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Collaboration with foreign universities for innovation: Evidence from Chinese manufacturing firms


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While prior research has focused upon the role of university-industry linkage in the promotion of innovation, there has been little research distinguishing the different roles of domestic and foreign universities in this pursuit. This paper explores the different impacts of domestic and foreign universities on innovation performance in emerging economies using a national firm-level survey database from China. It finds that international innovation collaboration with leading universities in other developing countries, especially with those in the Newly Industrialised Economies and the emerging South, is fruitful in enhancing the creation of ground-breaking innovations in indigenous Chinese firms. In contrast, the contribution of universities in the USA/EU/Japan is significantly beneficial to foreign-invested firms. Collaboration with domestic universities has played a significant role in the diffusion of advanced technology in China.
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nt. J. Technology Management, Vol. 70, Nos. 2/3, 2016 193
Copyright © 2016 Inderscience Enterprises Ltd.
Collaboration with foreign universities for innovation:
evidence from Chinese manufacturing firms
Xiaolan Fu
Technology and Management Centre for Development,
Department of International Development,
University of Oxford,
3 Mansfield Road, Oxford, OX1 3TB, UK
Jizhen Li*
Research Center for Technological Innovation,
School of Economics and Management,
Tsinghua University,
Beijing, 100084, China
*Corresponding author
Abstract: While prior research has focused upon the role of university-industry
linkage in the promotion of innovation, there has been little research
distinguishing the different roles of domestic and foreign universities in this
pursuit. This paper explores the different impacts of domestic and foreign
universities on innovation performance in emerging economies using a national
firm-level survey database from China. It finds that international innovation
collaboration with leading universities in other developing countries, especially
with those in the Newly Industrialised Economies and the emerging
South, is fruitful in enhancing the creation of ground-breaking innovations
in indigenous Chinese firms. In contrast, the contribution of universities
in the USA/EU/Japan is significantly beneficial to foreign-invested firms.
Collaboration with domestic universities has played a significant role in the
diffusion of advanced technology in China.
Keywords: innovation; international collaboration; university-industry linkage;
technology management; China.
Reference to this paper should be made as follows: Fu, X. and Li, J. (2016)
‘Collaboration with foreign universities for innovation: evidence from Chinese
manufacturing firms’, Int. J. Technology Management, Vol. 70, Nos. 2/3,
Biographical notes: Xiaolan Fu is a Professor of Technology and International
Development, Fellow of Green Templeton College and Founding Director
of the Technology and Management Centre for Development at the University
of Oxford. Her research interests include innovation, technology and
industrialisation; trade, foreign direct investment and economic development;
emerging Asian economies; innovation and productivity in the UK and USA.
uand J. Li
Jizhen Li is an Associate Professor at the School of Economics and
Management, Tsinghua University. He is also Research Fellow and Vice
Director at the Research Center for Technological Innovation, Tsinghua
University. His research interests include management of technological
innovation, science and technology policy, project management and SMEs
innovation and entrepreneurship.
1 Introduction
As an important player in national and regional innovation systems, universities are
widely regarded as a major contributor to advances in basic scientific research and
industrial innovation. However, most of the received wisdom on the role of universities is
based on experience and evidence from developed countries (e.g., Etzkowitz, 2002;
Gulbrandsen et al., 2011). Our understanding is still limited with regard to the effects of
university-industry collaboration (UIC) on imitative and ground-breaking novel
innovations in emerging economies. Moreover, innovation is increasingly a collaborative
and global undertaking due to the internationalisation of innovation and changes in
innovation mode from closed to open. Firms in emerging economies have started to
collaborate not only with domestic universities but also with foreign universities. It is
hence important to understand different roles of domestic and foreign universities in
industrial innovation in emerging economies, especially in their pursuit of catch-up and
This research attempts to fill in these gaps in the literature by examining the role of
intra- and inter-national UIC in novel and imitative innovations in emerging economies
using firm-level data from China. China provides a good case study for this research for
several reasons. First, China is one of the major emerging economies in the world. It is
attempting to develop a path of compressed development and leapfrog the conventional
latecomer path of imitative industrialisation and progression up the value chain. Second,
the industrial and university sectors in China have made impressive progress in
innovation since the reforms of 1978. In 2010, total research and development (R&D)
expenditure in China ranked the third in the world. Thirdly, the Chinese Government has
made great efforts to foster university-industry linkage and to encourage technology
transfer (Wu, 2007; OECD, 2008). So, China is a good case for research on the role of
universities in industrial innovation in emerging economies.
The research contributes to the literature in three ways. First, most of the research on
university-industry innovation collaboration has not distinguished collaboration with
foreign and domestic universities. In the globalisation era, firms are increasingly open
and internationalised in their R&D activities. It is hence important to understand the
effect of such international UICs. To our knowledge, this is the first research that
distinguishes effects of foreign from domestic universities in innovation collaboration.
This is one of the rare research projects that explores the contribution of international
collaboration to innovation, as well as the moderating roles of cultural differences and
geographic, technical and institutional distances in shaping the collaboration outcome.
Second, it deepens on our understanding of the role of a UIC in the middle-income
emerging economies given different effects of UIC on novel and imitative innovations.
We have to take account of various demands and motivations for university-industry
Collaboration with foreign universities for innovation 195
linkage and diverse capabilities in the industrial and university sectors at different
development stages. Finally, the paper provides the first empirical evidence of the role of
universities in emerging economies based on a large national firm-level survey dataset.
The remainder of the paper is organised as follows. Section 2 discusses the literature
and the theoretical framework. Section 3 discusses the methodology and data. Section 4
presents the results. Section 5 concludes.
2 The literature and the hypotheses
The literature on national innovation systems has highlighted the role of universities, not
only in training and education, but also as an active player in knowledge creation and
transfer (Braczyk et al., 1998; OECD, 2008). They disseminate knowledge to the real
economy by producing quality students and by interacting with firms through a number
of channels such as consulting, licensing, and collaborative research projects (Eom and
Lee, 2010). The emergence of open innovation as a new mode of innovation suggests that
universities may play a more important role in industrial innovation, as external
knowledge sources and co-producers of new products and processes. Firms adopt open
innovation to tap into external resources and talents, share uncertainties, diversify risks
and promote learning (Chesbrough, 2003; Keupp and Gassmann, 2009). Universities also
facilitate the attainment of complementary assets and allow firms to achieve goals which
they cannot pursue alone (Mowery et al., 1996; Powell and Grodal, 2005). As a result,
firms networking with others are found to have greater innovative performance (Goes and
Park, 1997; Tsai, 2001; Laursen and Salter, 2006). In recent years, open innovation has
not only been adopted rapidly at the firm level but also emerged at the system level as an
open regional innovation system if firms are open to external-to-the-region research
networks and knowledge sources (Belussi et al., 2010).
Given these trends, a growing number of countries are seeking to use universities as
an important driver of knowledge-based economic development and technical change
(Mowery and Sampat, 2005). Through interaction with the science base, firms are able to
access a diversified range of knowledge sources in comparison to intra-firm collaboration
(Kaufmann and Todtling, 2001), especially in relation to tacit and uncodified knowledge
(Yusuf, 2008). Learning organisations create important assets in the form of codified
(e.g., publications, patents, contract R&D projects) and tacit (e.g., collaborative research,
informal consultation) knowledge and transfer them to firms (Kodama, 2008). In sum,
there is a gathering belief that contracting out research to universities and collaborating
with universities can confer substantial advantages and hence offer a new vehicle for
catch-up (Lee et al., 2009). ‘Bridging researchers’ from universities are also found to be
an effective link to bring international sciences to domestic industry (Giuliani and
Rabellotti, 2012).
2.1 Motivations, capabilities, heterogeneity of knowledge and the role of
foreign universities
The impact of a UIC on innovation outcomes depends on the motivations and capabilities
of both parties. These factors first affect a partner’s willingness to engage in a UIC (Lai,
2011). Firms collaborate with universities for different reasons – from paradigm shifting
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to technical problem-solving. Universities also interact with firms for diverse reasons and
in diverse ways (Perkmann and Walsh, 2008). These include joint research, advisory
work and contracts, and technology commercialisation, among others. Each of this wide
array of interactions offers different opportunities to generate or disseminate knowledge.
Firms choose the mode of interaction and their suitable university partner according to
their specific needs and objectives. This further determines the impact of the UIC on the
type of innovation that these interactions will nurture. Therefore, the outcomes of the
university-industry interaction are diverse and the relationships may be nonlinear and
contingent (Gulbrandsen et al., 2011) depending on motivations and types of interactions
between the university and the firm. Moreover, universities are of different types. The
research capabilities of universities will have direct effects on the novelty of the
innovation produced from a UIC, because innovation is path-dependent and the skills of
researchers are the most important determinant of innovation.
The stage of development of an economy will influence the demand for external
knowledge sources and the motivations of its firms to collaborate with universities. As a
result, the behaviour and innovation impact of a UIC in an emerging economy such as
China will be different from that in developed economies. On the demand side, since they
are at an intermediate stage of development, the emerging economies have long been at a
stage at which the assimilation of foreign technology has been a major source of
technology upgrading and hence the priority was to absorb technology transferred from
abroad (Hershberg et al., 2007). As a result, universities in emerging economies are likely
to engage more in the diffusion of frontier technology than the creation of such
technology. Moreover, firms in latecomer economies have less R&D and absorptive
capacity (Eun et al., 2006; Lee et al., 2009). Owing to this constraint on the local
industry, there is a need to tap into the expertise of science and engineering experts in
universities to help with the assimilation and adaptation of foreign technology.
Collaboration with universities will help to accelerate the adoption of foreign technology
(Motohashi, 2005; Lyu and Gunasekaran, 1993). This need for technical assistance in the
economy will induce government policy responses that pull or even push the domestic
universities to prioritise the problem-solving tasks as raised by the industry.
With regard to capabilities, the extent of development of an economy is often in line
with the level of research capabilities of its universities and hence the type of innovation
they are able/going to create. In Asian countries, universities began to pay more attention
to research in recent decades, being prompted by government incentives (Hershberg
et al., 2007). The higher education sector in these countries is capable of collaborating
with the industrial sector to assimilate the transferred foreign technology and make the
adaptations necessary for foreign technology to fit within the local technical, economic
and social context. Some alliances between industry and leading research universities are
also capable of leading to a deep assimilation of foreign technology through reverse
engineering and R&D to make modifications to the transferred foreign technology.
However, so far, the capability of domestic universities in China to carry out frontier
research at the world level is still limited, except in a few subject areas at several elite
research universities. For example, China’s share in science and engineering articles has
risen since the mid-1990s, accounting for 8.3% of the global research output in 2009,
next to that of the US. However, the quality of these papers as measured by the number
of citations per paper was 5.87 in 2010, which was substantially lower than the world
average at 10.57 (CSTII, 2010). The average number of citations per paper published by
authors from the USA, the Netherlands and the UK is about three times higher than that
Collaboration with foreign universities for innovation 197
by the Chinese authors. This gap in the quality in published papers between authors from
China and the world average exists in almost all subject areas except engineering
technology and maths where the gap is relatively small. This gap in research capabilities
and the concurrent high level demand for technical assistance from the private sector will
lead to the rise of demand for knowledge from foreign universities.
Moreover, due to the heterogeneity of knowledge in different industries and in
different locations, the supply of knowledge in one specific field in a given location may
be limited even in advanced economies. This induces firms to look for knowledge from
foreign sources. In the context of emerging economies, inflows of knowledge and
technology from foreign sources have long been essential components of learning and
innovation processes. International knowledge diffusion is therefore an important driver
of economic growth (Coe and Helpman, 1995). External knowledge can be diffused
between firms and across countries in several ways. These include international trade;
inward and outward foreign direct investment; migration of highly skilled personnel; and
integration into global value chains (Fu et al., 2011; Zucker and Darby, 2007).
Another possible channel for domestic firms to interact with international knowledge
sources is to collaborate directly with foreign universities. In an open national innovation
system, firms can collaborate not only with various domestic universities but also with
foreign universities. Given the differences in research capabilities between domestic and
foreign universities, firms in emerging economies may also collaborate with innovative
foreign universities. The extent of the collaboration depends on the objectives of the
mission, the research capability of the universities, the communication and financial
capabilities of the firm, as well as the language, culture and geographic distance between
the partners. Firms are technologically heterogeneous and their knowledge base is, as a
whole, rather differentiated (Nelson and Winter, 1982). Talents and institutions with
expertise in a particular field tend to cluster together (Audretsch and Feldman, 1996). For
firms in a particular industry, potential collaborators are limited in order to get cutting
edge technology. As a result, firms may cross country borders to collaborate with foreign
organisations, including foreign universities, for frontier research and innovation. In
return, such cross border UICs are more likely to produce ground-breaking innovations
due to a broader knowledge base and the input of leading researchers who have world-
class expert knowledge in a particular area. Therefore, we have the following
H1a Collaboration with domestic universities is likely to make a greater contribution
to the diffusion of imitative innovation than the creation of ground-breaking
novel innovations in China.
H1b Collaboration between Chinese firms and foreign universities is likely to be
associated with innovations of greater novelty.
2.2 Cultural and technological distances and outcomes of international UICs
The success of cross-border UICs will be dependent upon several factors which affect the
knowledge exchange, integration and co-production process in addition to the research
capability and absorptive capacity of partners (Cohen and Levinthal, 1990; Kodama,
2008). In the context of international UIC, differences in culture and technological
capabilities as well as geographical distances may all affect the outcome.
uand J. Li
Cultural differences: an innovation collaboration involves the integration of
heterogeneous knowledge resources, which each of the partners possesses. This requires
a wide range of cognition activities. It is found that the innovative performance of inter-
firm alliances bears an inverted-U shaped function with cognitive distance and that an
optimal cognitive distance depends on absorptive capacity (Nooteboom, 2009). Cognition
includes a broad range of mental activities, including proprioception, perception, sense
making, categorisation, inference, value judgments, emotions, and feelings. National and
organisational cultural differences are important factors that determine the cognitive
distances between partners and hence the knowledge integration and co-production
process. Although cultural distance may raise communication problems between partners,
it is also argued to have a positive effect on international knowledge transfer due to an
enlarged knowledge base (Vaara et al., 2012). Distinguishing cultural differences at
national and organisational levels, Sarala and Vaara (2010) find that national cultural
differences are positively associated with knowledge transfer in international
acquisitions. However, organisational cultural differences can have both negative and
positive effects on knowledge transfer in acquisitions. A UIC often involves a formal
collaboration between organisations. Therefore, cultural distance, especially
organisational cultural distance, will affect the effectiveness of a UIC between Chinese
firms and universities in the industrialised economies (Lin and Berg, 2001; Sirmon and
Lane, 2004).
Technological distances: technological distances between two parties involved in
knowledge transfers are found to be an important factor for moderating the effectiveness
of the transfer process. Some studies find that spillovers are present when technology
gaps are moderate (Kokko et al., 1996). Girma and Görg (2007) argue that the
relationship between the technical gap and the strength of spillovers from foreign to local
firms follows an inverted-U shape. Therefore, in the context of an innovation
collaboration, a moderate technological gap between the collaborative partners is likely to
result in maximum knowledge transfer and integration. Hence elite universities in other
advanced developing countries such as the newly industrialised economies (NIEs) may
be a better match for normal indigenous Chinese firms while universities in advanced
economies may be a suitable match for the affiliates of multinational enterprises (MNEs)
in China.
With regard to geographical proximity, evidence from the UK and the Netherlands
suggests that firms give priority to quality of research over geographical proximity
(Laursen, et. al., 2011) and that knowledge spillovers through a UIC are not limited to the
regional scale (Ponds, 2010). Once such social, cultural and institutional connectors have
been allowed for, MNEs are as likely to set up R&D labs in nearby as in more remote
locations (Castellani et al., 2013). Therefore, the influence of geographical distances on
the performance of a UIC is limited.
Above discussions suggest that the effect of collaboration in innovation between
Chinese firms and foreign universities is likely to be greater when the national cultural
distance between the collaborating countries is larger, the organisational cultural distance
is smaller, and the technological distance is moderate. Given the differences in culture,
technology and market demand between Chinese firms and foreign universities in
advanced and other developing economies, as well as the characteristics of indigenous
and foreign invested firms in China, we have the following corollaries:
Collaboration with foreign universities for innovation 199
H2 Collaboration with foreign universities in the industrialised economies is likely to
have a greater impact on innovation in foreign-invested firms in China than that
in indigenous Chinese firms.
H2b Collaboration with foreign universities in other advanced developing countries is
likely to have greater impact on innovation in indigenous Chinese firms than that
in foreign-invested firms in China.
3 Methodology and data
3.1 Measurement
Innovation outcome: innovation could be measured in different ways, for example, R&D
expenditure, number of patents, or a dummy variable which equals 1 for innovation and 0
for no innovation based on firms’ self assessment. Each has its advantages and
limitations. In this study we use the sales of new or improved products as a measure of
innovation output as this information is available in the survey dataset. In the survey,
firms are asked whether, besides being new to their firm or the country, the innovation
was also new to the world. This allows a distinction between innovations of the latter
kind – which may be termed ‘novel’ – and innovations of the former kind – which may
be considered as ‘imitative’ innovations. Since we are interested in the different roles of
universities in the creation of ground-breaking novel innovation and in translating,
deciphering and adapting transferred foreign technology, we use two dependent
variables: the proportion of sales accounted for by products which were ground-breaking
at the world level, and, secondly, of those which were new to the country or firm.
Although new product sales is the outcome of product innovation, research comparing
the various innovation indicators finds that this indicator reflects, to a greater extent, a
firm’s overall innovation performance in both product and process innovations (Fu,
2012). Of course, this distinction of novel versus imitative innovation is based on firms’
self assessment. The recognition of ‘novel’ innovation is subject to firms’ knowledge of
the new product and process development in the industry global-wise. Since some firms
may not be fully aware of such information, there is a likelihood of overestimating the
rate or proportion of ‘novel’ innovation in our sample. This is a limitation of the data that
we should take into account when drawing up conclusions.
University-industry collaboration: following the widely used proxy of inter-
organisational collaboration in the literature, we consider a number of variables in an
attempt to capture the direct university contribution to firms' innovation. We use a
dummy variable that equals 1 if firms collaborated in any innovation activities with
universities and public research institutions and 0 otherwise as a proxy for the presence of
a UIC. We distinguish between universities located in a firm’s own country, the NIEs,
EU, USA and Japan, and other countries to proxy the technological and cultural gap
between the collaborators. Alternatively, we also include an indicator reflecting whether
a firm cooperates with any other organisations, such as suppliers, customers, competitors,
universities and PRIs, consultants and commercial labs in the course of its innovation
activity. This allows for a direct test of the question as to whether a collaboration
significantly influences a firm's innovation performance.
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3.2 Control variables
Intramural R&D expenditure and Extramural R&D expenditure of the firm: investment
in R&D is often found to be a significant determinant of innovation. Firms which engage
in R&D are more likely to innovate because R&D directly creates new products and
processes and these firms are also more receptive to new external ideas. The size of
extramural R&D is also important as a control over the effects of other type of
collaborations, for example, with suppliers, customers, other firms in the same industry
and within the company group.
Labour force skills: labour force skills, especially qualified scientists and engineers,
are another widely recognised critical factor that contributes to the innovation
performance of firms (Hoffman et al., 1998). We use a dummy variable that equals 1 for
firms reporting a lack of qualified personnel as being of medium and high importance and
0 for others as a control variable. Skills also enhance the firms’ absorptive capacity
(Cohen and Levinthal, 1990).
Firm size: larger firms have a greater range of market opportunities through which to
exploit innovative opportunities. The size of the firm can therefore act as a proxy for this
enhanced incentive to innovate. Larger firms also have greater resources for innovation.
Firm age: Older firms may have accumulated more experience and knowledge and be
more capable in innovation. On the other hand, older firms may be constrained by
organisational rigidity and hence be less active in innovation.
Industry specific effects: since technological and innovation opportunities may occur
unevenly across sectors, we include industry dummy variables to proxy for these effects.
The full list of variables is summarised in Table 1.
Table 1 Definition of variables and descriptive statistics
Variable Definition Mean
newsal % of new sales 33.970
newsaln % of sales of products that are ground-breaking in world terms
newsald % of sales of products that are new to China or the firm or are
significantly improved (‘imitative’)
lrdin Ln(intramural R&D expenditure) 5.135
lrdex Ln(extramural R&D expenditure) 0.906
size Firm size dummy equally 1 for large firm and 0 for small firm. 0.675
age Firm age 16.705
lack_hc1 Human capital constraints dummy variable, 1 = the importance of
lack of qualified personnel to innovation is medium and high; and
0 = low or unimportant
co Innovation cooperation dummy variable, 1 = yes, 0 = no 0.483
cogd Dummy variable, 1 = cooperate with other firms within an
enterprise group; 0 = no
cosd Dummy variable, 1 = cooperate with suppliers; 0 = no 0.305
cocd Dummy variable, 1 = cooperate with customers; 0 = no 0.296
copd Dummy variable, 1 = cooperate with competitors or other firms in
the same industry; 0 = no
Collaboration with foreign universities for innovation 201
Table 1 Definition of variables and descriptive statistics (continued)
Variable Definition Mean
coprid Dummy variable, 1 = cooperate with private R&D institutions;
0 = no
counid Dummy variable, 1 = cooperate with universities and public
research institutions (PRIs); 0 = no
couni1 Dummy variable, 1 = cooperate with universities and PRIs in China;
0 = no
couni2 Dummy variable, 1 = cooperate with universities and PRIs in Newly
Industrialised Economies (Hong Kong, Taiwan, Singapore, Korea);
0 = no
couni3 Dummy variable, 1 = cooperate with universities and PRIs in
Europe, USA and Japan; 0 = no
couni4 Dummy variable, 1 = cooperate with universities and PRIs in other
countries not listed above; 0 = no
Two estimation problems arise in this model. The first is that the dependent variable, the
percentage of innovative sales, is constrained to a value between 0 and 100 and takes a
value of zero in a large proportion of the sample. The ordinary least squares (OLS)
estimates would thus be biased. Therefore a Tobit model should be introduced to reduce
the problem. The second problem is that a number of firms have not undertaken any
R&D activity at all and therefore have no sales of new or significantly improved
products. So there is a selection effect based on the decision to innovate or not. A
generalised Tobit (Hurdle) model needs to be employed to allow for the fact that firms
decide either to innovate or not, and, with respect to those that are innovative, for the
extent to which they are innovative (Mairesse and Monhen, 2002). As a robustness check,
we also report the results from the standard Tobit model.
3.3 Data
This paper is based on our study of innovation in Chinese firms. We use a national
innovation survey dataset of 1,408 manufacturing firms in China, the 2008 China
Innovation Survey (as the survey is called in a note to a table). This dataset contains data
on firms’ innovation activities over the 2005 to 2007 period. The survey was designed by
Tsinghua University and implemented by the National Statistical Bureau. It covers 42
cities in China in both the coastal and inland regions of China. A total of 1,408 valid
responses were received, with a response rate of 83.6%. The questionnaire demonstrates
high consistency and comparability with the design of the European Community
Innovation Survey (CIS). The sample includes firms of all major ownership types that
exist in China, and is composed of 9% state or collectively owned enterprises, 7%
privately owned, 53% share holding and limited liability companies, and 30% foreign
invested enterprises (FIEs) which are wholly-owned subsidiaries of MNEs or joint
ventures in which foreign investment accounted for more than 25% of total investment.
The survey oversampled large and innovative firms. About 50% and 17.5% of the firms
in the sample are medium- and large-sized firms which have between 300 to 2000 and
more than 2000 employees, respectively. The total reported R&D investment in the
sampled firms was RMB53350 million, accounting for 18.2% of China’s total R&D
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investment in the manufacturing sector in 2007. After careful data cleansing to exclude
observations with missing values for the necessary variables, the final dataset used in the
estimation contains 802 firms, of which 95% have innovated their products or processes.
Therefore, the results of this study reflect the role of universities in the innovation of
innovative Chinese firms rather than that in Chinese firms generally. This is a limitation
of the research that we shall bear in mind when drawing conclusions.
In the questionnaire, firms are asked about their innovation outputs based on their
self-assessment as well as their inputs into innovation such as intramural and extramural
R&D expenditures and R&D personnel. With regard to innovation outputs, firms are
asked to report whether in the past three years they have introduced any new product or
production process. For firms that have introduced new products, they are also asked to
report the proportion of their total sales in 2007 from products that are:
1 new to the firm
2 new to the country
3 new to the world.
The firms are also asked about their innovation collaboration behaviour, e.g., the type of
their collaborators, which includes other firms in the company group, suppliers,
customers, competitors, universities and PRIs, consultants and commercial labs.
Information is also collected about the geographic location of these collaborators, which
is divided into four groups mainly according to their technological advancement level as
well as geo-economic links to China:
1 mainland China
2 the fast growing NIEs nearby China including Hong Kong, Taiwan, Singapore and
South Korea
3 innovation leaders such as Europe, the USA and Japan
4 other countries.
On average, nearly half of the surveyed Chinese firms report that they collaborated with
external organisations1. Interestingly, universities and public research institutions are the
most popular collaborator for Chinese firms, with 35% of the firms reporting to have
collaborated with them for innovation during the sample period. This is not surprising
given that historically universities and public research institutions (PRIs) dominated the
innovation system in China and there was a strong government policy of pushing the
development of university-industry linkages. Most of the firms collaborate with Chinese
universities. Around 3% of the firms have collaborated with foreign universities.
Amongst these foreign university collaborators, 51% are from the USA, Europe and
Japan, 33% are from the NIEs including South Korea, Singapore, Taiwan and Hong
Kong, and 16% are from other countries (Table 2). Chinese firms which have
collaborated with foreign universities are mainly in the high- and medium-high
technology industries. Although most of them are in the coastal region of China, there are
still 37% of the firms from the inland region. This is interesting, especially given that
Collaboration with foreign universities for innovation 203
only 29% of the firms in the sample are located in the inland region. Breaking down
firms by ownership, domestic firms accounted for 65% of all the firms that collaborated
with foreign universities while FIEs accounted for 35%. This is more or less consistent
with the ownership distribution in the sample where FIEs accounted for 30% of the firms
in the sample.
Table 2 Distribution of foreign university collaborators in China
Home country of the foreign universities Total foreign
NIE EU&US&JP Other countries
By industry
Low&medium-low tech 7% 12% 0% 19%
High&medium-high tech 26% 40% 16% 81%
Total 33% 51% 16% 100%
By region
Inland 7% 19% 7% 33%
Coastal 26% 33% 9% 67%
Total 33% 51% 16% 100%
By ownership
Domestic owned firms 19% 35% 12% 65%
Foreign owned firms 14% 16% 5% 35%
Total 33% 51% 16% 100%
Source: Authors’ estimation based on the 2008 China Innovation Survey.
4 Results
4.1 What firms collaborate with foreign and domestic universities?
We first run a Logit model to examine what kinds of Chinese firm collaborate with
universities. We include in the model a vector of variables indicating a firm’s
technological capabilities which will shape their collaborative activities. These include a
firm’s intramural and extramural R&D expenditure and human resources. Productivity
level should also be an important determinant of a firm’s collaborative activities. A firm
of low level of productivity may not have the capacity to carry out collaborative
innovation. However, since R&D spending and human capital are two major sources of
productivity and we have already included these variables in our regression, we decided
not to include productivity into the regression to avoid (the problem of)
multicollinearity2. We also include a vector of variables representing firm-specific
characteristics such as size, age, industry and ownership. A dummy variable to represent
a firm receiving government support for innovation is also included as an explanatory
variable as this will enable a firm’s financial capacity for collaborative research,
especially as some government support schemes are specifically oriented to collaborative
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Table 3 Determinants of university-industry collaboration in China
Co with foreign university Co with domestic university
Coef. Std. err. Coef. Std. err.
Ln(intramural R&D exp) 0.206* 0.115 0.192*** 0.043
Ln(extramural R&D exp) 0.062 0.064 0.117*** 0.030
Firm size –1.628 1.033 –0.335 0.238
Firm age –0.043*** 0.016 0.004 0.006
Constraints in human capital –0.829* 0.492 –0.458* 0.273
Co with domestic uni 0.590 0.688
Co with foreign uni 0.560 0.831
Foreign-invested firm dummy 0.246 0.578 –1.304*** 0.246
Government support 0.766 0.591 1.273*** 0.220
Industry dummies yes yes yes yes
Constant yes yes yes yes
Observations 592 592
Log likelihood –74.690 –295.939
Notes: Robust standard errors are reported here. ***p < 0.01, **p < 0.05, *p < 0.1.
Table 3 reports the estimated results of determinants of the UIC from the perspective of
firms. Columns (1) and (3) show the estimated results of the determinants of
collaboration with foreign and domestic universities, respectively. Firms which invested
more in in-house R&D activity show a greater propensity to collaborate with both
domestic and foreign universities. However, extramural R&D appears to be significantly
associated with collaboration with domestic but not foreign universities. This is likely to
be due to geographical proximity between domestic universities and Chinese firms,
making extramural R&D more feasible. Human capital also appears to be a significant
factor in firms’ decisions to collaborate with universities, both domestic and foreign. The
greater constraints in human capital a firm feels, the less the likelihood that a firm will
engage in the UIC. Firm size does not appear to have a significant effect on firms’
propensity to collaborate with either domestic or foreign universities. Although large
firms appear to be active in the UIC due to greater capabilities, small firms with some
R&D activities are also found to engage in the UIC and benefit from such collaboration,
for example in Japan (Motohashi, 2005). This is consistent with evidence from East Asia
that firms with some R&D activities, regardless of size, are willing to engage with the
UICs (Lee et al., 2009).
As the results in column (1) indicate, younger firms such as start-ups are more likely
to collaborate with foreign universities. Many of the young firms, especially those in the
high-technology industry, are born with a global orientation (Filatotchev et al., 2009). A
considerable proportion of their top management team have been educated, trained or
worked abroad before they return to China. Young firms run by these returnees are more
likely to collaborate with foreign universities than other firms. Government financial
support on innovation does not appear to have a significant effect on firms’ engagement
Collaboration with foreign universities for innovation 205
with foreign universities. In contrast, as column (2) shows, firms with government
financial support are more likely to collaborate with domestic universities; this finding is
statistically significant. This suggests that government R&D programmes, specifically
designed to encourage university-industry linkages, have had a significant effect on the
formation of such UIC linkages. Not surprisingly, foreign-invested firms collaborate
significantly less with domestic universities than indigenous Chinese firms. This fact is
consistent with the findings from an earlier survey among MNE affiliates in China (Zhou,
2006) and research in semiconductor industries in China (Teece and Chesbrough, 2005).
This may be explained by intellectual property considerations and the skill and
capabilities mismatch between foreign invested firms in China and the Chinese
4.2 Foreign and domestic universities and innovation in the Chinese firms
The estimated results of the role of universities in industrial innovation in China using the
Generalised Tobit model corrected for potential selection bias are reported in Table 43.
Columns 1 to 3 report the regression results using the percentage of sales of products
which are ground-breaking innovations in world terms as the dependent variable.
Columns 4 to 6 report the results of regressions using imitative innovations as the
dependent variable. The results in columns (1) and (4) suggest that collaborations with
other firms or institutions have a positive impact and are significantly associated with the
creation of innovations that are new to the world and those which are new to the
country/firm. The magnitude of the estimated coefficients is of similar size but those in
the imitative innovation regression are of higher significance. However, treating all
universities as a whole without distinguishing between the types of universities, the
estimated coefficient of the ‘collaboration with universities’ dummy is not statistically
significant, although bearing the expected positive sign. The creation of a single dummy
variable to proxy the UICs between firms and different types of universities may have
covered up the real and diversified picture taken place in reality. Moreover, as Chen and
Kenney (2006) argue, although universities have formed an effective linkage and become
a critical source for industrial innovations in some regions in China, such as Beijing, the
fast growth of high-technology industries in many other regions is driven mainly by other
Breaking down universities according to their country of origin, the estimated results
exhibit some interesting findings in columns (3) and (6). Collaboration with domestic
universities exhibits a positive but insignificant effect on novel innovation. This can
probably be explained by the research quality of domestic universities in comparison to
the world innovation frontier during the sample period (Guan and Ma, 2007; CSTII,
2010). However, the effect of imitative innovation is positive and statistically significant.
The likely reasons are the priority given to technology transfer and assimilation in an
emerging developing country like China, the incentives provided by the government that
pushed universities to engage in many UICs for problem solving, and the current research
capacity of the majority of the universities in the country. This is not only the case in
high-technology industries where universities helped with reverse engineering, but also in
low-technology industries. For example, universities in Guangdong have played an active
role with respect to imitative innovation in the textile and apparel industry (Li and Hu,
uand J. Li
Table 4 Universities and firm innovation in China: generalised Tobit model estimates
Novel innovation Imitative innovation
1 2 3 4 5 6
Co with other org. 2.3 6.250**
(1.442) (2.824)
Co with universities 2.111 3.967
(1.414) (2.786)
Co with domestic uni. 1.045 5.239*
(1.402) (2.779)
Co with uni in NIEs 12.09** –0.693
(5.914) (11.99)
Co with uni in US/EU/Japan 8.429* –8.014
(4.696) (9.338)
Co with uni in other countries 17.08** –5.956
(8.088) (15.87)
Ln(intramural R&D exp) 0.409* 0.357 0.267 2.308*** 2.331*** 2.329***
(0.213) (0.224) (0.227) (0.427) (0.453) (0.45)
Ln(extramural R&D exp) 0.105 0.119 0.197 0.108
(0.185) (0.184) (0.363) (0.363)
Firm size 0.729 0.856 0.397 –5.874** –5.220* –5.732*
(1.502) (1.498) (1.501) (2.962) (2.968) (2.979)
Firm age –0.053* –0.056* –0.048 –0.021 –0.024 –0.040
(0.030) (0.030) (0.030) (0.058) (0.059) (0.059)
Constraints in human capital –0.205 –0.468 -0.484 –4.728 –5.600* –6.201**
(1.613) (1.573) (1.557) (3.171) (3.104) (3.112)
Industry dummies yes yes yes yes yes yes
Constant yes yes yes yes yes yes
Observations 817 817 817 802 802 802
Log Likelihood –3,366 –3,367 –3,354 –3,815 –3,819 –3,813
Notes: Robust standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.
As expected, international innovation collaboration between Chinese firms and
universities in the NIEs, namely Hong Kong, Taiwan, Singapore and Korea, appears to
have a significant and positive effect on the generation of innovations by Chinese
manufacturing firms. Internationally, joint scientific research between Chinese and
foreign researchers has grown rapidly since the late 1990s. This is evidenced by increased
joint publication which has benefited the research quality and impact of China (Guan and
Ma, 2007). Scientific collaboration amongst Asian economies (in particular, China, Japan
and Korea) has increased greatly since 1997. As a result, joint publication among authors
from these countries has increased significantly since the late 1990s (Li et al., 2012).
Moreover, the de facto active regional economic integration among business sectors and
geographical proximity have also facilitated cross border collaboration between the
Chinese firms and NIE universities. The active economic and scientific integration of the
Collaboration with foreign universities for innovation 207
East Asian economies has thus resulted in productive UIC in the East Asian regional
innovation system.
Moreover, firms that have innovation collaboration with universities in other
countries (e.g., Australia, Russia, Israel, India and Brazil) have a significantly higher
proportion of sales accounted for by products which are new at the world level. As
discussed earlier, despite the geographic and cultural distances between them, China and
these countries share some common characteristics and interests because of their similar
level of economic development. The similarity in stages of industrial development and
the cultural diversity between the partners appear to have enabled a fruitful collaboration.
Examples are the collaboration between Chinese firms and Australian universities in the
solar Photovoltaic industry. Nevertheless, firms that collaborated with foreign
universities in the ‘other countries’ group are concentrated in two industries: the
communication equipment, computer and other electronic equipment manufacturing
industry and special equipment manufacturing industry. These firms are very innovative:
the average share of innovative sales in these firms is 72%, with 31% accounted for by
‘novel’ innovations and 41% for ‘imitative’ innovations. Such high industry
concentration of this sub-sample may lead to an over-estimation of the impact of UIC
with universities in countries other than NIEs, US, Europe and Japan. Therefore, we
should be cautious about drawing strong conclusions on the general relationship between
collaborations with universities in the ‘other countries’ group and novel innovation in
Chinese firms.
Finally, linkages with universities in the major industrialised economies, i.e., USA,
Japan and Europe, although showing a positive effect, involve an estimated coefficient
which is only marginally significant at the 10% level. As discussed earlier, the
compatibility of innovation capability of the elite Western universities and Chinese firms,
their willingness to collaborate with each other, the significant organisational culture
distance between the partners, the substantial differences in market demand and technical
conditions between the partner countries, the absorptive capacity of the Chinese firms,
the consideration of intellectual property protection on the Western partner side, and the
lack of trust between the partners resulting from the above differences and concerns may
all be responsible for the weaker significance in comparison to the effect of innovation
collaboration between Chinese firms and the universities in the NIEs.
On the other hand, the significant effects of UIC with universities in NIEs in
comparison with the weaker benefits from UIC with universities in the
USA/Europe/Japan may also be due to the differences in the characteristics of the firms
that collaborated with them. As Table 2 indicates earlier, most of the firms that
collaborated with universities in the NIEs are foreign owned firms in the high technology
industries located in the coastal region. They have greater absorptive capacity, less
organisational culture distance to the universities in the NIEs than those domestic firms in
the inland regions and in the non-high technology sectors. Of the firms that collaborated
with universities in the advanced industrial countries such as US and Europe, more than
two thirds are domestic firms, more from the inland regions of China. Therefore, such
differences in ownership, regional and industry composition between the firms that
collaborated with universities in NIEs and advanced industrial countries give the
‘demand’ side explanation for why the UIC with universities in NIEs yields more novel
innovations than that in the USA and Europe.
uand J. Li
Firms’ intramural R&D appears to be insignificantly associated with their sales
intensity of novel products but is positive and significantly associated with their imitative
innovation. This result is consistent with the work of Fu and Gong (2011) which
examines the effect of indigenous R&D activities on technology upgrading in China
using a large firm-level panel dataset compiled by the National Statistical Bureau. As
regards the other control variables, smaller firms appear to have a greater propensity for
imitative innovation than larger ones. Moreover, younger firms appear to create more
novel innovations. This may be partly due to the fact that some young start-ups are spin-
outs of universities which have novel technologies to commercialise. Moreover, some
start-ups in China are born with a global background and orientation, with members
trained overseas (Filatotchev et al., 2009). These young firms with links close to the
global technology frontier (e.g., the young high-technology firms in the ZhongGuanCun
area) are more likely to create novel innovation.
Table 5 reports the results of a robustness check using the standard Tobit model. The
estimated results are broadly consistent with the generalised Tobit model estimates,
especially in respect to the pattern of the effect of different universities by country of
origin. The effect of collaboration with domestic universities remains significant with
respect to imitative innovation but not regarding novel innovation. International
innovation collaboration with foreign universities in the NIEs and other developing
countries all demonstrate a positive and significant effect. The estimated coefficients of
the US/Japan/EU university collaboration dummy remain positive, but fall slightly from
marginally significant to insignificant. In sum, the results on the role of different
universities in industrial innovation are generally robust.
Table 5 Robustness check: Tobit model estimates
Novel innovation Imitative innovation
1 2 3 4 5 6
Co with other org. 10.40** 9.828***
(5.111) (3.321)
Co with universities 5.344 4.276
(5.205) (3.513)
Co with domestic uni. 1.345 5.954*
(5.094) (3.532)
Co with uni in NIEs 35.67*** –5.64
(11.25) (17.1)
Co with uni in US/EU/Japan 17.29 –13.58
(10.72) (11.04)
Co with uni in other countries 35.32** –8.254
(15.97) (16.5)
Ln(intramural R&D exp) 2.106** 1.299 1.203 2.949*** 3.007*** 2.929***
(0.865) (0.937) (0.921) (0.544) (0.589) (0.595)
Ln(extramural R&D exp) 1.623** 1.690*** 0.0666 0.0009
(0.649) (0.647) (0.46) (0.461)
Collaboration with foreign universities for innovation 209
Table 5 Robustness check: Tobit model estimates (continued)
Novel innovation Imitative innovation
1 2 3 4 5 6
Firm size 5.967 4.837 3.428 –3.446 –5.144 –5.629
(5.553) (5.673) (5.642) (3.728) (3.932) (3.912)
Firm age –0.257 –0.244 –0.202 –0.0266 –0.024 –0.0467
(0.159) (0.168) (0.161) (0.0575) (0.0603) (0.0622)
Constraints in human capital –1.933 –2.811 –2.645 –2.605 –6.097 –6.154
(5.134) (5.171) (5.067) (3.63) (3.874) (3.869)
Industry dummies yes yes yes yes yes yes
Constant yes yes yes yes yes yes
Observations 928 817 817 910 802 802
F statistics 6.293 5.218 5.73 7.746 5.116 3.445
Log Likelihood –1,454 –1,298 –1,291 –3,741 –3,320 –3,314
Notes: Robust standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.
The collaboration variable is arguably determined simultaneously with the dependent
variable of innovation. In other words, there might be a potential endogeneity problem.
For example, firms that collaborate with other firms and universities are more likely to
have more innovative sales. However, it is also possible that more innovative firms might
collaborate to a greater extent with other firms and universities. Moreover, they are also
more likely to be invited into any innovation collaboration by other organisations. In
order to deal with the potential problem of endogeneity, we employ an instrumental
variable regression technique to correct this problem. The instrumental variables used are
all the exogenous variables in the model with the addition of two extra exogenous
variables: a firm location dummy which indicates whether a firm is located in the six
university concentrated cities; and a group dummy that equals 1 for firms that belong to a
corporation group. Moreover, the use of industry dummies in the regressions is also
designed to mitigate part of this potential endogeneity problem. We test whether the
assumption of endogeneity is borne out by the data at hand. The Wald tests of exogeneity
of the collaboration variables suggest there is no significant endogeneity problem.
Therefore, the generalised Tobit model estimates are preferred to the instrumental
variable model estimates. Consistent with the picture revealed in Tables 3 and 4, the
effect of university collaboration remains insignificant for firm innovation, of both novel
and imitative types 4. Nevertheless, given the cross-sectional nature of the data and the
difficulty of finding perfect instruments in practice, the relationship that can be inferred
from the results is more one of association than causality.
4.3 The role of universities in indigenous and foreign-invested firms
Table 6 reports the estimated results of Hypotheses 2 concerning the different effects of
foreign and domestic universities on novel innovation in indigenous Chinese and foreign-
invested firms, respectively. Consistent with the pattern shown in Tables 4 and 5, the
impact of domestic universities remains insignificant for both the indigenous and foreign-
invested firms. As expected, collaboration with universities in the NIEs and in other
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developing countries bears a significant and positive impact on novel innovation in
indigenous firms, which supports our Hypothesis 2b. The result is robust across models
using different estimation methods. In contrast, the link with universities in the
industrialised countries appears to have a significant positive impact on novel innovations
in foreign invested firms, which supports our Hypothesis 2a. As discussed earlier, this is
likely due to the reduced organisational cultural distances, moderate technological
distance and a stronger absorptive capacity in the foreign-invested firms for the advanced
technologies created in the USA, EU and Japan. Evidence from this table also suggests
that a moderate technological gap between partners and the absorptive capacity in the
receiving firm is an important factor shaping the outcome of international UICs.
Table 6 Universities and novel innovation in indigenous and foreign-invested firms
Indigenous firms Foreign-invested firms
Tobit Generalised
1 2 3 4
Co with domestic uni. 1.658 4.203 4.491 9.015
(1.337) (5.638) (4.051) (11.31)
Co with uni in NIEs 14.790** 39.910*** –19.270 –29.710
(6.164) (10.52) (16.45) (36.55)
Co with uni in US/EU/Japan 5.292 8.474 46.930** 87.070**
(4.132) (11.19) (19.45) (36.79)
Co with uni in other countries 27.190*** 46.300*** –14.690 –23.010
(8.522) (5.872) (19.21) (36.03)
Ln(intramural R&D exp) 0.846*** 3.798*** –0.443 –1.418
(0.228) (1.308) (0.504) (1.438)
Ln(extramural R&D exp) –0.083 1.007 0.530 2.576**
(0.176) (0.666) (0.476) (1.268)
Firm size –0.627 –0.149 –0.446 –0.478
(1.405) (0.133) (4.121) (0.55)
Firm age –0.034 –2.853 –0.121 6.247
(0.025) (6.038) (0.202) (11.94)
Constraints in human capital –1.344 -6.716 3.457 9.458
(1.475) (5.317) (4.192) (11.64)
Industry dummies yes yes yes yes
Constant yes yes yes yes
Observations 596 596 221 221
Log likelihood –2,348 –825 –949.9 –448.8
Notes: Dependent variable: novel innovation.
Robust standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.
Intramural R&D appears to be a significant driver of novel innovation in the indigenous
firms. The magnitude of the estimated coefficient and the statistical significance are both
higher than that in the larger sample which includes both indigenous and foreign invested
Collaboration with foreign universities for innovation 211
firms. In contrast, the estimated coefficient of intramural R&D is not significant for
foreign-invested firms. This may be explained by the fact that affiliates of MNEs are
often less active in frontier R&D due to their linkage to, and reliance on, R&D at their
headquarters, as Table 3 indicates. Firm size and age, however, do not appear to have
significant impacts on a firm’s performance in creating important novel innovations in
both indigenous and foreign-invested firms. Findings from this exercise, which
distinguishes indigenous firms and MNE affiliates, are important because of their
implications in collaboration partner selection for managers in both indigenous and MNE
affiliates. Moreover, for development purposes, the innovation capacity of indigenous
firms is a matter of special attention for policy makers in developing countries.
5 Conclusions and discussions
This paper attempts to investigate the role of foreign and domestic universities in
industrial innovation in emerging economies using a firm-level innovation survey
database from China. The key findings of this study can be summarised as follows. First,
international innovation collaboration with foreign universities benefits the creation of
ground-breaking radical innovations in China although the best-matched partners vary
between indigenous and foreign-invested firms. Second, collaboration with leading
research universities in other developing countries, especially with those in the NIEs,
such as Hong Kong, Taiwan, Singapore and Korea, appears to be fruitful in enhancing
the creation of ground-breaking innovations in indigenous firms. Third, collaboration
with universities in ‘other countries’, mainly the emerging South such as, Russia, Brazil
and India, also appears to be positively associated with firms’ performance in radical
innovation. Of course, given the small number of sampled firms that partnered with
universities in ‘other countries’ and their high industry concentration in only two
technology-intensive industries, we should be careful in drawing general conclusions on
the gains from collaborating with universities in these countries based on current
research. Nevertheless, our evidence does point out an alternative partnership other than
the traditional partners for effective international innovation collaboration.
Fourth, collaboration with universities in the industrialised economies appears to be a
significant external source of knowledge for foreign-invested firms in China. The USA
Europe and Japan are the main home countries of foreign universities that engaged in
innovation collaboration with Chinese firms. Firms that collaborated with them come
from both high- and low-technology industries, in both coastal and inland regions of
China. However, it does not appear to be as effective and fruitful for indigenous firms as
expected. The gap in innovation capability between the elite Western research
universities and Chinese firms, the significant organisational culture distance between the
partners, the differences in market demand between the partner countries, the absorptive
capacity of the Chinese firms, and the worry of weak intellectual property rights
protection in China may all be responsible for the weaker significance of UIC between
Chinese firms and universities in advanced industrial countries in comparison to that with
the universities in the NIEs. At the same time, most of the firms that collaborated with
NIE universities are foreign invested high technology companies located in the coastal
region. These characteristics of Chinese partners also determined the higher absorptive
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capabilities and smaller cultural distances for the UIC between them and universities in
the NIEs in comparison with those in the advanced industrial economies.
Finally, collaboration with domestic universities has played a significant role in the
diffusion of frontier technology and the creation of imitative innovations, while their
contribution to the creation of ground-breaking innovations is limited in the current stage
of development in China.
The overall pattern of the effectiveness of international UIC attests to the argument
regarding the importance of cultural and technological distances between partners. A
shared background in development stage and hence similar market demand for
technology amongst the partners is important for effective international innovation
collaboration. As a result, knowledge from universities in the NIEs appears to be more
compatible and more relevant to Chinese indigenous firms to nurture novel and
commercially relevant ideas. This is consistent with the argument of Acemoglu (2002)
that technologies originated in the South are more appropriate for technological
upgrading in the developing countries.
Findings from the current research have important policy and practical implications
for firms in both emerging and wider developing countries with regard to the processes
involved in tapping into knowledge and resources from universities to promote
innovation. Given the importance of technology transfer in developing countries,
especially in the early stage of industrialisation, policies in the developing countries
should greatly promote the UIC as a means of enhancing the absorptive capacity of the
indigenous economy. Moreover, opening up the innovation system to enable domestic
industries to collaborate with foreign universities should be an effective policy choice to
promote radical innovation, especially for those emerging economies which are
undergoing the crucial transformation from imitation to innovation. Domestic universities
are best positioned to help firms in developing countries to assimilate, grasp, adapt and
decipher transferred foreign technology.
Moreover, in their pursuit of greater novel innovation and catch-up, south-south
collaboration appears to be a fruitful choice for indigenous firms in emerging economies.
Increased economic integration and scientific collaboration have led to a closer
relationship between Chinese firms and the universities of the NIEs. In these cases, the
synergy, compatibility, and adequate technological gap between the two partners form a
creative and knowledge-enriching basis from which innovative products and processes
are co-produced. At the same time, collaboration with universities from the emerging
South also appears to make a robust positive contribution to the production of novel
innovations in indigenous firms. Therefore, firms in the developing countries seeking
international collaboration should not constrain themselves by considering only
universities in the several leading industrialised countries. Universities in other countries
also present alternative opportunities supported with well targeted and selected
Universities in major industrialised countries have been a useful knowledge source
for novel innovation in foreign-invested firms in China, although they do not appear to
benefit indigenous firms directly. This may be due to the stronger absorptive capability in
the affiliates of MNEs and the smaller organisational cultural differences between the
affiliates and the Western universities. If effective spillovers from foreign to domestic
firms are present, foreign-invested firms can serve as a gatekeeper, tapping Western
academic research into the Chinese economy. Moreover, as Chinese indigenous firms
move up the technology ladder and converge with Western firms in terms of management
Collaboration with foreign universities for innovation 213
culture and system in the future, they will be able to benefit more from a direct
collaboration with universities in major industrialised countries.
It is important to acknowledge the limitations of our research. First, due to data
limitations, our classification of cultural and technological differences is only roughly
defined based on the grouping of countries. Better measurement of these gaps for each
individual country instead of country group will provide more accurate insights on the
moderating roles of cultural and technological distances in international knowledge
transfer. Moreover, as discussed earlier, due to the possible endogeneity between
collaboration and innovation and the limitations in data and instrumental variables, the
relationship that can be inferred from the results is more one of association than causality.
Finally, future research should take into account more detailed information regarding
which university a firm has collaborated with and the research strength of the university
in this particular discipline. Therefore, we do not consider this analysis to be the final
word on the role of foreign and domestic universities in the emerging economies.
The authors would like to thank the Research Center for Technological Innovation of
Tsinghua University for the China Innovation Survey (2008) data, the generous funding
from the National Natural Science Foundation of China (Project No. 71273152) awarded
to Jizhen Li, and the High end Foreign Expert Program from State Administration of
Foreign Expert Affairs of China (Project No. GDW20131100024) awarded to Xiaolan
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1 The Chinese wording used in the survey (‘he zuo’) does not imply the subtle difference
between collaboration and cooperation. Firms may regard both arms-length close cooperation
and collaboration as being ‘he zuo’. We therefore translate the wording into ‘collaboration’
which may include recursive and sustained interactions in addition to arm-length cooperation.
This may be a factor in the not small proportion of Chinese firms report having collaborated
with external organisations for innovation.
2 We have tested the regression with labour productivity included as one of the explanatory
variables. The results are highly consistent with those reported in Table 3 although the
estimated coefficients of the labour productivity variable are insignificant, probably due to the
high correlation between productivity and R&D and human capital.
3 Estimated results of the first (selection) equation are reported in Appendix.
4 Due to space limitations, the estimated results are not reported here. They are available from
the authors on request.
Collaboration with foreign universities for innovation 217
Results of the selection equation of the generalised Tobit model
Novel innovation Imitative innovation
Ln(intramural R&D exp) 0.163** 0.113***
(0.067) (0.004)
Ln(extramural R&D exp) 10.820 0.008
(229.50) (0.009)
Firm size 0.617 –0.061
(0.413) (0.040)
Firm age 0.009 0.001
(0.008) (0.002)
Constraints in human capital –0.190 –0.147***
(0.578) (0.050)
Industry dummies Yes*** Yes***
Constant Yes Yes
Observations 817 802
Log likelihood –1,288 –3,813
Notes: Robust standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.
Results reported here are that of the selection equations of the full model for the
whole sample.
... In other words, organizations with extensive experience in foreign market expansion typically commit to a broader range of modes of access, while organizations lacking relevant international experience frequently prefer less resource-intensive entry modes. In prestigious universities in developed countries, international experience, in addition to international enrollment experience and experience cooperating with foreign institutions of HE-in this case, partnering with educational institutions in developing countries-also helps to promote innovation in HEIs in emerging industrialized developing countries [59,60]. ...
... In particular, country image, experience in recruiting postgraduate international students, and using social media to connect with students were the top-valued indicators under the abovementioned strategies, respectively. Even though previous studies [46,55,59,60,72] have mentioned these factors, this research has, for the first time, fully developed such definitions and surveyed students to gather their opinions regarding these indicators. ...
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Globalization and internationalization have received increasing attention from researchers in the field of education management over the past decade, who have found that internationalization is an indispensable component in the sustainable development of higher education (HE). This research aims to construct Innovative Marketing Strategies (IMS) for international student recruitment and contribute to the sustainable internationalization of higher education (IoHE). A literature review was used to construct an Innovative Marketing Strategies questionnaire. Data collected from 300 international students studying in Taiwan were used for a feasibility analysis in order to construct the final questionnaire, and survey data collected from 522 participants were used for more in-depth statistical analysis of the final strategies. The research results indicate that combining the marketing mix 4C with an innovative strategy (IS) approach provides sustainable recruitment marketing strategies toward higher education institution internationalization from a customer-oriented perspective. The IMS in this study includes 2 dimensions, 10 strategies, and 32 indicators. Additionally , a comprehensive analysis demonstrating the perspectives of international students with respect to the strategies was also conducted. This research contributes to the theoretical development and practical implementation of sustainable HE internationalization management. Discussions and recommendations based on the results of this study are also given.
... However, firms forging distant linkages would, therefore, only engage with distant universities if the required knowledge is located there (Huggins et al., 2008;D'Este and Iammarino, 2010;Berchicci et al., 2016). Arguments in favor of contract research over longer distances are the availability of highly talented researchers at lower cost (Huggins et al., 2008), and the presence of universities that are helpful with more ground-breaking innovation (D'Este and Iammarino, 2010;Fu and Li, 2016). More distant knowledge is deemed superior to locally available knowledge, and contract research to more distant universities could be supposed to be induced by universities better equipped to convey complex knowledge across larger distances (Lissoni, 2001). ...
... More distant knowledge is deemed superior to locally available knowledge, and contract research to more distant universities could be supposed to be induced by universities better equipped to convey complex knowledge across larger distances (Lissoni, 2001). These universities can be expected to be consulted for complex knowledge; either when there are no local alternatives to obtain that knowledge (Fu and Li, 2016); or if the university research results are cheaper (Maskell et al., 2007;Herstad et al., 2014). Knowledge developed at distant universities shows more variety (regional environments reduce the variety of knowledge, as the pool of knowledge risks becoming familiar but potentially redundant), and with distance the scope of available knowledge increases (Berchicci et al., 2016). ...
This article explores the role of geographical distance and regional co-location as a determinant of the size of contract research from firms to domestic universities. The analysis relies on a panel of three consecutive R&D surveys for Belgium, and controls for firm, university, and network characteristics. The findings reveal that firms tend to enter into larger-sized contract research with universities that are either located nearby or farther away. Regional co-location is also positively associated with the size of contract research from firms to universities.
... Although, some challenges faced by EMNCs engaged in U-I may overlap with those seen in traditionally studied domestic U-I collaborations, the international dimension inevitably adds further complications. For example, international U-I collaboration of Chinese MNCs are affected by a cultural gap between home and host country in terms ofinstitutions, language and business practices (Fu and Li, 2016). While this seems to be in line with previous studies on the impact of national culture on R&D internationalisation (Jones and Davis, 2000), the way MNCs mitigate it through organisational roles remains largely unexplored. ...
... LCMs leverage this in order to carry out their function of reducing cultural distance between Yangshuo and their partners while they collaborate on joint research projects. Consistent with Fu and Li (2016), we confirm the presence of a country-culture effect in Chinese organisations collaborating with foreign universities. It appears though that this culture effect is not associated with a common cultural and administrative misalignment between the West and China and its impact on management practices and styles. ...
This paper contributes to open innovation (OI) and boundary spanning literatures by providing a first understanding of industry‐based boundary spanners in university‐industry (U‐I) collaboration through case studies of Chinese multinational corporations (MNCs) and their international OI platforms. Organisational roles created by these platforms within the MNCs and their activity of bridging between organisations are examined. The analysis of 25 in‐depth interviews in two MNCs located in eight countries, along with internal documents, sheds light on U‐I collaboration practices implemented by Chinese MNCs. Two new boundary spanning roles are identified: Dual Cultural Bridger – as these OI platforms bridge organisational and national cultural gaps to prevent and solve problems in the collaborative process; and International Network Enhancer – as these OI platforms act as trust building and local knowledge listening posts for the MNC’s global network. Managerial and policy implications are provided.
... Several studies have confirmed the contribution of university-industry research cooperation in China to universities' revenue, firms' innovative capacity and regional economic development (Liu and Shi 2009;Ng and Li 2009;Yang and Ling 2009;Li et al. 2010;Fu and Li 2016;Hao et al. 2016). Quan (2010) surveyed R&D laboratories in companies that interact with universities in Beijing. ...
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Universities and public research institutes play a key role in enabling the application of scientific breakthroughs and innovations in the market- place or by government organizations. Their present and potential future contribution to the production and application of knowledge to innovation is undeniable. To further leverage this role, many countries – developed and developing alike – have implemented national strategies to support the application or commercialization of knowledge produced by public research organizations. In addition, individual universities and public research institutes have introduced practices to support these activities, for instance, by including knowledge transfer to promote innovation as a core part of their mission. As a result, a vital question for policymakers – and the enquiry of this book – is how to improve the efficiency of these knowledge transfer practices to help maximize innovation-driven growth and/or to seek practical solutions to critical societal challenges. Unfortunately, it is not straightforward for policymakers or knowledge transfer practitioners to access information on what works and what does not. Countries and institutions have garnered substantial experience with diverse approaches. Yet this information has not been distilled in a way that can provide policy guidance for specific sectors or for countries at varying levels of economic development. With this in mind, the book pursued the following three objectives: • to develop a conceptual framework to evaluate knowledge transfer practices and outcomes • to improve knowledge transfermetrics, surveys, and evaluation frameworks, resulting in a standardized method to assess national or insti- tutional strategies in an internationally comparable way • to generate findings on what works and what does not, and to propose related policy lessons.
... On the one hand, international collaboration is argued to provide an ideal platform for realizing inter-organizational learning and the enhancement of dynamic capabilities (Yao, Yang & Fisher, 2013Teece, 2014;Zhao, Liu, Andersson & Shenkar, 2021). On the other hand, it may also enhance innovation by facilitating the acquisition of a range of resources, increasing the diversity of the knowledge pool, and improving the dynamic capabilities of collaborating partners (Narin, Stevens & Whitlow, 1991;Wagner and Leydesdorff, 2005;Fu, 2015;Fu and Li, 2016;Lee, Spanjol & Sun, 2019). To this end, leading multinational enterprises (MNEs) from emerging economies work hard to catch up to and even leapfrog the technological frontier by acquiring novel knowledge and upgrading their technological capability via international collaboration. ...
This paper investigates the impact of international collaboration and its characteristics on the quality of the innovation of multinational enterprises (MNEs) in emerging markets. Using a unique dataset of 1428 international and comparable domestic collaboration projects over the 2010–2016 period, it finds that while international innovation collaborations are associated with high innovation quality, cultural distance has a negative effect on collaboration outcomes. Moreover, proximity to the focal firm's overseas R&D centres and the size of expenditure budgets play significant moderating roles in overcoming cultural barriers. Based on the RBV and dynamic capabilities theory, we investigate how firms from emerging markets can acquire these two crucial requisites for innovation. The characteristics of partners and intellectual property (IP) arrangements are also found to have a significant impact on the quality of innovations.
... Based on both self-verification and symbiosis theories, we explore the mechanism of millennials' entrepreneurial values, supporting the growth of new ventures. The entrepreneurial symbiosis network in the makerspace clarifies its realization path (Chen et al., 2016;Fu and Li, 2016). ...
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The fate of new ventures incubated by the same corporate ecosystem is different. Can entrepreneurs’ ideas affect the way out of incubating companies? Based on self-verification theory and symbiosis theory, we took millennial entrepreneurs as the research object, combined with entrepreneurial enterprises’ data in the makerspace. We analyzed the impact of millennials’ entrepreneurial values on new ventures growth and explored the mediating role of entrepreneurial symbiosis networks. The following conclusions are obtained by analyzing the questionnaire of 191 millennial entrepreneurs: Millennials’ entrepreneurial values significantly promote new ventures growth. The strength and scale of the entrepreneurial symbiosis network have a positive impact on new ventures growth. The entrepreneur symbiosis network acts as an intermediary between the millennials’ entrepreneurial values and new ventures growth.
... Economic output considers the impact of medium-to long-term effects on economic results, measured by total sales growth (Barge-Gil and Modrego 2011; Fu and Li 2016;Di Maria et al. 2019), sales growth of new products (Belderbos et al. 2004;Arranz and Fdez de Arroyabe 2008;Frenz and Ietto-Gillies 2009), and added value or productivity growth (Belderbos et al. 2004(Belderbos et al. , 2006Aschhoff and Schmidt 2008;Harris et al. 2013). The consensus on the effect on these outcomes is weaker in comparison with the technical outcome. ...
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The University–Industry (U–I) relationship is a fundamental part of innovation systems. A wide spread of public resources has been given to promote this relationship and a large number of studies has evaluated the results. However, while innovation theory identifies this relationship as a positive instrument to increase firms’ performance, evaluation literature reports a wide range of findings. The lack of conclusiveness results in theory and evaluation literature motivates this meta-regression analysis (MRA), built on fifty-one micro-level studies published since 1995. After controlling for publication selection bias, sample, and study heterogeneities, our results show a small effect on firms’ performance. Specifically, the size of the effect is more significant for technical outcomes than economic ones. These findings have a lot of relevance for universities, firms, and policymakers for determining open-innovation strategies and public policies.
... Several studies have confirmed the contribution of university-industry research cooperation in China to universities' revenue, firms' innovative capacity and regional economic development (Liu and Shi 2009;Ng and Li 2009;Yang and Ling 2009;Li et al. 2010;Kafouros et al. 2015;Fu and Li 2016;Hao et al. 2016). ...
... Studies from the advanced world have pointed to the distinctive effects of individual actors collaborating in different types of innovations and to the impact of spatial and non-spatial proximities among actors (Boschma 2005;De Maggio et al. 2009;Tsai 2009;Petruzzelli 2011;Iammarino et al. 2012;Ardito et al. 2015;Hansen and Mattes;2017;Del Guidice et al. 2019;Elia et al. 2019). However, existing studies have failed to provide a comprehensive overview of collaborative innovation in the emerging innovation systems of advancing countries (Da-Chang et al. 2012;Lin and Huang 2013;Barros 2016;Fu and Li 2016;Perri et al. 2017;Del Guidice et al. 2019;Saranga et al. 2019;Kirby and El Hadidi 2019). ...
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Firms often lack competences and capabilities for creation and commercialization of innovations. The solution to this problem lies in sharing or acquisition of relevant resources through collaboration. The success of collaborative relationships depends on the type and the quality of partners involved and proximity between them. This is particularly true in emerging innovation systems of countries in transition from middle to high income levels. The objective of this paper is to explore whether collaboration with suppliers, customers, universities and research institutes and intra-group collaboration with partners of different origin in nine emerging innovation systems of Central and Eastern Europe facilitates commercialization of existing marginally modified, incrementally novel and radically novel products. Results of treatment analysis on a sample of over 10.000 firms from the Eurostat’s Community Innovation Survey reveal that domestic innovation competences and capabilities mostly fuel commercialization of existing products and that firms rely on a diverse network of collaborators. We find evidence of positive impact of collaboration on commercialization of existing products and to a lesser extent on incremental and radical innovations. Relevance of individual collaboration channels differs over countries. Among foreign partners, collaboration with entities from other European Union member states facilitates commercialization of existing products while partners from United States, China and India have positive effects on commercialization of incremental and radical innovations suggesting that cognitive proximity is more important than geographical, social, organizational and institutional proximities. Recommendations for formulation of innovation policies in emerging innovation systems are provided.
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The first edition of Regional Innovation Systems introduced a newly developed theoretical and empirical construct as a means of planning and analysing industrial development on a regional scale. Since 1995, when the first edition was commissioned, there has been a worldwide innovation-led boom and subsequent slump, meaning enormous change has occurred in regional economies. The new edition registers this change and provides an interesting test of the robustness of the original arguments in the book. Not least, more industrial policy making is influenced by the RIS analysis, and many national and regional governments have adopted RIS approaches, along with related instruments like promotion of industry clusters, academic entrepreneurship, regional venture capital and science-led development strategies. Set within a broadly evolutionary economics perspective, accounts are given of the system interactions occurring between firms and the innovation support infrastructure. Cases are drawnworldwide from Asia, North America and Europe and include a new case study on Slovenia.
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This paper examines interfirm knowledge transfers within strategic alliances. Using a new measure of changes in alliance partners' technological capabilities, based on the citation patterns of their patent portfolios, we analyze changes in the extent to which partner firms' technological resources ‘overlap’ as a result of alliance participation. This measure allows us to test hypotheses from the literature on interfirm knowledge transfer in alliances, with interesting results: we find support for some elements of this ‘received wisdom’—equity arrangements promote greater knowledge transfer, and ‘absorptive capacity’ helps explain the extent of technological capability transfer, at least in some alliances. But the results also suggest limits to the ‘capabilities acquisition’ view of strategic alliances. Consistent with the argument that alliance activity can promote increased specialization, we find that the capabilities of partner firms become more divergent in a substantial subset of alliances.
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Interorganizational links-cooperative relationships among distinct but related organizations-are believed to enhance innovative processes in organizations. We conceptualize various types of interorganizational links as opportunities for learning and resource sharing in the pursuit of innovation, A study of over 400 California hospitals over ten years found considerable support for the relationship between interorganizational links and innovation, Structural, institutional, and resource-based links were all strongly related to innovation in hospital services and technologies.
Purpose: Various forms of regional cooperation in East Asia have greatly increased in the past two decades. Scientific collaboration is beneficial for both scientifically lagging countries (SLCs) and scientifically advanced countries (SACs) with respect to their S&T capacity and thus economic prosperity. This paper aims to provide a comprehensive description of intra-regional scientific collaboration in East Asia from 1985 to 2008 with the 10+3 (ASEAN ten plus China, Japan and South Korea) framework. Design/methodology/approach: The paper uses co-authored articles embodied by Science Citation Index Expanded (SCI-Expanded) to indicate cross-border scientific collaboration. Findings: Data show that heterogeneity in the publication output across East Asian countries is decreasing. Intra-ASEAN, intra- Northeast Asia, as well as ASEAN and Northeast Asia scientific collaboration greatly improved since 1997. Originality/value: This paper discusses factors influencing international scientific collaboration and focuses on explaining the impact of regional integration. Finally, it offers further suggestions on East Asian integration from the perspective of scientific collaboration.
The IMF Working Papers series is designed to make IMF staff research available to a wide audience. Almost 300 Working Papers are released each year, covering a wide range of theoretical and analytical topics, including balance of payments, monetary and fiscal issues, global liquidity, and national and international economic developments.
This article examines the roles of universities in industrial-economy national innovation systems, the complex institutional landscapes that influence the creation, development, and dissemination of innovations. The inclusion of a article on university research in a volume on innovation is itself an innovation-it is likely that a similar handbook published two decades ago would have devoted far less attention to the role of universities in industrial innovation. But scholarship on the role of universities in the innovation process, as opposed to their role in basic research, has grown rapidly since 1970. One important theme in this research is the reconceptualization of universities as important institutional actors in national and regional systems of innovation. Rather than "ivory towers" devoted to the pursuit of knowledge for its own sake, a growing number of industrial-economy and developingeconomy governments seek to use universities as instruments for knowledge-based economic development and change.
This paper shows that value creation by multinational enterprises (MNEs) is the result of activities where geographic distance effects can be overcome. We submit that geographic distance has a relatively low impact on international research and development (R&D) investments, owing to the spiky nature of innovation, and to the unique ability of MNEs to absorb and transfer knowledge on a global scale. On the one hand, MNEs need to set up their labs as close as possible to specialized technology clusters where valuable knowledge is concentrated, largely regardless of distance from their home base. On the other, MNEs have historically developed technical and organizational competencies that enable them to transfer knowledge within their internal networks and across technology clusters at relatively low cost. Using data on R&D and manufacturing investments of 6320 firms in 59 countries, we find that geographic distance has a lower negative impact on the probability of setting up R&D than manufacturing plants. Furthermore, once measures of institutional proximity are accounted for, MNEs are equally likely to set up R&D labs in nearby or in more remote locations. This result is driven by MNEs based in Triad countries, whereas for non-Triad MNEs the effect of geographic distance on cross-border R&D is negative and significant.
This paper gives an overview of the main elements of a cognitive theory of the firm, and organization more widely, with a focus on innovation. The theory claims that the main purpose of an organization is to align cognition, in a wide sense of perceptions, meanings, interpretations, values and norms of behaviour, thus limiting 'cognitive distance' between people within an organization, in order to achieve coordination. This is done on the basis of an organizational 'cognitive focus' that has a 'competence side' related to capabilities and knowledge, and 'governance side' related to norms and values of conduct. The paper starts with a review of related theories of the firm: the competence view from Penrose, the transaction cost view from Williamson, and the evolutionary view from, in particular, McKelvey and Aldrich. Next comes a note on the relation between the notions of firms and organizations. The paper proceeds with an outline of the underlying theory of 'embodied' cognition, the notions of cognitive distance and organizational focus, inter- and intra industry differences between organizations, organizational boundaries, organization between organizations, and learning and invention.
Formal collaboration between universities and industries is a recent phenomenon. Currently the role of an industry–university collaboration (IUC) is to close the gap between industry and academia. This study aims to analyze the willingness to engage in technology transfer (TT) in IUCs from the three vantage points of the technology transferor (university), the technology transferee (industry), and the TT intermediary institute. This study mainly observes the pairwise relationships between influencing variables and sub-variables and willingness to participate in TT in an IUC. From the vantage point of universities, this study shows that the “transferor's incentive” and “capability of transferor” variables positively influence willingness to participate in TT in an IUC. From the vantage point of industry, the results indicate that “capability of transferee” and “incentive for establishing technological resources” have major influences on willingness to participate in TT in an IUC. From the vantage point of TT intermediary institutes, the results show that “intermediary's fundamental resources” and “intermediary's transferring process” have a positive impact on willingness to participate in TT in an IUC.