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Does collaborative R&D policy work? The effect of collaborative R&D policy on innovative activities of firms in Korea
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Content
DOES COLLABORATIVE R&D POLICY WORK?
THE EFFECT OF COLLABORATIVE R&D POLICY ON INNOV ATIVE
ACTIVITIES OF FIRMS IN KOREA
by
Kwang Jun Ryu
A Dissertation presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirement for the Degree
DOCTOR OF PHILOSOPHY
(POLICY , PLANNNG, AND DEVELOPMENT)
August 2015
Copyright 2015 Kwang Jun Ryu
To my parents, Jong-mook and Soon-ja,
for their unconditional and unmeasurable love made me what I am.
To my wife, Sangmin Rha,
for her priceless devotion led me here.
And to my kids, Minna and Daniel,
may their dreams come true.
i
Table of Contents
List of Tables……………………………………………………………………………………..ⅳ
Abstract………………………………………………………………………………………….. ⅴ
CHAPTER 1: INTRODUCTION…………………………………………………………………1
1.1. Background and research questions………………………………………………….…...1
1.2. Research contribution………………………………………………………………...…...4
1.3. Organization of the dissertation …………………………………….…………………..…6
CHAPTER 2: REVIEW OF COLLABORATIVE R&D POLICIES……………………………..7
2.1. Collaborative R&D policies in the major advanced countries……………………….…..7
2.1.1 The United States………………………………………………………………………..9
Cooperative Research and Development Agreement (CRADA)………………………….....9
Advanced Technology Program (ATP)…………………………………………………......10
SEMATECH……………………………………………………………………………......12
2.1.2 Europe………………………………………………………………………………….14
Framework Programmes (FPs)……………………………………………………………..15
National level collaborative R&D policies………………………………………..……..17
2.1.3 Japan…………………………………………………………………………………...19
VLSI association………………………………………………………………………....19
SELETE and ASET…………………………………………………………………………20
2.2. Collaborative R&D policy in Korea……………………………………………………...22
ii
2.2.1. Establishing industry associations for knowledge transfer……………………………23
2.2.2. National R&D programs for industry-government R&D collaboration………………24
Made-in Korea computer……………………………………………………………..….....26
4M DRAM semiconductor………………………………………………………………....27
Mobile telecommunication and CDMA……………………………………………...……..29
2.3. Summary……………………………………………………………………………….....31
CHAPTER 3: THEORY OF COLLABORATIVE R&D POLICY…………………………...…34
3.1. Collaborative R&D and technology innovation……………………………………...….34
3.2. Collaborative R&D policy as a tool for transferring knowledge……………………….36
3.3. Empirical model and hypotheses……………………………………………………..….40
CHAPTER 4: EMPIRICAL ALAYSIS ON COLLABORATIVE R&D………………………...46
4.1. Data and Sample…………………………………………………………………...……..47
4.1.1. Dependent variables……………………………………………………………...……48
4.1.2. Independent variables…………………………………………………………………48
4.1.3. Control variables………………………………………………………………………49
4.2. Data analysis…………………………………………………………………………......52
4.2.1. The effect of collaborative R&D policies………………………………………..……52
4.2.2. The effect of non-collaborative R&D policies………………………………….……..53
4.2.3. The effect of the control variables…………..………………………………….........54
4.2.4. Estimation of results using the 2008 and 2010 CIS datasets……………….…………57
iii
CHAPTER 5: CONCLUSION…………………………………………………………………..63
5.1. Summary of the findings and implications.….…………………………………………..63
5.2. Limitation of the research……..…………………………………………………………66
5.3. Future directions……………..………………………………………………………......68
Bibliography……………………………………………………………………………………..70
iv
List of Tables
Table 1: Descriptive statistics of 2012 CIS dataset……………………………………………51
Table 2: Empirical results of 2012 CIS dataset………………………………………………..56
Table 3: Descriptive statistics of 2008 and 2010 CIS dataset………………...………….........57
Table 4: Estimation results of 2008 CIS dataset………………………………………...…….59
Table 5: Estimation results of 2010 CIS dataset…………………………...………………….61
Table 6: Summary of the findings ……………………………...……………………………..64
v
Abstract
This research examines whether and how different collaborative R&D policies affect
innovative activities of firms in Korea. In particular, collaborative R&D policy can be classified
into two types: “strong” collaborative R&D policy and “weak” collaborative R&D policy. The
former is considered to be more effective in transferring tacit knowledge than the latter. Using
data from the Korean Community Innovation Survey (CIS), the effects of those two policies on
innovative activities of firms are investigated. The innovative activities are measured by four
variables: R&D expenditure, the number of patent applications, product innovation, and process
innovation. The results of empirical analysis show that the firms’ R&D expenditure and the
number of patent applications are positively affected by strong collaborative R&D policy
represented by participating in government R&D projects. However, joining industry
associations, a proxy of weak collaborative R&D policy, does not have any effect on innovative
activities. This result indicates that collaborative policy which drives companies to participate in
R&D projects is relatively more helpful in fostering firms’ innovative activities. This is because
the strong policy mainly concentrates on transferring tacit knowledge which is essential for
innovation. When the government designs an R&D policy, therefore, it needs to consider that the
effect of policy may vary according to the type of the policy.
1
CHAPTER 1
INTRODUCTION
1.1. Background and research questions
Although collaboration across sectors has been a matter of great interest in the field of
public administration since the 1960s (Provan & Milward, 2001), even until the late 1990s,
scholars and practitioners did not appear to be fully prepared to collaborate with others (O'Toole,
1997). Now, however, collaboration, as new management guidance, is becoming more and more
ubiquitous, because the nature of public problems today is getting increasingly complex and
specialized. Thus, government alone can no longer deal with those problems effectively through
traditional management models (O’Toole, 1997; O’Leary, Gazley, McGuire, & Bingham, 2009).
As Frederickson (1999) noted, the field of public administration or policy has been proceeding to
“theories of cooperation,” and most researchers in the field recognize the importance and
indispensability of collaboration among stakeholders to tackle major public problems (Agranoff,
2007, 2012; Bingham & O’Leary, 2006; Emerson, Nabatchi, & Balogh, 2012).
In the field of public affairs, the literature on collaboration with other actors outside the
public arena, otherwise known as collaborative governance, is considerably rich and extensive
(Bryson, Crosby, & Stone, 2006; McGuire, 2006; Provan, Fish, & Sydow, 2007). Recent research
demonstrates a wide range of interests and covers various subjects including managerial lessons
for public managers on collaborating with others (Agranoff, 2003, 2006; Milward & Provan,
2006; O’Leary, Choi, & Gerard, 2012), requirements for successful collaboration (Berardo,
Heikkila, & Gerlak, 2014; Einbinder, Robertson, Garcia, Vuckovic, & Patti, 2000; Margerum,
2001; Sørensen & Torfing, 2011), trust as a key factor of collaborative interactions (Aldrich,
2
2014), knowledge sharing or collective learning (Gerlak & Heikkila, 2011; Leach, Weible, Vince,
Siddiki, & Calanni, 2014), incentive management for enhancing collaboration (Tang & Tang
2014), evaluation and accountability problems (Acar, Guo, & Yang, 2008; Considine, 2002;
Leach, Pelkey & Sabatier, 2002; Manring, 2005; Provan & Milward, 2001; Romzek, LeRoux,
Johnson, Kempf, & Piatak, 2014), conflicts related to collaboration (Fleming, McCartha, &
Steelman, 2015), collaboration strategies at the local level (Agranoff & McGuire, 2003; Graddy
& Chen, 2006; McGuire, 2000; Zhang, 2012), democratic prospects of new governance (Bevir,
2006; Bogason & Musso, 2006), citizen participation and involvement through collaboration
(Bingham, Nabatchi, & O’Leary, 2005; Cooper, Bryer, & Meek, 2006; Frieling, Lindenberg, &
Stokman, 2014; Fung, 2006; Rummery, 2006), cross-sectoral collaboration for emergency
response (Gazley & Brudney, 2007; Kapucu, 2005; Kapucu & Garayev, 2013; McEntire, 2002;
Moynihan, 2005; Waugh & Streib, 2006), and dark networks as a pathological phenomenon
(Raab & Milward, 2003).
Additionally, the prevalence of collaboration is not restricted within the field of public
administration and goes further into other relevant fields such as urban planning (Chaskin, 2005;
Healey, 2006; Innes & Booher, 1999; Margerum, 2002), environmental management (Connick &
Innes, 2003; Frame, Gunton, & Day, 2004; K. Lee, 2003), health administration (Lasker, Weiss,
& Miller, 2001; Mitchell & Shortell, 2000; Roussos & Fawcett, 2000; Weiner, Alexander &
Zuckerman, 2000), children’s services system (Romzek et al., 2014), cultural policy (Gugu &
Dal Molin, 2015), climate policy (Elgin, 2015), crime prevention or police service (Brainard &
McNutt, 2010; Choi & Choi, 2012; Kelman, Hong, & Turbitt, 2013), and education (Akkerman,
Torenviled, & Schalk, 2011; Selden, Sowa, & Sandfort, 2006; Wohlstetter, Malloy, Hentschke, &
Smith, 2004).
3
One relevant field this research would like to tackle is the field of R&D policy, where a
variety of government policy instruments are implemented to encourage collaborative R&D
activities among various actors in order to foster innovative activities, and eventually to support
economic development and maintain the level of sustainable economic development (Wu, 2005).
Contemporary collaborative R&D can be traced back to the 1970s, but the importance of
collaborative R&D and its associated policies was not fully recognized and appreciated until the
1980s. The United States and Japan were viewed as leading countries in the growth and
development of collaborative R&D policies, and European countries had also tried to enhance
collaborative R&D policies to foster innovation (Howells, Nedeva, & Georghiou, 1998;
Hagedoorn, Link, & Vonortas, 2000; European Commission [EC], 2011).
Since the 1990s, researchers have examined the issue of intra and inter-organizational
knowledge transfer. Those researchers argue that tacit knowledge is more crucial than explicit
knowledge for competitiveness (V oelpel, Dous, & Davenport, 2005), and tacit knowledge is
difficult to transfer easily among organizations (Teece, 1992; Nonaka, V on Krogh, & V oelpel,
2006). Meanwhile, a group of scholars focused on the channel of knowledge transfer and
knowledge characteristics. Dinur (2010) analyzed the relationship between knowledge
characteristics and knowledge transfer mechanisms in multinational corporations, and argued
that tacit knowledge requires more intensive interaction among organizations. Moreover, V oelpel
et al. (2005) argued that the richness of the channel can determine the type of knowledge that is
easily transferable.
Having this in mind, the main purpose of this research is to explore the effects of
different collaborative R&D policies on the innovative activities of firms. In particular, I argue
that collaborative R&D policies can be channels for knowledge transfer from the public to the
4
private sectors, which affects the degree of innovative activities of firms. Furthermore, those
channels can have different effects on the degree of transferring tacit knowledge, which is crucial
for innovative activities of firms.
In this vein, I propose the following hypothesis: a collaborative R&D policy, which
drives firms to participate in government R&D projects, enables transfer of tacit knowledge,
which can foster innovative activities of firms. There can be two different collaborative R&D
policies in terms of the degree of transferring tacit knowledge: One is called “strong”
collaborative R&D policy that can transfer tacit and explicit knowledge, and the other is called
“weak” collaborative R&D policy that can transfer explicit knowledge rather than tacit
knowledge. Strong collaborative R&D policy involves close face-to-face interaction between
researchers of firms and government-funded research institutes, as it enables firms to directly
participate in government R&D projects. Thus the strong collaborative R&D policy is more
effective in transferring tacit knowledge than the weak one.
1.2. Research contribution
This research contributes to framing collaborative R&D policy as a knowledge transfer
channel, and showing that collaborative R&D policy which has firms directly participate in
government R&D projects has a positive effect on innovative activities of firms through the
process of transferring tacit knowledge (Belderbos, Carree, & Lokshin, 2004).
Unfortunately, the effect of collaborative R&D between the public and private sector on
innovative activities of firms has not been studied much, even though governments in the US,
European Union (EU) and Japan have increasingly and continuously funded collaborative R&D
policies for the last 40 years (Hagedoorn et al., 2000).
5
In advanced countries, governments have designed collaborative R&D policies to foster
innovative activities of firms. Collaborative R&D policies can be broadly classified into two
types in terms of the degree of interaction: One is strong collaborative policy interaction, and the
other is weak collaborative policy interaction. The former is a policy that generates frequent
interaction. For example, if a firm gets a chance to participate in a government funded research
project, it gets a chance to learn about new technologies that the government is funding for the
future of the nation, and also gets a chance to train its researchers to absorb cutting-edge
technologies, which may not be possible within the firm’s internal R&D budget. In Chapter 2, an
overview of major collaborative R&D examples is provided.
Weak collaborative R&D policy is characterized by less interaction. It includes
information and knowledge being provided through leaflets, and through regular meetings held
with members of the industry consortia (Sakikabara & Cho, 2002). Note that governments often
form industry consortia to foster collaborative R&D (Schacht, 2007).
Moreover, this research includes empirical analyses in order to examine the effect of
collaborative R&D policy on innovative activities of firms, using the Community Innovation
Survey (CIS) dataset of Korean manufacturing firms. This dataset is an official firm-level survey
on innovative performance (e.g. patenting, product and process innovation) and related activities
(e.g. R&D, marketing, and collaboration) of the firms in the manufacturing sector. The empirical
models use collaborative R&D policy variables that are the proxies of strong and weak
collaboration with the government, and they investigate the effect of each policy type on
innovative activities of firms.
The empirical evidence and qualitative cases examined in this research suggest
meaningful policy implications supporting the importance of collaborative R&D policy. This
6
research will be a stepping stone for further research and policy design that can advance
knowledge in the field of collaborative R&D policy.
1.3. Organization of the dissertation
The remainder of this paper consists of four parts. Chapter 2 provides an overview of
collaborative R&D policies that fostered innovative activities of firms. In particular, cases in the
US, such as the Cooperative Research and Development Agreement (CRADA) and Advanced
Technology Program (ATP), and cases in European countries, such as the Framework
Programmes (FPs) are briefly examined, along with the cases in Japan. Then, the Korean cases
are introduced to provide further understanding of collaborative R&D policy.
In Chapter 3, a theoretical overview of existing literature is provided with rationales for
collaborative R&D policy, which may overcome the appropriability problem and then transfer
knowledge necessary for firm innovation. Subsequently, some testable hypotheses are proposed.
Next, in Chapter 4, empirical analyses are conducted using a dataset of responses from
the Korean Community Innovation Survey (CIS). Finally, policy implications are discussed
based on the findings from the empirical analyses as well as policy cases.
7
CHAPTER 2
REVIEW OF COLLABORATIVE R&D POLICIES
This chapter describes actual cases of collaborative R&D policies in the major advanced
countries and Korea, which were intended to foster innovative activities of firms. In particular,
collaborative R&D policies have been designed and implemented for a variety of technology
sectors and industries in the major countries and Korea. One common feature of these policies is
that the government conducted these collaborative R&D policies to foster innovative activities of
the firms in those sectors and industries. Another is that the collaborative R&D policy with close
interaction among private firms and public entities is crucial for successful technology
innovation. The government set up a policy providing opportunity for firms to closely interact
with the government. The policy objective and the tools for implementing the collaborative R&D
policy should be consistent and clear in forming a constructive collaboration among partners to
foster innovation.
2.1. Collaborative R&D policies in the major advanced countries
Collaborative R&D policies have been introduced and implemented worldwide since the
1980s in order to deal with fierce global competition. This competition was triggered by Japan,
as the Japanese government collaborated closely with firms, especially with semiconductor firms
through the very large scale integrated circuit (VLSI) Technology Research Association, in order
to catch up with the US and the EU in the 1970s. This private-public collaboration was known to
be successful, as the Japanese semiconductor firms caught up with firms in the US and the EU in
terms of technological competitiveness. In the early 1980s, both the US and the EU launched
8
new R&D policies to deal with Japan’s challenge, focusing on collaborative R&D systems.
The collaborative R&D policy in the US aimed to relax its strict antitrust laws and to
foster an environment for collaboration among the market players in the field (Hagedoorn et al.,
2000). Here, a brief review of US Cooperative Research and Development Agreements
(CRADA), Advanced Technology Program (ATP), and SEMATECH will be presented.
The collaborative R&D policy in the EU was intended to create an integrated cross-
national system that can stimulate innovative activities across nations, and share cutting-edge
technologies (EC, 2011). This idea was structured into the Framework Programmes (FPs) on
research and technology development, which has been continuing for more than 30 years.
Meanwhile, there has been a variety of collaborative R&D policies at the national level in
Europe. The FPs, the European Research Coordination Agency (EUREKA), and some of the
major collaborative R&D policies are briefly examined to understand their policy implications.
There has been an up and down in Japanese collaborative R&D policy. After World
War II, the Japanese government launched collaborative R&D policies for various industries to
catch up with those of the advanced countries. The initial purpose was to let industries learn and
adopt the advanced technologies quickly, but it changed its focus to developing state-of-the art
technologies to lead the world in the late 1960s and 1970s. This was led by the Japanese
government, specifically the Ministry of International Trade and Industry (MITI), which formed
industry associations for each industry involved. However, the Japanese government stopped
collaborative R&D in the 1980s, though it again pushed for collaborative R&D in the 1990s.
Below are selective examples of collaborative R&D policy in the major advanced
countries.
9
2.1.1. The United States
Cooperative Research and Development Agreements (CRADA)
In the US, policies to support collaborative R&D have a long history, reaching back to
WWII, when major collaborative efforts were launched in pharmaceuticals, petrochemicals,
synthetic rubber, and atomic weapons (Mowery, 1998). However, US policy took a step forward
in the early 1970s, when the National Science Foundation established a program funding the
Industry-University Cooperative Research Centers. Moreover, faced with intense international
competition, the US government expanded its policy focus beyond just funding R&D to meet
national needs including economic growth. Although the government knew that the private
sector is mainly in charge of commercializing technology, the government itself fostered
innovation and technological change in the major industries (Hagedoorn et al., 2000).
The Bayh-Dole Act (P.L. 96-517), passed in 1980 and later amended in 1986, was
another significant step towards the creation of the Cooperative Research and Development
Agreements (CRADA). CRADA is a written agreement between one or more government
laboratories and one or more private firms. This agreement is a contract to share facilities,
equipment, resources, and personnel of a lab and a firm to complete a joint project (Rogers,
Carayannis, Kurihara, & Allbritton, 1998). CRADA aims to encourage government laboratories
and private firms to collaborate in the field of science and technology so that both can benefit
more from it. A main feature of CRADA is that government laboratories do not transfer any
funds to private firms, which means that funding for a certain project should be provided by the
firms participating (Mowery, 1998). It was designed to be a mechanism that could form various
types of collaborative R&D between federal and non-federal organizations, which eventually
10
contributed to the transfer of technologies developed by federally funded R&D to the private
sector (Hagedoorn et al., 2000).
The Bayh-Dole Act simplified the process for firms and non-profit institutions to patent
and license the results from publicly-funded research. By amending the Stevenson-Wydler
Technology Innovation Act of 1980 (P.L. 96-480) which aimed to encourage government
laboratories to participate in technology transfer, the Bayh-Dohl Act allowed CRADA in 1986
(Mowery, 1998). In 1984, the US Congress passed the National Cooperative Research Act (P.L.
98-462) to solve the concerns associated with joint research being treated as monopolistic
behavior, and firms not participating in long-term risky R&D projects, which were often costly
for a private firm (Hagedoorn, 2000). This legislation clarified that collaborative R&D ventures
should not be treated as illegal behavior (Williamson, 2010). Then, in 1989, the Technology
Transfer Act (P.L. 99-502) was modified to allow contract-operated federal laboratories to also
enter CRADAs.
Since 1995, the US Congress has changed its policy to encourage federal laboratories to
license technologies obtained through R&D collaboration with private firms. Between 1995 and
2001, the number of CRADAs declined by more than 60%, but the number of licenses has more
than tripled (Getz, 2011).
Advanced Technology Program (ATP)
The Advanced Technology Program (ATP) is a collaborative approach to R&D between
the government and the industry for conducting high-risk research with the goal of creating
and/or developing technologies that promise significant commercial payoffs and wide-spread
benefits to the U.S. economy (National Institute of Standards and Technology [NIST], 2014).
11
ATP was created by the Omnibus Trade and Competitiveness Act of 1988 (P.L. 100-418).
Collaborative R&D efforts were encouraged and developed with financial support. ATP
provided public seed money, matched by private sector investments, to form a team or
consortium comprised of universities, companies, and/or government laboratories for the
development of generic technologies that had broad application across industry sectors (Schacht,
2007). According to NIST, by the end of 2007, 824 projects were funded with $1.6 billion by the
government, and approximately 28% were joint ventures (NIST, 2014).
ATP was replaced by the Technology Innovation Program (TIP) in 2008. TIP was
similar to ATP, except that it was designed and operated somewhat differently to encourage
collaborative R&D. In particular, comparing the guidelines of ATP and TIP, funding of TIP was
limited to small and medium-sized firms, whereas that of ATP was available to all firms.
Moreover, in the ATP guideline, joint ventures were required to include two separately owned
for-profit firms and could include universities, government laboratories, and other research
establishments as participants in the project but not as grant recipients. Under TIP, a joint
venture could be formed by two separately owned for-profit companies, but it could also be
comprised of one small or medium-sized firm and a university. A single company was able to
receive up to $2 million for up to three years under the ATP scheme, but under TIP, the
participating company, which must be a small or medium-sized business, could receive up to $3
million for up to three years. Also, in ATP, small and medium-sized companies were not
required to share costs, while large firms provided 60% of the total cost of the project. In TIP,
there was a 50% cost sharing requirement which, again, only applied to the small and medium-
sized businesses that were eligible. There was no five-year limit in funding for joint ventures
under ATP, while TIP limited joint venture funding to $9 million for up to five years.
12
The Advisory Board that was created to assist the Advanced Technology Program
included industry representatives, federal government officials, and representatives from other
research organizations. Vonortas (2000) reported that members of two ATP-sponsored research
partnerships experienced gains in their R&D efficiency. These gains were realized from reduced
duplication of research costs and curtailing cycle times.
Additionally, analyzing studies of the Advanced Technology Program (ATP), Ruegg
and Feller (2003) found that there was considerable evidence that ATP-funded projects generated
innovative outputs that would potentially lead to knowledge spillovers. They also reported that
an average of 72% of the participants felt that the collaborative nature of the ATP programme
benefited them by stimulating creative thinking, followed by 58% responding that it had been
beneficial towards obtaining R&D expertise. Moreover, they argued that “ATP funding leverages
and accelerates R&D, refocuses R&D on more technically challenging problems and enabling
platforms of technologies, and fills a significant funding gap” (p. xxv).
SEMATECH
Another important collaborative R&D policy was the SEMATECH (Semiconductor
Manufacturing Technology) consortium, which was a collaborative R&D partnership between
the government and a coalition of private firms, formed in order to revive the U.S.
semiconductor industry. In the mid-1980s, believing that the Japanese collaborative R&D
program had played a significant role in the success of Japanese semiconductor firms, the
leading U.S. semiconductor firms came to the conclusion that a collaborative mechanism could
be helpful in improving the quality of their products and eventually enhancing their competitive
advantage (Browning & Shetler, 2000). As a result, the SEMATECH consortium was formed. It
13
was a new way of setting the government-industry collaboration in technology development in
the US. However, the firms associated with the semiconductor industry hesitated to collaborate
with each other, and even to collaborate with the government, because it was an unusual
collaborative effort for both the government and the firms in the US (Browning & Shetler, 2000).
To some extent, the collaborative structure of SEMATECH benchmarked Japan’s
successful VLSI projects of the late 1970s. The U.S. Department of Defense of the US
government treated the semiconductor industry as a national security issue and paid half of the
cost of the consortium with a $200 million annual budget. The membership was restricted to U.S.
companies only (Grindley, Mowery, & Silverman, 1994). In addition, the U.S. semiconductor
materials and equipment producers formed a sister organization, SEMI/SEMATECH, in 1987.
This organization was established to collaborate with SEMATECH and it received a seat on the
SEMATECH board of directors, and then supported SEMATECH in gathering research ideas
and organizing project teams (Wessner, 2003).
This unprecedented collaborative R&D policy in the US, encompassing all the
semiconductor firms, including suppliers of the materials and equipment, is deemed to have
contributed to a resurgence of the US semiconductor industry (Wessner, 2003). In particular,
from the industry’s perspective, it is said that the product quality and international
competitiveness of the U.S. equipment and supplies industry have been improved due to the
consortium. In combination with US-Japan Semiconductor Trade Agreements, the U.S.
semiconductor industry increased its market share in Japan over 20% by December 1992, when
Japan was the world’s largest semiconductor market (Procassini, 1995). Further evidence was
that the industry participants of SEMATECH were willing to provide matching funds for its
operation, and those firms continued to fund the consortium even when the government stopped
14
funding it (Browning & Shetler, 2000).
Relatedly, a similar experience with the organizations’ collaborative efforts was
reported by the members of SEMATECH. Link, Teece and Finan (1996) estimated that research
collaboration through SEMATECH earned its member firms a return of about 63% on their
membership dues, which was realized primarily through reduction of duplicated research costs
(Link et al.,1996). However, Congress and the Executive branch were debating the government
funding for R&D in a specific industry. Note that federal funding for SEMATECH ended after
1996 at the industry’s request, and some argued that the role of the government should be limited,
because the federal funding seemed like “corporate welfare” (Rogers et al., 1998).
2.1.2. Europe
Similar to the US, the EU entered the 1980s with increasing anxiety over the gradual
loss of its global competitiveness in high-technology industries. The EU had historical and
structural factors influencing its policy in addition to the widely perceived change in the global
forces affecting R&D and innovation. First, there was a wide gap among the member countries
in terms of technological capability. Second, the R&D policies and infrastructures had been
established, but they were different among the member countries (Hagedoorn et al., 2000).
Nevertheless, taking all the differences and common aspects into account, the EU
formed the Framework Programmes (FPs) to induce innovation within the European continent. It
started with the pilot program called European Strategic Program for R&D in Information
Technology (ESPRIT) in 1981, which was a response to rapid technological advances and the
loss of global market share of the European electronic companies. Being established with the
twelve largest European electronics firms, ESPRIT aimed to support cooperative R&D and pre-
15
competitive research far from commercialization in the fields, such as microelectronics, micro
processing systems and software packages, and manufacturing integration (Tassey, 1997).
Framework Programmes (FPs)
In 1984, the Framework Programme (FP) was started as an integrated and
comprehensive R&D program, encompassing all research and technology development (RTD, a
term for R&D in Europe) policies in the EU. From 1984 to 2013, a total of seven FPs have
already been completed, and the eighth phase of FP, named Horizon 2020, started in 2014.
Starting with €3.75 billion in FP1, the estimated funding amount has increased up to €80 billion
during Horizon 2020 (Artis & Nixon, 2007).
The FP for RTD was fully established when the Single European Act (SEA) was passed
in 1987. The overall objective of this law was to strengthen the scientific and technological basis
of the industry, and thus to increase the global competitiveness of European Community (EC)
countries. Moreover, the SEA had a provision that the EU’s FP for RTD policy should be
coordinated with all other policies concerned with the general well-being of citizens. The basic
idea of the FP for RTD policy was also stimulating collaborative R&D. The SEA stipulated that
the policy would be implemented via a set of specific programmes in certain specified fields over
several years (Article 130f). The 1992 Treaty in the European Union included the idea of
strengthening the FP by giving priority to it over all other RTD projects of the EU. The FP
consisted of a broad range of programmes from basic research to marketable development-
oriented projects (Georghiou, 2001).
Over the past several decades, the FPs have funded excellent inter-disciplinary and
collaborative research on a wide range of topics. They have brought the research capabilities of
16
participating organizations together and settled collaborative R&D processes from which SMEs
and start-ups can benefit. One example is the "Future and Emerging Technologies" (FET)
programme, in FP6 and FP7, which triggered explorative research and had a strong effect on
strengthening the competitiveness of participating organizations (EC, 2011).
More broadly, a major contribution of the FPs was the creation of an innovative
landscape that could be systematically planned, coordinated, and implemented across Europe. If
it were not for the FPs, the European Research Council (ERC) would not have been created.
Rather, the EU would then have been left with fragmented national research councils with no
funding mechanism to promote EU-wide competition for funds and to encourage cutting-edge
research (EC, 2009). For example, a pan-European strategy on research infrastructure has been
developed and is now being implemented, which aims to foster collaborative R&D projects,
international cooperation actions, and inter-disciplinary R&D across Europe (EC, 2011). Without
the FPs, such a project would not have been possible.
Martin (1996) argued that firms in the European semiconductor industry domestically
and internationally engaged in collaborative R&D to further their competitive strategic goals. In
addition, firms in Europe reached their competitive strategic goals thanks to the collaboration
prompted by public policies. Moreover, one of the programs included in the FPs, the
Competitiveness and Innovation Programme (CIP), has increased innovation in SMEs by
forming public-private partnerships and collaborative networks with international organizations.
For example, in their recent study, Barajas, Huergo, and Moreno (2011) analyzed the
productivity of Spanish manufacturing firms between 1995 and 2005, and concluded that
participating in the R&D program had a positive impact on firms’ technological capabilities.
The EU is now executing a new policy strategy, Horizon 2020, which is the eighth
17
phase of FP. Horizon 2020 focuses on smart growth with specific objectives. These include
increasing investment in R&D and innovation up to 3% of the gross domestic product (GDP) in
the EU by 2020, improving conditions for R&D and innovation, strengthening the links in the
innovation cycle, and refocusing R&D and innovation policy on major challenges for society,
such as climate change, energy and resource efficiency, and health and demographic change (EC,
2011).
National level collaborative R&D policies
The European Research Coordination Agency (EUREKA) is a well-known bottom-up
collaborative R&D consortium established by European companies to develop marketable
products, which are often funded by national governments. The main purpose of EUREKA is to
harmonize various efforts of R&D participants regarding innovation. The first major
collaborative project in microelectronics designed by EUREKA was the Joint European
Submicron Silicon Initiative (JESSI). JESSI, from 1988 to 1996, was quite successful in
developing devices producing semiconductors for the telecommunication market. Then, JESSI
was succeeded by the MEDEA microelectronics project, which began in 1997, and MEDEA was
extended into MEDEAPlus, a nine-year follow-up program. Almost 40% of its total funding
came from the governments of the member countries.
In addition, the Inter-university Microelectronics Centre (IMEC), originally founded by
the regional government of Flanders in Belgium, is a large, independent research center that
contracts with European governments and both European and foreign companies to conduct
advanced microelectronics R&D. IMEC has spun off numerous semiconductor associated firms
and has won global acclaim for a succession of technological achievements (Wessner, 2003).
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The Laboratory for Electronics, Technology, and Instrumentation (LETI) was established by the
French government to conduct microelectronics R&D activities. This laboratory also transferred
technologies to private companies, supported the creation of startups, and sponsored consortia
(Wessner, 2003).
The LINK Collaborative Research Scheme, since 1986, has been one of the UK
government’s main mechanisms for the promotion of collaboration among companies,
universities, and research organizations in pre-commercial or strategic research. An independent
strategic review in 2003 found that the LINK Collaborative Research Scheme had provided good
value for the money and led to substantial economic benefits for participating companies (Smith
et al., 2003). Since its inception, it is estimated to have increased the profits of participating
companies and raised the number of related jobs by up to 25,000 (Cunningham & Gök, 2012). It
was well regarded by both business and university users. Moreover, Public and Corporate
Economic Consultants (PACEC) (2011) calculated that the Collaborative R&D program in the
UK had created 13,350 jobs and also generated Gross Value Added (GVA) of £2.9 billion.
However, the EU recognizes many challenges that it has to confront to take the FPs to
the next level. First, Europe still needs greater technological leadership and innovation capability.
Its share of global patents is decreasing, and the high-technology trade deficit is increasing,
compared to those of the US and East Asia (EC, 2011). There needs to be more innovative
companies in the high-technology sectors.
Moreover, Hughes and his colleague (Cosh & Hughes, 2007; Hughes, 2008), through
their Centre for Business Research (CBR) surveys in the UK since 1991, found that the increase
in the amount of collaborative activities, especially between universities and industries, is due to
increased importance given to university-industry collaboration in UK innovation policy in the
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1990s and 2000s. In a series of evaluations of the UK higher education, “the third stream
activities” supported by the Higher Education Funding Council for England (HEFCE) had a
positive impact on government funding of university and industry links especially in comparison
with such activities in the US (Cosh & Hughes, 2007). Note that the third stream activities are
known as revenue-raising activities that universities undertake along with their traditional
functions such as teaching and research. A typical example of third stream activities is
knowledge transfer through university-industry collaboration (PACEC, 2009).
2.1.3. Japan
VLSI association
After World War II, collaborative R&D supported by the government was led by Japan.
One typical way of conducting collaborative R&D in Japan is to form an industry association
where government and industry works together. Japan initially benchmarked the idea of research
associations from the UK, and used it to help firms and industries to adapt and distribute
technological information in high technology industries in the first place (Hagedoorn et al.,
2000). Following the Mining and Manufacturing Industry Technology Research Association of
1961, a large number of Japanese Engineering Research Associations (ERAs) were established in
a wide variety of sectors (Sigurdson, 1986). Note that 237 ERAs were set up between 1959 and
1992 (Sakakibara, 1997)
The role of ERAs changed from supporting firms and industries to adapt and learn
specific technologies, to supporting them so that they could catch up with leading-edge
technologies and to conduct pioneering research (Oshima & Kodama, 1986). The famous success
20
story is related to the VLSI association where the Japanese government and related firms came
together to catch up with the rival firms in the US that were taking the lead of the world at that
time. The VLSI association supported Japanese firms in acquiring cutting-edge capabilities in
manufacturing both memory devices and logic circuits through collaborative R&D (Tyson, 1992).
Accordingly, the worldwide DRAM market share of U.S. firms dropped to 10% around 1984 or
1985, while it had been around 90% in the late 1970s (Macher, Mowery, & Hodges, 1999).
However, as the firms got bigger, they resisted following the government-initiated collaborative
R&D policy, and the decreasing government budget also hindered the collaborative R&D policy
from operating properly in the 1980s (Ham, Linden, & Appleyard, 1998). However, it was
generally perceived as a key success factor which brought Japanese semiconductor technology
up to world-class levels in the late 1970s (Wessner, 2003).
SELETE and ASET
In the 1990s, Japanese firms again started to conduct collaborative R&D in the
semiconductor industry. Based on the proposal of the Semiconductor Industry Research Institute
of Japan (SIRIJ), founded in 1994, as a think tank for the Japanese semiconductor industry, the
Semiconductor Leading Edge Technologies, Inc. (SELETE) and the Association of Super-
Advanced Electronics Technologies (ASET) were established in 1996.
SELETE was established to promote and evaluate technologies, to develop advanced
technologies, and to carry out special projects. To do so SELETE’s key activity is arranging
collaboration among the key stakeholders in the industry as well as the government. In the case
of pre-commercial semiconductor research, proposals initiated by the industry were transmitted
to the Ministry of International Trade and Industry (MITI) through SELETE. If the proposal is
21
accepted, MITI may request the necessary budget. After the budget proposal is approved, MITI
assigns research funds through New Energy and Industrial Technology Development
Organization (NEDO), which publicly announces the new project (Wessner, 2003).
SELETE also collaborated with equipment suppliers and academia. SELETE developed
performance metrics for process, reliability, and productivity, and provided evaluation and
feedback to the equipment suppliers. SELETE developed a framework to collaborate with
academia and focused on three-dimensional process simulation for developing computer-aided
design (CAD) technology. Especially in the semiconductor industry, simulation is considered
important for equipment development and implementation of each concept as modelled (Morino,
2002).
The Association of Super-Advanced Electronics Technologies (ASET), established by a
special law, is a consortium of firms in the electronics device industry with 41 members,
including equipment and materials suppliers. Setoya (2003) argued that the objective of this
consortium was to perform research between the basic and applied levels, and the Japanese
government provides funding for all the projects, and all the outcomes would be opened to the
public.
ASET is unique in having multiple research areas, while other research consortia are
established for a specific research area. The difference between ASET and SELETE is the nature
of the funding for collaborative R&D. ASET is funded by the government, whereas SELETE is
100 % funded by the industry, although in both cases, development is done by assignees from
individual member companies (Inoue, 1998). In the case where the developers are universities,
most funds come from the government, while industries are encouraged to make joint
agreements for funding the research (Wessner, 2003).
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Although these R&D collaboration policies were perceived to be successful in Japan,
Sakakibara (1997) argued that the overall effects were modest and reported that the “participants
do not perceive R&D consortia to be critical to the establishment of their competitive position”
(p.468). According to Branstetter and Sakakibara (1998), there was a small but positive statistical
link between government’s support and the amount and productivity of Japanese firms’
innovative activities. Therefore, designing and operating an effective collaborative R&D policy
seems to be more important than the level and amount of resources invested in it in terms of
explaining research outcomes (Cunningham & Gök, 2012). In particular, the government may
have to be careful in deciding the size of the matching fund of the firms, and also in leading the
direction of collaborative R&D policy.
2.2. Collaborative R&D policy in Korea
Since the Ministry of Science and Technology (MOST) was established in 1967, the
Korean government has proactively designed and aggressively implemented policies to foster
innovation for almost 50 years (Yoon, 2014). The R&D expenditure has increased to up to 4% of
the GDP, and the number of patents applied has dramatically increased, with annual patent
applications amounting to over 400,000 in 2014 (NIST, 2014). Now, Korea is one of the top
countries investing in R&D for sustainable economic growth.
Along this line, the Korean government designed and implemented a collaborative R&D
policy when it started a series of five-year economic development plans in the 1960s. First, the
Korean government established industry associations for industry–government collaboration.
This collaborative R&D through industry association has been a major knowledge transfer
channel until now. As Korean firms did not have enough resources and technological capability
23
to conduct collaborative R&D extensively, the Korean government focused on setting up
industry associations to provide information to the firms and to educate them as to what
advanced countries were doing (Chung, 2010; Yoon, 2014).
Second, the Korean government implemented an intensive collaborative R&D policy
mandating that firms participate in government R&D projects through large-scale national R&D
programs in the 1980s. The Korean government benchmarked a Japanese collaborative R&D
system following the policy direction of advanced countries. As Korea was close to Japan
geographically and historically, Korea was quick to imitate the Japanese system and structure to
conduct collaborative R&D (Sakakibara & Cho, 2002).
Note that the ratio of R&D expenditures by the private sector and public sector became
50:50 in 1981 (Kim, 1997). In addition, private sector R&D rapidly increased up to more than 70%
of the total national R&D expenditure by 2010.
2.2.1. Establishing industry associations for knowledge transfer
The Korean government set up and operated numerous industry associations funded
jointly by the government and firms. These industry associations were used as a channel for
transferring technological and business information and knowledge to the firms in Korea. Any
firm could join industry associations if it wanted to. According to an interview with Dr. Hwang-
Hee Cho, a former vice-president of the Science and Technology Policy Institute (STEPI) in
Korea, he argued that although establishing and operating industry associations can be thought of
as a low-level collaborative R&D policy only providing technological information and
educational materials, the role of industry associations was crucial in the early stage of
technology development in Korea (Interview with Dr. Cho, 2015). He also mentioned that the
24
knowledge transferred through these industry associations was mostly codified and explicit in the
forms of leaflets, journals, and annual reports. For example, the government and the firms
producing apparel in 1962 jointly established the Korea Apparel Industry Association. Those
apparel firms were all small start-ups, and the Korean government played a parental role in
guiding small firms to grow through exporting their products.
The firms pay a membership fee to join the association, and the government funds the
industry association for basic operation and research activities. The research activities include
sharing information about relevant new technology developments by the government-funded
research institute or the firms in the industry, as well as international technology development
trends and other business trends.
Thus, the industry associations are a knowledge transfer channel between the industry
members and the government, allowing the flow of explicit or codified knowledge. For example,
relevant technological and innovative information is shared among the member firms and the
government gets information and the opinions of the firms through the association.
2.2.2. National R&D programs for industry –government R&D collaboration
The Korean government actively developed and used various types of technology
policies to facilitate firms’ innovative behaviors. Along this policy direction, the Korean
government designed and implemented a large-scale national R&D program, where private firms
can closely interact with a government-funded research institute by participating in government
R&D projects. The process of participating in national R&D programs is open to any Korean
firm. There is a variety of government R&D projects a firm can choose to take part in. In general,
the procedure of selecting firms for projects is similar to a tryout. The government selects
25
qualified companies from among applicants according to certain criteria announced in advance.
On the other hand, there are regulations preventing large enterprises with higher R&D capability
from applying for some projects (see www.keit.re.kr for more details). That is, once a firm’s
R&D capability is over a certain level, the Korean government intends to provide a fair
opportunity to other firms.
Through this government R&D participation policy, the transfer of tacit knowledge can
occur, because private firms closely interact with the government research institutes by directly
participating in the government R&D projects with their matched funds and with their own
researchers (Hwang & Kim, 2000). This policy directly attempts to form a collaboration scheme
between private firms and the government. Through this policy, the firms become informed and
trained, so they can conduct innovative work on their own in the future (Bransletter &
Sakakibara, 1998).
The first national R&D program was the Specific R&D Program launched by the MOST
in 1982. The R&D program has two sub-programs: one track that required the matching of funds
by private firms and the other with no fund matching. The sub-programs requiring the matching
of funds consist of projects that are closed to the market and the sub-programs with no fund
matching are primarily at a pre-commercial stage. This Specific R&D Program ended in 2001.
Since this Specific R&D Program was successfully launched and implemented, other national
R&D programs, such as the Industrial Generic Technology Development Program and
Information Communication Technology (ICT) R&D Program, were launched with the aim to
develop technologies that could solve problems in areas with a high economic impact (MOST,
2008).
26
Below I introduce two projects of the Specific R&D Programs and one project of the
ICT R&D Program that are evaluated to be successful in terms of the collaboration between the
government-funded research institutes and private firms.
Made-in-Korea computer
Based on the successful government–industry collaborative R&D efforts, which were
followed by commercialization in the growing IT industry, in the early 1980s, the Korean
government continuously designed and implemented national R&D programs consisting of sub-
programs and projects in a variety of industry sectors.
The Specific R&D Program, the first national R&D program, was a successful industry–
government collaborative R&D project aimed at developing a made-in-Korea computer. As a
matter of fact, considering Korea’s level of technological capability in the field of computer
science at that time and the complexity of the technology in manufacturing companies, it was
almost a miracle to succeed in producing it without any defects.
The Electronics and Telecommunication Research Institute (ETRI) was established
through the combined funding of the Korean government and the International Bank of
Reconstruction and Development (IBRD) in 1976. Dr. Sang-Chun Lee, a former president of the
Korea Institute of Machinery and Materials, recalls that it took around four years for this
research institute to construct the semiconductor production line, which was the best and up-to-
date production line in Korea at that time. As the government set up a new semiconductor
production line, it wanted to show the citizens that the government had done something
meaningful with the tax money. Then the government planned a project to manufacture a
computer through private–public collaborative R&D using the indigenous technological
27
capability, so that private firms and people in general could gain confidence in the government’s
policy direction and in what the government was pursuing.
The 8-bit computer manufacturing project was started in 1982 by the government and
several firms, including Samsung Electronics, LG Electronics, and Sambo Computer. The
government and the firms started a research fund at around KRW 800 million, which is
approximately $1.1 million at the exchange rate in that year, and the project succeeded in 1983
(Hwang & Kim, 2000).
Along with the manufacturing of the 8-bit computer, a project to develop a 16-bit UNIX
computer was launched in 1982. This project was also a collaborative R&D effort of the ETRI as
government-funded research institute, and the Korean Electronics and Telecommunication
Company, which is now the famous Samsung Electronics. The size of this project was around
KRW 800 million, with funding matched by Samsung Electronics (MOST, 2008). The firms that
participated in the project were the major members of the Korean Electronics Industry
Association, established in 1967. These firms, such as Samsung Electronics and LG Electronics,
later became not just major electronics and computer companies in Korea, but also grew to
become global multinational companies by the beginning of this century.
4M DRAM semiconductor
The 4M DRAM semiconductor development project is also a success story of industry–
government collaborative R&D designed by the Specific R&D Program in Korea. When the
collaborative R&D in semiconductor research was a global issue in the 1980s, the ETRI (a
government-funded research institute) and the industry began to make memory chips by using
the technologies imported from the US and Japan. In particular, the ETRI succeeded in
28
developing a 32K DRAM in 1982, and Samsung Electronics Company succeeded in producing a
64K DRAM in the following year. The news of success in Korea was very astonishing
worldwide, as semiconductor technologies were dominated entirely by the US, the EU, and
Japan (Hwang & Kim, 2000).
Despite all the efforts of the Korean government and the firms, the speed of technology
development was too fast to catch by a single entity. In addition, the amount of funding needed
to jump into the technology competition was so enormous that a single company or a
government in a developing country could not afford to do so. Even the advanced countries, such
as the US, the EU, and Japan, formed consortia to deal with the pre-commercial research
necessary to compete globally in the semiconductor industry.
Knowing the amount of funding necessary for this semiconductor technology
competition, the Korean government hesitated as to whether it should support semiconductor
technology development, as large companies, such as Samsung and LG, are involved mostly due
to the nature of the industry. Dr. Chul-Koo Min, a senior research fellow of STEPI, said that
supporting Samsung Electronics, LG Electronics, and Hyundai Electronics, which were
Chaebols (i.e., conglomerates), was a political burden for the MOST, as there were other urgent
policy priorities to fund, although developing technologies related to the semiconductor was also
urgent in terms of enhancing global competitiveness (Interview with Dr. Min, 2015). However,
the consensus that semiconductor technology would be the future of the nation began to emerge,
and the industry, academic, and public sector researchers and scientists met and suggested the
launch of a project to develop a 4M DRAM through the Specific R&D Program (Hwang & Kim,
2000).
29
The project of developing a 4M DRAM was designed to be a large-scale industry–
government collaborative R&D project, where four major ministries and three large electronics
companies came to collaborate on the development of technologies to produce a single chip. The
four ministries included the Economic Planning Bureau, the Ministry of Commerce and Industry,
the Ministry of Postal and Communications, and MOST, and the three electronics companies
included Samsung Electronics, LG Electronics, and Hyundai Electronics (Ko, Kwon, & Lee,
2003). The project managing organization was the ETRI, and the sub-projects were designed to
be executed by each team consisting of researchers from the government-funded research
institutes and the companies (MOST, 2008).
The prototype was developed in 1988, which was earlier than the plan, and the
development was successful. Prof. Karpsoo Kim of the Korea Advance Institute of Science and
Technology and a former senior researcher of STEPI recalled that President Doo-Hwan Chun
was so pleased that he invited the researchers to the Blue House for dinner in 1988 (Interview
with Prof. Kim, 2015) . All three firms acquired the necessary product and process technologies
from collaborating with the government. As a result, it was reported that semiconductor
development technology capabilities of the Korean firms were only six months behind those of
companies in advanced countries (Ko et al., 2003).
Mobile telecommunication and CDMA
Finally, the CDMA project of the ICT R&D Program, another national R&D program
launched in 1995, was developing a mobile technology called Code Division Multiple Access
(CDMA) technology. This R&D project pushed the level of the technological capability of Korea
to the point where it could indigenously develop and commercialize the mobile
30
telecommunication devices. Qualcomm, a US firm founded in 1985, originally developed
CDMA technology for mobile telecommunications in 1988 and have had a monopoly position in
the market.
The ETRI grasped that Qualcomm developed a CDMA technology, which was new in
the field of mobile telecommunication, and it persuaded MOST to make an official joint
development agreement contract with Qualcomm in 1991 (MOST, 2008). Note that the ETRI
already had experience forming a collaborative R&D consortium consisting of Korean
electronics companies, such as Samsung Electronics, LG Electronics, and Hyundai Electronics,
in 1989 (Hwang & Kim, 2000).
This project was designed to develop key and complementary technologies, including
the mobile device, no later than 1996. The total R&D budget was KRW 99.6 billion
(approximately $146 million based on the exchange rate of 1989), with around 1,000 researchers
from government-funded research institutes, electronic companies, and universities involved
annually (C. Lee, 2003).
The CDMA technology development project was operated in two phases. The first phase
is a collaborative division of R&D phase where the firms and the government-funded research
institutes closely collaborated for R&D. In the second phase, the firms had to compete in the
mobile device market. Thus ETRI played a role in designing the basic system and developing the
technology with the help of Qualcomm, and the firms developed and manufactured their devices,
components, and telecommunication systems using the basic design of ETRI, but they
customized the technology based on their own specifications. As well, the government supported
the CDMA technology to become an international standard for mobile communications (MOST,
2008).
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A collaborative R&D policy for developing and commercializing the CMDA technology
enhanced the technology capability of the Korean electronics industry in a very short period.
Based on the ETRI’s analysis, from 1996 to 2002, the annual CDMA mobile telecom market
growth rate in Korea was 37.2%, and cumulative domestic production was KRW 4.2 trillion. In
2002, it was reported that the made-in-Korea telecom components were around 70% of all the
components needed for a mobile phone, with 60 component manufacturers and 13 firms
manufacturing CDMA facilities, while there had been only four firms manufacturing CDMA
facilities in 1996 (C. Lee, 2003).
Now, over 50 countries have adopted CDMA technology for providing mobile
telecommunication services, and the Korean companies, such as Samsung Electronics and LG
Electronics, have a significant market share in the mobile telecommunication device market.
Note that in the 1980s, US firms, such as Motorola, or EU firms, such as Nokia, dominated the
mobile telecommunication device market (C. Lee, 2003). As a result, the technological
capabilities of the firms were enhanced in the microelectronics industry and mobile
telecommunication industry, and the firms were eventually able to compete at a global level.
2.3. Summary
To sum up, the major advanced countries and Korea all recognized collaborative R&D
policies as an important policy instrument for fostering firm innovation. The success factor that
is emphasized is the direct participation of the firm in government projects. Direct participation
means the firms and the government-funded research institutes jointly fund and encourage their
researchers to interact closely. This direct participation is likely to foster the transfer of tacit
knowledge from the public to the private sector. For example, CRADA made firms directly
32
participate in government R&D projects, even mandating that firms match the funds for the
R&D projects. Moreover, it required the government-funded research institutes in the US to put
forth efforts to transfer knowledge associated with the projects under CRADA. The FPs of EU
and the industry consortia of Japan, such as VLSI, also enabled firms to join the government
R&D projects, where the government and the firms match the funds for the R&D project.
The Korean government designed and implemented collaborative R&D policies to
foster innovative activities among the firms. Two types of policies were there: one is a
collaborative R&D policy which mostly transfers explicit knowledge to the firms through
industry association; the other is a collaborative R&D policy which transfers tacit knowledge
from the government research institutes to a firm when the firm participates in a government
R&D project.
The collaborative R&D policy that transfers explicit knowledge is usually done through
industry associations. These associations provide current technology trends and R&D
information. They provide annual reports to their members and try to set up a channel for
communications between the government and the industry.
The collaborative R&D policy that transfers tacit knowledge is usually about
participating in the government R&D projects in Korea. If a firm participates in the government
R&D projects, the firm’s researchers closely interact with the researchers of the government-
funded research institutes. This implies that tacit knowledge can be transferred directly by
participating in government projects, which may eventually foster innovative activities of the
firms. Note that Samsung Electronics and LG Electronics have become technologically
competitive multinational companies, while they were small local firms participating in the
government R&D projects in the 1970s.
33
Further theoretical discussion of the type of knowledge and the transferring channels of
that knowledge will be presented in detail along with associated hypotheses in Chapter 3.
34
CHAPTER 3
THEORY OF COLLABORATIVE R&D POLICY
3.1. Collaborative R&D and technology innovation
It is well known that technology innovation is crucial for firms to maintain their
competitive advantage, and that it eventually enables sustainable economic development
(Griliches, 1990; Wu, 2005). Thus firms have continuously conducted various innovative
activities, and the government has designed and implemented various policies to support the
innovative activities of firms.
Recent literature on technology innovation posits that there are two ways of acquiring
knowledge necessary for innovation. One is conducting internal R&D, and the other is acquiring
external knowledge through various channels. However, there are concerns about less technology
innovation being introduced to society due to the appropriability problem associated with
knowledge creation. In particular, once new knowledge is created, which requires sizable R&D
investment, studies have shown that there is no substantial cost in reproducing it or copying it
(Glader, 2006). For example, reverse engineering and the mobility of employees suggest how
competitors might take advantage of a firm’s innovation efforts. Approximately 60% of
successful innovations in the chemical, drug, electronics and machinery industries are imitated
within four years at a cost of 65% of producing the original innovation (Kattan, 1993). So,
scholars argue that the level of investment in R&D is below the level that is socially desirable
(Cohen, 1995).
Hence, on one hand, the government establishes government-funded research institutes
to create new knowledge through R&D activities. On the other hand, the government makes
35
collaborative R&D policies to facilitate knowledge exchange among the R&D entities, such as
government-funded research institutes, universities and private firms, expecting that the firms
can eventually increase technology innovation.
According to the literature, it is possible for collaborative R&D to be formed between
private and public sectors, which fosters transfer of tacit knowledge developed in the public
sectors. One example of collaborative R&D can be collaborations with universities.
Collaborative R&D with universities can be funded by their own fund as well as by the
government. Firms pursue R&D collaboration with universities in order to gain access to
complementary research activity and results, so as to innovate their products or processes
(Blumenthal, Gluck, Louis, Stoto, & Wise, 1986; Mansfield, 1981; Zucker, Darby, & Armstrong,
1994). There are some empirical studies finding that a firm’s sales and R&D productivity
increases once it engages in R&D collaboration with a university (Cohen, Nelson, & Walsh,
2002). Initially, firms started to collaborate with universities to establish networks with key
faculty members in the field (Rosenberg & Nelson, 1994), but now commercializing the applied
research is a crucial task for faculty collaborating with the firms (Henderson, Jaffe, & Trajtenber,
1995).
Another example of R&D collaboration can be collaboration between government-
funded research institutes and private firms. There are government policies and funding
programs encouraging firms to participate in government research projects. The strongest
collaboration type is a firm participating in government R&D projects. By participating in a
government R&D project, a firm collaborates to develop new products or new process
technologies for the government R&D project, and a firm can acquire new knowledge by joining
the project. Thus, collaborative R&D policy brings in human and capital resources from both
36
private and public sectors, and each partner shares its knowledge and experience for the
technology innovation process (O’Kane, 2008).
However, there are concerns over firms engaging in collaborative R&D from an anti-
trust policy perspective. In other words, the concern of the government is that collaborative R&D
may lead to a decrease in competition (Scott, 1993). On a national level, in particular, the
decrease of social welfare due to less competition may be greater than the benefit of innovation
due to collaborative R&D (Vonortas, 2000). Regardless of this concern, governments even in the
US and EU have pursued R&D policies which intended to foster collaboration among firms in
the same industry, as well as between public and private organizations, because of fierce global
competition. In the next section, the rationale for collaborative R&D policies is examined in
more depth.
3.2. Collaborative R&D policy as a tool for transferring knowledge
Based on previous literature, recombining new and existing knowledge is essential for
technology innovation (Hall, Jaffe, & Trajtenberg, 2005; Feldman & Yoon, 2012). Knowledge
can be broadly classified into explicit knowledge and tacit knowledge (Polanyi, 1967). Garud
(1997) says that explicit knowledge is the content that has been captured in a tangible format,
such as documents, audio recordings or images. Thus explicit knowledge is associated with
declarative knowledge and “know why”, and declarative knowledge and “know why” consist of
descriptive elements (Garud, 1997).
On the other hand, tacit knowledge is defined as “things that we know but cannot tell”
(Polanyi, 1967). While tacit knowledge has become more important for technology innovation,
tacit knowledge does not spill over inexpensively (Teece, 1992), contrary to the idea of
37
knowledge as a public good (Arrow, 1962). Moreover, tacit knowledge is not easily articulated or
formalized, and is difficult to put into words or text, as it is rooted in action, procedures, and the
heads of individuals (Nonaka et al., 2006).
Meanwhile, based on the nature of knowledge being transferred, knowledge transfer
channels can be personal or impersonal. Personal knowledge transfer channels are channels that
involve face-to-face interaction, such as a mentor-mentee relationship or an apprenticeship,
which may be more effective for distributing highly contextual knowledge (Tounkara, 2013).
Thus, tacit knowledge can be transferred through close interaction and learning-by-doing (Grant,
1996), and by the cognitive efforts of a sender and a receiver (Dalkir, 2011). Impersonal
knowledge transfer channels are channels that involve a non-personal medium that plays a role in
transferring knowledge, such as knowledge repositories or databases or images, which may be
most effective for knowledge that can be readily codified and generalized to other contexts
(Tounkara, 2013). Thus, transfer of explicit knowledge can occur by documents, journals and
reports.
Based on the previous literature, it would be possible to argue that collaborative R&D
policy is a channel for knowledge transfer. And collaborative R&D policy can be classified into
two types, according to the type of knowledge transferred.
One is what I call a “strong” collaborative R&D policy, which transfers tacit knowledge
effectively. A typical example of strong collaborative R&D policy is participating in a
government R&D project. A firm participating in a project usually sends its own researchers to
the project site. This participation requires face-to-face personal interaction, and hence, it
provides a solid foundation that can easily transfer tacit knowledge. In the related literature, this
strong collaborative R&D policy is a so-called “high-‘richness’ knowledge transfer channel”
38
including close personal interaction (V oelpel et al., 2005). It is proposed that this type of channel
refers to face-to-face interactions and team-based communication mechanisms, such as intensive
meetings, training sessions, and workshops (Dinur, 2010). The other type is what I call a “weak”
collaborative R&D policy, which refers to interchange of written documents, manuals, reports,
databases, electronic media, and so on (Dinur, 2010). This weak collaborative R&D policy
involves information and knowledge sharing among industry members and the government
through industry consortia (Hagedoorn et al., 2000).
A strong collaborative R&D policy is likely to transfer knowledge, including tacit
knowledge, more effectively and sufficiently, because the firms participating in the government
R&D project are actually conducting R&D activities together with the government-funded
research institutes to accomplish the objective of the project, while firms involved in the weak
collaborative R&D policy only share explicit knowledge. Based on an interview with Dr. Yong-
Soo Hwang, at STEPI, in practice, when a firm participates in a government R&D project, a firm
sends its researchers to the government R&D site, or to a research project team. By joining the
project team, researchers from the firm closely interact with the researchers in government-
funded research institutes, and get a chance to learn and absorb tacit knowledge, which is
difficult to get just from technical documents or reports.
From the government side, the researchers from the government-funded research
institutes are required to transfer the knowledge or technology they developed, including all the
know-how and intangible knowledge, once a project team is established. For example, the
Stevenson-Wydler Technology Innovation Act (P.L. 96-480) mandated the government-funded
research institutes to establish offices or laboratories for technology transfer from the federal
government to the private sector (Schacht, 2012).
39
Meanwhile, a firm also collaborates with the government and government-funded
research institutes on a regular basis by joining an industry association. In particular, the
government, especially the Korean government, establishes the industry association in order to
foster communication and to share information and knowledge with industry members. The
government-funded research institutes present their new findings and the firms also share their
best practices through periodicals, journals and special reports. For example, the Korean
Electronics Association (KEA), a major industry association in Korea established in 1981 by the
Korean government, plays that role of providing a basic foundation for the private-government
collaboration. Since its establishment, a high-level government official has been appointed as a
vice president, while the president was elected from the industry (see www.gokea.org). In the
KEA’s articles of association, the major tasks are providing industry and technical information,
industry statistics, and member information for networking. To do so, KEA issues leaflets and
periodic reports for the members.
Note that there are some studies that investigate the relationship between collaborative
R&D policy and the innovative activities of firms. For instance, a positive statistical relationship
was found between the R&D spending of firms and government funding through collaborative
R&D programs (Watanabe, Kishioka, & Nagamatsu, 2004). Czarnitzki, Ebersberger, and Fier
(2007) confirm this finding in Finland, but do not find this in Germany. Also, there are studies
investigating the effect of government subsidies on collaborative R&D between firms. For
example, Kang and Park (2012) and Bozeman and Gaughan (2007) found a positive relationship
between collaboration and government support. Moreover, Branstetter and Sakakibara (1998)
found a positive relationship between participating in government funded R&D consortia and
research productivity, while Miotti and Sachwald (2003) found that the firms which actively
40
collaborated with public research institutes were more likely to be in science-related sectors
where the firm could acquire frontier technological knowledge.
3.3. Empirical model and hypotheses
Based on the previous section, I modeled the effect of collaborative R&D policy on
innovative activities of the firm and constructed relevant hypotheses. The innovative activities of
the firm are modeled by four different variables. One is R&D expenditure, which is often used as
a valid proxy of a firm’s innovative activities (Griliches, 1990). Another is the number of patent
applications, which also has been used as both an innovative input and output factor to measure
the degree of innovation (Jaffe & Trajtenberg, 2002).
The third variable is product innovation, and the fourth is process innovation. These
variables are also employed as proxies for innovative outcomes, as both variables are typical
outcomes of a firm’s innovative efforts (Cohen, 1995). These two variables are used widely when
explaining the effect of market demand (e.g., Crépon, Duguet, & Mairesse, 1998) and user-
supplier relationships (e.g., V on Hippel, 1998) on the innovative activities of the firm. In
particular, Crépon et al. (1998) examined the effect of technology demand on product innovation
and process innovation, using 1990 survey data. Von Hippel (1998) provided cases where the
designs of application-specific integrated circuits (commonly referred to as ASICs) and computer
telephony integration (CTI) were improved by specific users, and thus argued that managing the
user-supplier network was crucial for innovation.
Government R&D policies promoting innovation can be classified into collaborative
and non-collaborative R&D policies. The former is divided into two types: a strong collaborative
R&D policy, and a weak collaborative R&D policy. A strong one is a policy that is designed to
41
transfer tacit knowledge to the private sector. As transferring tacit knowledge requires close face-
to-face interaction among the R&D partners, the strong collaborative R&D policy requires firms
to participate in the government R&D projects directly. For example, the fact that a firm
participates in government R&D means letting the firm’s researchers closely interact with the
researchers in the government-funded research institutes. They often form a specific research
team aiming to achieve certain technological goals. Hence, whether a firm participates in a
government R&D project or not is a proxy for the strong collaborative R&D policy. As the
transfer of tacit knowledge is crucial for innovative activities (V on Hippel, 1998; Teece, 1992), I
argue that the strong collaborative R&D policy positively affects the innovative activities of the
firm.
A weak collaborative R&D policy is a policy that is designed to transfer explicit
knowledge to the private sector. And this policy is implemented in the form of establishing and
operating industry associations. The government establishes an industry association in order to
foster communication and facilitate the sharing of information between the private sector and the
government sector. For example, government-funded research institutes disclose their new
findings and the firms also share their best practices through periodicals, journals and reports.
Hence, whether a firm gets technological information through industry association or not is a
proxy for the weak collaborative R&D policy.
The non-collaborative R&D policy includes direct R&D funding and R&D tax
incentives. First, direct R&D funding is the most popular policy instrument that directly supports
firms in developing new technologies. This policy covers areas ranging from basic and applied
science to industrial technology development (Mowery, 1995). Government R&D funding data
has been shown in various studies to have both a positive and negative effect on the innovative
42
activities of firms (David, Hall, & Toole, 2000). As in most countries with a structured national
R&D system, the Korean government designs and implements various R&D policies to foster
innovative activities of firms. Previous studies on the effect of direct R&D funding on private
firms’ R&D investment in Korea are not consistent. One set of studies shows that government
R&D funding increases the level of R&D investment by a firm (Park, 2002; Yoon & Park, 2007),
whereas Ko et al. (2003) show that direct R&D funding is not effective, but rather is substituting
for private R&D investment in Korea.
Second, R&D tax incentives are widely used to promote R&D in private firms in Korea.
The basic idea is that the government cannot fine-target the R&D projects that are socially
desirable. Thus, the government should just provide incentives to innovate through R&D tax
deduction, which would not restrict resource allocation (Hall & Van Reenen, 2000). In Korea, a
major R&D tax incentive is called the Technology Development Reserve Fund, where a firm can
set aside up to 20% of profits before tax in any one year to be used for R&D work in the
following 4 years (Park, 2002). R&D tax credits show different effects on the innovative
activities of firms in different countries (Hall & Van Reenen, 2000). Some previous studies show
that the policy increased firms’ R&D spending by more than 10% (Mansfield, 1981). However,
there are also studies showing that the tax incentive is not effective for increasing firms’ R&D
expenditure (Hall, 1993). Especially in Korea, Ko et al. (2003) report that the R&D tax incentive
policy is not effective for increasing firms’ R&D investment.
With due regard for the characteristics of government R&D policy, I propose the first set
of hypotheses that a company’s R&D expenditure is positively affected by the collaborative
R&D policies, and the effect size of the strong collaborative R&D policy is greater than that of
the weak collaborative R&D policy:
43
H1-1: A firm is likely to increase its internal R&D when it participates in government
R&D projects
H1-2: A firm is likely to increase its internal R&D when it gets information through
industry associations
H1-3: The effect of R&D participation on a firm’ s internal R&D is greater than that of
getting information through industry associations
The second set of hypotheses is that the number of patent applications is increasing in
the collaborative R&D policies, and that the effect size of the strong collaborative R&D policy is
greater than that of the weak collaborative R&D policy:
H2-1: A firm is likely to obtain more patents when it participates in government R&D
projects
H2-2: A firm is likely to obtain more patents when it gets information or knowledge
through industry associations
H2-3: The effect of R&D participation on the firm’ s patenting is greater than that of
getting information or knowledge through industry associations
Thirdly, I put forward two sets of hypotheses that both the product and process
innovations are positively affected by participating in government R&D projects, and the effect
size of the strong collaborative R&D policy is greater than that of the weak collaborative R&D
policy:
44
H3-1: A firm is likely to carry out product innovations when it participates in
government R&D projects
H3-2: A firm is likely to carry out product innovations when it gets information or
knowledge through industry associations
H3-3: The effect of R&D participation on the firm’ s product innovation is greater than
that of getting information or knowledge through industry associations
H4-1: A firm is likely to carry out process innovations when it participates in
government R&D projects
H4-2: A firm is likely to carry out process innovations when it gets information or
knowledge through industry associations
H4-3: The effect of R&D participation on the firm’ s process innovation is greater than
that of getting information or knowledge through industry associations
Finally, I add a hypothesis that a non-collaborative R&D policy also positively affects
innovative activities of the firm:
H5-1: R&D funding by the government is likely to increase the innovative activities of
the firm
H5-2: R&D tax incentive is likely to increase the innovative activities of the firm
In order to test all the hypotheses proposed above with the sample based on the CIS
dataset of Korean firms, a couple of empirical models need to be used. First, a multiple
45
regression model is employed for the set-up with R&D expenditure and the number of patent
applications as dependent variables. Second, a probit model is also used, because other
dependent variables, product and process innovation, are binary variables with values of 0 and 1.
46
CHAPTER 4
EMPIRICAL ANALYSIS ON COLLABORATIVE R&D
In this chapter, the effect of collaborative R&D policies on innovative activities of firms
is closely examined. As mentioned in the previous chapter, collaborative R&D policies can be
classified into strong and weak types. The former is a collaborative R&D policy where a firm
directly participates in government R&D projects, which transfers tacit knowledge. The latter is a
collaborative R&D policy where the government interacts with firms in a more impersonal way
through industry associations, by which explicit knowledge is transferred. Moreover, in the
empirical model, I added the variables of direct R&D funding and tax incentive, in order to
examine the effects of non-collaborative R&D policies on innovative activities of the firm.
In this context, I selected four major innovative activities as dependent variables: R&D
expenditure, the number of patent applications, product innovation, and process innovation.
These four variables are officially collected and used as innovation measures in the Oslo Manual
developed by the Organization for Economic Cooperation and Development (OECD), which is
the “foremost international source of guidelines for the collection and use of data on innovation
activities in industry” (OECD, 2015). This research uses the measures collected and submitted
for the official OECD Community Innovation Survey (CIS) database.
In the following sections, the empirical results from testing the hypotheses of the effect
of collaborative R&D policies on innovative activities of the firm are provided.
47
4.1. Data and sample
The main data source for this research is the before-mentioned CIS in Korean
manufacturing sectors managed by STEPI. This CIS database is publicly available, but those
who need access to it have to submit an official letter of request to STEPI. The CIS database is
the official data based on the Oslo Manual used to compile the OECD statistics. The OECD
mandates member countries to collect information about the innovation activities of the firms in
all manufacturing and service sectors. OECD constructed the Oslo Manual to measure the input,
output and outcomes of innovation activities, where input is R&D expenditure, output is patents,
and outcomes are product and process innovations. The CIS dataset also includes information
about complementary activities of the firm, such as marketing, collaboration, and the source of
knowledge (see the Oslo Manual for more details).
The survey, first published in 1996, is conducted every three years to collect data on
manufacturing firms’ innovation activities (i.e., CIS-2012 contains 4086 firms’ innovation
information during 2010~2012). The CIS in Korea is undertaken by STEPI, a government-
funded research institute. Companies with ten employees or more are surveyed. The survey
process starts with identifying a valid sample of manufacturing firms in Korea. In the 2012
survey, for example, 4,086 manufacturing firms were sampled from a total of 43,810 firms listed
in the Statistics Korea (KOSTAT) (Ha, Kang, Song, & Kim, 2012). Ha et al. (2012) document
that, before actually carrying out the survey, STEPI holds a conference inviting the firms
included in the sample in order to enlist their full cooperation. The contact person at the firm is
the head of an R&D department or a chief technology officer (CTO). The whole process usually
takes about 10 months (Ha et al., 2012).
Unfortunately, these surveys were not constructed in a panel data setting, which makes it
48
impossible to take advantage of panel data analyses. Thus, I conducted cross-sectional analyses
of data from CIS-2008, 2010 and 2012, respectively. I estimated the dataset separately for each
year and ran the same model. Note that after eliminating observations with missing values in the
relevant variables used, the total observations which can be used are 1489: 441 observations in
2008, 532 observations in 2010, and 516 observations in 2012.
4.1.1. Dependent variables
First of all, R&D expenditure is a proxy for the innovative activity of a firm. In this
study, I used the amount of internal R&D expenditure, which excludes R&D funds from other
organizations. I used the variable transformed with the natural log. I logged the variable to
normalize the distribution of R&D expenditures.
The number of patent applications is employed as a measure for innovative activity as
well. Note that a patent can be innovative input, for example, when a patented technology is used
to produce a new product or develop a new process, whereas it is often thought of as an
innovative output (Griliches, 1990; Jaffe & Trajtenberg, 2002). I logged it to normalize the
distribution of the variable.
The product and process innovation variables are measures of innovative outcomes. In
the CIS database, each of them is a binary questionnaire. The questions asked the firms whether
there was a product or process innovation within the last three years.
4.1.2. Independent variables
The first two independent variables are the participation of firms in government R&D
projects and participation in industry associations. Both of them are measured by a binary
49
variable which has the value of 1 when a firm participated in government R&D projects, and
when it got information from the government via industry association during 2009-2011, while 0
if not. These variables indicate whether a firm actually collaborated with the government by
participating in government R&D projects, or got useful information from the government
through industry consortia or unions.
The R&D funding variable is used as a proxy for direct government R&D funding
policy. The survey is designed only to answer in a binary way whether a firm received R&D
funding from the government; it does not report the amount of funding. Thus, this variable is also
a binary variable, where it is 1 if a firm was awarded government funding, and 0 if not.
Finally, the R&D tax incentive variable is employed as a proxy for R&D tax credit
policy. Whether a firm takes advantage of the R&D tax incentives is also surveyed in the Korean
CIS dataset. The tax incentive for R&D is also a binary variable, which is 1 if a firm received
any tax benefit, 0 if not. The amount of tax deduction a firm got is not available. The Korean
National Tax Services does not share the related information because it is considered a business
secret.
4.1.3. Control variables
I also controlled for other major characteristics of the firms which may affect their
innovative activities. First, the number of researchers is used as a control variable to capture the
effect of the internal innovative capacity of a firm. If a firm has a greater number of researchers,
a firm may conduct more R&D in order to enhance its internal capacity (Cohen & Klepper,
1996). Furthermore, if a firm’s internal capacity is higher, the firm may be efficient in conducting
R&D, which results in conducting fewer R&D projects, regardless of the size of the projects
50
(Arora, Fosfuri, & Gambardella, 2001).
Second, the size of the firm is controlled and it is measured by the number of employees.
R&D expenditures are inclined to increase if the number of employees increases (Cohen &
Klepper, 1996). At the same time, however, the number of employees is likely to increase if the
R&D expenditures increase or the innovative activities affecting the firm’s revenue or
profitability increases (Cohen, 1995). Fortunately, the dataset provides the absolute number of
researchers and employees for each year. Hence, I used the number of researchers and employees
of the earliest year in the dataset to reduce the concern of reverse causality where R&D
expenditures or innovative activities increase the size of the firm.
In addition, the annual revenue of the firm is added to control for firm size. However,
this variable is measured as an interval variable with six intervals: below KRW 100 million,
KRW 100-500 million, KRW 500-1000 million, KRW 1000-5000 million, KRW 5000-10000
million, and above KRW 10000 million. So I created an interval variable where the value ranges
from 0 to 5. The interval below KRW 100 million is 0, and above KRW 10000 million is 5.
Finally, major industry dummy variables are used to control for unobserved industry
characteristics in Korea. Those industries include healthcare and pharmaceuticals, automobiles,
machines and other heavy industry, electronics, steel and metal, and chemicals, which are
relatively R&D intensive in Korea.
Table 1 below shows the descriptive statistics of the variables for the 2012 CIS dataset.
Note that since the data are collected from three different datasets — 2008, 2010, and 2012 — I
review empirical results separately for each year.
51
Table 1: Descriptive statistics of 2012 CIS dataset
Variable Obs. Mean
Std.
dev.
Min Max
Dependent variable
Ln(R&D expenditure) 516 3.99 3.31 0.00 12.27
Ln(# of patent applications) 516 1.08 1.03 0.00 4.83
Product innovation 516 0.07 0.26 0.00 1.00
Process innovation 516 0.08 0.27 0.00 1.00
Independent variable
Participating in gov. R&D project 516 0.36 0.48 0.00 1.00
Participating in industry association 516 0.04 0.19 0.00 1.00
R&D tax incentive 516 0.15 0.36 0.00 1.00
R&D funding 516 0.31 0.46 0.00 1.00
Control variable
Ln(# of researchers) 516 1.66 1.44 0.00 9.12
Ln(# of employees) 516 3.93 1.20 1.61 11.51
Revenue 516 2.26 1.41 0.00 5.00
Healthcare & Pharmaceuticals 516 0.06 0.24 0.00 1.00
Automobile & Parts 516 0.06 0.24 0.00 1.00
Machine & Heavy industry 516 0.10 0.30 0.00 1.00
Electronics & Telecom. 516 0.05 0.22 0.00 1.00
Steel & Metallic products 516 0.06 0.08 0.00 1.00
Chemical materials & Products 516 0.14 0.34 0.00 1.00
52
4.2. Data analysis
In order to test the hypotheses proposed in the previous chapter with the sample based
on the Korean CIS data, a multiple regression model and probit model are used. I provide
empirical results of the 2012 dataset in Table 2. Additionally, the empirical results using the CIS
dataset of 2008 and 2010 are provided later.
4.2.1. The effect of collaborative R&D policies
According to the empirical results, a firm increases its R&D expenditure when it works
together with the government by participating in government R&D projects (β
1
= 1.18, p-value
< .001 in Model 1). This result supports the hypothesis that a firm boosts its R&D activities
when it collaborates with the government (H1-1). Also, the result is qualitatively in concordance
with the results shown in previous studies regarding the effect of collaborative R&D policy on
the innovative activities of a firm (Schacht, 2012). Moreover, the effect of participating in a
government R&D project on the number of patent applications is positive and significant (β
1
=
0.33, p-value < .001 in Model 2). This result confirms the hypothesis H2-1, and is also consistent
with the previous literature arguing that close interaction between research partners can transfer
tacit knowledge, which can foster firms’ ability to increase innovative outputs (Dinur, 2010).
Lastly, the effect of participating in government R&D projects on product innovation is positive,
but not significant (β
1
= 0.27, p-value = .156 in Model 3). Likewise, the effect of participating in
government R&D projects on process innovation is positive but not significant (β
1
= 0.15, p-value
= .458 in Model 4). These results do not support hypotheses H3-1 and H4-1.
Next, the effects of participating in industry associations (β
2
) on innovative activities are
not significant, although they show positive signs. The effects on R&D expenditure and the
53
number of patent applications are not significant (β
2
= 0.19, p-value = .781 in Model 1; β
2
= 0.02,
p-value = .920 in Model 2). Moreover, the effect of participating in industry associations (β
2
) on
product innovation is positive but not significant (β
2
= 0.11, p-value = .783 in Model 3), and also
the effect on process innovation is positive but not significant (β
2
= 0.33, p-value = .369 in Model
4). These results do not support the hypotheses proposed before regarding the effect of weak
collaborative R&D policy on the innovative activities of a firm (H1-2, H2-2, H3-2, and H4-2). In
short, it is assumed that the effect of strong collaborative R&D policy is greater than that of weak
collaborative R&D policy. Maybe just acquiring explicit knowledge through industry association
is not enough for facilitating innovative activities that often require a complete set of explicit and
tacit knowledge combined.
Lastly, these results partly support the hypotheses regarding the effect of strong
collaborative R&D policy on the innovative activities of a firm, compared to the weak
collaborative R&D policy, since the effect of strong collaborative R&D policy is significant,
while weak collaborative R&D policy is not (H1-3 and H2-3 are supported, H3-3 and H4-3 are
not supported). In other words, the effect size of the strong collaborative R&D policy on R&D
expenditure and the number of patent applications is bigger, while the effect size on product and
process innovation is not different.
4.2.2. The effect of non-collaborative R&D policies
Direct R&D funding and R&D tax incentives also have a positive effect on the R&D
expenditure of a firm, although the effect of R&D tax incentive is significant (β
3
= 0.92, p-value
= .023 in Model 1) and the effect of direct R&D funding is weakly significant (β
4
= 0.17, p-value
= .082 in Model 1). These results are also consistent with the empirical results of previous
54
studies which reported the positive effect of R&D tax incentives and direct R&D funding on the
R&D expenditure of firms in developing countries (Mowery, 1995) and especially in Korea (W.
Y . Lee, 1984; Park, 2002). However, R&D tax incentive on the number of patent applications
(β
3
= 0.07, p-value = .82 in Model 2), and the effects of R&D funding on the number of patent
applications (β
4
= 0.11, p-value = .23 in Model 2) are not significant.
Meanwhile, the effects of R&D tax incentives on product and process innovation are
not significant (β
3
= -0.22, p-value = .338 in Model 3; β
3
= -0.02, p-value = .932 in Model 4). The
effect of direct R&D funding on product innovation is weakly significant (β
4
= 0.33, p-value
= .094 in Model 3), while its effect on process innovation is not significant (β
4
= 0.30, p-value
= .138 in Model 4).
The positive effect of direct R&D funding on product innovation shown in the empirical
results seems to indicate the natural outcome of the Korean government policy direction, as the
policy has emphasized new innovative products or services. However, the effect of R&D tax
incentives on product and process innovations needs further study, as there are no previous
studies to my knowledge.
4.2.3. The effect of the control variables
The effects of the number of researchers (β
5
) on R&D expenditure and the number of
patent applications are positive and significant (β
5
=0.26, p-value = .038 in Model 1; β
5
=0.19, p-
value < .001 in Model 2). These results indicate that internal R&D capability matters for
innovative activities, which is in line with previous studies (Cohen, 1995). The number of
researchers (β
5
) has a weak positive effect on process innovation (β
5
= 0.15, p-value = .065 in
Model 4), while it does not affect product innovation (β
5
= 0.03, p-value = .751 in Model 3). This
55
demonstrates that the scale of R&D capability matters for process innovation, and also reflects a
characteristic of Korean manufacturing industry. The major Korean manufacturing industries are
capital-intensive with highly complex facilities which require considerable R&D investment. It is
reported that process innovation is affected by the scale of R&D capability, meaning that the
process innovation in the chemical or semiconductor industry sectors can be possible when there
is a sizable number of researchers (Cohen & Klepper, 1996).
The number of employees and firm revenue are proxies of a firm’s size. The effect of
the number of employees on R&D expenditure is positive and significant (β
6
= 0.42, p-value
< .001 in Model 1), while the effect on the number of patent applications is positive but weakly
significant (β
6
= 0.10, p-value = .07 in Model 2). This seems to show that firm size matters for
R&D activities, which is consistent with previous research on firm size and R&D (Cohen, 1995).
However, both the number of employees (β
6
) and revenue do not significantly affect product or
process innovation in the model. Note that the revenue variable does not significantly affect these
innovative activities, although it has positive sign for all four innovation variables.
56
Table 2: Empirical results of 2012 CIS dataset
Dependent
variables
Independent &
Control variables
Model 1
Ln(R&D
expenditure)
Model 2
Ln(# of patent
applications)
Model 3
Product
innovation
Model 4
Process
innovation
Constant (β
0
)
0.74
(0.54)
-0.01
(0.16)
-1.48
(0.38)
***
-1.53
(0.33)
***
Participating in gov. R&D
Project (β
1
)
1.18
(0.30)
***
0.33
(0.09)
***
0.27
(0.19)
0.15
(0.20)
Participating in industry
association (β
2
)
0.19
(0.70)
0.02
(0.21)
0.11
(0.42)
0.33
(0.37)
R&D tax incentive (β
3
)
0.92
(0.40)
**
0.07
(0.12)
-0.22
(0.25)
-0.02
(0.24)
R&D funding (β
4
)
0.17
(0.09)
*
0.11
(0.10)
0.33
(0.20)
*
0.30
(0.20)
Ln(# of researchers) (β
5
)
0.26
(0.13)
**
0.19
(0.04)
***
0.03
(0.09)
0.15
(0.08)
*
Ln(# of employees) (β
6
)
0.42
(0.19)
***
0.10
(0.06)
*
-0.08
(0.13)
-0.08
(0.12)
Revenue (β
7
)
0.14
(0.13)
0.11
(0.14)
0.04
(0.09)
0.07
(0.09)
Healthcare &
Pharmaceuticals
1.95
(0.55)
***
0.11
(0.17)
0.25
(0.31)
1.53
(0.33)
***
Automobile & Parts
0.49
(0.56)
0.00
(0.17)
0.29
(0.31)
0.16
(0.31)
Machine & Heavy
Industry
1.26
(0.45)
***
0.04
(0.13)
0.19
(0.32)
0.86
(0.46)
*
Electronics & Telecom.
0.61
(0.61)
0.18
(0.18)
0.20
(0.35)
0.26
(0.32)
Steel & Metallic products
1.85
(1.73)
-0.77
(0.52)
1.22
(0.77)
-0.66
(0.48)
Chemical materials
& Products
0.94
(0.41)
**
0.12
(0.12)
-0.45
(0.34)
-0.10
(0.25)
Obs.
R-sq. / Pseudo R-sq.
516
0.20
516
0.25
516
0.06
516
0.07
Sig. level: *** 1%, ** 5%, * 10%
57
4.2.4. Estimation of results using the 2008 and 2010 CIS Datasets
In this section, additional empirical analyses are conducted, using the CIS datasets from
2008 and 2010, in order to examine whether the empirical results are consistent throughout this
period. The number of observations used for these analyses is 441 and 532, respectively (see
Table 3). The same empirical models as in the previous section, with the same dependent,
independent, and control variables, are used for the analyses.
Table 3: Descriptive statistics of 2008 and 2010 CIS dataset
2010 dataset
Variables Obs. Mean Std. dev. Min Max
Ln(R&D expenditure) 532 6.84 1.69 0.69 12.35
Ln(# of patent applications) 532 2.03 1.32 0.00 6.21
Product innovation 532 0.45 0.50 0.00 1.00
Process innovation 532 0.58 0.49 0.00 1.00
Participating in gov. R&D project 532 0.69 0.46 0.00 1.00
Participating in industry association 532 0.21 0.41 0.00 1.00
R&D tax incentive 532 0.40 0.49 0.00 1.00
R&D funding 532 0.51 0.50 0.00 1.00
Ln(# of researchers) 532 2.28 1.10 0.00 5.31
Ln(# of employees) 532 4.74 1.15 1.95 6.91
Revenue 532 2.14 1.41 0.00 5.00
Healthcare & Pharmaceuticals 532 0.08 0.28 0.00 1.00
Automobile & Parts 532 0.02 0.15 0.00 1.00
Machine & Heavy Industry 532 0.10 0.29 0.00 1.00
Electronics & Telecom. 532 0.08 0.27 0.00 1.00
Steel & Metallic products 532 0.09 0.29 0.00 1.00
Chemical materials & Products 532 0.09 0.29 0.00 1.00
58
2008 dataset
Variables Obs. Mean Std. dev. Min Max
Ln(R&D expenditure) 441 7.38 1.64 2.71 13.70
Ln(# of patent applications) 441 2.05 1.68 0.00 11.51
Product innovation 441 0.41 0.49 0.00 1.00
Process innovation 441 0.52 0.50 0.00 1.00
Participating in gov. R&D project 441 0.82 0.38 0.00 1.00
Participating in industry association 441 0.15 0.36 0.00 1.00
R&D tax incentive 441 0.31 0.46 0.00 1.00
R&D funding 441 0.45 0.50 0.00 1.00
Ln(# of researchers) 441 2.36 1.28 0.00 7.38
Ln(# of employees) 441 4.95 1.45 1.95 10.13
Revenue 441 3.20 1.51 0.00 5.00
Healthcare & Pharmaceuticals 441 0.05 0.23 0.00 1.00
Automobile & Parts 441 0.02 0.13 0.00 1.00
Machine & Heavy industry 441 0.10 0.30 0.00 1.00
Electronics & Telecom. 441 0.05 0.21 0.00 1.00
Steel & Metallic products 441 0.00 0.07 0.00 1.00
Chemical materials & Products 441 0.04 0.20 0.00 1.00
The empirical results using the CIS dataset of 2008 are provided in Table 4. First, the
effect of a strong collaborative R&D policy on the innovative activities of the firm is consistent
with the estimation results using the CIS dataset of 2012 provided in Table 2. In particular, the
R&D expenditure and the number of patent applications increase when the firms participated in
the government R&D project (β
1
= 0.16, p-value = .041 in Model 5; β
1
= 0.23, p-value = .049 in
Model 6). However, the strong collaborative R&D policy does not significantly affect the
product and process innovation (β
1
= 0.10, p-value = .563 in Model 7; β
1
= -0.09, p-value = .568
59
in Model 8). On the other hand, a weak collaborative policy, which is a firm participating in
industry associations, does not have a significant effect on any of the innovative activities,
including the level of R&D expenditure and the number of patent applications.
Meanwhile, the non-collaborative R&D policies also have qualitatively similar effects
on the innovative activities. The effect of R&D funding by the government on the firm R&D is
positive and significant (β
4
= 0.29, p-value = .005). This result is consistent with the results using
2012 data in Table 2, and with previous studies (Park, 2002; Yoon & Park, 2007). Note that the
R&D tax incentive does not have a positive effect on firm R&D expenditure, the number of
patent applications, or product innovation, while it has a weakly positive effect only on process
innovation (β
3
= 0.27, p-value = .062).
The number of researchers and the number of employees positively affect these
innovative activities, while the effect of annual revenue does not show a consistent effect on
these innovative activities.
Table 4: Estimation results of 2008 CIS dataset
Dependent
variables
Independent &
Control variables
Model 5
Ln(R&D
expenditure)
Model 6
Ln(#of patent
applications)
Model 7
Product
innovation
Model 8
Process
innovation
Constant (β
0
)
4.28
(0.23)
***
-1.03
(0.33)
***
-0.61
(0.32)
**
-1.10
(0.32)
***
Participating in gov. R&D
Project (β
1
)
0.16
(0.08)
**
0.23
(0.12)
**
0.10
(0.17)
-0.09
(0.17)
Participating in industry
association (β
2
)
0.00
(0.13)
-0.31
(0.19)
-0.33
(020)
-0.07
(0.19)
R&D tax incentives (β
3
)
0.07
(0.10)
0.05
(0.15)
0.23
(0.14)
0.27
(0.14)
*
R&D funding (β
4
)
0.29
(0.10)
***
0.10
(0.14)
0.07
(0.14)
0.14
(0.14)
Ln(# of researchers) (β
5
)
0.89
(0.05)
***
0.52
(0.08)
***
0.16
(0.07)
**
0.03
(0.07)
60
Ln(# of employees) (β
6
)
0.24
(0.08)
***
0.57
(0.11)
***
0.08
(0.11)
0.30
(0.11)
***
Revenue (β
7
)
0.05
(0.06)
0.24
(0.09)
***
-0.16
(0.09)
-0.14
(0.09)
Electronics & Telecom.
-0.04
(0.21)
-0.03
(0.30)
0.13
(0.30)
-0.06
(0.30)
Machine & Heavy industry
-0.07
(0.16)
0.37
(0.22)
*
-0.53
(0.23)
0.52
(0.23)
**
Automobile & Parts
-0.68
(0.35)
-0.48
(0.50)
0.25
(0.49)
-0.47
(0.50)
Chemical materials
& Products
-0.02
(0.23)
0.37
(0.32)
0.28
(0.31)
-0.31
(0.32)
Steel & Metallic products
0.25
(0.66)
0.55
(0.94)
0.41
(0.89)
0.46
(0.92)
Healthcare
& Pharmaceuticals
0.04
(0.20)
0.12
(0.28)
-0.14
(0.28)
-0.37
(0.28)
Obs.
R-sq. / Pseudo R-sq.
441
0.68
441
0.40
441
0.04
441
0.06
Sig. level: *** 1%, ** 5%, * 10%
The empirical results using the CIS dataset of 2010 are provided in Table 5. First of all,
the effect of a strong collaborative R&D policy on the innovative activities of firms is positive
and significant, which is consistent with the results using the CIS dataset of 2012 provided in
Table 2. In particular, the effects of participation in the government R&D project on the R&D
expenditure, the number of patent applications, and the product innovation are positive and
significant (β
1
= 0.52, p-value < .001 in model 9; β
1
= 0.20, p-value = 0.24 in model 10; β
1
= 0.32,
p-value = .038 in model 11). Yet the strong collaborative R&D policy does not significantly
affect process innovation.
In contrast, a weak collaborative policy does not have a significant effect on any of the
innovative activities. In particular, the effects of a firm participating in industry associations on
the level of R&D expenditure, the number of patent applications, product innovation, and
process innovation are not significant. Meanwhile, the non-collaborative R&D policies also do
not have significant effects on these innovative activities.
61
Table 5: Estimation results of 2010 CIS dataset
Dependent
variables
Independent &
Control variables
Model 9
Ln(R&D
expenditure)
Model 10
Ln(# of patent
applications)
Model 11
Product
innovation
Model 12
Process
innovation
Constant (β
0
)
4.11
(0.31)
***
-0.40
(0.26)
0.21
(0.29)
-0.96
(0.30)
***
Participating in gov. R&D
Project (β
1
)
0.52
(0.16)
***
0.20
(0.09)
**
0.32
(0.15)
**
0.08
(0.15)
Participating in industry
association (β
2
)
-0.21
(0.16)
-0.16
(0.13)
0.18
(0.15)
-0.01
(0.15)
R&D tax incentives (β
3
)
0.12
(0.14)
-0.01
(0.11)
0.10
(0.13)
0.25
(0.13)
*
R&D funding (β
4
)
0.16
(0.15)
0.14
(0.12)
0.03
(0.14)
0.06
(0.14)
Ln(# of researchers) (β
5
)
0.68
(0.08)
***
0.44
(0.06)
***
0.01
(0.07)
0.00
(0.07)
Ln(# of employees) (β
6
)
0.18
(0.07)
***
0.24
(0.06)
***
0.09
(0.07)
0.19
(0.07)
***
Revenue (β
7
)
0.01
(0.04)
0.05
(0.04)
-0.03
(0.04)
0.01
(0.04)
Electronics & Telecom.
-0.10
(0.23)
0.12
(0.19)
-0.15
(0.22)
0.77
(0.26)
***
Machine & Heavy
Industry
0.07
(0.22)
0.23
(0.18)
0.26
(0.20)
-0.13
(0.21)
Automobile & Parts
-0.24
(0.40)
-0.04
(0.33)
-0.86
(0.44)
**
0.14
(0.38)
Chemical materials
& Products
-0.64
(0.21)
***
0.04
(0.17)
-0.01
(0.20)
-0.01
(0.20)
Steel & Metallic products
-0.44
(0.22)
***
0.11
(0.18)
0.05
(0.20)
-0.04
(0.21)
Healthcare &
Pharmaceuticals
0.04
(0.22)
-0.14
(0.18)
0.06
(0.21)
0.00
(0.21)
Obs.
R-sq. / Pseudo R-sq.
532
0.36
532
0.30
532
0.03
532
0.06
Sig. level: *** 1%, ** 5%, * 10%
62
Note that some of the effects of both internal R&D capability and firm size in terms of
the number of employees are positive and significant. In Table 5, the number of researchers and
the number of employees positively affect R&D expenditure (β
5
= 0.68, p-value < .001 in Model
9; β
6
= 0.18, p-value < .001 in Model 9) and the number of patent applications (β
5
= 0.44, p-value
< .001 in Model 10; β
6
< 0.24, p-value < .001 in Model 10). The annual revenue variable does
not have a significant effect on any of the innovative activities.
In short, I found that the empirical results using the CIS datasets from 2008 and 2010
are not substantially different from the estimation results using the CIS dataset from 2012. When
a firm participates in a government R&D project, a proxy of a strong collaborative R&D policy,
it is likely to increase R&D expenditure and the number of patent applications. Meanwhile, it
does not show a consistently significant effect on either product or process innovation. Note that
participating in the industry association policy, a proxy of a weak collaborative R&D policy,
does not show a significant effect on these innovative activities.
63
CHAPTER 5
CONCLUSION
This study examines the effect of collaborative R&D policy on innovative activities of
firms in Korea. In particular, I classified collaborative R&D policies into two types: strong
collaborative R&D policy and weak collaborative R&D policy, and then empirically investigated
to what extent these two types of policies exert influence on innovative activities of firms.
Moreover, the effect of non-collaborative R&D policy on innovative activities was also
evaluated. Some characteristics of the firms were controlled to identify and estimate the actual
policy impacts. The innovative activities were examined using multiple regression models and
probit models with four dependent variables: R&D expenditure, the number of patent
applications, product innovation, and process innovation.
5.1. Summary of the findings and implications
The empirical results show that the effect of participating in a government R&D project
on both R&D expenditure and the number of patent applications of a firm is positive and
significant (see Table 6). This indicates that strong collaborative R&D policy is likely to increase
innovative input and output of a firm, which is consistent with the empirical findings of previous
studies. From this result, it is assumed that participating in a government R&D project is
effectual in transferring tacit knowledge which leads a firm to generate new knowledge.
However, process innovation is not affected by any type of collaborative R&D policy. This
seems to result from the fact that process innovation requires a firm’s internal R&D capability to
rise above a certain level, and then usually takes a longer period of time to occur than other
64
variables representing innovative activities. In fact, in the absence of any published studies
addressing this issue, further qualitative research is needed.
Table 6: Summary of the findings
Innovative activity
Policy and others
R&D
expenditure
# of
patent
applications
Product
innovation
Process
innovation
Collaborative R&D policy
Strong: Participating in
gov. R&D project
O O △ X
Weak: Participating in
industry association
X X X X
Non-collaborative R&D policy
R&D tax incentive O X X X
R&D funding △ X △ X
Control variables
# of researchers O O X △
# of employees O △ X X
Revenue X X X X
O: significant, △: weakly significant, X: not significant
Additionally, traditional factors such as the number of employees, a proxy of firm size,
and the number of researchers, a proxy of internal R&D capability, have a positive effect on both
R&D expenditure and the number of patent applications. This result is not different from that of
65
previous empirical literature, which can be seen as confirming the stability of the models and
data of this research.
Based on these empirical findings, I suggest four policy implications. First, the feature
of strong collaboration should be included in government-funded R&D programs as much as
possible in order to encourage firms to increase their innovative input and output. Recent R&D
policy direction of the Korean government appears to focus on inducing more collaboration
among stakeholders when it conducts an R&D project. I think that the empirical results of this
research provide convincing evidence to support this policy direction. Compared to other OECD
member countries, Korea remains at a low level of collaboration on innovation, both intra-
nationally and internationally (OCED, 2010). The government should consider additional
policies which emphasize the synergy from close collaboration among government-funded
research institutes, firms, and other partners.
Second, the traditional non-collaborative R&D policy needs to be carefully but
unconventionally re-designed, if the Korean government wants to continuously promote
innovative activities of firms. Since the effects of the non-collaborative R&D policy instruments
are not consistent, it is important to investigate the prospective impacts of different mixtures of
these instruments. The role of the government is to find the optimal combination of policy
instruments in given conditions.
Third, I would suggest both a reform in government regulations which hinder
collaborative R&D efforts and development of devices which would accelerate collaboration.
For example, the Act on the Performance Evaluation and Management of National Research and
Development Projects and related decrees do not have any performance indicators that can
stimulate collaboration in Korea (Ministry of Science, ICT and Future Planning [MISP], 2013).
66
While ATP mandated collaboration efforts in the US, researchers in the government-funded
research institutes in Korea are not given an incentive to get involved actively in collaboration
with outside partners, because their performance is judged mostly by personal achievements and
goals set individually. In this context, a well-designed performance indicator needs to be
incorporated into the current performance evaluation system.
Lastly, universities have to be more aggressive in taking part in collaborative R&D
endeavors. While this research mainly focuses on cooperative relationships between government-
funded research institutes and firms, there is no doubt that universities are also an important
player in collaborative R&D policy. Generally speaking, teaching and doing research are
regarded as the primary roles of universities. In addition, a third role may be collaborating with
firms to transfer knowledge which is the fruit of academic research. Thus the government needs
to develop a policy that attracts the faculties of various universities to collaborative projects and
provides them with incentives to meet firms’ R&D needs for innovation, especially the demand
of small and medium companies whose R&D capability is quite limited.
5.2. Limitations of the research
Although I tried to develop and test a reasonable model of collaborative R&D policies
affecting innovative activities of firms, this research inevitably has several limitations. The first
one has to do with data availability. Ideally speaking, a panel dataset of innovative activities and
R&D policies would provide more complete information about the phenomena under
investigation. However, there is no dataset publicly available in the world with information on
key variables related to R&D policies and innovative activities. No data other than the CIS
dataset collected by OECD member countries is currently available for this research.
67
Unfortunately, this dataset is not constructed in a panel structure, and the firms are not
individually identified so as to protect personal information and privacy.
Another concern is the possibility of reverse causality, which means that the government
might have selected firms with relatively higher R&D capability to participate in government
R&D projects. However, due to the lack of a panel dataset as mentioned above, it is impossible
to model and examine the causal relationships among these variables. Instead, I tried to control
for firms’ R&D capability by using the number of researchers as a control variable in the models,
to some extent addressing this potential endogeneity problem.
In addition, the measurement of the dependent and independent variables is limited in
terms of not providing more information. Although the CIS dataset is the only one available at
this point in time, information about firm involvement in the collaborative R&D policies is
ascertained by a simple yes or no question. In the future, I hope that data on collaborative R&D
policy and innovative activities, such as product and process innovation, can be measured in a
more sophisticated way. Especially, the effect of collaborative R&D policy on process innovation
showed lack of significance in the analyses. This may have arisen from the lack of concern on
the part of stakeholders including policy makers, since process innovation is difficult to measure
and sometimes difficult to achieve within the policy cycle.
The final limitation of this study is that the scope of analysis is limited to Korean
manufacturing firms, although its results are consistent with other studies in advanced countries
such as the US, France, and Germany. Actually, a simple international comparative analysis is
not so easy to accomplish. Yet I expect that more detailed data on collaborative R&D policy and
innovative activities will be integrated so that global-level comparative studies can be
successfully conducted sooner or later.
68
5.3. Future directions
For the last three decades, collaborative R&D policies have caught both academic and
professional attention as a way of promoting firms’ innovative efforts. Although each country’s
collaborative R&D policy has been designed and implemented in different policy environments,
the objective of those policies was commonly focused on improving innovative performance of
firms, which would eventually enhance global competitiveness at the national level.
Nowadays, the scope and dimensions of collaborative R&D policy are changing. In
many countries, R&D policy to foster international collaboration is now in the spotlight. For
example, the EU incorporated international collaboration in the FPs, while the Korean
government started a national R&D program which gave an advantage to projects in which
foreign firms, research institutes or scholars are involved. Furthermore, the dimension of
collaborative R&D policy does not remain just at the development level, but also encompasses
basic and applied research.
This change of scope and dimension of collaborative R&D policy is reflective of the
trend of “open” innovation. Chesbrough (2003) defines that “open innovation is a paradigm that
assumes that firms can and should use external ideas as well as internal ideas, and internal and
external paths to market, as the firms look to advance their technology” (p. xxiv). Basically, I
think that open innovation is a set of collaborative innovation models including technology
licensing, strategic alliances, merger and acquisition as well as joint R&D. Although the concept
of open innovation is not new, the frame of reference suggested by Chesbrough (2003) inspires
scholars to further their studies with wider angles. In the meantime, as open innovation is now
perceived as a necessary frame of reference by the Korean government, it is designing and
implementing some collaborative R&D policies based on the concept of openness. For example,
69
the Korean government has set open innovation as a policy objective for its basic R&D policy,
and then included collaboration in the evaluation metrics in 2013 (MSIP, 2013).
Therefore, I look forward to seeing more in-depth research with more detailed data and
cases in the near future. In this regard, I expect to get a chance to construct a sophisticated
database and models associated with collaborative R&D policy and innovative activities of firms
in the global context, and analyze network-based collaborative R&D performance. Furthermore,
I also hope to see improved measurements of product innovation and process innovation, and to
examine the determinants of these innovations to provide meaningful policy implications. As
collaborative R&D networks are getting more complex, modeling the policy linkages and
examining the effect of such networks on innovative performances can be another contribution in
the field. Especially, in-depth research employing the open innovation framework will play an
important role in identifying the exact features of process innovation.
When all is said and done, one thing that is obvious is that collaboration is the critical
factor of R&D efforts both now and in the future. Even in the field of R&D policy, collaboration
matters to all the stakeholders.
70
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Abstract (if available)
Abstract
This research examines whether and how different collaborative R&D policies affect innovative activities of firms in Korea. In particular, collaborative R&D policy can be classified into two types: “strong” collaborative R&D policy and “weak” collaborative R&D policy. The former is considered to be more effective in transferring tacit knowledge than the latter. Using data from the Korean Community Innovation Survey (CIS), the effects of those two policies on innovative activities of firms are investigated. The innovative activities are measured by four variables: R&D expenditure, the number of patent applications, product innovation, and process innovation. The results of empirical analysis show that the firms’ R&D expenditure and the number of patent applications are positively affected by strong collaborative R&D policy represented by participating in government R&D projects. However, joining industry associations, a proxy of weak collaborative R&D policy, does not have any effect on innovative activities. This result indicates that collaborative policy which drives companies to participate in R&D projects is relatively more helpful in fostering firms’ innovative activities. This is because the strong policy mainly concentrates on transferring tacit knowledge which is essential for innovation. When the government designs an R&D policy, therefore, it needs to consider that the effect of policy may vary according to the type of the policy.
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The future of work: defining a healthy ecosystem that closes skills-gaps
Asset Metadata
Creator
Ryu, Kwang Jun
(author)
Core Title
Does collaborative R&D policy work? The effect of collaborative R&D policy on innovative activities of firms in Korea
School
School of Policy, Planning and Development
Degree
Doctor of Philosophy
Degree Program
Policy, Planning, and Development
Publication Date
07/28/2015
Defense Date
06/22/2015
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
collaboration,collaborative R,Community Innovation Survey,innovative activity,Korea,OAI-PMH Harvest
Format
application/pdf
(imt)
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
Robertson, Peter John (
committee chair
), Tang, Shui Yan (
committee member
), Wilber, Kathleen H. (
committee member
)
Creator Email
junryu@hanmail.net,kwangryu@usc.edu
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-c3-608621
Unique identifier
UC11299591
Identifier
etd-RyuKwangJu-3688.pdf (filename),usctheses-c3-608621 (legacy record id)
Legacy Identifier
etd-RyuKwangJu-3688.pdf
Dmrecord
608621
Document Type
Dissertation
Format
application/pdf (imt)
Rights
Ryu, Kwang Jun
Type
texts
Source
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
Access Conditions
The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...
Repository Name
University of Southern California Digital Library
Repository Location
USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA
Tags
collaboration
collaborative R
Community Innovation Survey
innovative activity