by the company in collaboration with universities and public research
organizations. The budget and the destination of that budget will be the most
important factors to explain this ¯nancing. On the other hand, the public sector
tends to ¯nance smaller companies more with some dedication to R&D than larger
companies, with large R&D departments [Ballesteros and Rico (2001)]. Scandura
[2016] investigated the impact of collaboration between U–I based on public funding,
in R&D projects and he concluded that there is a positive impact on companies
looking to increase the level of private investment in R&D, creating knowledge in the
economy and increasing job opportunities in the labor market.
The theory of economic knowledge suggests that innovation and new knowledge
create opportunities for technological changes, obtained through cooperation be-
tween entrepreneurial companies and research institutions. This cooperation also
generates regional development, hence the need to create an active public policy
[Lehmann and Menter (2018); Slavtchev (2013)].
Industrial clusters have played a leading role in innovation, with several countries
that have developed speci¯c promotion policies for these clusters. The hhIndustrial
Cluster Projectii aims, in addition to building collaboration networks between U–I,
N. Figueiredo & C. Fernandes
2130001-22
the development of regional industries. Thus, to improve the R&D e±ciency of local
companies, it is important to build collaborative networks within and beyond the
clusters [Nishimura and Okamuro (2011)].
3.4. Clusters 4: Strategics alliances
Table 6present the articles that are part of cluster 4, which addresses the type of
alliances that can be established between U–I, the characteristics necessary for them
to materialize and the results that can be obtained.
Since knowledge is considered a criticalresource to guarantee the growth and survival
of companies, they are motivated to cooperate to achieve this transfer of knowledge
[Santoro and Saparito (2006)]. The technological knowledge (innovations in products
and processes) of companies is not obtained only throughinternal learning processes, but
using external sources such as universities [Sherwood and Covin (2008)]. According to
Johnson and Johnston [2004], knowledge conversion processes are more relevant when
examined together, instead of separately, and that the facilitating factors depend on the
organizational context under analysis. It is known that knowledge and skills are key
factors for innovation. However, the process is complicated and di±cult to manage due
to the various actors involved in the network [Gubbins and Dooley (2014)]. It is then
necessary to understand the nature, process and content that these cooperations can
bring both in the formulation of governmental policies and in the elaboration of cor-
porate strategies [Carayannis et al. (2000)].
The more interactions between partners, in this case U–I, the more and better
knowledge sharing and absorption capacity will be, since the actors will better un-
derstand the speci¯cs of each context [Gubbins and Dooley (2014)]. There are others
factors that can a®ect partnerships and the success of knowledge acquisition such as
familiarity, formal collaboration teams, communications from technology specialists
[Sherwood and Covin (2008); Plewa et al. (2013)], R&D Capacity and size of com-
panies [Fontana et al. (2006)]. The greater the degree of trust between partners, the
greater the transfer of knowledge and the performance of innovation. Thus, U–Iwill
win if they are able to develop an environment of trust [Bstieler et al. (2015); Santoro
and Saparito (2006)].
The existing cooperation between U–I can o®er a series of bene¯ts both for the
parties involved and for the economy in general [Plewa et al. (2013)]. In addition,
these partnerships also serve to accelerate organizational learning and coordinate
transorganizational \innovation communities" [Carayannis et al. (2000)]. Lee [1996]
intended to understand what role academics played in technology transfer, and in
industrial innovation, and how they could collaborate with private industry. For
these authors, universities actively participate in local and regional economic de-
velopment. To this end, cooperation mechanisms have been developed, such as
consortia, alliances, research and collaborative development projects, sta® exchange
and individual interaction between teachers and professionals in the sector. How-
ever, universities should not engage in close commercial partnerships with private
industry, such as investment in stocks.
Cooperation University-Industry
2130001-23
Table 6. Articles of cluster 4.
Authors Article Journal Contribution
Lee [1996] Technology transfer' and the research
university: A search for the boundaries
of university–industry collaboration
Research Policy De¯nition of the role of universities in technology transfer
through alliances such as consortia, alliances, collaborative
research and development projects, sta® exchange and in-
dividual interaction between professors and professionals in
the sector
Sherwood and Covin [2008] Knowledge acquisition in university in-
dustry alliances: An empirical investi-
gation from a learning theory
perspective
Journal of Product
Innovation Man-
agement
Factors such as: trust, familiarity, formal collaboration teams
and communications from technology experts, which are
inherent to the context of knowledge acquisition can a®ect
the transfer of technology from universities to industry
Carayannis et al. [2000] Leveraging knowledge, learning, and in-
novation in forming strategic govern-
ment–university–industry (UIG) R&D
partnerships in the US, Germany, and
France
Technovation If there is an understanding of the nature, process and contents
of the collaboration between government, university and
industry actors, results such as knowledge sharing, social
capital and innovation can be achieved
Plewa et al. [2013] University–industry linkage evolution: An
empirical investigation of relational
success factors
R and D Manage-
ment
The relational success factors necessary for creating alliances
are communication, trust, understanding and individuals
Bstieler et al. [2015] Trust formation in university–industry
collaborations in the U.S. biotechnol-
ogy industry: IP policies, shared gov-
ernance, and champions
Journal of Product
Innovation Man-
agement
The importance in de¯ning the roles of universities' Intellectual
Property policies and shared management in building trust
between universities and industry
Johnson and Johnston
[2004]
Organizational knowledge creating pro-
cesses and the performance of univer-
sity–industry collaborative R&D
projects
International Jour-
nal of Technolo-
gy Management
Analysis of the e®ects of enablers and knowledge creation
processes in a collaborative environment
Simeth and Ra®o [2013] What makes companies pursue an Open
Science strategy?
Research Policy Explore which are the motivations of companies that dissemi-
nate research results in a scienti¯c format
Santoro and Saparito [2006] Self-interest assumption and relational
trust in university–industry knowledge
transfers
IEEE Transactions
on Engineering
Management
Examine the role of self-interest and relational trust in the
transfer of knowledge between U–I
N. Figueiredo & C. Fernandes
2130001-24
Table 6. (Continued )
Authors Article Journal Contribution
Gubbins and Dooley [2014] Exploring Social Network Dynamics
Driving Knowledge Management for
Innovation
Journal of Manage-
ment Inquiry
Analyze how a social network can in°uence the knowledge
management process for innovation
Fontana et al. [2006] Factors a®ecting university–industry R
and D projects: The importance of
searching, screening and signalling
Research Policy One of the determinants of research cooperation between
companies and public research organizations is the
\absolute size" of the industrial partner. Companies that
outsource research and development and patents to protect
innovation and signal competencies have higher levels of
collaboration
Cooperation University-Industry
2130001-25
There are several studies that focus on universities as part of commercial activ-
ities, but few that refer to companies as disseminators of scienti¯c knowledge.
According to Simeth and Ra®o [2013], companies are more likely to adopt academic
principles when they need scienti¯c knowledge considered important, namely for
driving innovation.
4. Discussion
The conceptual model presented in Fig. 4summarizes and links the four clusters
obtained, which address the importance of government in the cooperation process,
namely through Triple Helix, the strategic alliances that can be established between
U–I, the determining factors for cooperation and the transfer of knowledge that can
get, as a result of that connection.
From the analysis of Fig. 4, it is possible to see that the cooperation process can
be positively or negatively in°uenced by several determining factors. The strategic
alliances that are established between U–I are fundamental for the development of
countries and companies, because on the one hand it allows economic development
and, on the other hand it allows development, creation and improvements through
the transfer of knowledge. Thus, the government plays a fundamental role in this
cooperation, through the so-called Triple Helix, namely by measures, policies and
Fig. 4. Conceptual model.
N. Figueiredo & C. Fernandes
2130001-26
even government support that it can provide so that success and understanding
between the intervening parties is possible and the objectives achieved.
5. Conclusion
Given the growing importance of university knowledge and its cooperation with
industry, notably due to its in°uence on economic growth, the role that the gov-
ernment plays is fundamental. However, there are many barriers that may nega-
tively in°uence this cooperation, especially in less developed countries.
This paper was based on an SLR whose theme is the cooperation between U–I.
Through a bibliometric analysis, it was possible to identify four clusters: (1) Triple
Helix, (2) Knowledge Transfer, (3) Determinants of Cooperation and (4) Strategic
Alliances.
It was possible to conclude that the cooperation between U–I has a crucial role
not only for the development of the companies, but also for the economic develop-
ment of the countries. Although the bene¯ts of such cooperation are recognized,
there are determinants and barriers that must be considered in order for success and
goals to be achieved by all stakeholders. Thus, the government, namely that of
Triple Helix, has a fundamental role through its policies, measures, and government
support, which may facilitate and even be a mechanism that drives the process. In
this sense, a conceptual model was proposed that could serve as a contribution to
future analysis of the theme. However, by the present SLR, it is possible to perceive
the need to carry out further studies on this theme as there is still much to be
investigated.
This study contributed to the literature by highlighting the most relevant the-
matic areas in U–I cooperation, analyzing and systematizing the main investigations
carried out in the area, thus allowing a deeper knowledge of the theme and the
identi¯cation of possible future lines of investigation. This SLR also presents con-
tributions to the practice as this theme has generated great interest on the part of
governments, policy makers, researchers, industry and the university, more speci¯-
cally, in order to allow the evaluation of other determining factors for cooperation, to
identify other types of partnerships, as well as the achievement of results that may
result from this cooperation.
From the analyzed literature and based on the ¯rst cluster, it was possible to
perceive that the intervention of governments, through their political measures,
incentive programs, government support and even as a factor that streamlines the
cooperation process, may be fundamental in U–I cooperation, given that university
and industry may have di®erent objectives. Thus, if academics want publications,
industries want ¯nancial gain. As such, the integration in the Triple Helix model
may allow a dynamic advantage for the parties involved. Thus, for future research, it
would be interesting to see if the selective process in creating links between U–I
generates improvement in the dissemination of knowledge, both regionally and na-
tionally [Giuliani and Arza (2009)]. On the other hand, future research may involve
analysis based on various sectors of activity and longitudinal studies to analyze the
dynamic nature of the connections that are created [Zubielqui et al. (2015);Jones
Cooperation University-Industry
2130001-27
and Zubielqui (2017)]. Finally, future research should be carried out based on
Quadruple Helix, University–Industry–Government–Citizen, insofar as it suggests
that democracy ¯ts and changes the conditions of innovation also for the citizen [Suh
(2016); Pennacchio et al. (2018)].
The second line of research addresses knowledge transfer from universities to
industry and its importance as a vital strategy for boosting business, encouraging
and developing innovation. Thus, universities' competitive advantage will depend on
their ability to create knowledge. It would be important in future studies to examine
the e®ects of regulatory structures on R&D contracts that are established with
industry [Berbegal-Mirabent et al. (2015)]. On the other hand, and since the coop-
eration between U–I has not yet reached its maximum, it would be interesting to
develop studies that allow the formulation of hypotheses that involve more research
on the e®ectiveness of promoting technological innovation among U–I. Factors such
as personal and professional characteristics a®ect and in°uence the cooperation
process between U–I and the transfer of knowledge. Thus, future research should pay
attention to the elaboration of inter-university and international studies in order to
compare di®erent institutional contexts [Franco et al. (2014)].
In the third line of research, it was possible to identify some determining factors in
U–I cooperation. Thus, the following aspects were mentioned: the sector of activity
[Schartinger et al. (2002)], the size of the company [Fontana et al. (2006)], the
geographical proximity [Maietta (2015); Slavtchev (2013)], the training programs
[Maietta (2015)], educational, research activities, related to technical infrastructures
and consultancy [Arvanitis et al. (2008)], intensity in R&D, educational level of
managers and intellectual property policies [Okamuro and Nishimura (2013)].
According to Maietta [2015], the size of the company does not in°uence the process
of cooperation, innovation and development. However, in future research, it would
be interesting to analyze whether the process of cooperation, innovation and de-
velopment advances in micro-companies as in medium and large companies. Al-
though previous studies have shown the importance of cooperation in various sectors
of activity, it would be important to understand how it has evolved over time. Thus,
it would be interesting for future research to carry out studies with longitudinal data
[Arvanitis et al. (2008)]. Another result perceived by the literature review is the fact
that cooperation between U–I depends on the strategies established between part-
ners, namely with regard to intellectual property. In future research, it would be
interesting to analyze the impact on U–I cooperation in a more dynamic way, con-
sidering the evolution of the university's intellectual property policies [Okamuro and
Nishimura (2013)]. The cooperation between U–I develops the economic activity of
the countries, with the role of the government being central to the cooperation to
develop e®ectively. Thus, future research should focus more on research organiza-
tions and investigate public policy measures that try to develop public-private
partnerships [Lehmann and Menter (2018)].
In the fourth and ¯nal line of research, it was possible to see that there are many
advantages in cooperation between U–I, both for companies and for the economic
development of countries. Thus, it is necessary to develop mechanisms that allow the
e®ectiveness of the established strategic alliances, since technological knowledge and
N. Figueiredo & C. Fernandes
2130001-28
development of innovations are not obtained only through internal learning pro-
cesses. As such, it would be interesting in future research, namely through the
analysis of case studies, to understand the role of inter-organizational management
mechanisms in U–I collaborations [Bstieler et al. (2015)]. On the other hand, future
research must be carried out to identify the various factors that allow classifying
partners and competitors [Park et al. (2015); Wickramasinghe and Malik (2018)].
Hence, Table 7sets out the contextual and methodological orientations for co-
operation U–I research as well as the shortcomings in the indicative knowledge and
insights for future research.
Although this paper brings important collaborations to the literature on coop-
eration between U–I, namely through the systematization of research areas and, due
to these, articulating possible lines of future research, it is not free of some limita-
tions. One of the limitations of the study is the fact that it resorted to the use of a
single database for the collection of literature (WoS). On the other hand, the cri-
terion for grouping articles into clusters (Bibliographic Coupling) may have limited
the scope of the study. Despite the limitations underlying any research, we believe
that this study produces important implications for the cooperation U–I¯eldof
research given its analysis of the co-citation data and the recourse to a quantitative
Table 7. Contextual and methodological orientations and future research directions for cooperation U–I.
Theory .Which theories hold greatest relevance to the study of cooperation U–I?
.Should new theories be developed?
.How may existing theories be developed and enriched to better explain cooperation U–I
practices?
.Which cooperation U–I theory holds the potential in terms of conceptual contributions to
develop a broader reaching literature?
.How might we interrelate the structure, the organization, the cooperation U–I?
Context .What are the similarities and di®erences in the various SL approaches?
.What are the similarities and di®erences in cooperation U–I according to company strat-
egies?
.What factors explain these di®erences?
.What importance do informal relationships hold to the success or non-success of cooper-
ation U–I?
.What institutional pressures are at stake? Within the same sector, what con¯gurations
change from company to company? Across di®erent sectors, what similarities are there in
company organizations?
Content .What is the role played by resources and capacities in de¯ning cooperation U–I practices?
.Which factors measure the cooperation U–I relationship —with what results at the in-
stitutional, organizational, and individual levels?
.How do institutional logics interrelate with cooperation U–I?
.Why do some business leaders attribute more/less importance to cooperation U–I?
Method .How are we able to signi¯cantly measure cooperation U–I?
.How might we measure the impact of the utilization or otherwise of cooperation U–Iina
company? Are these distinctive or similar metrics?
.Do the di®erent levels of cooperation U–I require di®erent methodologies?
.How might we combine various methods to explore cooperation U–I based on the di®erent
levels of analysis?
.How might we develop large scale databases for measuring cooperation U–I performance?
.Do researchers need to modify the underlying assumptions of the methodologies applied to
studying cooperation U–I?
Cooperation University-Industry
2130001-29
approach resulting in the mapping of the scienti¯c publications and their intellectual
structure as well as de¯ning the trends ongoing in cooperation U–I theoretical
research.
Last but not the least we think it is important to re°ect on the Covid-19 pan-
demic and U–I cooperation. As the OECD [2020] re°ected the pandemic, it brought
several challenges to the educational system. However, something that we can take
as positive was the increase in U–I cooperation in terms of several innovative pro-
jects. Portugal appears in front of the countries analyzed by the Organization for
Economic Cooperation and Development (OECD)
a
as the one with the largest
number of innovative projects in the ¯ght against Covid-19. The entity highlights 19
initiatives, placing Portugal ahead of Ireland and the United Kingdom. According
OECD [2020] as we enter the COVID-19 recovery phase, it will be critical to re°ect
on the role of educational systems and particularly vocational education in
fostering resilient societies. The global health crisis and the lockdown that followed
have brought to the fore professions that have often been taken for granted,
renewing our awareness of their value to society. This has helped restore a sense of
esteem for those workers who have worked relentlessly during this time to keep
economies a°oat. The outlook is very uncertain. But, if anything, the pandemic has
exposed our vulnerability to crises and revealed how precarious and interdependent the
economies we have built can be. Disruptions on the scale we have just witnessed are not
limited to pandemics, but may also result from natural, political, economic and envi-
ronmental disorder. Our capacity to react e®ectively and e±ciently in the future will
hinge on governments' foresight, readiness and preparedness. Through their role in
developing the competencies and skills needed for tomorrow's society, education systems
will need to be at the heart of this planning. This means working in close collaboration
with other government sectors and the private sector to increase the attractiveness and
labor-market prospects of certain professions, including those considered paramount for
the common good. Real change often takes place in deep crises, and this moment holds
the possibility that we won't return to the status quo when things return to \normal".
While this crisis has deeply disruptive implications, including for education, it does not
have predetermined outcomes. It will be the nature of our collective and systemic
responses to these disruptions that will determine how we are a®ected by them. In this
sense, the pandemic is also a call to renew the commitment to the Sustainable Devel-
opment Goals. Ensuring that all young people have the opportunity to succeed at school
and develop the knowledge, skills, attitudes and values that will allow them to contribute
to society is at the heart of the global agenda and education's promise to our future
society. The current crisis has tested our ability to deal with large-scale disruptions. It is
now up to us to build as its legacy a more resilient society.
Acknowledgment
Nat
alia Figueiredo would like to thank; National Funds for their funding through
the FCT Foundation for Science and Technology, I.P., within the scope of the
a
https://oecd-opsi.org/covid-response/.
N. Figueiredo & C. Fernandes
2130001-30
project Ref. a UIDB/05583/2020. Furthermore, we would like to thank the Research
Centre in Digital Services (CISeD) and the Polytechnic of Viseu for their support.
Cristina Fernandes would like to thank The Portuguese Foundation for Science
and Technology (Grants and NECE- UIDB/04630/2020) provided support for this
study.
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Biography
Nat
alia de Lima Figueiredo She is a PhD Student in Management at Faculty of
Social and Human Sciences, University of Beira Interior (UBI), Portugal. She is a
member of the research center NECE –Center for Studies in Business Sciences at the
University of Beira Interior and a member of the research center CISeD –Center of
Investigation in Digital Services, at the Polytechnic of Viseu. Degree in Management
from Higher School of Technology of Viseu, Polytechnic Institute of Viseu, Portugal.
Master’s degree in management from ISCTE - University Institute of Lisbon, Por-
tugal. Employee of Instituto Polytechnic of Viseu since 2000.
Cristina Fernandes She is Assistant Professor with accreditation at the University
of Beira Interior (UBI), Portugal. She holds a PhD in Management from the Uni-
versity of Beira Interior. She is currently the scienti¯c coordinator of the Entre-
preneurship, Competitiveness and Innovation research line of the research center
NECE - Center for Studies in Business Sciences at the University of Beira Interior.
Centre for Corporate Entrepreneurship and Innovation at Loughborough Univer-
sity, UK. She is part of the editorial board of Management Decision; has several
dozen scienti¯c articles published in international journals including: Journal of
Technology Transfer; Journal of Knowledge Management; R&D Management and
Journal of Business Research. Actively participates in scienti¯c meetings and in-
ternational conferences on these topics, having been distinguished several times with
awards for best article. Has participation in several international projects.
N. Figueiredo & C. Fernandes
2130001-36
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