Pasteur Quadrant Goes to China:
Scientific Publication and Patenting Within the Chinese Academy of
Sciences
[SECOND DRAFT]
Ke Wen
Institute of policy and management
Chinese Academy of Sciences
15 Zhongguancun BeiYiTiao
Beijing, China 100190
[email protected]
Scott Stern
MIT Sloan School of Management
Massachusetts Institute of Technology
100 Main Street e62-476
Cambridge, MA 02142
& National Bureau of Economic Research
[email protected]
DRAFT DATE: August 16, 2013
DO NOT CITE OR QUOTE WITHOUT PERMISSION OF THE AUTHORS
Pasteur Quadrant Goes to China:
Scientific Publication and Patenting Within the Chinese Academy of
Sciences
Wen Ke1 and Scott Stern2
Abstract
Over the past decade, there has been an extraordinary rise in the scale and scope
of the Chinese Innovation System(OECD, 2007). By and large, the extant analysis of
the Chinese Innovation System has drawn on frameworks appropriate to North
American and European innovation system models, and attempted to impose those
frameworks on the complexity (Liu & White, 2001;Sun, 2002;Gu & Lundvall,
2006;Chen & Kenney, 2007;OECD, 2007). National Innovation System is generally
recognized as comprising the complex functions and interactions among various actors
including government, enterprises, universities, public and private research institutes,
bridging institutes, and other institutions and institutions (Freeman, 1987; Lundvall,
1992; Nelson, 1993), which is essentially centered on innovative capability or
absorptive capability of enterprises to see knowledge diffusion. However, transforming
from a planned economy system to a market economy system, China built its innovation
system originally beginning with promoting scientific and technological achievements
of public research institutions to market. Then, the analysis of Chinese innovation
1
Institute of Policy and Management Chinese Academy of Sciences
2
MIT Sloan School of Management and NBER
system should test system level structure and its distinctive features, which means it is
need to pay more attention to links between science and industry from the perspective
of science base. This is not to say that is more suitable for Chinese national innovation
system with a linear model, but indicate that realizing low innovative capability of local
enterprises, Chinese government reformed public research institutes to encourage
technology transfer that result in the exploration of Chinese public research institutes
in Pasteur Quadrant. And this is precisely an important characteristics of the Chinese
innovation system ignored by OECD (2007). The objective of this paper is to provide
a preliminary exploration on performance of Chinese innovation system while taking a
science-side perspective.
In this paper, we studied the relationship between publications and patents of
Chinese scientists based on the survey of more than 3000 scientists working in CAS.
The significant positive correlation between papers and patents shows that publishing
and patenting are complementary while not substitute. In the case of CAS, it is on the
process of making success in Pasteur’s quadrant. For Chinese scientists, the relationship
between publishing and patenting is significantly moderated by the combination of
industry interaction and organizational structure, which indicates the performance of
Chinese innovation system. Policy implication is that rather than focusing on potential
for spillovers of technology, policy should reorient to evaluate the interactions and
inter-relationships between institutional mechanism that allow knowledge to be
diffused and exploited by both scientific and technological communities.
I.
INTRODUCTION
Over the past decade, there has been an extraordinary rise in the scale and scope of the Chinese
Innovation System (OECD, 2007). By and large, the extant analysis of the Chinese Innovation System
has drawn on frameworks appropriate to North American and European innovation system models, and
attempted to impose those frameworks on the complexity (Liu & White, 2001;Sun, 2002;Gu &
Lundvall, 2006;Chen & Kenney, 2007;OECD, 2007). However, transforming from a planned economy
system to a market economy system, China built its innovation system originally beginning with
promoting scientific and technological achievements of public research institutions to market. Then,
the analysis of Chinese innovation system should test system level structure and its distinctive features,
especially pay more attention to links between science and industry from the perspective of science
base. This is not to say that is more suitable for Chinese national innovation system with a linear model,
but indicate that realizing low innovative capability of local enterprises, Chinese government reformed
public research institutes to encourage technology transfer, which result in the exploration of Chinese
public research institutes in Pasteur Quadrant. And this is precisely a distinctive characteristics of the
Chinese innovation system ignored by OECD (2007).
As the commercialization of academic research has been viewed as a key driver of
competitiveness, a central issue of Chinese innovation system is the links between industry and science
(Xue, 1997;Liu & White, 2001;Gu & Lundvall, 2006). A salient feature of Chinese Innovation System
under the centrally planned regime was the separation of R&D centers from productive enterprises
(Xue, 1997). Public Research Institutes (PRIs) were responsible for conducting the majority of China’s
basic and applied research, with Chinese Academy of Sciences (CAS) assigned to be the national top
organization for comprehensive natural and engineering science, and PRIs under different sector
ministries in charge of applied research and development in their corresponding areas. Sector ministries
also took the responsibility for planned production tasks as well. Given that innovation as a process of
knowledge transferring from basic research to product prototype, pushing PRIs more responsive to
market needs for science and technology (S&T) input has been the critical task of Chinese Innovation
3
System (Xue, 1997; Fang & Liu, 2004). A range of initiatives seek to promote the links between science
and industry, such as scientific and technological achievements can be legally evaluated as the
intangible assets into enterprises’ registered capital, the related scientists contributing the technology
achievements can be rewarded the 20% of intangible equity or technology transfer revenues, etc..
However, weak industrial R&D in China haunted the technology transfer from PRIs (Xue, 1997), policy
efforts for allocating science and technology resources from research to commercialization resulted in
a bunch of spin-offs run by universities and PRIs in purpose of transferring respective
technologies(Xue, 2005).
Obviously, all extant researches based on NIS approach help us to understand the Chinese
Innovation System and its evolution, but they cannot provide practical guidelines for policy makers to
improve Chinese innovation system. Within the perspective of innovation system, China’s Gross
Domestic Expenditure on R&D (GERD) has growing in the average annual rate of more than 20% in
last ten years, among them from the business accounting for about 70%; papers publication and
inventions application have also increasing at the same rate of 20%; technology market transactions
scale amount reached 470 billion Yuan in 20113. But now, China is still facing the separate between
science and industry under the context that rapid development of each part of Chinese Innovation
System (OECD, 2007). The main challenge, as Edquist (2004) and Klein Woolthuis et al. (2005)
indicated, is that National innovation system until now is not a theory but only a conceptual framework,
we need a practically useful analysis that allows for assessment of system performance as well as the
identification of factors influencing performance (Anna Bergek etc, 2008).
The objective of this paper is to provide a preliminary exploration on distinctive features of the
relationships between basic research and technology innovations in Chinese innovation system while
taking a science-side perspective. In fact, the relationship between basic research and technology
3
From the website of Science and Technology Statistics Information Center, the internet address is
http://www.sts.org.cn/sjkl/index.htm.
4
innovation has been always the core of building Chinese innovation system (Fang & Liu, 2004;
Motohashi & Yun, 2007). Departure from the uneasy relationship between basic research and
technology innovation mediated by the priority-based reward system (Partha & David, 1994;Stern,
2004) and by potential economic returns (Stern, 2004), we evaluated research practices and
collaboration by researchers working within the research institutes that operate under the auspices of
the Chinese Academy of Sciences. In particular, after a detailed examination of the Chinese Academy
of Sciences, we report the results of a novel survey of more than 3,000 research scientists working
within that system. Building on a recent literature emphasizing the complex relationship between
scientific publication, patenting, and the institutional structures underlying basic research and
technology innovation, we focus in particular on the patenting and publication behavior of scientists in
different institutional environments.
Though we are cautious as our results are based on a single cross-section and the Chinese
Innovation System is itself evolving rapidly, we are able to document a number of novel and important
findings.
First, we find a broadly positive relationship between patenting and publication at the
individual level – those researchers with the highest levels of publication are those with the highest
level of patenting. However, this finding by itself significant masks how the institutional environment
seems to impact this relationship. Second, something about overall attitudes and the fact that basic
science and collaboration with industry is viewed positively except that there is insufficient resource to
motivate such interactions. Third, more than examining Pasteau Quadrant based on the relationship
between paper and patent (Fiona & Scott, 2007), we specifically test the moderations of researchers’
experience of collaboration with industry and of the establishment of TTO on the relationship. However,
the moderation is one-way, it facilitates knowledge transfer from papers into patents, while not inspire
use-driven basic research. These findings have important implications for our emerging understanding
of the Chinese Innovation System.
Relative to their counterparts in the United States, Chinese
researchers seem to exhibit a low level of concern about the negative impact of industry collaboration
5
on more basic research, but the ability of CAS researchers to both patent and public seems to depend
on the institutional environment and research organization.
The rest of the paper is organized as follows. The section 2 addressed the research setting of this
paper. Section 3 sets out the main research questions in this paper and put forward the main hypotheses
through a review of literatures. A description of the methodology used in the analysis is contained in
Section 4, and section 5 presents the main empirical results. Section 6 presents our discussions and
section 7 points out the limitations of our research and suggestions for future research. A brief
conclusion is drawn in the last section.
II.
RESEARCH SETTING: CHINESE ACADEMY OF SCIENCES
This paper mainly set research on the context of Chinese Academy of Sciences (CAS). CAS is a
leading academic institution and comprehensive research and development center in natural science,
technological science and high-tech innovation in China. While comparable to scientific institutions in
other countries in some respects (resembling, variously, Germany’s Max Planck Society and U.S.
national labs and in its honorific functions, the Royal Society and the U.S. National Academy of
Sciences), CAS is truly unique in its size and the range of activities and functions it attempts to
accomplish (Suttmeier et al., 2006). To date, as a union of research institutes, CAS has more than 100
research institutes, 2 universities, and more than 100 national key laboratories and engineering centers.
CAS was founded in Beijing on 1 November 1949, one month after the founding of the People’s
Republic of China. In the early days, the CAS was mandated as the key force of the new China’s
scientific research system, undertaking missions of defining scientific research orientations,
restructuring its research institutions, encouraging and helping overseas Chinese scientists to return
home, training and properly allocating professionals, outlining strategies for the nation’s future
scientific and technological development while contributing to the national economic and social
development4. Since the publication of the Central Committee’s Resolution on the Structural Reform
4
http://english.cas.cn/ACAS/BI/200908/t20090825_33882.shtml.
6
of the Science and Technology System in March 1985 (CCCPC, 1985), CAS has always been a major
driving force in Science and Technology System Reform and the exploration of National Innovation
System. When facing for drastically reduction of financial funding in 1980s, CAS put forward the
concept of “one Academy, two systems”- as research institution and commercial technology agent,
promoting its constituent institutes into a series of commercial ventures. Lenovo was the typical spinoff of Institute of Computing Technology of CAS at that time.
In May 1995, Chinese government issued “Decision on Accelerating Scientific and Technological
Development”, and proposed the national strategy of “Revitalize the Country through Science and
Education” on the following National Conference on Science convened at the end of May 1995.
Consequence, new plans and programs were introduced to expand basic research, increase R&D
investment, and advance the ongoing reforms of the science and technology system. In 1997, CAS
submitted to the central leadership a report titled “The Coming of the Knowledge-Based Economy and
the Construction of the National Innovation System”, in this report, CAS incorporated the concept of
national innovations system in China’s evolving science and technology policies (Suttmeier et al.,
2006). Intuitively, this report directly fuse leading to the launch of knowledge innovation project,
what’s more, its deeper influence is that the concept of innovation is more and more important in China.
Chinese government realized it is the need to promote the science and technology regime reform from
the perspective of innovation, while, the experience of china’s reform is beginning with a pilot.
Therefore, in 1998 the “Knowledge Innovation Program” (KIP) of CAS was initiative as a phased “pilot
project” of creating national innovation system. KIP comprised three stages. From 1998 to 2000 was
first “experimental” phase (1998-2000), then followed by a five year implementation phase (20012005), and the last phase was optimization phase (2006-2010).
CAS aimed at reorganizing itself and developing its ambidexterity by the implementation of KIP,
which means leading the exploration of Chinese science community on basic research as well as
providing technology driver for rapid economic development. When KIP began, CAS was running
7
about 120 institutes, many of which were seriously overstaffed and overlapping in missions. Research
agendas of the institutes, on one hand, were away from the needs of industries, on the other hand, lagged
behind international research frontiers. KIP remade CAS from inside and outside two aspects. Inside,
major measures were made on structural adjustment and revitalizing human resource base. In order to
reduce duplication and rationalize missions, CAS reorganized some institutes even hive off some of
the applied research institutes as commercial entities, transferring traditional disciplinary orientations
to new international knowledge networks of relevance to the IT-Bio-Nano revolutions(Suttmeier et al.,
2006), and provided KIP directional projects supporting elite scientists in fundamental research, strategic
high technology, and science and technology for managing resources and the environment. “100 talents”
program is the most important approach for CAS to recruit group leaders from “brain drain” scientists
working abroad as well as from prominent young scientists in China. In order to incentive scientists
transfer their scientific achievements to market, many institutes of CAS began to regulate invention
patent as work and project assessment indicator, while papers still have the most important weight for
professional position.
Outside, CAS increased collaborations with other actors in Chinese Innovation system as well as
international famous research institutes. Centering on technology transfer, CAS explored a variety of
collaboration mechanisms. In 2001, CAS established the specific Bureau of Academy-Locality
Cooperation being responsible for collaborations with local governments and large enterprises. The
Bureau on behalf of CAS communicates with local governments, promoting two sides to sign strategic
collaboration agreements or frameworks. Under the collaboration frameworks, the two sides usually
together build institutional structures, for example, collaboration special program, technology transfer
center, etc. convenient for contract research, the licensing of proprietary technologies and the spinning
off of companies from CAS institutes. Corresponding to the establishment of Bureau of AcademyLocality Cooperation, the institutes also strengthen technology transfer team building. Part of them set
up a dedicated technology transfer office, in the case of Institute of Computing Technology that has
8
begun to set up TTO branches all over the country since 2002. And, most of the rest institutes also set
full time jobs responsible for technology transfer, although they didn’t establish TTOs.
There is no doubt that today the Chinese Academy of Sciences has made great achievements. From
2001 to 2010, the Chinese Academy of Sciences as the first author to be indexed by the SCI (Extended
Edition) has increased 133% ( from 6725 to 15655), patent applications increased 3.76 times (from
2523 to 9487)and granted patents increased 4.52 times ( from 1001 to 4522). And, Chinese Academy
of Sciences has established a nationwide collaboration framework with 31 provinces and 11 cities with
independent planning, more than 10,000 scientists collaborating with industry. In 2011, 1774
technology contracts were signed between research institutes of CAS and local enterprises, the total
amount of which reached ￥ 1.67 billion.
5
After KIP, the CAS started “Innovation 2020” Strategy
in 2011, by its implement in next ten years, one-third of research institutes of CAS will become the
world-class research institutes, and technology innovation capability of the CAS can strongly support
industry upgrading and new industry emerging.
20000
15000
patent applications
10000
granted patent
SCIE
5000
0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Data source: CAS Statistics Year Book 2002 - 2012.
Obviously, the efforts of the Chinese Academy of Sciences provide the best context for us to
examine the distinctive features of Chinese Innovation System centering on the relationship between
basic research and technology innovation from the science base.
5
From the Chinese Academy of Sciences Annual Report (2012) , P60.
9
III.
MAIN HYPOTHESES
Reward system of science and knowledge disclosure
Science reward system mediates the uneasy relationship between science as a knowledge
production process and as in input into technology innovation (Stern, 2004), which is based on the
priority of discovery or development (Browne, 1932;Merton, 1957;Partha & David, 1994). Priority was
protected in two institutional spheres: public rights (peer-reviewed publications) and private property
rights (patent system) that shape the rules of knowledge disclosure, access and reward (Partha & David,
1994;Fiona & Scott, 2007;Murray & Stern, 2007). Given two separate incentives, policy studies have
always centering on the linkage between science and technology (Bush, 1945;Pavitt, 1998), especially
on the evolution of universities or public research institutions and their contribution to innovation
(Rosenberg & Nelson, 1994;Henderson et al., 1998;Pavitt, 1998;Etzkowitz & Leydesdorff,
2000;Kaufmann & T dtling, 2001;Aghion et al., 2009).
The traditional linear model makes a clear distinction between science and technology by relating
science to basic research and attaching technology to applied research (Bush, 1945;Stokes, 1997). Basic
research focuses on questions of fundamental scientific interests while applied research focuses on
potential commercial application, and in the linear model, basic scientific research serves as a
foundation for subsequent technological innovation and economic growth (Fiona & Scott, 2007). The
idea of a spectrum of research extending from basic to applied is a “static” one-way form, however, the
development of science in postwar paradigm call into question the dynamic form as well(Stokes, 1997),
the feedback mechanism between science and technology are ignored(S. & Rosenberg, 1986) By
highlighting the dynamics form between science and technology and the potential duality of some
research projects, Stokes (1997) illustrated “use-inspired basic research (Pasteur’s Quadrant)”,
indicating a single discovery perhaps possessing both applied and basic characteristics. The theory of
Pasteur’s Quadrant challenges the traditional linear model: science and technology are not
consequential and substitute but can be jointly produced in a single research, and then knowledge
10
generated in Pasteur’s Quadrant presents scientists with a choice: publication or patenting(Fiona &
Scott, 2007).
Thus, science reward system of maintaining science and technology in dynamic balance becomes
very important (Partha & David, 1994). Since the exploration of American government launching
Bayh–Dole Act
in 1980(Mowery et al., 2001), many countries provided blanket permission for
performers of federally funded research to file for patents on the results of such research and to grant
licenses for these patents,
which facilitated university patenting and licensing (source).
From the
beginning of the enactment of the Patent Act of 1984, the Chinese government has encouraging
scientists of universities and public research institutions to apply for patents. However, universities and
research institutions are in the culture of scientific community, publications are regarded the most
important for scientist’s career development. Patenting was ignored even be considered a proper job.
Moreover, patenting is time-consuming, so the researchers have no motivations to patenting. 863 Plan,
for example, the program funded by governments can be termed as mission oriented research aiming
at helping to high-tech research and commercialization which is consistent with Pasteur's Quadrant.
From 1986 to 2000, the proportion of patents to publications funded by 863 Plan was about 1 vs. 30.
In order to lead universities and research institutions orienting to innovation, in 1999 the General Office
of the State Council forwarded “certain provisions on promoting scientific achievements transfer” made
jointly by seven ministries including the Ministry of Science and Technology, Ministry of Education,
etc. regulating that research achievements of universities and public research institutes can be assessed
and transferred to the enterprise. Followed by “Certain provisions on intellectual property management
of the national research project” released by the Ministry of Science and Technology and the Ministry
of Finance in 2002, and “Provisions of strengthening intellectual property management of National
Science and Technology Plan” published by the Ministry of Science and Technology in 2003, both are
clearly defining that intellectual property rights of public funded research were granted to commitment
universities and research institutions(Yan Liu, 2007). Besides these regulations incentive patent
application of public research by shifting the ownership of research output from the state to research
11
institutes and universities, since 1999 each province of China successively launched patent subsidy
programs that subsidize costs and fees normally incurred during patent application, substantial
examination, and maintenance, which directly result in the dramatic growth of patenting of patent
applications from both universities and research institutes(Li, 2012). Therefore, facing the changes of
science reward system, we argue:
H1： Chinese researchers are “pragmatic” in their pursuit of patents versus papers (or both).
Industry interactions and knowledge disclosure
Universities collaborations with industry for technology/knowledge transfer have had a long history,
however, of late, there has been a substantial increase in these partnerships (Ankrah, 2007). Along with
the change of science reward system, there is a growing amount of governmental and institutional
funding available for public-private R&D projects (Ambos et al., 2008), which strengthen the close
relationship between academics and industries.
AgrawalandHenderson (2002) suggest that interaction with industry may steer scientists and
engineers towards patenting. Interaction with industry can lead to increased interest in patenting, not
only because industry often has a patent focus and patent “know-how” but also because industry directs
its research to questions that are well suited to patenting (Stephan et al., 2007). At the same time,
scientists and engineers also often gain inspiration for their research through interaction with industry
(Mansfield, 1995;Siegel et al., 2003). Besides accessing to better scientific infrastructures and
equipments of firms, which means a greater possibility to do more research, academic scientists are
inspired new research ideas or finding new research questions when discussing with industrial scientists
(Siegel et al., 2003). Many empirical studies have demonstrated that academic scientists who
collaborate with industry are more productive of publication and patenting (Landry et al., 1996;Ding et
al., 2006). However, collaboration with enterprises may also lead researchers overly concerned with
commercialization activities to reduce their research time and energy(Zucker & Darby, 1996), thereby
12
decreasing knowledge production in terms of papers and patents(Powell & Owen-Smith, 1998;Zucker
et al., 2002;Toole & Czarnitzki, 2010). Generally speaking, academic scientists will not take a risk to
deviate from knowledge disclosure based on priority reward system to seek commercial
accomplishments, because commercial activities do not often carry weight in tenure and promotion
decisions(Owen-Smith & Powell, 2001). But commercial activities may change the culture of academic
research institutions, which could weaken the traditional educational and research missions (Powell &
Owen-Smith, 1998).
Currently, there is no literature talking about the impacts of Chinese scientist collaborations with
industry on their research productivity.
However, the authors observed that as patents involved into
reward system of science, it is easier for those scientists who have research findings and in a higher
academic position towards patenting and collaborating with industry. Carayol (2007) also indicated
university researchers are more likely to patent later in their careers. But for scientists who just begin
their scientific research career, industry interaction will perhaps also be more conductive to find
research questions and obtain research achievements faster. A young scientist of CAS we have
interviewed noted:
“Before cooperating with local enterprise, my research subjects are from literatures, following the
advanced studies of American or European. Collaboration with industry helped me know the demand
of industry and find research question need to study, and soon I published my findings as well as
applying one patent”.
Chinese firms, on the other hand, are seeking out well-known scientists to collaborate, just like
Owen-SmithandPowell (2001) found that local firms often seek out highly visible scientists.
For
scientists, it has long been recognized that publications is the basis for legitimate reputation-building
claims (Merton, 1968;Partha & David, 1994), so in China researchers who have been invited to
collaborate by industry are usually those with more publications. In turn, this commercial involvement
raises their awareness of value of intellectual property. At least, publication and patenting are not
13
mutually exclusive, many scientists feel patenting increases their academic visibility and status by
reaffirming their novelty and usefulness of their work. Then, we argue:
H2: When scientists are engaged in industry collaboration, more “complementarity” between
patents and publications; while scientists are NOT engaged in industry collaboration, much lower
relationship between patenting and publication.
Organizational institutions, Industry interaction and knowledge disclosure
The changes of science reward system and the trend of science-based innovation have created
tensions between facilitating diffusion of knowledge as a public good and protecting its private
ownership and value(Argyres & Liebeskind, 1998;Gans et al., 2011) , both at individual and at
organizational levels(Ambos et al., 2008). Owen-SmithandPowell (2001) indicated that many scientists
are often reluctant to patenting their findings, the link between patent capacity and disclosure is
mediated by their perception of costs and benefits of patenting. Then, universities are facing the issues
of helping researchers easier to disclose their inventions as well as designing a proper incentive scheme
specifying an adequate share for inventors in commercialization (Siegel et al., 2007), which are conflict
with traditional scientific research management. Fortunately, it may be very difficult but possible for
organizations to reconcile conflicting demands at the individual level, given that organizations can
create a supportive context in which individuals can make their own informed judgments about how
they should allocate their time to meet the conflicting demands for alignment and adaptability(Gibson
& Birkinshaw, 2004;Chang et al., 2009).
Facing the tension between academic and commercial demands, the organizational reactions of
universities include the establishment of dedicated technology transfer offices
(TTO) (Debackere &
Veugelers, 2005), and the incorporation of supportive policies, activities and incentives designed to
legitimize commercial activities(Owen-Smith & Powell, 2001;Chang et al., 2009). These current
attempts of universities to reconcile newer commercial demands with traditional academic ones fit well
14
with the concept of ambidexterity (Ambos et al., 2008). Based on ambidexterity theory, the dominant
approach to analyze entrepreneurial universities focuses on a dedicated specific function - TTO
(Bercovitz et al., 2001;Debackere & Veugelers, 2005;Ambos et al., 2008).
Superficially, TTO is the
best dual structure designed to deal with dual demands of open science and technology
commercialization (Bercovitz et al., 2001), moreover, it is also signaling the positive support of
research institution leader toward patenting and technology commercialization, and leaders drive
organizational culture by signaling technology commercialization to be expected, valued and likely to
be rewarded(Bercovitz & Feldman, 2008).
In CAS system, each research institute is an independent legal unit, and the director of research
institute is legal representative who takes responsibility of almost all things of institutes. The
establishment of TTO by some RIs usually reflects the director’s support for academic
commercialization, as well as signaling organizational orientation to science-industry links. From the
function of TTO, it plays an important role in the patenting process by raising awareness about IP
protection, disseminating practical information about invention disclosure and patenting, providing
assistance and service to scientists, determining which patents and licenses to file and pursue, and
facilitating the transfer of technology from researchers to the marketplace(Chang et al., 2009;Huang et
al., 2011), most academic patent inventors are more likely to rely on the assistance from TTOs in filing
patent application. Given that empirical studies generally support the idea that the establishment of a
TTO has a positive effect on university patenting activities (Sellenthin, 2009), it can be expected that
the existence of a formally established TTO with specific full time staffs should increase the likelihood
of patenting from academic research as well as enhance the capability of supporting co-development
between science and technology of RIs.
H3: When scientists are from RIs with TTO, very high “complementarity” between patents and
publications; while scientists are in RIs without TTO, much lower relationship between patenting and
publication.
15
Generally, TTO is regarded as the formal gateway between the university and industry(Rothaermel
et al., 2007), which facilitates commercial knowledge transfers of IP resulting from university research
through licensing to existing firms or start-up companies of inventions or other forms(Siegel et al.,
2007). TTOs vary in their mandates and capabilities (Siegel et al., 2003). While licensing is the primary
activity for TTOs in America (Thursby & Thursby, 2002) , TTOs in RIs of CAS often act as information
intermediates between academic and industry, besides assisting scientists in filing patent application.
Comparing to individual scientists or teams, a TTO has the advantages of identifying opportunities,
lower costs of searching for potential collaborators, and then offer more opportunities for scientists to
collaborate with firms. On the other hand, in order to protect the interest of research organizations (Jain
& George, 2007), TTOs will also actively urge and help scientists to patenting their findings in the
process of collaboration with firms.
H4: There is a three way moderation effect among TTO, industry interaction and publications: the
highest levels of patents are expected when publication is high, engaging in Collaboration with industry
and in a context with TTO. Relatively high levels of patents are expected when two of three variables
are beneficial. Reduced levels of patents are expected when only one of three variables is high. The
lowest levels of patents are expected when publication is low, no industry interaction and a context
without TTO.
IV.
DATA, VARIABLES AND METHODS
Sampling Frame and Data Collection
The data mainly come from a survey at individual level of scientists conducted between September
and November 2010. In 2010, there are about 100 research institutes with about 25,000 full time
research scientists affiliated to CAS, distributing in physics, chemistry, earth science, information
technology, space and so on, almost cover all natural science disciplines. Departure from our research
16
purpose, we remove several social science study institutes such as Institute of Policy and Management,
Institute for the History of Natural Science etc. as well as several new launched institutes in 2010 out
of our samples. Therefore, we constrained our research to 94 natural science and applied research
institutes of CAS. We planned to send out 5000 questionnaires within these 94 research institutes. That
means the survey would investigate about 20 percent of research scientists of each sampled research
institute. According to this ratio, we calculated the number of questionnaires going to each research
institutes, and slightly adjusted each number to the integer times of 10. Finally, we determined the
investigation sample at individual level is 5700. We coded all these 5700 questionnaires, as well as
coding 94 research institutes.
Under the help of Bureau of Academy-Locality Cooperation of CAS, our surveys were addressed
to 94 sample research institutes affiliated to CAS. Questionnaires were sent randomly out by institutes’
support staff. Even though we put forward the random rule, it still has a bias of sample that those
scientists who are familiar by support staff had a bigger possibility involved in our sample. But we
don’t think such a bias will impact on our research questions. 3453 scientists responded to us. After
cleaning the data by deleting observations that missed required information 6 , we finally got 3085
observations affiliated to 84 research institutes.
Besides socio-demographic questions such as age, gender, degree, discipline, etc, we asked
respondents about: the amount of patent (specific indicating invention) applications and paper
publications between 2007 and 2009, whether or not ever collaborated with enterprises before 2009.
Here, collaboration mainly indicates contract R&D projects. Worthy of note is that, it is almost
impossible to obtain paper-patent pairs like those in Murray (2004) and MurrayandStern (2007),
because there is no citation indexing of patents in database of SIPO in China. Then, we can’t match
papers and patents by indexing, but we still can assume the intensive relationship between papers and
6
Firstly, we dropped off 89 observations missing age information, and consequently 64 missing gender information, 176
observations didn’t respond funding level, 27 missing degree information, 6 missing career stage information. Besides
deleting all observations missing information, we also dropped off 6 observations in psychology discipline.
17
patents based on common knowledge accumulation embedded in one person.
The reasons to collect
three years data stem from three considerations: ① the existing researches often use one or two years
as time lag to analyze the relationship between paper and patents, we regard papers and patents in a
continuous period of three years can cover the time lag effect. ②CAS entered into the Third Phase of
KIP in 2006, research outputs between 2007 and 2009 were in a relatively stable institutional
environment. ③For respondents, it is obviously easier to give three years data than five years data.
Variables
Dependent and independent variables
In this paper, referring to Stephan et al. (2007) we use the number of patent application between
2007 and 2009 as an indicator of patent activity and the number of articles published from 2007 to 2009
as a measure of publishing activity. The number of patent applications, PATENT, depends on the
number of articles published, SCI, and other observed and unobserved factors, while PATENT and SCI
do not measure the quality of these publications, they do capture the scientist’s capacity to disclose
their research findings.
With raw data, the number of patent application ranges from 0 to 227 and the number of paper
published ranges from 0 to 180. Even if quantity-oriented scientific evaluation systems in China push
Chinese scientists paying more attention to knowledge disclosure, however, more than 60 publications
or patents per year is abnormal in research productivity. In order to exclude potential abnormal
observations distorting the relationship between variables, we modified the each tail of both
distributions at 0.01 level by use of winsor command in Stata 12. After the modification, the dependent
variable PATENT ranges from 0 to 20 with a mean of 1.95, and the independent variable SCI ranges
from 0 to 40 with a mean of 4.02.
Both the PATENT and SCI measures are significantly skewed, as shown in Table. The distribution
of patents is a little more skewed, however, than that of publications.
18
TABLE I Percentage distribution of number of SCI and PATENT
All sample(N=3085)
Field
0
36.69
48.56
48.62
2.82
0.00
0.00
Female(N=830)
36.39
57.10
4.22
1.69
0.60
58.43
40.12
1.45
0.00
0.00
Male(N=2255)
36.81
52.55
6.96
2.04
1.64
44.92
51.75
3.33
0.00
0.00
PHD(N=2008)
19.22
67.33
8.72
2.79
1.94
42.08
54.18
3.74
0.00
0.00
70.43
27.98
1.16
0.43
0.00
63.19
35.94
0.87
0.00
0.00
Bachelor(N=387)
67.18
29.46
2.33
0.25
0.78
56.07
42.38
1.55
0.00
0.00
ResourceEnvironment(N=356)
30.90
63.48
4.50
0.56
0.56
59.83
37.64
2.53
0.00
0.00
Metal&Meterials(N=343)
21.28
69.97
5.83
2.63
0.29
33.82
64.14
2.04
0.00
0.00
26.27
62.92
7.00
1.90
1.91
59.32
38.56
2.12
0.00
0.00
TripleE(N=801)
56.05
40.08
2.62
1.00
0.25
36.70
60.30
3.00
0.00
0.00
Chemistry(N=399)
25.31
52.38
13.54
5.26
3.51
40.60
54.64
4.76
0.00
0.00
Physics(N=714)
38.52
51.26
6.72
1.54
1.96
60.64
36.84
2.52
0.00
0.00
Master(N=690)
Biotechnology(N=472)
1-10
53.78
11-20
6.22
21-30
1.95
>30
1.36
Among the sample, male is more likely to patent. Not surprisingly, papers and patents of scientists
with PH.D is obviously much more that those of master’s and undergraduates. From the perspective of
disciplines, Physics scientists are least likely to patent, and Metal & Meterials scientists are the most
likely to patent. Also, Metal&Meterials scientists are the ones most likely to publish. New technologies
like Nanotech are emerging in Metal&Meterials discipline, research activities are active with a huge
industrial prospects. The gap between publications and patents is big in biotechnology discipline, which
perhaps owe to the broad criteria used to define biotechnology, encompassing those working in all areas
such as agricultural and food sciences, not only narrowly defined areas such as microbiotechnology
and biotechnology.
Moderators
19
Many measure instruments were developed to describe industry interaction of scientists (Bozeman
& Gaughan, 2007;Deste & Patel, 2007;Okamuro, 2007;Ambos et al., 2008). In this paper, we are going
to examine whether the experience of industry interaction impacts the relationship between publications
and patents, rather than in depth study of the impact of different interaction mode or variety on the
relationship. Therefore, we use a dummy variable COLLABORATION indicating the collaboration
experience of respondents, 1 shows collaboration experience and 0 indicates none. 1,270 respondents
have contracted research with industry from 2007 to 2009, less than 50% of the sample.
TTO represents organizational structure of public science supporting technology innovation.
Scholars developed variables of breath or scale, experience or age, and establishment of TTO to
examine its function in innovation system (Chapple et al., 2005;Ambos et al., 2008). In this research,
TTO is measured with a dummy variable TTO to indicate whether research institutes have a Technology
Transfer Office or not. Among 84 research institutes, there are 45 institutes with TTO and 39 without
TTO. Also, we find knowledge productivity of research institutes with TTO is higher than that of
institutes without TTO (shown in Table IV).
Table IV
Mean
COLLABORATION=1
COLLABORATION=0
TTO=1
TTO=0
5.12
3.10
3.28
1.10
4.28
2.32
3.56
1.29
SCI
PATENT
Controls
Scientist characters were controlled by age, gender, degree and disciplines (Stuart & Ding,
2006;Haeussler & Colyvas, 2011) . AGE is measured in years.
The respondents are on average 35.84
years old. GENDER is a binary variable, 1 for female and 0 for male. 2255 among respondents are male.
Two to thirds of respondents are with PH.D, which nearly reflect the degree structure of CAS
researchers. Scientific discipline was regarded as an important factor influencing the researchers’
20
engagement in technology transfer (Deste & Patel, 2007), in this paper, we also control the variance
between disciplines by entering six binary variables of Resource&Environment, Metal&Meterials,
Biotechnology, TripleE, Chemistry and Physics7.
Methods
In this paper, PATENT are count variable and take on only nonnegative integer values. The linear
regression OLS model is obviously inadequate for modeling such variables because the distribution of
residuals will be heteroscedastic nonnormal. Stephan et al. (2007) argue that two families of count data
models based on the maximum likelihood approach and GMM, taken together, are suitable for the
analysis of the patent–publication data with special features, including discreteness of the measures
of productivity (i.e., both patent and publication counts), skewed distribution, extremely high
proportion of individuals who do not patent, and, more importantly, potential correlation between
publications and unobserved heterogeneity.
According to our hypotheses, the expected number of patent applications 𝑦=PATENT is specified
as:
E[𝑦|x1 , x2 , 𝑧; 𝜔] = exp(𝛽1 x1 + β2 x2 + β12 x1 x2 + δz) ω
（1）
x1 is the number of publications SCI, x2 is the vector of moderators including collaboration
with industry COLLABORATION (dummy, 1-Yes) and TTO (dummy, 1-Yes), and z is a set of variables,
including the constant term and independent control variables. The two measures of productivity of
scientists-patenting and publishing-are likely to be strongly correlated, we use ω referring to the
unobserved heterogeneity component. As such, x1 is correlated with ω . Given that SCI is also a non-
7
We coded scientists’ scientific discipline referring to the National Discipline Classification of China.
21
negative discrete variable, we apply non-linear instrumental variable estimation of models of patenting
and publication.
We use two instrumental variables for explanatory variable SCI: ResearchFellow and Funding.
ResearchFellow is a dummy variable with 1 indicating scientists with Professor or Associate professor
title who mainly pursue the success in basic research. In CAS, there are two professional tracks for
scientists career development, one is research fellow and the other is engineer track, for scientists in
research fellow track, publications are the most important basis for career development. Hence, we use
ResearchFellow as an instrument of SCI. Funding is a variable indicating the relative position of
funding scientists obtained comparing the average level of their peers. It was confirmed that funding is
positive related to research outputs (Arora and Gambardella, 2006; Jacob and Lefgren, 2007). We ask
the respondents to score the five-point scale. About 43% of respondents consider themselves below the
average, 40% consider at the average, only 17% above the average.
Our hypothesis predicted positive moderations of Industry Interaction and TTO on the relationship
between patenting and publishing. To test for the moderation effects, we must examine the interaction
term contributing significantly to the variance explained in the dependent variable. Since SCI is
endogenous, interaction terms between SCI, COLLABORATION and TTO will probably be endogenous
too. Therefore, we also need to find instruments to correct the endogeneity of the interaction terms. By
Woodrige (2010) and BalliandSørensen (2012), we get the instruments for interaction terms in three
steps: ① Estimate a reduced form probit for SCI using all exogenous variables and two instrumental
variables, getting the fitted probabilities, say xg.
②
Construct instrumental variables
xg_w=xg*COLLABORATION, xg_z=xg*TTO, xg_wz=xg*COLLABORATION*TTO ③ We use xg,
xg_w xg_z and xg_wz as instruments for SCI and endogenous interaction terms.
Mullahy (1997) argued that maximum likelihood (ML), quasi-ML (QML), nonlinear least squares
(NLLS) is not suitable for count data models when the unobserved heterogeneity is correlated with
explanatory variable, and proposed GMM approach. Given that SCI is regarded as endogenous in our
22
model, we give up Zero-inflated negative binomial (ZINB) model of the likelihood-based method and
estimate a GMM model to test the hypotheses. As Mullahy (1997) suggests, for such exponential mean
regression in Eq.1, the appropriate moment restriction underlying the GMM estimator is:
E[𝑦 ∗ exp(−（𝛽1 x1 + β2 x2 + β12 x1 x2 + δz）) − 1|𝑤] = 0
(2)
where 𝑤 is a vector of instrumental variables, and 𝑦 ∗ exp(−（𝛽1 x1 + β2 x2 + β12 x1 x2 +
δz）) − 1 is the residual function.
We use two-step GMM method in STATA 12 to estimate the model. Nevertheless, the
interaction terms are treated as separate variables in regression, not a product of two variables that
appear elsewhere in the model. That means, the coefficients on the interaction terms does not provide
the change in the partial effect of either variable on the conditional mean function if the function is
nonlinear. The interaction effect requires computing the cross derivative (Ai & Norton, 2003). Based
on Greene (2010), we calculate the marginal effects of x1 and x2 in the model as follows:
∂E[𝑦|x1 , x2 , 𝑧]
= (𝛽1 + β12 x2 ) ∗ exp(𝛽1 x1 + β2 x2 + β12 x1 x2 + δz)
∂x1
(3),
ΔE[𝑦|x1 , x2 , 𝑧]
= E[𝑦|x1 , x2 = 1, 𝑧] − E[𝑦|x1 , x2 = 0, 𝑧]
Δx2
= exp(𝛽1 x1 + β2 + β12 x1 + δz) − exp(β1 x1 + δz) (4),
and for the interaction effect, it should be calculated with Eq. (5),
∂(ΔE[𝑦|x1 , x2 , 𝑧] ∕ Δx2 )
∂x1
= (𝛽1 + β12 ) ∗ exp(𝛽1 x1 + β2 + β12 x1 + δz) − β1 ∗ exp(β1 x1
+ δz)
(5)
The marginal effect of the interaction term shows how the partial effect of x1 varies with a moderate
switch of x2. Greene (2010)argue that the process of statistical testing about partial effects, and
interaction terms in particular, produces generally uninformative and sometimes contradictory and
23
misleading results in nonlinear models, and suggested graphical analysis. Then, we further probe the
interaction effect in graphical presentations adjunct to numerical statistical results. In order to reduce
potential multicollinearity and to get conditional effects, we first mean centered continuous indicator,
including predicted instrument variable, and then computed crossproducts (Aiken & West, 1991).
24
V.
RESULTS
As a prelude to the analysis, Error! Reference source not found. presents summary statistics
of the dependent and independent variables, moderators, instrumental variables and control
variables. Columns 2 and 3 list the mean and standard deviation for the whole sample, and column
4-19 list the correlations between variables.
25
Table II
Variables
Mean
Std.D
(1)
(2)
(3)
(4)
(5)
(6)
(7)
CORRELATIONS
(8)
(9)
(10)
(11)
(12)
(13)
(14)
PATENT
1.950405
3.317085
1.0000
SCI
4.024311
6.812125
0.4485*
1.0000
AGE
35.83922
7.893508
0.2608*
0.3197*
1.0000
PH.D
.6508914
.4767656
0.1726*
0.3025*
0.0812*
1.0000
MASTER
.2236629
.4167662
-0.1440*
-0.2364*
-0.2430*
-0.7329*
1.0000
BACHALOR
.1218801
.3272003
-0.0649*
-0.1389*
0.1807*
-0.5087*
-0.2000*
1.0000
Resource&Environmen
t
.1153971
.3195523
-0.0497*
-0.0358‡
0.0814*
0.0410‡
-0.0453‡
0.0019
1.0000
Metal&Materials
.1111831
.3144098
0.0662*
0.0307＃
-0.0238
0.0687*
-0.0686*
-0.0120
-0.1277*
1.0000
Biotechnology
.1529984
.3600443
-0.0756*
0.0408‡
-0.0153
0.0015
-0.0077
0.0068
-0.1535*
-0.1503*
1.0000
TripleE
.2596434
.43851
0.0624*
-0.1728*
-0.0996*
-0.0998*
0.1328*
-0.0195
-0.2139*
-0.2095*
-0.2517*
1.0000
Chemistry
.1293355
.3356253
0.0958*
0.1739*
0.0798*
0.0350＃
-0.0469*
0.0100
-0.1392*
-0.1363*
-0.1638*
-0.2282*
1.0000
Physics
.2314425
.4218229
-0.0882*
0.0107
0.0091
-0.0076
-0.0087
0.0140
-0.1982*
-0.1941*
-0.2332*
-0.3250*
-0.2115*
1.0000
COLLABORATION
.4262561
.4946121
0.2981*
0.1262*
0.2685*
0.0496*
-0.0993*
0.0576*
-0.0138
0.0725*
-0.0714*
0.0233
0.0584*
-0.0534*
1.0000
TTO
.6353323
.4814149
0.1509*
0.0514*
-0.0619*
-0.0350＃
0.0592*
-0.0203
-0.0741*
0.2572*
0.0788*
0.1968*
0.0191
-0.4225*
0.0675*
1.0000
ResearchFellow
.8350081
.3712334
0.1213*
0.1994*
0.1339*
0.3981*
-0.1617*
-0.3576*
0.0922*
-0.0122
-0.0076
0.0083
0.0074
-0.0687*
0.0494*
0.0352＃
Funding
2.502755
1.080908
0.2417*
0.2185*
0.2807*
0.1419*
-0.1626*
0.0009
-0.0028
-0.0119
-0.0619*
0.0837*
0.0182
-0.0377‡
0.2243*
0.0022
(15)
(16)
1.0000
0.1187*
1.0000
*,p<0.01;‡, p<0.05;＃,p<0.1
26
Table III reports GMM regression coefficients with significance for dependent variable (PATENT),
and corresponding margins (partial effects) of variables on PATENT calculated in Eq. (3)-(5) are
presented in Table IV. The general rule of thumb is that if the coefficient on a variable is significant,
the marginal effect is significant (Stephan et al., 2007). Hansen’s J-test statistic for over identifying
restrictions was calculated, and showing that there is no indication of misspecification at conventional
levels of significance.
TableIII GMM centered result-coefficients
PATENT
SCI
Model1
0.124
(0.013)***
COLLABORATION
SCI×COLLABORATION
Model2
0.065
(0.012)***
0.771
(0.068)***
-0.011
(0.008)
TTO
Model3
0.069
(0.013)***
0.383
(0.092)***
-0.011
(0.010)
SCI×TTO
COLLABORATION×TTO
SCI×COLLABORATION×
TTO
AGE
GENDER
PHD
MASTER
ResourceEnvironment
MetalMaterials
Biotechnology
TripleE
Chemistry
Constant
Hansen’s J(p value)
N
-0.026
(0.014)*
-0.049
(0.106)
-0.526
(0.220)**
-0.334
(0.153)**
0.673
(0.246)***
0.913
(0.193)***
-0.062
(0.188)
1.227
(0.196)***
0.171
(0.171)
-0.097
(0.157)
0.6321
3,085
0.019
(0.006)***
-0.103
(0.066)
0.346
(0.133)***
0.067
(0.123)
0.194
(0.124)
0.558
(0.098)***
-0.078
(0.115)
0.749
(0.097)***
0.303
(0.095)***
-0.525
(0.113)***
¶
3,085
0.026
(0.007)***
-0.182
(0.070)***
0.324
(0.134)**
-0.016
(0.125)
0.162
(0.142)
0.511
(0.119)***
-0.166
(0.125)
0.690
(0.122)***
0.264
(0.100)***
-0.303
(0.123)**
¶
3,085
Model4
0.093
(0.020)***
1.144
(0.134)***
-0.046
(0.021)**
0.654
(0.115)***
-0.038
(0.019)**
-0.507
(0.152)***
0.044
(0.023)*
0.020
(0.007)***
-0.096
(0.066)
0.364
(0.131)***
0.045
(0.122)
0.145
(0.140)
0.387
(0.120)***
-0.166
(0.126)
0.619
(0.121)***
0.221
(0.102)**
-0.913
(0.144)***
¶
3,085
Notes. Continuous variables are centered. Standard errors are in parentheses.
* p<0.1; ** p<0.05; *** p<0.01
‡ ResearchFellow and Funding are the instruments for SCI, p value >0.1 that means we can’t reject the null hypothesis.
¶ xg and xg_w are instruments for SCI and interaction term SCI*COLLABORTION. It is no need to do overid test.
xg and xg_z are instruments for SCI and interaction term SCI*TTO. It is no need to do overid test.
xg, xg_w and xg_z wz xg_wz are instruments for SCI and interaction terms. It is no need to do overid test.
27
We find patents and publications to be positively and significantly related (Model 1), scientists with
more publications are more likely to have more patents. A 1% increase in publications raises the number
of patent applications by approximately 0.242% (in TableI). Conversely, scientists with less
publications also applied fewer patents. Since patents were introduced into science award system,
scientists will be more willing to disclose their findings in both papers and patents that will enhance
their bargaining power to get more funding. Our hypotheses 1 is supported. Some demographic
characteristics have a significant impact on the expected number of patents, field of discipline also
plays a key role in determining the number of patents. Consistently we find that scientists of TripleE
make more patent applications than the benchmark of those working in physics.
Table II The impact of publishing on patenting estimates
PATENT
SCI
Model1
Coefficient
Margin
0.124
0.242
(0.013)***
Model2
Coefficient
Margin
0.065
0.112
(0.012)***
Model3
Coefficient
Margin
0.069
0.118
(0.013)***
Model4
Coefficient
Margin
0.093
0.109
(0.020)***
COLLABORATION
0.771
(0.068)***
1.326
1.144
(0.134)***
1.296
SCI×
COLLABORATION
-0.011
(0.008)
0.058
-0.046
(0.021)**
0.049
TTO
0.383
(0.092)***
0.557
0.654
(0.115)***
0.487
-0.011
(0.010)
0.015
-0.038
(0.019)**
0.015
COLLABORATION
×TTO
-0.507
(0.152)***
0.063
SCI×
COLLABORATION
×TTO
N
0.044
(0.023)*
0.058
SCI×TTO
3,085
3,085
3,085
3,085
Notes. Continuous variables are centered. Standard errors are in parentheses.
* p<0.1; ** p<0.05; *** p<0.01
However, industry interaction did not moderate the relationship between publication and patenting
(model 2). That means, scientists won’t disclose their findings in two ways of papers and patents for
the reason of collaborating with industry. Hypotheses 2 is not supported. Similarly, TTO did not show
moderation function on the relationship between publication and patenting (model 3), which does not
28
support hypothesis 3. Nevertheless, the presence of TTO has a significant impact on patenting between
male scientists and females (GENDER=1 if female).
When combining the moderations of TTO and industry interaction together, however, we find the
results displayed in model 4 indicate that the three-way interaction is significant at 0.1 level. The margin
of the product term shows the interaction effect, a 1% increase in publications raises the number of
patent applications by approximately 0.442% for scientists who have collaborations with industry and
in a context of TTO. To probe for the interaction effect, we figured the impact of publication on
patenting with different combinations of collaborations with industry and TTO using the procedure
outlined by Greene (2010). Figure 1 depicts the pattern of moderated results related to hypothesis 4.
The highest levels of patenting are observed when publication is high, with industry interaction and in
a context of TTO. This figure also shows that when publication is high, COLLABORATION equal to
1, but TTO equal to 0, levels of patenting are also high. The more moderate expectations for two further
combinations are also apparent in figure 1, which are high publication, no collaboration and in a context
with TTO, and low publication, having collaboration and in a context without TTO. The lowest level
of patent is observed when publication is low, neither collaboration nor TTO. The pattern of results is
largely consistent with our predictions, offering support for Hypothesis 4.
Figure 1. Industry interaction and TTO as moderators of the relationship between publications
and patents
29
VI.
DISCUSSION
In this paper, we studied the relationship between publications and patents in the case of CAS
scientists and the impacts of institutions on this relationship to examine the new features of Chinese
innovation system from science-side perspective. Our study makes three unique contributions to the
literature. First, we hypothesize and find that the significantly positive relationship between
publications and patents of Chinese scientists, the results further verify the proposition in the China’s
context that a single research investment can simultaneously yield scientific knowledge as well as
new technology (Stokes, 1997;Fiona & Scott, 2007). Second, we hypothesize and find the
combination of industry interaction and organizational structure play a significantly positive
moderation on the relationship between papers and patents, which indicates the performance of
Chinese innovation system. Third, the results extend the work of Henderson et al. (1998)
Murray&Stern (2007) by examing the moderation of institutional environments, finding the visible
hand pushing Chinese public research into Pasteur quadrant.
In China, that IPR was regulated into science reward system has greatly bringing the dramatic
increase of academic patenting in universities and public research institutes. While, the positive
correlation that we find between publishing and patenting suggests that the commercialization has
not come at the expense of the production of knowledge. Patenting is perceived to contribute to
scientists’ scholarly reputation and influence, and then for scientists with both papers and patents will
have more bargaining power to obtain more scientific resources. In other words, the rise of academic
patenting may have reinforced the highly stratified power structure of academic science (Murray &
Stern, 2007). The results show that older scientists with PH.D have applied for more patents than
those younger and less educated scientists. Scientists who have traditionally been in weak positions
may be further disadvantaged by their lack of patents. Therefore, even though academic patenting
promotes scientific research activities into Pasteur Quadrant, however, which on the other hand may
exacerbate the "Matthew Effect" of scientific community.
30
Logically speaking, industry interaction will on the one hand encourage scientists to disclose
their research findings in patents, and on the other hand spirit scientists getting new ideas of
knowledge production. However, our research did not find a moderation function of industry
interaction on the relationship between publishing and patenting. Why not? One potential possibility
is that, because enterprises always require to protect innovation rents firstly in the form of patents,
scientists usually miss good time to publish, which results in papers substituted by patents (Thursby
& Thursby, 2000). Another more possibility is that, the vast majority of research funding of CAS is
financial support funded by both central and local governments, only a few from industry. CAS
scientists often disclose their research findings firstly in publications and then in patenting, so as to
enhance their bargaining power with other research scientists in lobbying for governments support.
Moreover, influenced by linear innovation model, CAS scientists often regard collaborating with
industry as transferring their research outputs into enterprises to earn a direct commercial advantage,
rather than generating new research ideas spirited by market demand and co-studying with enterprises.
In other words, before collaborating with industry CAS scientists have already disclosed their findings
in both ways, industry interaction has not significantly stimulated knowledge production.
The establishment of TTO does not positively moderate the relationship between publication
and patenting either. That means, comparing to those in research institutes without TTO, scientists
from research institutes with TTO are not more likely to disclose their research findings in both ways.
In fact, almost all dedicated TTOs of RIs of CAS were set up after 2000, some of them even after
2006 when CAS entered the third period of KIP. Full time employees of most of these TTOs are not
more than 4, who are either recruited fresh graduated students or researchers considered to be with
lower research capability. Carlsson and Fridh (2002) argued that the larger the TTO, the broader is
the in-house expertise, and the more aggressive the pursuit of patents. Obviously, the short time and
lack of expertise hinder established TTOs of CAS playing their role.
31
Although neither industry interaction nor TTO separately positively moderate the relationship
between papers and patents, the combination of two moderators play a significantly positive
moderation on the relationship. The slope of the equation mapping the association of papers and
patents is most positive when TTO=1 and COLLABORATION =1, the combination of
COLLABORATION =1 and TTO=0 shows the next most positive slope, and the combination of
COLLABORATION =0 and TTO=1 shows the third positive association. Overall, it appears that
scientists with high publications, having the experience of industry interaction and working in a
context with TTO, result in the highest level of patents. However, scientists with high publications
having industry interaction can still have high levels of patents, even when they working in research
institutes without TTO. Although we expected that the combination of TTO=1 and industry
interaction =0 would result in a relatively high patents, we did not predict that the expected patents
would be similar to those when TTO=0 and COLLABORATION=1. At the other end of the spectrum,
the lowest slope for the graphed relationship between papers and patents is as expected when TTO=0
and COLLABORATION=0. Taken together, our results indicate that having an internal driver and
an experience of industry interaction are more important than having a supporting context of TTO.
This could be in part because TTO is only an organizational structure of promoting collaboration
between science and industry. For scientists, they must have motivations to collaborate with industry,
and seeking to find new research ideas in the process of industry interaction but not following foreign
research.
VII.
LIMITATIONS AND DIRECTIONS FOR FUTURE RESEARCH
Several limitations to the current study suggest opportunities for future research. One limitation
is the potential for same source bias affecting results. Our data reflect the situation of Chinese
scientists under the auspice of CAS, although CAS is the typical case of Chinese public research
institutions and the sample includes more than 3000 observations, but the relatively absolute size of
the sample raises the question of generalizability to the national innovation system. Therefore, future
research might address multi sources of observations who work in other public research institutes or
32
universities. Western scholars always concern the role of universities in national innovation
system(Ekwitz, 2000; Rothamatel et al., 2007), ignoring public research institutes, comparison
between universities and public research institutes may be worth investigating in contributing
theoretical development of national innovation system.
Second, most of our data are self-reported. However, we attempted to mitigate concerns about
response accuracy (and common method bias) by controlling questionnaire’s length and designing
questions easy to answer. It has been proposed that scientists may be more likely to exaggerate their
research outputs, but what we are caring is the relationship between papers and patents, we think
the…
Third, the cross-sectional design of the current study limits our ability to make causal inferences
about the observed relationships. The fact that time lag exists between publishing and patenting, leads
scholars studying the causal directions between publications and patents to reflect the relationship
between basic research and applied research(Henderson et al, 2005; Geuna, 2006 ) . However, a
prerequisite for such studies is to match papers with patents to be paper-patent pairs (Fiona, 2002;
Fiona & Scott, 2005). Although the patent database of SIPO couldn’t offer index information of
patents, future studies might embrace efforts to generate paper-patent pairs by keywords searching
and to collect longitude data.
Finally, TTO entered the model as an institutional variable, which means we supposed that
scientists of research institutes with TTO had got the assist from TTO in the process of patenting. In
fact, however, scientists perhaps didn’t get the support of TTO, or their patenting activities were not
influenced by TTO but only for personal motivations. Therefore, future studies should explore more
in depth in the mechanisms of TTO in academic patenting and technology transfer.
VIII. CONCLUSION
The first study on China’s innovation system – IDRC’s (1997) report has described only elements
of the system, including categories of actors, institutions and policies, so does OECD’s report (2007)
33
ten years later. Liu & White (2001) tried to analyze system level characteristics of Chinese innovation
system to describe its evolution. But, all of them failed to show how elements of the system interacted
and the performance of Chinese innovation system. In this paper, we show a detailed analysis of how
scientists into innovation system under the interaction of institutions and policies. In particular, we
suggest the change of Chinese innovation system from traditional linear form focusing on technology
spillovers and complex interactions between science and technology to Pasteur’s Quadrant in which
policy should reorient to evaluate the interactions and inter-relationships between institutional
mechanism that allow knowledge to be diffused and exploited by both scientific and technological
communities.
ACKNOWLEDGEMENT：
We thank ***, Participants in *** conferences and seminars for comments and suggestions.
Hongyu Su and Jing Zhang provided excellent research assistance. Financial support for this research
was provided by the National Natural Science Foundation, under grant no. 71103174 and Senior
Visiting Scholar Exchange Program of CAS.
34
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