Description
This paper analyzes a total of 1930 collaboration projects, including contract research, joint research,
technology transfer, and incubation models, between National Cheng Kung University (NCKU) and firms
during the period between 2001 and 2009. Effects of both firm size and geographical proximity on the
frequency of different models of collaboration are addressed. Results show that large-sized enterprises
prefer adopting the contract research model and implementing it in a more comprehensive cooperation
mechanism or with long-term cooperation relationships. By contrast, small and medium-sized enterprises
prefer to give equal prominence to both contract research for research subject dominance and
joint research due to low investment requirements. Results also show that geographical proximity is an
important factor during the interaction between enterprises and NCKU. However, the influence is quite
different when the cooperation models are compared individually
Original article
Effects of ?rm size and geographical proximity on different models of interaction
between university and ?rm: A case study
Tien-Chu Lin
a, *
, Shiann-Far Kung
b
, Hei-Chia Wang
c
a
Faculty of Research and Services Headquarters, National Cheng Kung University, Tainan, Taiwan
b
Faculty of Urban Planning, National Cheng Kung University, Tainan, Taiwan
c
Faculty of Industrial and Information Management, National Cheng Kung University, Tainan, Taiwan
a r t i c l e i n f o
Article history:
Received 12 July 2012
Accepted 23 September 2013
Available online 30 March 2015
Keywords:
Firm size
Geographical proximity
Industryeuniversity collaboration
a b s t r a c t
This paper analyzes a total of 1930 collaboration projects, including contract research, joint research,
technology transfer, and incubation models, between National Cheng Kung University (NCKU) and ?rms
during the period between 2001 and 2009. Effects of both ?rm size and geographical proximity on the
frequency of different models of collaboration are addressed. Results show that large-sized enterprises
prefer adopting the contract research model and implementing it in a more comprehensive cooperation
mechanism or with long-term cooperation relationships. By contrast, small and medium-sized enter-
prises prefer to give equal prominence to both contract research for research subject dominance and
joint research due to low investment requirements. Results also show that geographical proximity is an
important factor during the interaction between enterprises and NCKU. However, the in?uence is quite
different when the cooperation models are compared individually.
© 2015, College of Management, National Cheng Kung University. Production and hosting by Elsevier
Taiwan LLC. All rights reserved.
1. Introduction
1.1. Models of industryeuniversity collaboration
Since the late 1970s, university missions have been widely dis-
cussed and have moved progressively from teaching and research
to teaching, research, and public service. This means that besides
sharing existing knowledge and advancing new knowledge, uni-
versities have the newmission of transferring academic knowledge
and resources to the public, and generating technological spillover
through universityeindustry interaction (Etzkowitz & Leydesdorff,
2000; Leydesdorff & Meyer, 2003; Lin, Chang, & Chung, 2012a,
2012b; Mans?eld, 1995) The interaction includes recruitment of
university graduates, personnel exchanges, joint research, contract
research, consulting, patents and publications, licensing, spin-off
companies, industry-funded laboratories and other facilities, and
also through informal contacts such as meetings and conferences
[D’Este & Patel, 2007; Muscio, 2010; Organisation for Economic Co-
operation and Development (OECD), 1999; Schmoch, 1999]. Such
interaction between industry and university usually bene?ts both
parties, such as enhancing the research productivity of ?rms (Link,
Scott, & Siegel, 2003), increasing the innovation activity of ?rms,
especially for large manufacturing ?rms (Loof and Brostrom, 2000),
enhancing research performance and publications of university
professors (Guldbrandsen & Smeby, 2005), and shifting university
curriculums from basic toward applied research (Link & Scott,
2003).
These kinds of links between university and industry are the
primary knowledge-?ow channels in national innovation systems,
which are keys for improving technology performance (OECD, 1999).
Manyearlier studies (Hanel &St-Pierre, 2006; Loof &Brostrom, 2008;
OECD, 1996, 2003; O'Gorman, Byrne, & Pandya, 2008) of knowledge
transfer have concentrated on patenting, licensing, and spin-offs as
the main contributions of universities in terms of technology diffu-
sion. Systematic analysis of knowledge transfer in models such as
joint research projects, consultancy, and training has been largely
neglected (D'Este & Patel, 2007). However, as some researchers have
mentionedor noted, whencomparedwithother formal mechanisms,
suchas contract researchor joint researchagreements, patenting and
licensing account only for a small proportion of academiceindustry
* Corresponding author. Faculty of Research and Services Headquarters, National
Cheng Kung University, Tainan 70101, Taiwan.
E-mail address: [email protected] (T.-C. Lin).
Peer review under responsibility of College of Management, National Cheng
Kung University.
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Asia Paci?c Management Review 20 (2015) 90e99
interactions (Agrawal & Henderson, 2002; Cohen, Nelson, & Walsh,
2002; D'Este & Patel, 2007; Mans?eld & Lee, 1996; Mowery &
Sampat, 2005; Schartinger, Schibany, & Gassler, 2001). For example,
Agrawal and Henderson (2002) used data from the departments of
mechanical and electrical engineering at MIT to demonstrate that
patents are a relatively small source of knowledge transfer (less than
10%) out of the university. Cohen et al. (2002) used data from the
CarnegieMellonSurveyonR&Dperforming?rms inthe UnitedStates
and concluded that most patents and licenses were less important
models compared with publications, conferences, informal in-
teractions, and consulting. Consequently, more emphasis should be
placed on investigations of knowledge ?ow through contract
research, joint research, publications, conferences, and informal
consulting.
It can be understood that different ?rms appear to use quite
different models to access knowledge from universities (Agrawal &
Henderson, 2002). However, the major barrier for a researcher who
measures the interaction between universities and ?rms is the lack
of information from both university and ?rm (Schartinger et al.,
2001). Traditionally, the main indicator that measures the R&D
cooperation between university and industry can only be obtained
from the annual reports of the university and by surveying uni-
versity faculties and ?rms (OECD, 1999). However, it has been
generally dif?cult to obtain details of all individual indus-
tryeuniversity cooperation projects because they are mostly clas-
si?ed “con?dential” by individual universities. Thus, existing
impediments for getting access to the information concerning the
enterprises experiencing such industryeuniversity cooperation
and the rare availability of such information had also contributed to
the relevant reference documents. In this research, size of ?rms and
geographical proximity were investigated because in the literature,
they are important determinants related to industryeuniversity
collaboration (Brostrom, 2010; Elyse, 2006; Monjon & Waelbroeck,
2003; Schartinger et al., 2001). Data collected from National Cheng
Kung University (NCKU; the most active university that collabo-
rated with enterprises) are used, which includes 1930 collaboration
cases of contract research, joint research, technology transfer, and
incubation between NCKU and enterprises during the period from
2001 to 2009. Such a large number of cases allows for a wide grasp
of the factors involved in the decision making that concerns en-
terprises in selecting an industryeuniversity cooperation model
and the in?uential signi?cance levels of geographical proximity.
1.2. Industry and university environment in Taiwan
Taiwan has a land surface of approximately 36,000 km
2
and a
population of roughly 23 million people. Businesses are mainly
composed of small and medium-sized enterprises (SMEs), which
make up97.9% of the total enterprises (Small and MediumEnterprise
Administration, Ministry of Economic Affairs, 2010). In terms of
technologytrade, theexpenditures of Taiwaneseenterprises inrecent
years have always beenmuchgreater thantheir incomes. In2008, for
example, thenational technologytrade incomeandexpenditure ratio
was 0.26 (National Science Council, 2010); this shows that Taiwan's
technology trade is still in a de?cit situation, and enterprises' inno-
vative operations still lack suf?cient power for R&D. Furthermore,
according to the Global Competitiveness Report, the economic devel-
opment can be classi?ed into factor-driven, ef?ciency-driven, and
innovation-drivenstages (WorldEconomic Forum, 2008). For several
decades, Taiwan has been characterized by an ef?ciency-driven
economy. This image was symbolized by many original equipment
manufacturers in the electrical and electronics industry with high
production ef?ciency, but with low returns.
In attempting to transform the economy to the innovation-
driven stage (Taiwan has been characterized as being in transition
from ef?ciency-driven to innovation-driven according to the
report), the Taiwanese government is aware of the importance of
innovation creation and knowledge spillover, and has placed great
emphasis on the promotion of a national innovation system,
especially from the universities end. For example, R&D expendi-
tures of high educational institutions (HEIs) have increased rapidly
during the past 10 years, as illustrated in Fig. 1. Among which, R&D
expenditures appropriated from government, business enterprises,
and other sectors have all been increasing upward annually be-
tween 2000 and 2009. However, the annual percentage of R&D
expenditure contributed by business enterprises has just kept pace.
Such a tendency can be demonstrated by the ever-increasing
prevalence of industryeuniversity projects over the past 10 years;
however, this situation is far from the need of industry to create
innovation and knowledge spillover from university.
According to the analysis of Shi (2009), there are two major
shortcomings in the allocation of Taiwan's R&D resources: a
shortfall in allocation of universityeindustry R&D resources, and a
gap in the innovative connection of R&D activities between enter-
prises and educational institutes. The shortfall of uni-
versityeindustry resource allocation is caused by the majority of
personnel with doctorate quali?cations (72.1% of all PhDs) being in
the academic realm, whereas the gap in the innovative connection
of R&D activities between enterprises and educational institutes is
caused by a division of focus in technology development and aca-
demic research, respectively.
From the results of Shi's analysis, it is particularly important for
Taiwan's government to enhance industryeuniversity collabora-
tion to bridge the gap in the innovative connection of R&Dactivities
between enterprises and educational institutes, and thus, release
the capability of personnel with doctorate quali?cations from
university to industry. Therefore, the objective of this research is to
understand the different models that different ?rms appear to use
in order to access knowledge and innovation from universities,
especially the ?rm size and geographical proximity between the
university and the ?rm. Consequentially, using the statistical results
of the industryeuniversity cooperation models over the past
decade, an understanding of what concerns will be factored into
the decision making of industryeuniversity models and more
provisions of references will be made available for the policy-
makers.
The remainder of this paper is organized as follows. The “Data
and Methods” section sets out the de?nition of interaction channels
between the university and the ?rms, as well as the data and
Fig. 1. R&D expenditure of the higher education sector, 2000e2009. Note. Data from
“Indicators of Science and Technology Taiwan,” by National Science Council (2010),http://www.nsc.gov.tw/tech/index.asp.
T.-C. Lin et al. / Asia Paci?c Management Review 20 (2015) 90e99 91
method used in the analysis. The “Results and Discussion” section
describes the main empirical results regarding the impact of ?rm
size and geographical proximity. Finally, the “Conclusion” section
concludes the article.
2. Data and methods
Because NCKU
1
is one of the premier research institutions and
the most active university interacting with industries in Taiwan,
this paper analyses NCKU to understand the intensity of the uni-
versityeindustry relationship. According to statistics of the Higher
Education Evaluation and Accreditation Council of Taiwan
regarding the performance of universityeindustry collaboration
from 2006 to 2009, among public Taiwanese HEIs, NCKU won ?rst
prize in “the broadness of participating in universityeindustry
collaborative activities” for 4 consecutive years (2006e2009), and
in “actively striving for universityeindustry collaborative funding
and ef?cacy” for 3 consecutive years (2006e2008). This indicates
that the universityeindustry collaborative model of NCKU can
serve as a worthy reference for other Taiwanese universities (Lin
et al. 2012a, 2012b).
Take the top four Taiwanese universities as examples: National
Taiwan University (NTU), NCKU, National Chiao Tung University
(NCTU), and National Tsing Hua University (NTHU; Fig. 2) were
granted annual R&D funding of less than US$200 million, of which,
R&D funds from enterprises totaled less than US$13 million. These
two amounts of funding were far less than those granted to inter-
nationally renowned universities. A comparison of the proportion
of corporate funding in the total R&D funding granted to these four
Taiwanese universities indicates that the proportion for NCKU was
10.8% in 2009, which is roughly equal to that of other universities
that are well-known globally, followed by NTHU (6.8%), NCTU
(3.4%), and NTU (2.0%).
The statistics presented in this research was based on data
collected for a period of 9 years, from 2001 to 2009. The data
collected included all industryeuniversity collaboration projects
(1930 projects in total) between NCKUand the enterprises involved
in terms of four models, namely, “contract research,” “joint
research,” “technology transfer,” and “incubation.” The de?nition of
the four models of industryeuniversity collaboration in this
research is as follows:
Joint research refers to collaboration agreements between NCKU
and industry that involve research work undertaken by both
parties. Contract research refers to work commissioned by industry
and undertaken only by NCKU researchers. Technology transfer re-
fers to patent licensing or technology transfer from NCKU to in-
dustry. Incubation refers to collaboration projects by enterprises
incubated in NCKU or counseling projects offered by managers in
the NCKU incubation center.
All the corporate paid-in capital and duly organized addresses
cited in this research are adopted from publicized statistics in the
“Commerce Industrial Services Portal,” Dept. of Commerce, Minis-
try of Economic Affairs and the “eTax Portal, Ministry of Finance.”
For the purpose of calculating the linear geographic distance be-
tween each individual collaborating enterprise and NCKU, the
“Coordinative Reference System” (developed by the Center for
Geographic Information Science, Research Center for Humanities
and Social Sciences, Academia Sinica) was used in this research for
creating a corresponding point layer (longitude/latitude) in the X
and Y coordinates of each individual enterprise. Second, the “two-
degree transverse Mercator” methodology for converting WGS84
(World Geodetic System) into TWS67 (Taiwan Datum) was applied,
and was followed by the application of Pythagorean theorem to
obtain the linear geographic distance. Another in-depth analysis
included in this research is the adoption of a “location quotient”
(LQ) for evaluating the enterprise concentration rate for each in-
dividual industryeuniversity collaboration model. Being an indi-
cator commonly used in regional economics and economic
geography, the LQ has also been frequently employed for under-
standing the space distribution of all collaborated enterprises. LQ is
calculated as follows:
LQ ¼
C
i
=C
tot
S
i
=S
tot
(1)
where C indicates the amount of the enterprises sharing collabo-
ration projects with NCKU; S refers to the amount of enterprises;
the subscript i refers to district classi?cation, and tot refers to the
total amount in Taiwan. The geographical distribution statistics of
all the enterprises sharing collaboration projects with NCKU had
been further used for evaluating the corresponding ratio of total
enterprises in every district by means of the LQ, in case the urban
scale and economic compositions are not factored in.
3. Results and discussion
3.1. Demographics and descriptive statistics
The statistics adopted by this research included data collected
over a 9-year period in total (i.e., from 2001 to 2009). With respect
to all the collaboration projects of NCKU (Table 1), which consti-
tuted four models inclusive of contract research, joint research,
technology transfer, and incubation, the total number of samples
amounted to 1930. Of this total number, 1254 are contract research
projects, representing a ratio of 65.0% of the total number of pro-
jects, followed by 358 joint research projects, 155 technology
transfer contracts, and 163 incubation projects. Enterprises sharing
collaboration projects with NCKU amounted to 1049 (including
branches), in which the registered corporation information was
Fig. 2. Comparison of industrial research funding and total research expenditure of
universities. Note: From Association of University Technology Managers, 2009; the
Inter-Ministerial Project Of?ce for Academia-Industry Collaboration of Taiwan, 2009;
Kyoto University, 2009; Tokyo University, 2007; Taiwan University, 2008.
1
NCKU was established in 1931. Through 80 years of cultivation, NCKU gradually
developed nine colleges, which include Liberal Arts, Science, Engineering, Electrical
Engineering and Computer Science, Planning and Design, Management, Medicine,
Social Sciences, and Bioscience and Biotechnology. Currently, NCKU is the most
essential academic research institution in Southern Taiwan.
T.-C. Lin et al. / Asia Paci?c Management Review 20 (2015) 90e99 92
mainly based on the initial records fromthe administrator of NCKU,
and the corporate capital was subject to the paid-in capital.
With regard to the industryeuniversity collaboration models of
NCKU in the past 9 years, the enterprises demonstrated a prefer-
ence for the “contract research” model with NCKU(65.0%), followed
by “joint research” (18.6%); by contrast, the “technology trans-
fer”(8.0%) and “incubation”(8.4%) models had limited popularity.
These results nearly coincide with those of Schartinger et al. (2001),
whose study involved a questionnaire survey of ?rms in Austria.
The results of their study indicated that the innovative ?rms mainly
adopted the “contract research” and “joint research” models, ac-
counting for 32% and 23%, respectively, whereas the “technology
transfer” model constituted the rather poor ratio of 7%. Several
studies have noted that patenting and licensing account for just a
small proportion of industryeuniversity interactions (Agrawal &
Henderson, 2002; Cohen et al., 2002; D'Este & Patel, 2007;
Mans?eld & Lee, 1996; Mowery & Sampat, 2005; Schartinger
et al., 2001).
Among the aforementioned four models, a signi?cant variance
is demonstrated with respect to the average project scale (Table 1).
The average project scale for the case of “contract research” had the
highest value, as much as NT$1.35 million, whereas “technology
transfer” received NT$ 1.22 million, ranked second; by contrast,
“joint research” and “incubation” received as little as NT$0.53
million and NT$0.42 million, respectively, further supporting its
poor favorability among enterprises. The “joint research” model
adopted by enterprises generally varies with the subsidy applica-
tion offered by the government, but stipulates that most of the
intellectual property rights derived from such a model belong to
the counterparty university, which contrasts with partial owner-
ship or the pre-emptive technology rights for the enterprises
involved. Thus, most enterprises would not invest heavily in this
model because most of their motivations would stem from the
adoption of a mutually cooperated mechanism for the purpose of
establishing a preliminary-phase industryeuniversity cooperation
relationship, and obtaining access to the relevant expertise in the
?elds concerned and creating awareness of academic research
development. In addition to the ownership ratio of the subsequent
R&D outputs available to the enterprises, exerting in?uence on
professors' research subjects for promoting accessibility to the
enterprises involved is one of the major purposes contributing to
the fact that the “contract research” model is much more preferred
by enterprises involved than “joint research.”
3.2. Firm-size impact analysis
Firm size is important and has a clear relation with innovation
(Hsu &Liu, 2008; Inmyxai &Takahashi, 2012). According to surveys
(Schartinger et al., 2001) of Austrian innovative ?rms, there is a
clear relationship between ?rm size and the valuation of highly
skilled, university-educated personnel. Through empirical analysis
of a survey questionnaire, C aceres, Guzman, and Rekowski (2011)
also concluded that ?rm size is highly related with training, R&D,
and collaboration.
According to the research of Tien, Chiu, and Chen (2011), ?rm
size can signi?cantly moderate momentum only on the dimension
of plant and equipment newness, whereas ?rm age can moderate
momentum on the dimensions of nonproduction overhead and
advertising intensity. Furthermore, previous research on innova-
tion adoption identi?es many factors, which in?uence organiza-
tions to adopt new ideas, products, technologies, or services. Some
of these factors are support from top management and innovation
champions, attitude toward innovation, competitive advantages,
and innovation characteristics. These previous studies demon-
strate that innovation adoption is in?uenced by a complex dy-
namic of multiple factors. For example, Choi (2000) found that
environmental factors, such as environmental uncertainty,
competition, and IT intensity, are important for improving inno-
vation capabilities among SMEs in Korea. Copus, Skuras, and
Tsegenidi (2008) examined innovation capabilities among SMEs
in six European Union members and found that social and insti-
tutional capital were important factors for innovation perfor-
mance. Kim and An (2004) found that innovation adoption in large
Korean companies was affected by attitudes of usefulness only,
whereas in small Korean companies it was affected by the strategy
and the industry environment, as well as usefulness. However, the
study did not look at innovation adoption at the organization-
level. Chen and Fu (2001) found that SMEs in China were heavi-
ly in?uenced by the market, whereas large ?rms in China were
in?uenced by ?rm size.
Regarding collaborated models with which ?rms access
knowledge and innovation from universities, D'Este and Patel
(2007) found that joint research agreements will enable re-
searchers to access industry skill and facilities. This implies that
researchers motivated to interact with industry are likely to do so
through a variety of forms rather than via a single mechanism. Such
variety enables them to reap both larger pecuniary and non-
pecuniary bene?ts. Schartinger et al. (2001) studied collaboration
models adopted by different ?rms (size based) through a ques-
tionnaire survey of ?rms in Austria. The results showed that ?rms
adopted the “contract research”, “joint research,” “technology
transfer,” methods for the collaboration models. Their study also
found that innovative ?rms mainly adopted the “contract research”
and “joint research” models than “technology transfer.” The au-
thors also found that large ?rms value the bene?t of universities
higher than small ones by making use of highly skilled personnel,
directly supporting the development process, and utilizing the
university's consulting services. However, the reciprocal relation-
ship between ?rm size and collaboration models are not under-
stood clearly from literatures, and therefore, a deeper study to
understand this relationship is needed.
In the following section, an in-depth analysis of the relevancy
between the individual ?rm size and its willingness to adopt
different models of collaboration projects is presented by exam-
ining the participation frequency. The analysis procedure begins by
separating the enterprises involved into two categories, namely,
“large-sized enterprises (LEs)” and “SMEs,” according to the paid-in
capital of an enterprise. As stipulated by the “Small and Medium
Enterprise Development Promotion Guidelines,” “whichever an
enterprise dealing in manufacture, construction, mining, or earth-
works industry with a paid-in capital of no more than NT$80
million,” or “a paid-capital of no more than NT$100 million in the
event of dealing in any industry other than the previously pre-
scribed” shall be legitimate for being classi?ed into the category of
SMEs. Based on this stipulation, the classi?cation standards for this
research have been further simpli?ed as follows: whichever en-
terprise with paid-in capital of no more than NT$ 80 million shall be
classi?ed under SMEs; and if the paid-in capital is more than NT$80
million, the enterprise shall be classi?ed under LEs.
Table 1
Types of interaction between National Cheng Kung University and ?rms during
2001e2009.
Type of interaction Number of
projects
% of projects Average project
scale (million NT$)
Contract research 1254 65.0 1.35
Joint research 358 18.6 0.53
Technology transfer 155 8.0 1.22
Incubation 163 8.4 0.42
Total 1930 100.0 1.11
T.-C. Lin et al. / Asia Paci?c Management Review 20 (2015) 90e99 93
The simpli?ed classi?cation standards have been applied in
calculating the total collaboration projects, projects/?rms, and the
average project scale (Table 2) in the two categories of enterprises,
LEs and SMEs, that have established a partnership with NCKU from
2001 to 2009. As shown in the Table 2, the projects involving SMEs
amount to 835, constituting 43.3% of the total, whereas those
concerned with the LEs amount to 1095, accounting for 56.7% of the
total. With regard to the overall Taiwan business ecosystem struc-
ture, SMEs constitute an overwhelming 97.91% of the total busi-
nesses, whereas the ratio of LEs is con?ned to only 2.09% (Small and
Medium Enterprise Administration, Ministry of Economic Affairs,
2010). Thus, it can be said that LEs are considerably more inter-
ested in collaboration with NCKU than SMEs.
In consideration to other evaluation results, such as the
projects-to-?rms ratio (projects/?rms) and the average project
scale with NCKU, the average value of total projects/?rms
contributed by the category of LEs is 2.83, which is signi?cantly
better than the value of 1.67 attributed to SMEs (in which the
former is 1.69 times the latter). The average project scale resulting
fromthe LEs is NT$1.46 million, which is again much more than the
average amount of only NT$0.64 million achieved by SMEs. It thus
can be evidenced precisely that LEs will perform better if evaluated
in terms of both cooperation sustainability tendency and invest-
ment values. The overall investment potentiality of the LEs is also
much greater than that of the SMEs. The aforementioned statistics
can validate a distinctive feature that innovation demands among
Taiwanese corporate and the industryeuniversity cooperation en-
vironments have a signi?cantly distorted orientation, whereas
SMEs, constituting 97.91% of the total, performed worse than LEs
when compared in either the ratio of total cooperation projects
with NCKU or the cooperation sustainability and potentiality, or
even the average investment value. Such analysis results simply
support the fact that SMEs suffer from shortage of R&D talent,
funds, facilities, and innovation models for supporting their busi-
ness operations. As such, SMEs have dif?culty in improving their
R&D due to limited ?nancial resources available for participation in
such industryeuniversity cooperation. Therefore, it is hereby
advised that policy-makers devise more af?rmative and effective
policies that offer SMEs greater access to business innovation
opportunities.
Possessing certain in-house R&D resources, LEs support rapid
industry development advancement, prefer to succeed in concep-
tualizing more innovative business ideas, and shorten the durations
of completing the targeted R&D by adopting industryeuniversity
collaboration models. The systematic R&D activities carried out by
LEs within structured laboratories are more effective than the oc-
casional R&D activities undertaken by SMEs (Santarelli &
Sterlacchini, 1990). Because SMEs often conduct their innovative
activities without speci?c ?nancial and managerial resources and,
in particular, without formalized procedures, they tend to under-
take a signi?cant amount of innovative activities in their design,
production, and even in sales departments rather than in their R&D
departments. Thus, they more frequently develop incremental in-
novations rather than product innovations, which are mostly ach-
ieved by LEs. Another perspective regarding the R&D of SMEs from
a survey of 3000 Dutch ?rms (Kleinknecht, 1989) should be noted:
if informal R&D is taken into account, the R&D commitment of
SMEs is considerably higher than that reported by of?cial sources;
however, due to a lack of information in the literature, the in?uence
level surrounding this issue is not clear and worthy of future
research.
The statistics in Table 2 can be further utilized for analyzing the
decision-making tendency of industryeuniversity cooperation
involving LEs and SMEs. With respect to the adopted model be-
tween the LEs and NCKU, the “contract research” model was the
most prevalent (75.9%), creating 3.18 projects on average per en-
terprise, which is much more than the value resulting from other
cooperation models (such as the average of 1.48 projects for “in-
cubation,” 1.29 projects for the “joint research,” and 1.24 projects
for “technology transfer”). Therefore, a reasonable estimation can
be assumed that in addition to achieving relevant R&D successes,
LEs commonly choose the “contract research” cooperation model to
mainly motivate professors to focus on relevant research subjects
and also to cultivate long-term human resources suitable for their
own enterprises. This situation is consistent with Schartinger et al.
(2001) that LEs value the bene?t of employing educated and highly
skilled personnel more than SMEs.
Thus, it can be concluded that LEs commonly prefer to adopt the
“contract research” model for achieving more comprehensive or
long-term cooperation relations. This implies the lack of R&D
personnel (generally with doctorate quali?cations) in ?rms, and
usually adopting research achievements from universities to meet
their requirements. This also veri?ed the result of Shi (2009). The
policy-makers should also make a note of the limited project scale
(0.94 million) and lower ratios of projects per ?rm (1.29) in cases of
joint research model involving LEs and NCKU. It implies the lack of
deep reciprocal relationship between LEs and universities to build
long-term and large-scale cooperated projects by focusing on
topics that bene?t both entities. It thus has limited functions such
as enhancing research performance and publications of university
professors, and shifting university curriculums from basic toward
applied research toward recent collaboration projects between LEs
and universities. Proper mechanisms such as “matching fund,”
which implies focusing on topics that bene?t both entities and
establishing long-termcollaboration projects, are needed. Thus, the
deep reciprocal relationship between LEs and universities can ful?ll
and overcome the two major shortcomings in the allocation of
Taiwan's R&D resources analyzed by Shi (2009).
Furthermore, in the category of LEs, among all cooperation
models, the “technology transfer” model had the highest average
investment value of NT$1.95 million, instead of the “contract
research” model, which had an average investment value of
NT$1.59 million. This indicates that although LEs prefer to conduct
assignments or transfers by spending higher amounts relative to
obtaining the relevant intellectual property in case of any available
critical technology or patent, the total investment values resulting
fromthe “technology transfer” model constitute only a limited ratio
in proportion to the overall investment.
Therefore, a reasonable estimation can be assumed that in
addition to obtaining relevant R&D achievements, LEs commonly
choose the “contract research” cooperation model to mainly
motivate professors to focus on relevant research subjects and also
to cultivate long-term human resources suitable for their own en-
terprises. This situation is consistent with Schartinger et al. (2001)
Table 2
Comparison of interactions of National Cheng Kung University with LEs and SMEs
during 2001e2009.
Project number Projects/?rm Average project scale
(million NT$)
With LEs With
SMEs
With LEs With
SMEs
With LEs With
SMEs
Contract
research
831 423 3.18 1.54 1.59 0.86
Joint research 101 257 1.29 1.44 0.94 0.36
Technology
transfer
67 88 1.24 1.31 1.95 0.65
Incubation 96 67 1.48 1.24 0.48 0.32
All projects 1095 835 2.83 1.67 1.46 0.64
LEs ¼large-sized enterprises; SMEs ¼small and medium-sized enterprises.
T.-C. Lin et al. / Asia Paci?c Management Review 20 (2015) 90e99 94
that LEs value the bene?t of employing educated and highly skilled
personnel more than SMEs. However, in the category of LEs, among
all cooperation models, the “technology transfer” model resulted in
the highest average investment value of NT$1.95 million, instead of
the “contract research” model, which had an average investment
value of NT$1.59 million. This indicates that although LEs prefer to
conduct assignments or transfers by spending higher amounts
relative to obtaining the relevant intellectual property in case of any
available critical technology or patent, the total investment values
resulting from the “technology transfer” model constitute only a
limited ratio in proportion to the overall investment.
Factors such as shortages of R&D personnel, funds, and facilities
are the reasons why SMEs prefer the “contract research” model for
achieving dominance in the decision-making of research subjects
(50.7%) and the “joint research” model (30.8%) featuring a low-
investment benchmark. These two models have also achieved
success in carrying out more cooperation projects with individual
SMEs, with the former model having 1.54 projects on average and
the latter having 1.44. When compared with LEs, SMEs demonstrate
less interest in establishing a comprehensive or long-term coop-
eration relation. In terms of the average project investment value in
the category of SMEs, the “contract research” model has the highest
value (NT$ 0.86 million), instead of the “technology transfer” model
(NT$0.65 million), which again suggests that SMEs demonstrate
substantive business strategies or goals in adopting the “technology
transfer” model; in addition, SMEs also tend to be less interested in
obtaining costly intellectual property rights.
To completely understand the industryeuniversity models and
average corporate project scale with respect to “?rmsize,” the “?rm
size” was adopted as the X coordinate and the “research grant/
royalty” was adopted as the Y coordinate for constructing the
industryeuniversity cooperation pattern structure of NCKU (Fig. 3).
The research data considered in this analysis are inclusive of all the
contracts in terms of “contract research,” “joint research,” “tech-
nology transfer,” and “incubation” models established with NCKU
between 2001 and 2009. As shown in Fig. 3, enterprises that have
established industryeuniversity cooperation with NCKU show
extensive variation in terms of the paid-in capital, which varies
from less than NT$1 million to more than NT$100 billion. As such,
the investment values of industryeuniversity cooperation projects
range between NT$7250 and NT$43,200,000. It can be found in
Fig. 3 that the “joint research” model is distributed in the lower left-
hand corner, whereas the “contract research” model has a broad
distribution range in the X direction.
Firm sizes were further separated into seven categories, as
illustrated in Fig. 4, including those with paid-in capital of less than
NT$1 million, between NT$1 million and no more than NT$10
million, between NT$10 million and no more than NT$100 million,
between NT$100 million and less than NT$1 billion, from NT$1
billion to less than NT$10 billion, from NT$10 billion to less than
NT$100 billion, and more than NT$100 billion. When calculating
the weight ratio of technology transfer, incubation, contract
research, and joint research projects as adopted by regional en-
terprises and NCKU, the tendency of various enterprises (SMEs vs.
LEs) to adopt different models of industryeuniversity collaboration
is rather signi?cant. The percentage of contract research increased
along with the increase in ?rm size. For enterprises whose capital
exceeded NT$1 billion, 80% of the projects in collaboration with
NCKU belonged to contract research, whereas for ?rms whose
capital exceed hundreds of billions have 100% adoption of contract
research. However, the percentage of joint research and technology
transfer tends to decrease with the increase of ?rm size, and in-
cubation does not reveal signi?cant variance with regard to ?rm
size.
In summary, these research results imply that LEs generally
adopt the “contract research” model when cooperating with NCKU,
and also establish more diversi?ed cooperation effects or longer
cooperation relationships with this cooperation model. Moreover,
the tendency to adopt the “contract research” model varies
increasingly with the ever-increasing paid-in capitals of LEs. When
any critical technologies or patents are required, LEs would rather
procure intellectual property rights at values that are commonly
regarded more costly. SMEs are equally interested in “contract
research” for achieving dominance in determining research sub-
jects and “joint research,” which features lower investment funds,
but they are not interested in achieving more comprehensive
cooperation dimensions or maintaining long-term mutual
cooperation.
One of the conclusions from this research analysis is that there
still exists a signi?cant distorted orientation in Taiwan between the
motivation and demands of enterprises seeking innovation within
the industryeuniversity collaboration ecosystem. Even though
SMEs constitute an overwhelming 97.91% of the total corporations
in Taiwan, the cooperation projects with NCKU and all investment
values contributed by SMEs are both much less than those of LEs.
This is because SMEs generally suffer a shortage of R&D personnel,
investment funds, facilities, and innovative business operation
modes for supporting their business operations, as well as
Fig. 3. Pattern of collaboration projects between National Cheng Kung University and
?rms. Fig. 4. Project ratio of four collaboration models in seven different categories.
T.-C. Lin et al. / Asia Paci?c Management Review 20 (2015) 90e99 95
availability of limited channels for SMEs to improve their R&D ac-
tivities. Policy-makers should thus create valid and effective pol-
icies, such as modifying existing resource and tax policies, or create
cut-to-?t collaboration models for enabling SMEs to gain greater
access to more business innovation opportunities.
It thus can be concluded that LEs commonly prefer to adopt the
“contract research” model for achieving more comprehensive or
long-term cooperation relations. It implies the lack of R&D
personnel (generally with doctorate quali?cations) in ?rms, and
usually adopting achievements from universities to meet their re-
quirements. This also veri?ed the result reported previously by Shi
(2009). The policy-makers should also make a note of the limited
project scale (0.94 million) and lower ratios of projects per ?rm
(1.29) in cases of joint research model involving LEs and NCKU. It
implies the lack of deep reciprocal relationship between LEs and
universities to build long-term and large-scale cooperated projects
by focusing on topics that bene?t both entities. It thus has limited
functions such as enhancing research performance and publica-
tions of university professors, and shifting university curriculums
from basic toward applied research toward recent collaboration
projects between LEs and universities. Proper mechanisms such as
“matching fund,” which implies focusing on topics that bene?t both
entities and establishing long-term collaboration projects, are
needed.
3.3. Geographical proximity impact analysis
Start-up companies, as well as new technology-based ?rms
working with universities, are more likely to be in close proximity
to the licensing institution in order to capture the bene?ts of sub-
sequent innovations (Elyse, 2006). Proximity between ?rms and
universities promotes the natural exchange of ideas through both
formal and informal networks (Deeds, Decarolis, & Coombs, 2000;
Lindelof & Lofsten, 2004). Formal methods include licensing and
cooperative alliances (Lane & Lubatkin, 1998), whereas informal
methods include mobility of scientists and engineers, social
meetings, and discussions (Pouder & St. John, 1996).
Various studies have indicated that geographical proximity has
positive effects on the willingness of enterprises to adopt indus-
tryeuniversity cooperation and also on the subsequent perfor-
mance of the cooperation projects (Anselin, Varga, & Acs, 1997;
Fischer & Varga, 2003; Fritsch & Slavtchev, 2007; Jaffe, 1989;
Mans?eld, 1995; Moreno, Paci, & Usai, 2005; Oerlemans & Meeus,
2005). For example, Jaffe (1989) conducted an empirical analysis
using the “knowledge production function” developed by Griliches
(1986), and veri?ed that the more innovative the R&D activities are
carried out by universities, the better performance of innovation a
state will achieve afterward. This therefore supports that
university-involved R&Dprojects can stimulate more enterprises to
participate in R&D activities and thus achieve better performance
in terms of the amount of patent applications ?led. Moreover, based
on a survey (Mans?eld, 1995) of 200 academic researchers and 66
enterprises belonging to major manufacturing industries, the
mutual distances between the enterprises and the corresponding
cooperative universities are on average within 100e1000 mi
(approximately within 161e1609 km), making face-to-face contact
more convenient between the two cooperating parties, which
could lead to increased cooperation and better project
performances.
Yun and Lee (2012) compared the regional knowledge creation,
utilization, and transfer between South Korea and Taiwan's science
parks. They found that the Hsinchu Science Park in Taiwan is more
active toward cooperative relations than the Daedeok Innopolis in
Korea, making the innovation institutions in different regions as
different micro units of Triple Helix. Thus, it is important for
innovation participants from university, industry, and government
to play the role of creating knowledge space, consensus space, and
innovation space, as it is important for innovation participants to
develop co-evolving relationships. Yeh, Lin, and Kung (2011)
investigated the effect of geographical proximity on the charac-
teristic of industryeuniversity collaboration by analyzing second-
ary data. Results revealed that urbanization, population, resource
partitioning, industrial structure, and the social economy indeed
have a positive effect on geographical distribution of enterprises
that cooperated with universities.
This research has thus been initiated by statistically analyzing all
the cooperation projects carried out between the years 2001 and
2009 to calculate the average distances between NCKU and the
enterprises involved according to the model adopted. Results show
(Fig. 5) that the average distance of all enterprises involved in
technology transfers with NCKU is 167 km, which was the longest;
the average distances of all enterprises involved in contract
research and joint research were 143 and 117 km, respectively; and,
the average distance for the incubation model was 66 km, which
was the shortest. It turns out that the shortest average distance
results from enterprises migrating into NCKU or the enterprises
receiving consultancy support from the incubation manager of
NCKU. The “technology transfer” model demonstrated the longest
geographic distance, and thus, it is completely free of geographic
distance restraint.
The conversion outputs of the aforesaid “TWS67” coordinates
were then mapped out, as shown in Fig. 6, indicating that most
enterprises concerned with cooperation projects are located within
the top ?ve biggest metros (including Taipei City, Hsinchu City,
Taichung City, Tainan City, and Kaohsiung City). Such location dis-
tributions are in complete conformity with the natural geography
of Taiwan. As the Central Mountains extend from the north of the
island to the south, all populous cities are distributed along the
west island seashore. This geographic obstacle has also resulted in
most enterprises being located within those populous cities along
the west island seashore. Scholars have adopted various measure-
ment methodologies for evaluating how the geographical prox-
imity might in?uence knowledge spillover in a speci?c region;
however, as yet, there is not a set of measurement standards among
those scholars. For example, both Mans?eld (1995) and Gittelman
(2007) claimed that enterprises conducting industryeuniversity
cooperation projects are all located within an average distance
between 1000 and 1500 mi (equal to 1609e2414 km
Fig. 5. Average distances of ?rms that have cooperated with National Cheng Kung
University in different channels.
T.-C. Lin et al. / Asia Paci?c Management Review 20 (2015) 90e99 96
approximately). Because the longest linear distance of Taiwan is no
more than 400 km, which is much less than the aforesaid longest
distance of 2414 km, all cases of cooperation in this research are
within this region. Anselin et al. (1997) estimated the spatial
spillover between university research and high-technology in-
novations based on the well-known GrilicheseJaffe knowledge
production function (Jaffe, 1989), and con?rmed that a positive and
signi?cant relationship exists. They also found that a range of 50 mi
for the research spillover from metropolitan statistical areas and
result can be extended up to 75 miles in electronic industries
(Anselin, Varga, & Acs, 2000).
With logical derivation, it can thus be deduced that the ranges of
geographical proximity of these enterprises in Taiwan shall be
con?ned within acceptable ranges of knowledge spillover in
conjunction with the geographic obstacles of the island. As most
scholars have asserted that the spatial spillover is con?ned within
50 mi (about 80.5 km) (Anselin et al., 2000; Gittelman, 2007),
which is accordance with the current national administration
zoning of Taiwan, seven counties and cities
2
in southern Taiwan are
de?ned as being in geographical proximity districts with NCKU,
including Pintung County, Kaohsiung County, Kaohsiung City,
Tainan City, Tainan county, Chiayi County and Chiayi City, as
detailed in Fig. 6.
With respect to the distribution analysis of enterprises that have
established cooperation with NCKU, as illustrated in the Fig. 6, the
total projects between NCKU and the enterprises located in the
geographical proximity districts are 916, constituting 47.5% of the
overall number of collaboration projects, whereas the projects be-
tween NCKU and the enterprises outside of the geographical
proximity districts amount to 1014, representing 52.5% of the
overall number. Therefore, nearly half of the projects are within the
effective knowledge spillover district as de?ned by Gittelman
(2007) and Anselin et al. (2000), but the other half of the total
projects is still outside of the knowledge spillover district. Taiwan's
territory is pretty limited and differs greatly with that of the Eu-
ropean countries and the US. Most projects involve enterprise
clusters, with only two effective knowledge spillover districts
(Tainan City and Kaohsiung City), but three cities outside these
districts are de?ned by this research (Taichung City, Hsinchu City
and Taipei City). Such a unique mixture phenomenon is justi?able
for further research in the future.
After the aforesaid districts of effective knowledge spillover
have been applied for examining all the enterprises that have
established cooperation projects with NCKU, the survey results for
this research, as illustrated in Fig. 7, indicate that only 35% of the
total enterprises that adopted the “technology transfer” model are
located within the aforesaid zonal range. The values for the enter-
prises that adopted the “contract research,” “joint research,” and
“incubation” models are 38%, 55%, and 77%, respectively. The sig-
ni?cant differences among the aforesaid comparison ratios can
seemingly explain that the “preference ratios” for the enterprises
located within the aforementioned effective districts for partici-
pation in the “incubation” and “joint research” models are much
more than that of the enterprises located outside of these effective
districts; in addition, it can be seen that the ratio attributable to the
“incubation” model shows the greatest difference. However, as the
overall Taiwan business ecosystem is composed of atypical
nonuniformdistributions (in that only 20.7% of the total enterprises
in Taiwan are located within the aforesaid geographical proximity
districts), the “normalization” effect can be used for individually
examining the participation ratios of all enterprises involved with
all the individual models.
This research was conducted by applying the LQ for analyzing
the enterprise concentration levels for all individual indus-
tryeuniversity cooperation models. Being an indicator commonly
used in the ?elds of economics and economic geography, LQ has
frequently been applied for the measurement of speci?c industries
in the corresponding area for fully characterizing the enterprise
distributions geographically. The subsequent calculation results of
Fig. 6. Districts of geographical proximity and location of ?rms that collaborate with
National Cheng Kung University (NCKU).
Fig. 7. Percentage of ?rms with geographical proximity and location quotient (LQ).
2
According to the Taiwan Administration Territory Planning, Kaohsiung County
has been merged into Kaohsiung City; Tainan County has been merged into Tainan
City; Taichung County has been merged into Taichung City; as well as Taipei County
was upgraded to New Taipei City on December 25, 2010. These four cities along
with Capital Taipei are the ?ve Municipalities in Taiwan currently.
T.-C. Lin et al. / Asia Paci?c Management Review 20 (2015) 90e99 97
this research are illustrated in Fig. 7, whereby the LQ of ?rms with
geographical proximity in the technology transfer model is 2.6,
compared with 3.0 for contract research, 10.1 for joint research, and
16.0 for incubation (5.0 of all 4 models).
The phenomenon can validate that the enterprises executing the
industryeuniversity cooperation projects with NCKU demonstrate
the tendency in conformity with the geographical proximity. For
example, the participating rate of the enterprises in the
geographical proximity districts is ?ve times the average value of all
enterprises in Taiwan. Moreover, the LQs for the enterprises within
the geographical proximity districts participating in “technology
transfer” and “contract research,” are only 2.6 and 3.0. These two
values can indicate the limited relevancy between geographical
distances and the participating rate of the enterprises with the two
models. By contrast, the LQ for the enterprises within the
geographical proximity districts participating in the “incubation”
model is overwhelmingly 16 times of that in Taiwan overall. It
demonstrates that enterprises incubated in NCKU do feature
stronger geographical proximity compared with other models,-
which is compliance with the empirical research results among all
individual types of questionnaires conducted by Elyse (2006).
The results of this research thus indicate that the indus-
tryeuniversity collaboration administrator of a university should
pay more attention to the relevancy between the promotion
strategy of all individual cooperation models and the geographical
proximity of the enterprises involved, so as to strengthen regional
enterprise networks and interactions for the promotion of “incu-
bation” and “joint research” models. They should also broadening
marketing scope to all Taiwan and even launch international en-
terprise cooperation in the case of “technology transfer” and
“contract research” models, which feature limited relevancies with
geography restraints.
4. Conclusions
In this research, effects of the size of enterprises and geographic
proximity were investigated through the comparison of 1930
collaboration projects, including contract research, joint research,
technology transfer, and incubation, between enterprises and
NCKU, which is a representative university of Taiwan, during period
between 2001 and 2009. The collaboration projects were evaluated
to explore the counteracting relevancies in order to understand the
factors involved while making decisions for selecting an appro-
priate industryeuniversity cooperation model and the in?uential
signi?cance levels.
Results show that LEs prefer to adopt the “contract research”
model for industryeuniversity cooperation and implement the
model more comprehensively. Such a tendency varies positively
with the paid-in capital of ?rms. LEs would rather procure intel-
lectual property rights at values that are commonly regarded more
costly if they ?nd the patents or critical technologies useful.
However, the ratio of this cooperation model is limited in propor-
tion to the overall cases. Conversely, SMEs are equally interested in
“contract research” to dominate research subjects, whereas “joint
research” attracts lower investment funds. SMEs are interested in
neither achieving more comprehensive cooperation dimensions
nor maintaining long-term mutual cooperation.
Based on the results derived from this research, it can also be
af?rmed that the demand for seeking innovation and the
ecosystem of industryeuniversity cooperation in Taiwan are char-
acterized by extremely distorted orientations. Even though SMEs
constitute 97.91% of the total corporations, the cooperation projects
with NCKU and all investment values contributed by SMEs are both
much less than those for LEs. This is because SMEs generally suffer
from shortages of R&D personnel, investment funds, facilities, and
innovative business operation modes for supporting their business
operations, in addition to the limited collaboration activities
available for improving their R&D activities.
Policy-makers are recommended to create valid and effective
policies, such as adjusting the existing resource and tax policies, or
create cut-to-?t collaboration models for enabling SMEs to gain
greater access to more business innovation opportunities. The
policy-makers should also make a note of the limited project scale
(0.94 million) and lower ratios of projects per ?rm (1.29) in cases of
joint research model involving LEs and NCKU. It implies the lack of
deep reciprocal relationship between LEs and universities to build
long-term and large-scale cooperated projects by focusing on
topics that bene?t both entities. It thus has limited functions such
as enhancing research performance and publications of university
professors, and shifting university curriculums from basic toward
applied research toward recent collaboration projects between LEs
and universities. Proper mechanisms such as “matching fund,”
which implies focusing on topics that bene?t both entities and
establishing long-termcollaboration projects, are needed. Thus, the
deep reciprocal relationship between LEs and universities can ful?ll
and overcome the two major shortcomings in the allocation of
Taiwan's R&D resources.
With regard to the geographical proximity, results showthat it is
increasingly in?uential for enterprises that have established
industryeuniversity cooperation with NCKUdthe participation
ratio of enterprises within the geographical proximity is ?vefold to
that of all other aspects. Moreover, the participation ratios of en-
terprises within the geographical proximity vary signi?cantly with
each of the individual cooperation models, among which the “in-
cubation” and “joint research” can be de?ned as being in
geographical proximity, while the “technology transfer” and “con-
tract research” models demonstrate limited orientation. It is
therefore advised that policy makers should be more devoted to the
signi?cance of geographical proximity. In the cases of the “incu-
bation” and “joint research” models, they should strengthen
regional enterprise networks and interactions. Otherwise, for the
cases of “technology transfer” and “contract research” models, they
should clarify the attributes of all individual industry sectors and
interactive effects among industry chains, and then extend the
industryeuniversity cooperation scope to the whole of Taiwan and
even globally.
Con?icts of interest
All contributing authors declare no con?icts of interest.
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T.-C. Lin et al. / Asia Paci?c Management Review 20 (2015) 90e99 99
doc_957436981.pdf
This paper analyzes a total of 1930 collaboration projects, including contract research, joint research,
technology transfer, and incubation models, between National Cheng Kung University (NCKU) and firms
during the period between 2001 and 2009. Effects of both firm size and geographical proximity on the
frequency of different models of collaboration are addressed. Results show that large-sized enterprises
prefer adopting the contract research model and implementing it in a more comprehensive cooperation
mechanism or with long-term cooperation relationships. By contrast, small and medium-sized enterprises
prefer to give equal prominence to both contract research for research subject dominance and
joint research due to low investment requirements. Results also show that geographical proximity is an
important factor during the interaction between enterprises and NCKU. However, the influence is quite
different when the cooperation models are compared individually
Original article
Effects of ?rm size and geographical proximity on different models of interaction
between university and ?rm: A case study
Tien-Chu Lin
a, *
, Shiann-Far Kung
b
, Hei-Chia Wang
c
a
Faculty of Research and Services Headquarters, National Cheng Kung University, Tainan, Taiwan
b
Faculty of Urban Planning, National Cheng Kung University, Tainan, Taiwan
c
Faculty of Industrial and Information Management, National Cheng Kung University, Tainan, Taiwan
a r t i c l e i n f o
Article history:
Received 12 July 2012
Accepted 23 September 2013
Available online 30 March 2015
Keywords:
Firm size
Geographical proximity
Industryeuniversity collaboration
a b s t r a c t
This paper analyzes a total of 1930 collaboration projects, including contract research, joint research,
technology transfer, and incubation models, between National Cheng Kung University (NCKU) and ?rms
during the period between 2001 and 2009. Effects of both ?rm size and geographical proximity on the
frequency of different models of collaboration are addressed. Results show that large-sized enterprises
prefer adopting the contract research model and implementing it in a more comprehensive cooperation
mechanism or with long-term cooperation relationships. By contrast, small and medium-sized enter-
prises prefer to give equal prominence to both contract research for research subject dominance and
joint research due to low investment requirements. Results also show that geographical proximity is an
important factor during the interaction between enterprises and NCKU. However, the in?uence is quite
different when the cooperation models are compared individually.
© 2015, College of Management, National Cheng Kung University. Production and hosting by Elsevier
Taiwan LLC. All rights reserved.
1. Introduction
1.1. Models of industryeuniversity collaboration
Since the late 1970s, university missions have been widely dis-
cussed and have moved progressively from teaching and research
to teaching, research, and public service. This means that besides
sharing existing knowledge and advancing new knowledge, uni-
versities have the newmission of transferring academic knowledge
and resources to the public, and generating technological spillover
through universityeindustry interaction (Etzkowitz & Leydesdorff,
2000; Leydesdorff & Meyer, 2003; Lin, Chang, & Chung, 2012a,
2012b; Mans?eld, 1995) The interaction includes recruitment of
university graduates, personnel exchanges, joint research, contract
research, consulting, patents and publications, licensing, spin-off
companies, industry-funded laboratories and other facilities, and
also through informal contacts such as meetings and conferences
[D’Este & Patel, 2007; Muscio, 2010; Organisation for Economic Co-
operation and Development (OECD), 1999; Schmoch, 1999]. Such
interaction between industry and university usually bene?ts both
parties, such as enhancing the research productivity of ?rms (Link,
Scott, & Siegel, 2003), increasing the innovation activity of ?rms,
especially for large manufacturing ?rms (Loof and Brostrom, 2000),
enhancing research performance and publications of university
professors (Guldbrandsen & Smeby, 2005), and shifting university
curriculums from basic toward applied research (Link & Scott,
2003).
These kinds of links between university and industry are the
primary knowledge-?ow channels in national innovation systems,
which are keys for improving technology performance (OECD, 1999).
Manyearlier studies (Hanel &St-Pierre, 2006; Loof &Brostrom, 2008;
OECD, 1996, 2003; O'Gorman, Byrne, & Pandya, 2008) of knowledge
transfer have concentrated on patenting, licensing, and spin-offs as
the main contributions of universities in terms of technology diffu-
sion. Systematic analysis of knowledge transfer in models such as
joint research projects, consultancy, and training has been largely
neglected (D'Este & Patel, 2007). However, as some researchers have
mentionedor noted, whencomparedwithother formal mechanisms,
suchas contract researchor joint researchagreements, patenting and
licensing account only for a small proportion of academiceindustry
* Corresponding author. Faculty of Research and Services Headquarters, National
Cheng Kung University, Tainan 70101, Taiwan.
E-mail address: [email protected] (T.-C. Lin).
Peer review under responsibility of College of Management, National Cheng
Kung University.
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1029-3132/© 2015, College of Management, National Cheng Kung University. Production and hosting by Elsevier Taiwan LLC. All rights reserved.
Asia Paci?c Management Review 20 (2015) 90e99
interactions (Agrawal & Henderson, 2002; Cohen, Nelson, & Walsh,
2002; D'Este & Patel, 2007; Mans?eld & Lee, 1996; Mowery &
Sampat, 2005; Schartinger, Schibany, & Gassler, 2001). For example,
Agrawal and Henderson (2002) used data from the departments of
mechanical and electrical engineering at MIT to demonstrate that
patents are a relatively small source of knowledge transfer (less than
10%) out of the university. Cohen et al. (2002) used data from the
CarnegieMellonSurveyonR&Dperforming?rms inthe UnitedStates
and concluded that most patents and licenses were less important
models compared with publications, conferences, informal in-
teractions, and consulting. Consequently, more emphasis should be
placed on investigations of knowledge ?ow through contract
research, joint research, publications, conferences, and informal
consulting.
It can be understood that different ?rms appear to use quite
different models to access knowledge from universities (Agrawal &
Henderson, 2002). However, the major barrier for a researcher who
measures the interaction between universities and ?rms is the lack
of information from both university and ?rm (Schartinger et al.,
2001). Traditionally, the main indicator that measures the R&D
cooperation between university and industry can only be obtained
from the annual reports of the university and by surveying uni-
versity faculties and ?rms (OECD, 1999). However, it has been
generally dif?cult to obtain details of all individual indus-
tryeuniversity cooperation projects because they are mostly clas-
si?ed “con?dential” by individual universities. Thus, existing
impediments for getting access to the information concerning the
enterprises experiencing such industryeuniversity cooperation
and the rare availability of such information had also contributed to
the relevant reference documents. In this research, size of ?rms and
geographical proximity were investigated because in the literature,
they are important determinants related to industryeuniversity
collaboration (Brostrom, 2010; Elyse, 2006; Monjon & Waelbroeck,
2003; Schartinger et al., 2001). Data collected from National Cheng
Kung University (NCKU; the most active university that collabo-
rated with enterprises) are used, which includes 1930 collaboration
cases of contract research, joint research, technology transfer, and
incubation between NCKU and enterprises during the period from
2001 to 2009. Such a large number of cases allows for a wide grasp
of the factors involved in the decision making that concerns en-
terprises in selecting an industryeuniversity cooperation model
and the in?uential signi?cance levels of geographical proximity.
1.2. Industry and university environment in Taiwan
Taiwan has a land surface of approximately 36,000 km
2
and a
population of roughly 23 million people. Businesses are mainly
composed of small and medium-sized enterprises (SMEs), which
make up97.9% of the total enterprises (Small and MediumEnterprise
Administration, Ministry of Economic Affairs, 2010). In terms of
technologytrade, theexpenditures of Taiwaneseenterprises inrecent
years have always beenmuchgreater thantheir incomes. In2008, for
example, thenational technologytrade incomeandexpenditure ratio
was 0.26 (National Science Council, 2010); this shows that Taiwan's
technology trade is still in a de?cit situation, and enterprises' inno-
vative operations still lack suf?cient power for R&D. Furthermore,
according to the Global Competitiveness Report, the economic devel-
opment can be classi?ed into factor-driven, ef?ciency-driven, and
innovation-drivenstages (WorldEconomic Forum, 2008). For several
decades, Taiwan has been characterized by an ef?ciency-driven
economy. This image was symbolized by many original equipment
manufacturers in the electrical and electronics industry with high
production ef?ciency, but with low returns.
In attempting to transform the economy to the innovation-
driven stage (Taiwan has been characterized as being in transition
from ef?ciency-driven to innovation-driven according to the
report), the Taiwanese government is aware of the importance of
innovation creation and knowledge spillover, and has placed great
emphasis on the promotion of a national innovation system,
especially from the universities end. For example, R&D expendi-
tures of high educational institutions (HEIs) have increased rapidly
during the past 10 years, as illustrated in Fig. 1. Among which, R&D
expenditures appropriated from government, business enterprises,
and other sectors have all been increasing upward annually be-
tween 2000 and 2009. However, the annual percentage of R&D
expenditure contributed by business enterprises has just kept pace.
Such a tendency can be demonstrated by the ever-increasing
prevalence of industryeuniversity projects over the past 10 years;
however, this situation is far from the need of industry to create
innovation and knowledge spillover from university.
According to the analysis of Shi (2009), there are two major
shortcomings in the allocation of Taiwan's R&D resources: a
shortfall in allocation of universityeindustry R&D resources, and a
gap in the innovative connection of R&D activities between enter-
prises and educational institutes. The shortfall of uni-
versityeindustry resource allocation is caused by the majority of
personnel with doctorate quali?cations (72.1% of all PhDs) being in
the academic realm, whereas the gap in the innovative connection
of R&D activities between enterprises and educational institutes is
caused by a division of focus in technology development and aca-
demic research, respectively.
From the results of Shi's analysis, it is particularly important for
Taiwan's government to enhance industryeuniversity collabora-
tion to bridge the gap in the innovative connection of R&Dactivities
between enterprises and educational institutes, and thus, release
the capability of personnel with doctorate quali?cations from
university to industry. Therefore, the objective of this research is to
understand the different models that different ?rms appear to use
in order to access knowledge and innovation from universities,
especially the ?rm size and geographical proximity between the
university and the ?rm. Consequentially, using the statistical results
of the industryeuniversity cooperation models over the past
decade, an understanding of what concerns will be factored into
the decision making of industryeuniversity models and more
provisions of references will be made available for the policy-
makers.
The remainder of this paper is organized as follows. The “Data
and Methods” section sets out the de?nition of interaction channels
between the university and the ?rms, as well as the data and
Fig. 1. R&D expenditure of the higher education sector, 2000e2009. Note. Data from
“Indicators of Science and Technology Taiwan,” by National Science Council (2010),http://www.nsc.gov.tw/tech/index.asp.
T.-C. Lin et al. / Asia Paci?c Management Review 20 (2015) 90e99 91
method used in the analysis. The “Results and Discussion” section
describes the main empirical results regarding the impact of ?rm
size and geographical proximity. Finally, the “Conclusion” section
concludes the article.
2. Data and methods
Because NCKU
1
is one of the premier research institutions and
the most active university interacting with industries in Taiwan,
this paper analyses NCKU to understand the intensity of the uni-
versityeindustry relationship. According to statistics of the Higher
Education Evaluation and Accreditation Council of Taiwan
regarding the performance of universityeindustry collaboration
from 2006 to 2009, among public Taiwanese HEIs, NCKU won ?rst
prize in “the broadness of participating in universityeindustry
collaborative activities” for 4 consecutive years (2006e2009), and
in “actively striving for universityeindustry collaborative funding
and ef?cacy” for 3 consecutive years (2006e2008). This indicates
that the universityeindustry collaborative model of NCKU can
serve as a worthy reference for other Taiwanese universities (Lin
et al. 2012a, 2012b).
Take the top four Taiwanese universities as examples: National
Taiwan University (NTU), NCKU, National Chiao Tung University
(NCTU), and National Tsing Hua University (NTHU; Fig. 2) were
granted annual R&D funding of less than US$200 million, of which,
R&D funds from enterprises totaled less than US$13 million. These
two amounts of funding were far less than those granted to inter-
nationally renowned universities. A comparison of the proportion
of corporate funding in the total R&D funding granted to these four
Taiwanese universities indicates that the proportion for NCKU was
10.8% in 2009, which is roughly equal to that of other universities
that are well-known globally, followed by NTHU (6.8%), NCTU
(3.4%), and NTU (2.0%).
The statistics presented in this research was based on data
collected for a period of 9 years, from 2001 to 2009. The data
collected included all industryeuniversity collaboration projects
(1930 projects in total) between NCKUand the enterprises involved
in terms of four models, namely, “contract research,” “joint
research,” “technology transfer,” and “incubation.” The de?nition of
the four models of industryeuniversity collaboration in this
research is as follows:
Joint research refers to collaboration agreements between NCKU
and industry that involve research work undertaken by both
parties. Contract research refers to work commissioned by industry
and undertaken only by NCKU researchers. Technology transfer re-
fers to patent licensing or technology transfer from NCKU to in-
dustry. Incubation refers to collaboration projects by enterprises
incubated in NCKU or counseling projects offered by managers in
the NCKU incubation center.
All the corporate paid-in capital and duly organized addresses
cited in this research are adopted from publicized statistics in the
“Commerce Industrial Services Portal,” Dept. of Commerce, Minis-
try of Economic Affairs and the “eTax Portal, Ministry of Finance.”
For the purpose of calculating the linear geographic distance be-
tween each individual collaborating enterprise and NCKU, the
“Coordinative Reference System” (developed by the Center for
Geographic Information Science, Research Center for Humanities
and Social Sciences, Academia Sinica) was used in this research for
creating a corresponding point layer (longitude/latitude) in the X
and Y coordinates of each individual enterprise. Second, the “two-
degree transverse Mercator” methodology for converting WGS84
(World Geodetic System) into TWS67 (Taiwan Datum) was applied,
and was followed by the application of Pythagorean theorem to
obtain the linear geographic distance. Another in-depth analysis
included in this research is the adoption of a “location quotient”
(LQ) for evaluating the enterprise concentration rate for each in-
dividual industryeuniversity collaboration model. Being an indi-
cator commonly used in regional economics and economic
geography, the LQ has also been frequently employed for under-
standing the space distribution of all collaborated enterprises. LQ is
calculated as follows:
LQ ¼
C
i
=C
tot
S
i
=S
tot
(1)
where C indicates the amount of the enterprises sharing collabo-
ration projects with NCKU; S refers to the amount of enterprises;
the subscript i refers to district classi?cation, and tot refers to the
total amount in Taiwan. The geographical distribution statistics of
all the enterprises sharing collaboration projects with NCKU had
been further used for evaluating the corresponding ratio of total
enterprises in every district by means of the LQ, in case the urban
scale and economic compositions are not factored in.
3. Results and discussion
3.1. Demographics and descriptive statistics
The statistics adopted by this research included data collected
over a 9-year period in total (i.e., from 2001 to 2009). With respect
to all the collaboration projects of NCKU (Table 1), which consti-
tuted four models inclusive of contract research, joint research,
technology transfer, and incubation, the total number of samples
amounted to 1930. Of this total number, 1254 are contract research
projects, representing a ratio of 65.0% of the total number of pro-
jects, followed by 358 joint research projects, 155 technology
transfer contracts, and 163 incubation projects. Enterprises sharing
collaboration projects with NCKU amounted to 1049 (including
branches), in which the registered corporation information was
Fig. 2. Comparison of industrial research funding and total research expenditure of
universities. Note: From Association of University Technology Managers, 2009; the
Inter-Ministerial Project Of?ce for Academia-Industry Collaboration of Taiwan, 2009;
Kyoto University, 2009; Tokyo University, 2007; Taiwan University, 2008.
1
NCKU was established in 1931. Through 80 years of cultivation, NCKU gradually
developed nine colleges, which include Liberal Arts, Science, Engineering, Electrical
Engineering and Computer Science, Planning and Design, Management, Medicine,
Social Sciences, and Bioscience and Biotechnology. Currently, NCKU is the most
essential academic research institution in Southern Taiwan.
T.-C. Lin et al. / Asia Paci?c Management Review 20 (2015) 90e99 92
mainly based on the initial records fromthe administrator of NCKU,
and the corporate capital was subject to the paid-in capital.
With regard to the industryeuniversity collaboration models of
NCKU in the past 9 years, the enterprises demonstrated a prefer-
ence for the “contract research” model with NCKU(65.0%), followed
by “joint research” (18.6%); by contrast, the “technology trans-
fer”(8.0%) and “incubation”(8.4%) models had limited popularity.
These results nearly coincide with those of Schartinger et al. (2001),
whose study involved a questionnaire survey of ?rms in Austria.
The results of their study indicated that the innovative ?rms mainly
adopted the “contract research” and “joint research” models, ac-
counting for 32% and 23%, respectively, whereas the “technology
transfer” model constituted the rather poor ratio of 7%. Several
studies have noted that patenting and licensing account for just a
small proportion of industryeuniversity interactions (Agrawal &
Henderson, 2002; Cohen et al., 2002; D'Este & Patel, 2007;
Mans?eld & Lee, 1996; Mowery & Sampat, 2005; Schartinger
et al., 2001).
Among the aforementioned four models, a signi?cant variance
is demonstrated with respect to the average project scale (Table 1).
The average project scale for the case of “contract research” had the
highest value, as much as NT$1.35 million, whereas “technology
transfer” received NT$ 1.22 million, ranked second; by contrast,
“joint research” and “incubation” received as little as NT$0.53
million and NT$0.42 million, respectively, further supporting its
poor favorability among enterprises. The “joint research” model
adopted by enterprises generally varies with the subsidy applica-
tion offered by the government, but stipulates that most of the
intellectual property rights derived from such a model belong to
the counterparty university, which contrasts with partial owner-
ship or the pre-emptive technology rights for the enterprises
involved. Thus, most enterprises would not invest heavily in this
model because most of their motivations would stem from the
adoption of a mutually cooperated mechanism for the purpose of
establishing a preliminary-phase industryeuniversity cooperation
relationship, and obtaining access to the relevant expertise in the
?elds concerned and creating awareness of academic research
development. In addition to the ownership ratio of the subsequent
R&D outputs available to the enterprises, exerting in?uence on
professors' research subjects for promoting accessibility to the
enterprises involved is one of the major purposes contributing to
the fact that the “contract research” model is much more preferred
by enterprises involved than “joint research.”
3.2. Firm-size impact analysis
Firm size is important and has a clear relation with innovation
(Hsu &Liu, 2008; Inmyxai &Takahashi, 2012). According to surveys
(Schartinger et al., 2001) of Austrian innovative ?rms, there is a
clear relationship between ?rm size and the valuation of highly
skilled, university-educated personnel. Through empirical analysis
of a survey questionnaire, C aceres, Guzman, and Rekowski (2011)
also concluded that ?rm size is highly related with training, R&D,
and collaboration.
According to the research of Tien, Chiu, and Chen (2011), ?rm
size can signi?cantly moderate momentum only on the dimension
of plant and equipment newness, whereas ?rm age can moderate
momentum on the dimensions of nonproduction overhead and
advertising intensity. Furthermore, previous research on innova-
tion adoption identi?es many factors, which in?uence organiza-
tions to adopt new ideas, products, technologies, or services. Some
of these factors are support from top management and innovation
champions, attitude toward innovation, competitive advantages,
and innovation characteristics. These previous studies demon-
strate that innovation adoption is in?uenced by a complex dy-
namic of multiple factors. For example, Choi (2000) found that
environmental factors, such as environmental uncertainty,
competition, and IT intensity, are important for improving inno-
vation capabilities among SMEs in Korea. Copus, Skuras, and
Tsegenidi (2008) examined innovation capabilities among SMEs
in six European Union members and found that social and insti-
tutional capital were important factors for innovation perfor-
mance. Kim and An (2004) found that innovation adoption in large
Korean companies was affected by attitudes of usefulness only,
whereas in small Korean companies it was affected by the strategy
and the industry environment, as well as usefulness. However, the
study did not look at innovation adoption at the organization-
level. Chen and Fu (2001) found that SMEs in China were heavi-
ly in?uenced by the market, whereas large ?rms in China were
in?uenced by ?rm size.
Regarding collaborated models with which ?rms access
knowledge and innovation from universities, D'Este and Patel
(2007) found that joint research agreements will enable re-
searchers to access industry skill and facilities. This implies that
researchers motivated to interact with industry are likely to do so
through a variety of forms rather than via a single mechanism. Such
variety enables them to reap both larger pecuniary and non-
pecuniary bene?ts. Schartinger et al. (2001) studied collaboration
models adopted by different ?rms (size based) through a ques-
tionnaire survey of ?rms in Austria. The results showed that ?rms
adopted the “contract research”, “joint research,” “technology
transfer,” methods for the collaboration models. Their study also
found that innovative ?rms mainly adopted the “contract research”
and “joint research” models than “technology transfer.” The au-
thors also found that large ?rms value the bene?t of universities
higher than small ones by making use of highly skilled personnel,
directly supporting the development process, and utilizing the
university's consulting services. However, the reciprocal relation-
ship between ?rm size and collaboration models are not under-
stood clearly from literatures, and therefore, a deeper study to
understand this relationship is needed.
In the following section, an in-depth analysis of the relevancy
between the individual ?rm size and its willingness to adopt
different models of collaboration projects is presented by exam-
ining the participation frequency. The analysis procedure begins by
separating the enterprises involved into two categories, namely,
“large-sized enterprises (LEs)” and “SMEs,” according to the paid-in
capital of an enterprise. As stipulated by the “Small and Medium
Enterprise Development Promotion Guidelines,” “whichever an
enterprise dealing in manufacture, construction, mining, or earth-
works industry with a paid-in capital of no more than NT$80
million,” or “a paid-capital of no more than NT$100 million in the
event of dealing in any industry other than the previously pre-
scribed” shall be legitimate for being classi?ed into the category of
SMEs. Based on this stipulation, the classi?cation standards for this
research have been further simpli?ed as follows: whichever en-
terprise with paid-in capital of no more than NT$ 80 million shall be
classi?ed under SMEs; and if the paid-in capital is more than NT$80
million, the enterprise shall be classi?ed under LEs.
Table 1
Types of interaction between National Cheng Kung University and ?rms during
2001e2009.
Type of interaction Number of
projects
% of projects Average project
scale (million NT$)
Contract research 1254 65.0 1.35
Joint research 358 18.6 0.53
Technology transfer 155 8.0 1.22
Incubation 163 8.4 0.42
Total 1930 100.0 1.11
T.-C. Lin et al. / Asia Paci?c Management Review 20 (2015) 90e99 93
The simpli?ed classi?cation standards have been applied in
calculating the total collaboration projects, projects/?rms, and the
average project scale (Table 2) in the two categories of enterprises,
LEs and SMEs, that have established a partnership with NCKU from
2001 to 2009. As shown in the Table 2, the projects involving SMEs
amount to 835, constituting 43.3% of the total, whereas those
concerned with the LEs amount to 1095, accounting for 56.7% of the
total. With regard to the overall Taiwan business ecosystem struc-
ture, SMEs constitute an overwhelming 97.91% of the total busi-
nesses, whereas the ratio of LEs is con?ned to only 2.09% (Small and
Medium Enterprise Administration, Ministry of Economic Affairs,
2010). Thus, it can be said that LEs are considerably more inter-
ested in collaboration with NCKU than SMEs.
In consideration to other evaluation results, such as the
projects-to-?rms ratio (projects/?rms) and the average project
scale with NCKU, the average value of total projects/?rms
contributed by the category of LEs is 2.83, which is signi?cantly
better than the value of 1.67 attributed to SMEs (in which the
former is 1.69 times the latter). The average project scale resulting
fromthe LEs is NT$1.46 million, which is again much more than the
average amount of only NT$0.64 million achieved by SMEs. It thus
can be evidenced precisely that LEs will perform better if evaluated
in terms of both cooperation sustainability tendency and invest-
ment values. The overall investment potentiality of the LEs is also
much greater than that of the SMEs. The aforementioned statistics
can validate a distinctive feature that innovation demands among
Taiwanese corporate and the industryeuniversity cooperation en-
vironments have a signi?cantly distorted orientation, whereas
SMEs, constituting 97.91% of the total, performed worse than LEs
when compared in either the ratio of total cooperation projects
with NCKU or the cooperation sustainability and potentiality, or
even the average investment value. Such analysis results simply
support the fact that SMEs suffer from shortage of R&D talent,
funds, facilities, and innovation models for supporting their busi-
ness operations. As such, SMEs have dif?culty in improving their
R&D due to limited ?nancial resources available for participation in
such industryeuniversity cooperation. Therefore, it is hereby
advised that policy-makers devise more af?rmative and effective
policies that offer SMEs greater access to business innovation
opportunities.
Possessing certain in-house R&D resources, LEs support rapid
industry development advancement, prefer to succeed in concep-
tualizing more innovative business ideas, and shorten the durations
of completing the targeted R&D by adopting industryeuniversity
collaboration models. The systematic R&D activities carried out by
LEs within structured laboratories are more effective than the oc-
casional R&D activities undertaken by SMEs (Santarelli &
Sterlacchini, 1990). Because SMEs often conduct their innovative
activities without speci?c ?nancial and managerial resources and,
in particular, without formalized procedures, they tend to under-
take a signi?cant amount of innovative activities in their design,
production, and even in sales departments rather than in their R&D
departments. Thus, they more frequently develop incremental in-
novations rather than product innovations, which are mostly ach-
ieved by LEs. Another perspective regarding the R&D of SMEs from
a survey of 3000 Dutch ?rms (Kleinknecht, 1989) should be noted:
if informal R&D is taken into account, the R&D commitment of
SMEs is considerably higher than that reported by of?cial sources;
however, due to a lack of information in the literature, the in?uence
level surrounding this issue is not clear and worthy of future
research.
The statistics in Table 2 can be further utilized for analyzing the
decision-making tendency of industryeuniversity cooperation
involving LEs and SMEs. With respect to the adopted model be-
tween the LEs and NCKU, the “contract research” model was the
most prevalent (75.9%), creating 3.18 projects on average per en-
terprise, which is much more than the value resulting from other
cooperation models (such as the average of 1.48 projects for “in-
cubation,” 1.29 projects for the “joint research,” and 1.24 projects
for “technology transfer”). Therefore, a reasonable estimation can
be assumed that in addition to achieving relevant R&D successes,
LEs commonly choose the “contract research” cooperation model to
mainly motivate professors to focus on relevant research subjects
and also to cultivate long-term human resources suitable for their
own enterprises. This situation is consistent with Schartinger et al.
(2001) that LEs value the bene?t of employing educated and highly
skilled personnel more than SMEs.
Thus, it can be concluded that LEs commonly prefer to adopt the
“contract research” model for achieving more comprehensive or
long-term cooperation relations. This implies the lack of R&D
personnel (generally with doctorate quali?cations) in ?rms, and
usually adopting research achievements from universities to meet
their requirements. This also veri?ed the result of Shi (2009). The
policy-makers should also make a note of the limited project scale
(0.94 million) and lower ratios of projects per ?rm (1.29) in cases of
joint research model involving LEs and NCKU. It implies the lack of
deep reciprocal relationship between LEs and universities to build
long-term and large-scale cooperated projects by focusing on
topics that bene?t both entities. It thus has limited functions such
as enhancing research performance and publications of university
professors, and shifting university curriculums from basic toward
applied research toward recent collaboration projects between LEs
and universities. Proper mechanisms such as “matching fund,”
which implies focusing on topics that bene?t both entities and
establishing long-termcollaboration projects, are needed. Thus, the
deep reciprocal relationship between LEs and universities can ful?ll
and overcome the two major shortcomings in the allocation of
Taiwan's R&D resources analyzed by Shi (2009).
Furthermore, in the category of LEs, among all cooperation
models, the “technology transfer” model had the highest average
investment value of NT$1.95 million, instead of the “contract
research” model, which had an average investment value of
NT$1.59 million. This indicates that although LEs prefer to conduct
assignments or transfers by spending higher amounts relative to
obtaining the relevant intellectual property in case of any available
critical technology or patent, the total investment values resulting
fromthe “technology transfer” model constitute only a limited ratio
in proportion to the overall investment.
Therefore, a reasonable estimation can be assumed that in
addition to obtaining relevant R&D achievements, LEs commonly
choose the “contract research” cooperation model to mainly
motivate professors to focus on relevant research subjects and also
to cultivate long-term human resources suitable for their own en-
terprises. This situation is consistent with Schartinger et al. (2001)
Table 2
Comparison of interactions of National Cheng Kung University with LEs and SMEs
during 2001e2009.
Project number Projects/?rm Average project scale
(million NT$)
With LEs With
SMEs
With LEs With
SMEs
With LEs With
SMEs
Contract
research
831 423 3.18 1.54 1.59 0.86
Joint research 101 257 1.29 1.44 0.94 0.36
Technology
transfer
67 88 1.24 1.31 1.95 0.65
Incubation 96 67 1.48 1.24 0.48 0.32
All projects 1095 835 2.83 1.67 1.46 0.64
LEs ¼large-sized enterprises; SMEs ¼small and medium-sized enterprises.
T.-C. Lin et al. / Asia Paci?c Management Review 20 (2015) 90e99 94
that LEs value the bene?t of employing educated and highly skilled
personnel more than SMEs. However, in the category of LEs, among
all cooperation models, the “technology transfer” model resulted in
the highest average investment value of NT$1.95 million, instead of
the “contract research” model, which had an average investment
value of NT$1.59 million. This indicates that although LEs prefer to
conduct assignments or transfers by spending higher amounts
relative to obtaining the relevant intellectual property in case of any
available critical technology or patent, the total investment values
resulting from the “technology transfer” model constitute only a
limited ratio in proportion to the overall investment.
Factors such as shortages of R&D personnel, funds, and facilities
are the reasons why SMEs prefer the “contract research” model for
achieving dominance in the decision-making of research subjects
(50.7%) and the “joint research” model (30.8%) featuring a low-
investment benchmark. These two models have also achieved
success in carrying out more cooperation projects with individual
SMEs, with the former model having 1.54 projects on average and
the latter having 1.44. When compared with LEs, SMEs demonstrate
less interest in establishing a comprehensive or long-term coop-
eration relation. In terms of the average project investment value in
the category of SMEs, the “contract research” model has the highest
value (NT$ 0.86 million), instead of the “technology transfer” model
(NT$0.65 million), which again suggests that SMEs demonstrate
substantive business strategies or goals in adopting the “technology
transfer” model; in addition, SMEs also tend to be less interested in
obtaining costly intellectual property rights.
To completely understand the industryeuniversity models and
average corporate project scale with respect to “?rmsize,” the “?rm
size” was adopted as the X coordinate and the “research grant/
royalty” was adopted as the Y coordinate for constructing the
industryeuniversity cooperation pattern structure of NCKU (Fig. 3).
The research data considered in this analysis are inclusive of all the
contracts in terms of “contract research,” “joint research,” “tech-
nology transfer,” and “incubation” models established with NCKU
between 2001 and 2009. As shown in Fig. 3, enterprises that have
established industryeuniversity cooperation with NCKU show
extensive variation in terms of the paid-in capital, which varies
from less than NT$1 million to more than NT$100 billion. As such,
the investment values of industryeuniversity cooperation projects
range between NT$7250 and NT$43,200,000. It can be found in
Fig. 3 that the “joint research” model is distributed in the lower left-
hand corner, whereas the “contract research” model has a broad
distribution range in the X direction.
Firm sizes were further separated into seven categories, as
illustrated in Fig. 4, including those with paid-in capital of less than
NT$1 million, between NT$1 million and no more than NT$10
million, between NT$10 million and no more than NT$100 million,
between NT$100 million and less than NT$1 billion, from NT$1
billion to less than NT$10 billion, from NT$10 billion to less than
NT$100 billion, and more than NT$100 billion. When calculating
the weight ratio of technology transfer, incubation, contract
research, and joint research projects as adopted by regional en-
terprises and NCKU, the tendency of various enterprises (SMEs vs.
LEs) to adopt different models of industryeuniversity collaboration
is rather signi?cant. The percentage of contract research increased
along with the increase in ?rm size. For enterprises whose capital
exceeded NT$1 billion, 80% of the projects in collaboration with
NCKU belonged to contract research, whereas for ?rms whose
capital exceed hundreds of billions have 100% adoption of contract
research. However, the percentage of joint research and technology
transfer tends to decrease with the increase of ?rm size, and in-
cubation does not reveal signi?cant variance with regard to ?rm
size.
In summary, these research results imply that LEs generally
adopt the “contract research” model when cooperating with NCKU,
and also establish more diversi?ed cooperation effects or longer
cooperation relationships with this cooperation model. Moreover,
the tendency to adopt the “contract research” model varies
increasingly with the ever-increasing paid-in capitals of LEs. When
any critical technologies or patents are required, LEs would rather
procure intellectual property rights at values that are commonly
regarded more costly. SMEs are equally interested in “contract
research” for achieving dominance in determining research sub-
jects and “joint research,” which features lower investment funds,
but they are not interested in achieving more comprehensive
cooperation dimensions or maintaining long-term mutual
cooperation.
One of the conclusions from this research analysis is that there
still exists a signi?cant distorted orientation in Taiwan between the
motivation and demands of enterprises seeking innovation within
the industryeuniversity collaboration ecosystem. Even though
SMEs constitute an overwhelming 97.91% of the total corporations
in Taiwan, the cooperation projects with NCKU and all investment
values contributed by SMEs are both much less than those of LEs.
This is because SMEs generally suffer a shortage of R&D personnel,
investment funds, facilities, and innovative business operation
modes for supporting their business operations, as well as
Fig. 3. Pattern of collaboration projects between National Cheng Kung University and
?rms. Fig. 4. Project ratio of four collaboration models in seven different categories.
T.-C. Lin et al. / Asia Paci?c Management Review 20 (2015) 90e99 95
availability of limited channels for SMEs to improve their R&D ac-
tivities. Policy-makers should thus create valid and effective pol-
icies, such as modifying existing resource and tax policies, or create
cut-to-?t collaboration models for enabling SMEs to gain greater
access to more business innovation opportunities.
It thus can be concluded that LEs commonly prefer to adopt the
“contract research” model for achieving more comprehensive or
long-term cooperation relations. It implies the lack of R&D
personnel (generally with doctorate quali?cations) in ?rms, and
usually adopting achievements from universities to meet their re-
quirements. This also veri?ed the result reported previously by Shi
(2009). The policy-makers should also make a note of the limited
project scale (0.94 million) and lower ratios of projects per ?rm
(1.29) in cases of joint research model involving LEs and NCKU. It
implies the lack of deep reciprocal relationship between LEs and
universities to build long-term and large-scale cooperated projects
by focusing on topics that bene?t both entities. It thus has limited
functions such as enhancing research performance and publica-
tions of university professors, and shifting university curriculums
from basic toward applied research toward recent collaboration
projects between LEs and universities. Proper mechanisms such as
“matching fund,” which implies focusing on topics that bene?t both
entities and establishing long-term collaboration projects, are
needed.
3.3. Geographical proximity impact analysis
Start-up companies, as well as new technology-based ?rms
working with universities, are more likely to be in close proximity
to the licensing institution in order to capture the bene?ts of sub-
sequent innovations (Elyse, 2006). Proximity between ?rms and
universities promotes the natural exchange of ideas through both
formal and informal networks (Deeds, Decarolis, & Coombs, 2000;
Lindelof & Lofsten, 2004). Formal methods include licensing and
cooperative alliances (Lane & Lubatkin, 1998), whereas informal
methods include mobility of scientists and engineers, social
meetings, and discussions (Pouder & St. John, 1996).
Various studies have indicated that geographical proximity has
positive effects on the willingness of enterprises to adopt indus-
tryeuniversity cooperation and also on the subsequent perfor-
mance of the cooperation projects (Anselin, Varga, & Acs, 1997;
Fischer & Varga, 2003; Fritsch & Slavtchev, 2007; Jaffe, 1989;
Mans?eld, 1995; Moreno, Paci, & Usai, 2005; Oerlemans & Meeus,
2005). For example, Jaffe (1989) conducted an empirical analysis
using the “knowledge production function” developed by Griliches
(1986), and veri?ed that the more innovative the R&D activities are
carried out by universities, the better performance of innovation a
state will achieve afterward. This therefore supports that
university-involved R&Dprojects can stimulate more enterprises to
participate in R&D activities and thus achieve better performance
in terms of the amount of patent applications ?led. Moreover, based
on a survey (Mans?eld, 1995) of 200 academic researchers and 66
enterprises belonging to major manufacturing industries, the
mutual distances between the enterprises and the corresponding
cooperative universities are on average within 100e1000 mi
(approximately within 161e1609 km), making face-to-face contact
more convenient between the two cooperating parties, which
could lead to increased cooperation and better project
performances.
Yun and Lee (2012) compared the regional knowledge creation,
utilization, and transfer between South Korea and Taiwan's science
parks. They found that the Hsinchu Science Park in Taiwan is more
active toward cooperative relations than the Daedeok Innopolis in
Korea, making the innovation institutions in different regions as
different micro units of Triple Helix. Thus, it is important for
innovation participants from university, industry, and government
to play the role of creating knowledge space, consensus space, and
innovation space, as it is important for innovation participants to
develop co-evolving relationships. Yeh, Lin, and Kung (2011)
investigated the effect of geographical proximity on the charac-
teristic of industryeuniversity collaboration by analyzing second-
ary data. Results revealed that urbanization, population, resource
partitioning, industrial structure, and the social economy indeed
have a positive effect on geographical distribution of enterprises
that cooperated with universities.
This research has thus been initiated by statistically analyzing all
the cooperation projects carried out between the years 2001 and
2009 to calculate the average distances between NCKU and the
enterprises involved according to the model adopted. Results show
(Fig. 5) that the average distance of all enterprises involved in
technology transfers with NCKU is 167 km, which was the longest;
the average distances of all enterprises involved in contract
research and joint research were 143 and 117 km, respectively; and,
the average distance for the incubation model was 66 km, which
was the shortest. It turns out that the shortest average distance
results from enterprises migrating into NCKU or the enterprises
receiving consultancy support from the incubation manager of
NCKU. The “technology transfer” model demonstrated the longest
geographic distance, and thus, it is completely free of geographic
distance restraint.
The conversion outputs of the aforesaid “TWS67” coordinates
were then mapped out, as shown in Fig. 6, indicating that most
enterprises concerned with cooperation projects are located within
the top ?ve biggest metros (including Taipei City, Hsinchu City,
Taichung City, Tainan City, and Kaohsiung City). Such location dis-
tributions are in complete conformity with the natural geography
of Taiwan. As the Central Mountains extend from the north of the
island to the south, all populous cities are distributed along the
west island seashore. This geographic obstacle has also resulted in
most enterprises being located within those populous cities along
the west island seashore. Scholars have adopted various measure-
ment methodologies for evaluating how the geographical prox-
imity might in?uence knowledge spillover in a speci?c region;
however, as yet, there is not a set of measurement standards among
those scholars. For example, both Mans?eld (1995) and Gittelman
(2007) claimed that enterprises conducting industryeuniversity
cooperation projects are all located within an average distance
between 1000 and 1500 mi (equal to 1609e2414 km
Fig. 5. Average distances of ?rms that have cooperated with National Cheng Kung
University in different channels.
T.-C. Lin et al. / Asia Paci?c Management Review 20 (2015) 90e99 96
approximately). Because the longest linear distance of Taiwan is no
more than 400 km, which is much less than the aforesaid longest
distance of 2414 km, all cases of cooperation in this research are
within this region. Anselin et al. (1997) estimated the spatial
spillover between university research and high-technology in-
novations based on the well-known GrilicheseJaffe knowledge
production function (Jaffe, 1989), and con?rmed that a positive and
signi?cant relationship exists. They also found that a range of 50 mi
for the research spillover from metropolitan statistical areas and
result can be extended up to 75 miles in electronic industries
(Anselin, Varga, & Acs, 2000).
With logical derivation, it can thus be deduced that the ranges of
geographical proximity of these enterprises in Taiwan shall be
con?ned within acceptable ranges of knowledge spillover in
conjunction with the geographic obstacles of the island. As most
scholars have asserted that the spatial spillover is con?ned within
50 mi (about 80.5 km) (Anselin et al., 2000; Gittelman, 2007),
which is accordance with the current national administration
zoning of Taiwan, seven counties and cities
2
in southern Taiwan are
de?ned as being in geographical proximity districts with NCKU,
including Pintung County, Kaohsiung County, Kaohsiung City,
Tainan City, Tainan county, Chiayi County and Chiayi City, as
detailed in Fig. 6.
With respect to the distribution analysis of enterprises that have
established cooperation with NCKU, as illustrated in the Fig. 6, the
total projects between NCKU and the enterprises located in the
geographical proximity districts are 916, constituting 47.5% of the
overall number of collaboration projects, whereas the projects be-
tween NCKU and the enterprises outside of the geographical
proximity districts amount to 1014, representing 52.5% of the
overall number. Therefore, nearly half of the projects are within the
effective knowledge spillover district as de?ned by Gittelman
(2007) and Anselin et al. (2000), but the other half of the total
projects is still outside of the knowledge spillover district. Taiwan's
territory is pretty limited and differs greatly with that of the Eu-
ropean countries and the US. Most projects involve enterprise
clusters, with only two effective knowledge spillover districts
(Tainan City and Kaohsiung City), but three cities outside these
districts are de?ned by this research (Taichung City, Hsinchu City
and Taipei City). Such a unique mixture phenomenon is justi?able
for further research in the future.
After the aforesaid districts of effective knowledge spillover
have been applied for examining all the enterprises that have
established cooperation projects with NCKU, the survey results for
this research, as illustrated in Fig. 7, indicate that only 35% of the
total enterprises that adopted the “technology transfer” model are
located within the aforesaid zonal range. The values for the enter-
prises that adopted the “contract research,” “joint research,” and
“incubation” models are 38%, 55%, and 77%, respectively. The sig-
ni?cant differences among the aforesaid comparison ratios can
seemingly explain that the “preference ratios” for the enterprises
located within the aforementioned effective districts for partici-
pation in the “incubation” and “joint research” models are much
more than that of the enterprises located outside of these effective
districts; in addition, it can be seen that the ratio attributable to the
“incubation” model shows the greatest difference. However, as the
overall Taiwan business ecosystem is composed of atypical
nonuniformdistributions (in that only 20.7% of the total enterprises
in Taiwan are located within the aforesaid geographical proximity
districts), the “normalization” effect can be used for individually
examining the participation ratios of all enterprises involved with
all the individual models.
This research was conducted by applying the LQ for analyzing
the enterprise concentration levels for all individual indus-
tryeuniversity cooperation models. Being an indicator commonly
used in the ?elds of economics and economic geography, LQ has
frequently been applied for the measurement of speci?c industries
in the corresponding area for fully characterizing the enterprise
distributions geographically. The subsequent calculation results of
Fig. 6. Districts of geographical proximity and location of ?rms that collaborate with
National Cheng Kung University (NCKU).
Fig. 7. Percentage of ?rms with geographical proximity and location quotient (LQ).
2
According to the Taiwan Administration Territory Planning, Kaohsiung County
has been merged into Kaohsiung City; Tainan County has been merged into Tainan
City; Taichung County has been merged into Taichung City; as well as Taipei County
was upgraded to New Taipei City on December 25, 2010. These four cities along
with Capital Taipei are the ?ve Municipalities in Taiwan currently.
T.-C. Lin et al. / Asia Paci?c Management Review 20 (2015) 90e99 97
this research are illustrated in Fig. 7, whereby the LQ of ?rms with
geographical proximity in the technology transfer model is 2.6,
compared with 3.0 for contract research, 10.1 for joint research, and
16.0 for incubation (5.0 of all 4 models).
The phenomenon can validate that the enterprises executing the
industryeuniversity cooperation projects with NCKU demonstrate
the tendency in conformity with the geographical proximity. For
example, the participating rate of the enterprises in the
geographical proximity districts is ?ve times the average value of all
enterprises in Taiwan. Moreover, the LQs for the enterprises within
the geographical proximity districts participating in “technology
transfer” and “contract research,” are only 2.6 and 3.0. These two
values can indicate the limited relevancy between geographical
distances and the participating rate of the enterprises with the two
models. By contrast, the LQ for the enterprises within the
geographical proximity districts participating in the “incubation”
model is overwhelmingly 16 times of that in Taiwan overall. It
demonstrates that enterprises incubated in NCKU do feature
stronger geographical proximity compared with other models,-
which is compliance with the empirical research results among all
individual types of questionnaires conducted by Elyse (2006).
The results of this research thus indicate that the indus-
tryeuniversity collaboration administrator of a university should
pay more attention to the relevancy between the promotion
strategy of all individual cooperation models and the geographical
proximity of the enterprises involved, so as to strengthen regional
enterprise networks and interactions for the promotion of “incu-
bation” and “joint research” models. They should also broadening
marketing scope to all Taiwan and even launch international en-
terprise cooperation in the case of “technology transfer” and
“contract research” models, which feature limited relevancies with
geography restraints.
4. Conclusions
In this research, effects of the size of enterprises and geographic
proximity were investigated through the comparison of 1930
collaboration projects, including contract research, joint research,
technology transfer, and incubation, between enterprises and
NCKU, which is a representative university of Taiwan, during period
between 2001 and 2009. The collaboration projects were evaluated
to explore the counteracting relevancies in order to understand the
factors involved while making decisions for selecting an appro-
priate industryeuniversity cooperation model and the in?uential
signi?cance levels.
Results show that LEs prefer to adopt the “contract research”
model for industryeuniversity cooperation and implement the
model more comprehensively. Such a tendency varies positively
with the paid-in capital of ?rms. LEs would rather procure intel-
lectual property rights at values that are commonly regarded more
costly if they ?nd the patents or critical technologies useful.
However, the ratio of this cooperation model is limited in propor-
tion to the overall cases. Conversely, SMEs are equally interested in
“contract research” to dominate research subjects, whereas “joint
research” attracts lower investment funds. SMEs are interested in
neither achieving more comprehensive cooperation dimensions
nor maintaining long-term mutual cooperation.
Based on the results derived from this research, it can also be
af?rmed that the demand for seeking innovation and the
ecosystem of industryeuniversity cooperation in Taiwan are char-
acterized by extremely distorted orientations. Even though SMEs
constitute 97.91% of the total corporations, the cooperation projects
with NCKU and all investment values contributed by SMEs are both
much less than those for LEs. This is because SMEs generally suffer
from shortages of R&D personnel, investment funds, facilities, and
innovative business operation modes for supporting their business
operations, in addition to the limited collaboration activities
available for improving their R&D activities.
Policy-makers are recommended to create valid and effective
policies, such as adjusting the existing resource and tax policies, or
create cut-to-?t collaboration models for enabling SMEs to gain
greater access to more business innovation opportunities. The
policy-makers should also make a note of the limited project scale
(0.94 million) and lower ratios of projects per ?rm (1.29) in cases of
joint research model involving LEs and NCKU. It implies the lack of
deep reciprocal relationship between LEs and universities to build
long-term and large-scale cooperated projects by focusing on
topics that bene?t both entities. It thus has limited functions such
as enhancing research performance and publications of university
professors, and shifting university curriculums from basic toward
applied research toward recent collaboration projects between LEs
and universities. Proper mechanisms such as “matching fund,”
which implies focusing on topics that bene?t both entities and
establishing long-termcollaboration projects, are needed. Thus, the
deep reciprocal relationship between LEs and universities can ful?ll
and overcome the two major shortcomings in the allocation of
Taiwan's R&D resources.
With regard to the geographical proximity, results showthat it is
increasingly in?uential for enterprises that have established
industryeuniversity cooperation with NCKUdthe participation
ratio of enterprises within the geographical proximity is ?vefold to
that of all other aspects. Moreover, the participation ratios of en-
terprises within the geographical proximity vary signi?cantly with
each of the individual cooperation models, among which the “in-
cubation” and “joint research” can be de?ned as being in
geographical proximity, while the “technology transfer” and “con-
tract research” models demonstrate limited orientation. It is
therefore advised that policy makers should be more devoted to the
signi?cance of geographical proximity. In the cases of the “incu-
bation” and “joint research” models, they should strengthen
regional enterprise networks and interactions. Otherwise, for the
cases of “technology transfer” and “contract research” models, they
should clarify the attributes of all individual industry sectors and
interactive effects among industry chains, and then extend the
industryeuniversity cooperation scope to the whole of Taiwan and
even globally.
Con?icts of interest
All contributing authors declare no con?icts of interest.
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