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Does institutional context matter in building innovation capability?

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Our study investigates whether changes in China's reform policies had an influence on the national innovation capability building. Specifically, our study empirically examines the relationship between national innovation capability and the roles of key drivers from 33 administrative regions across the two periods of the reform (1991-1998 and 1999-2005) with four types of domestic patents during 1992-2009. The data is drawn from government official statistics, using STATA for panel analysis. Overall findings demonstrate that the innovation environment was changed and consequently changed the impact of drivers on China's innovation capability differently between the two periods, which helps provide a better understanding of the effect of innovation system reform in each phase in China. We extend research on innovation capability to emerging economies and enhance our understanding of how the government policies shape a country's innovation capability through mechanisms of key innovation drivers in emerging economies.
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Does Institutional Context matter in Building Innovation Capability?
Sukanlaya Sawang, PhD
Queensland University of Technology, Brisbane, Australia
Ying Zhou*, PhD
Nanjing Audit University, Jiangsu, China
Xiaohua Yang, PhD
University of San Francisco, California, U.S.A.
*Requests for reprints or further information should be made to:
Dr Ying Zhou
Email: yuxiyulu@126.com
Nanjing Audit University
77 Beiwei Rd, Gulou, Nanjing, Jiangsu, China.
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Does Institutional Context matter in Building Innovation Capability?
ABSTRACT
Our study investigates whether changes in China’s reform policies had an influence on the
national innovation capability building. Specifically, our study empirically examines the
relationship between national innovation capability (national patent data as a proxy) and the
roles of key drivers (i.e. industry actors, financial and human capital inputs, inward foreign
direct investment, international trade and domestic technology transfer) from 33
administrative regions across the two periods of the reform (1991-1998 and 1999-2005) with
time lag of four types of domestic patents during 1992-2009. The data is drawn from
Government official statistics, using STATA for panel analysis. Overall findings demonstrate
that the innovation environment was changed and consequently changed the impact of drivers
on China’s innovation capability differently between the two periods. The change of impact
provides indications of which strategies or innovations worked better during a specific
period, which in turn helps provide a better understanding of the effect of innovation system
reform in each phase in China. We contribute to the literature by extending research on
innovation capability to emerging economies like China and by enhancing our understanding
of how the government policies shape a country’s innovation capability through mechanisms
of key innovation drivers in emerging economies.
Keywords: China, Reform, Foreign Direct Investment, Employment, Science and
Technology, Patents
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Does Institutional Context matter in Building Innovation Capability?
1. Introduction
National innovative capability is the political and economic ability of a country to
create technological innovation (Furman, Porter and Stern 2002). The national innovative
capability framework draws on prior research of National Innovation Systems (NIS)
identifying country-specific drivers of innovation (Sharif 2006; Li and Cantwell 2010).
China’s economic reform, which began in 1978, aimed to improve its Research &
Development (R&D) system with a market-dominated economy by transforming its R&D
system from a centrally planned one to a system that was more market-orientated (Baark
2001). Using descriptive statistics before and after the reform policies, prior research
concluded that a number of key reform policies1 influenced the key drivers of innovation
capability, such as the expenditure on R&D activities, Foreign Direct Investment (FDI),
numbers of new enterprises, and the development of human resources in Science and
Technology (Chen, Chen and Vanhaverbeke 2011). Evidently, these studies suggest that
China’s innovation-related policies during the reform influenced these key drivers of
innovation. However, we know context matters (Yang and Terjesen, 2007) and how the
policy shifts during the different phases of the reform led to different impacts and outcomes
to innovation capability. The question remains: How did changes in the institutional context
in different phases impact on the national innovation capability in China?
The importance of studying innovation system in China has been widely
acknowledged and researchers have conducted much research in regions of China. Zhong
and Yang (2007) and Zhu and Tann (2009) investigated the long term reform process with a
1 Such as the 1995 Decision of the Central Committee of the Chinese Communist Party on
Accelerating Scientific and Technology Progress, and the 1999 Decision of the Central Committee of
the Chinese Communist Party on Strengthening Technological Innovation and Developing High
Technology and Realizing Industrialization.
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qualitative approach, while Liu and Chen (2003) compared innovation system across 12
regions. There are also studies based on overall regions in China. Wu and his colleagues
(2010) measured the performance of innovation capability across 30 regions in Mainland
China (in the following, referred to as China for short) during 2001 and in 2005 employed a
Data Envelope Analysis based model. Li (2009) focused on the impact of interactions
between components of innovation capability between 1998 and 2005, covering 30 regions in
China and using econometric models. Although there are many studies on innovation system
in China, to our knowledge, none of them employ a quantitative approach to systematically
investigate the differences between major drivers in different phases of the transitional
process.
To fill this gap, our study thus aims to firstly explore these questions in twofold.
Firstly, our study explains the context of China’s reform policies. These policies lead to
changing role of key enablers of innovation capability (such as industry sector (private
enterprise, higher education), employment of scientists and engineers, funding in science and
technology and foreign direct investment). Secondly, our study empirically examines the
extent to which the changes of these enablers impact on China’s innovation capability across
two major periods of the reform.
The remainder of this paper is organized as follows. In section 2, we review the
national innovation system framework and discuss the role of nation-based institutions in
national innovation performance. In section 3, we review the economic growth and historical
changes in the industry structure in China. In sections 4, we provide justification of using
various types of patenting as a proxy of China’s innovation capability and then we propose
key drivers of China’s innovation capability to be explored in section 5. The methods and
results of panel data analysis are provided in sections 6 and 7. We then conclude our paper
with a discussion of our results, future direction and limitations in sections 8 and 9.
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2. China’s innovation system through National Innovation System (NIS) lens
The concept of NIS appeared simultaneously in the academic world and policymaking
fields in the 1980s (Sharif 2006). It was developed to analyze economic growth, taking
innovation and learning into account when neoclassical economic thought was inadequate
(Lundvall 2007). Early NIS research was put into a historical, political and cultural context
(Balzat and Hanusch 2004) and historically there are three main stances taken by researchers
when conducting NIS studies: historical (Freeman 2004; Lundvall 2007), institutional
(Nelson 1993; Niosi et al. 1993), and evolutionary (Edquist 2004). Nonetheless, in most
studies the perspectives were combined to some extent, as the NIS approach itself employed
historical and evolutionary perspectives (Edquist 2004). Despite more than 20 years
development, a generally accepted definition of NIS is still lacking (Edquist 2004), with
researchers holding their own viewpoint on the meaning of NIS.
NIS was first formally defined by Lundvall (2007), focusing on knowledge and process of
learning. Next it was redefined by Nelson (2008), focusing on the analysis of institutions and
how countries set up their NIS, and finally by Edquist (2004). The first two definitions are
based on an institutional perspective while the last uses an evolutionary perspective.
Consequently, the two main definitions of NIS are:
“It is constituted by elements and relationships which interact in the production,
diffusion and use of new, and economically useful, knowledge and that a national system
encompasses elements and relationships, either located within or rooted inside the
borders of a nation state”. (Lundvall 1992, 2)
“The NIS is a set of institutional actors that, together, plays the major role in
influencing innovative performance”. (Nelson 1993, 4)
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From Lundvall’s (1992) perspective, NIS is comprised of elements such as innovation actors
and institutions, and their relationships in the production, diffusion and use of new
knowledge within the borders of a nation. Nelson’s (1993) definition focuses more on the
role of institutions in innovation activities. Differing from the previous two definitions,
Edquist (1997) argued NIS could be defined by identifying the determinants of innovations.
Hence, in a broad way, NIS includes all parts and aspects of economic structure and the
institutional set-up that may influence the development, diffusion, and use of innovations. In
other words, NIS consists of several sub-systems, such as an education and training system,
production system, marketing system, and financial system (Lundvall 1992). At the very least
organizations and institutions involved in innovation should be included. Consistent with the
sub-systems mentioned above, organizations can be, for example, governments, universities,
R&D departments, firms, banks, and financial agencies(Aeron and Jain 2015; Plechero and
Chaminade 2016). The major functions of these institutions are policy formulation,
promotion of human resources, performing R&D activities, financing R&D, technology
bridging, and promotion of technological entrepreneurship (Chang and Shih 2004).
Accordingly, the NIS approach highlights the importance of interactions and the role of
nation-based institutions in national innovation performance (Asheim and Coenen 2006).
NIS consists of all the factors that may affect innovation activities. A variety of
components can be considered part of these factors (Sawang & Unsworth, 2011), but
organizations and institutions are always considered to be the major ones (Edquist 2004).
Organizations refer to firms, banks, universities, research institutes, and government
agencies, and institutions are the “rules” (Scott 1995) organizations are embedded in
(Hamilton and Biggart 1988) and have to conform to (North 1990).
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3. Transition of China’s Economy and China’s Innovation System
It is worthwhile to provide a brief historical review of China’s economy reforms here
in order to understand the context and the justification of the two periods (1991-1998) in our
analysis. Since its foundation, the People’s Republic of China (PRC, referred to as China in
the following) has undergone tremendous change. The crucial shift took place in 1978 by the
reformists within the Communist Party of China, also known as Chinese Communist Party
(CPC), led by Deng Xiaoping. Since then China has stepped into the market-oriented
reforms and economic transition under the guidance of the central government, following
both a top-down and bottom-up approach, which has led to extraordinary economic
development.
The economic reforms also brought extraordinary changes in the innovation system.
However, before the reform in science and technology started in 1985, there was not exactly
an innovation system in China. Rather, it is more appropriate to call it a science and
technology system. An innovation system consists of elements of consequence to innovation,
such as firms, universities and research institutes, as well as economic, social and institutional
factors, and the relationships between the elements ((Edquist 1997; Cooke, Uranga and
Etxebarria 1997). Before the science and technology reform, China focused on developing
scientific technologies and innovations that mainly relied on research institutes. Meanwhile
there were few interactions between different innovation actors. Therefore, strictly speaking,
there was no innovation system in China at that time. With the progress of the science and
technology reform, the innovation system has been gradually built up.
The reform process of the economic and innovation system in China has been gradual
(Chow 2004; Bagnai and Ospina 2009; Yang and Li 2004): the government undertakes
experiments in a specific area and monitors the outcomes to decide whether to extend the
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reforms nationally (Yang and Li 2004). Our study focuses on two critical periods of
innovation policy changes: 1991-1998 and 1999-2005.
During 1991-1998, the government first implemented the outline of long and
medium-term development of Science and Technology (1991-2000), leading China to a new
era of the transformation of national innovation system. The role of government, both central
and regional, in innovation was transferring from mandatory to directing, and the
development emphasis was changing as well. Based on the outline of long and medium-term
development of Science and Technology, the government put great effort into building up
technology markets to facilitate technology transformation. During this period public
research institutes and universities were also encouraged to set up their own high-tech
enterprises. Researchers and teachers could take part-time or full-time jobs in the enterprises
or establish their own high-tech companies while remaining in their positions in public
research institutes or universities. Those activities led to 16,097 high-tech enterprises being
established in national high-tech development zones all over the country by the end of 1998
(Zhong and Yang 2007).
From 1999-2005, the strategy of “building the nation with science and education” was
reaffirmed and the objective of building China’s innovation system was highlighted. At this
stage the reform focused on the macro level, which is different from the micro level from last
period. The central government released the Decision on Strengthening Technological
Innovation, Developing High-Tech Firms, and Realizing Commercialization of New
Technologies, which highlighted the emphasis of this stage; strengthening China’s innovation
system and accelerating the transformation of science and technology achievements. The
decision recognized the complex relationships between reforms in the economy, science and
technology, education, and innovation (Zhong and Yang 2007). Therefore, to realize the
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objectives, innovation actors were encouraged to increase financial investment in innovation
activities and collaborate with each other.
4. Patenting as a proxy of China’s innovation capability
Domestic patents have been widely used to capture the national innovation activities
in the literature (e.g. Li 2009; Acs, Anselin and Varga 2002). Patent statistics offer the best
available output indicator for innovation activities (Freeman 2004). Because some innovative
products or services may not be patentable, some studies thus used alternative innovation
measures such as the number of new products (Fritsch 2002), new product sales (Liu and
White 1997), and literature-based innovation counts (Acs, Anselin and Varga 2002), which
still generate the similar pitfalls.
The State Intellectual Property Office (SIPO) has systematically collected domestic
patents since 1985 when China’s patent law came into force. According to patent law,
domestic patents are classified into three categories: invention, utility model and design.
Inventions represent the most technologically sophisticated innovation output, radical
innovations such as a new products or new methods. Utility models are less innovative
compared to inventions. They are incremental innovations, such as the structure change of a
product. Designs mainly reflect superficial novelty, such as changes to the shape and color of
a product. The basic condition for a patent to be granted is whether it differs from existing
technologies and designs, both domestically and internationally, regardless of the patent type.
For inventions and utility models, they have to be novel, inventive and practically applicable.
As a result, the variation of patent quality is remarkable across the three categories and they
differ from each other in terms of novelty, economic value, technological importance, and
resource commitment (Li 2009).
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We adopted the domestic patents because they are more comparable and all the
regions are subject to the same national patenting laws (Koo and Wright, 2010). Specifically,
in China there are 33 administrative regions in PRC, including 22 provinces2 ; five
autonomous regions3 ; and two special administrative regions.4 Therefore, domestic patents
are deemed to be appropriate for our study. Four types of domestic patents are used; (a)
overall patent applications, (b) overall granted patents; (c) granted invention patents; and (d)
granted utility model patents.
5. Key determinants of China’s innovation capability
Our study identifies key determinants of China’s innovation capability as (a) industry,
including firms, research institute and universities; (b) financial input through science and
technology funding; (c) human capital though numbers of scientists and engineers and
employment rate; (d) inward foreign direct investment; (e) international trade and (f)
domestic technology transfer. A proposed model is shown in Figure 1.
-----------------------------------------------
Figure 1 is about here
------------------------------------------------
Firms, research institute and universities are the organizations that can generate
innovations directly. Firms engage with productive activities; research institutes influence
idea generation; and universities transfer knowledge and provide human capital (Sawang,
2011). In our study, we employ the numbers of universities, special colleges, such as medical
schools and musical colleges, and professional technology colleges representing Higher
2 Anhui, Fujian, Gansu, Guangdong, Guizhou, Hainan, Hebei, Heilongjiang, Henan, Hubei, Jiangsu, Jiangxi, Jilin, Liaoning, Qinghai,
Shaanxi, Shandong, Shanxi, Sichuan, Yunnan, Zhejiang; four municipalities, Beijing, Shanghai, Tianjin, Chongqing (separated from
Sichuan in 1997)
3 Guangxi, Inner Mongolia, Ningxia, Tibet, and Xinjiang
4 Macau and Hong Kong
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Education Institutions (HEI) and the number of large and medium-sized industrial
enterprises. We propose:
Hypothesis 1: Higher education institutions and industrial enterprises will have a
positive influence on innovation capability (i.e. four types of domestic patents) in China.
Resource commitments such as funding for science and technology activities, R&D
expenditure (Freeman 2004; Lundvall 1992; Park and Park 2003; Pan 2007; Evangelista et al.
2001), and numbers of scientists and engineers (Lundvall 2007) are considered as the most
direct input factors to innovation activities. These key resources have also been reflected by
the policy reform as previously discussed. Our study uses the funding for science and
technology activities as a key indicator because it can be used for all science and technology
related activities, including research and development (R&D) activities, purchase or
construction of fixed assets and it may represent the effort put into innovation development
better than R&D expenditure. Apart from funding, the number of scientists and engineers are
also important for innovation capability building. However, scientists and engineers also
need support from other staff with general administrative issues. This is why employment
rate is also included. We propose:
Hypothesis 2: Financial input through funding for science and technology will have a
positive influence on innovation capability in China.
Hypothesis 3: Human capability through number of scientists and engineers,
employment rate will have a positive influence on innovation capability in China.
Inward Foreign Direct Investment (FDI) is one of the main channels transferring
technologies from the source countries to the host countries (Zhu and Jeon 2007). During
1991-1998, China FDI grew rapidly, creating US$45,463 million by 1998. However, the
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inward FDI has been dropped during 1999-2005 periods, due to Asian Financial Crisis and
the acquisition transactions. A part of inward FDI, the numbers of international trade and
domestic technology transfer is likely to reflect the innovation building capability in China
(Lin and Lin 2010; Chuang and Hsu 2004). Accordingly we propose broad hypotheses as
following:
Hypothesis 4: Inward FDI will have a positive influence on innovation capability in
China.
Hypothesis 5: International trade will have a positive influence on innovation
capability in China.
Hypothesis 6: Domestic technology transfer will have a positive influence on
innovation capability in China.
6. Methods
Our study employs secondary data to uncover the longitudinal impact of key drivers
on innovation activities in China. The data were drawn from the official documents and
statistics. Official statistics are permanent, and less time consuming or costly (Bryman and
Bell 2003; Emory and Cooper 1991). They can also result in unforeseen discoveries
(Saunders, Lewis and Thornhill 2003). What is more important is they are suitable for
longitudinal studies (Saunders, Lewis and Thornhill 2003; Bryman and Bell 2003) and they
can be used to make powerful comparisons between different groups, societies and nations
(Smith 2008). Moreover, official statistics, such as statistic yearbooks, are not based on
samples, so a complete picture can be obtained.
Our data samples are 33 administrative regions in China across two phases: Phase
One starts in 1991 and extends to 1998, which corresponds to the fourth stage of the reform.
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Phase Two extends from 1999 to 2005, which aligns with the fifth stage in the long run.
Considering time lag between input and output, the time range for dependent variables is
from 1992 to 2009.
Our dependent variables (proxies of national innovation capability) are the number of
(DV1) total patent applications (DV2), overall granted patents, (DV3) granted invention
patents and (DV4) granted utility model patents per million people as our dependent variables
(innovation activities). Our determinants of national innovation capability are the number of
higher education institutions (HEIs), industrial enterprises (IE), GDP per person, (GDP)
funding for science and technology activities (FUND), full-time employed scientists and
engineers per million persons (SCI-ENG), employment rate (EMPLOY), inward foreign
direct investment (FDI), international trade (TRADE) and domestic technology transfer
(TECH).
7. Results
As discussed earlier, the time frame of this study is divided into two phases according
to the reform process of innovation system. Phase One is 1991 to 1998 and Phase Two is
1999 to 2005. To explore whether there are any differences in China’s innovation drivers
between the two stages, the two stages were analyzed separately using panel data regression
with fixed effect models.
The comparison of means between the two phases is depicted in Table 1, which
shows the mean of each variable was higher in Phase Two than in Phase One, except for FDI.
Considering IVs, the difference between number of higher education institutions (HEI) is the
greatest, followed by skilled labor (SCI-ENG), domestic technology transfer (CONTRACT),
and financial input (FUND).
------------------------
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Table 1 about here
------------------------
7.1. Estimated results
In the following, the estimated results from fixed effect models for the two phases are
elaborated. We firstly present the results main effects (Table 2) of key drivers on the
innovation activities (DV1 to DV4). -------------------------
Table 2 about here
--------------------------
Testing hypothesis 1: the role of higher education institutions and industrial enterprises
Higher education institutions: The estimated results showed the impact of HEI differed
between Phase One and Phase Two. In Phase One the impact was negative and significant on
overall granted patents (β = -.378, p<.001), granted invention (β = -3.466, p<.001) and utility
model patents (β = -.325, p<.001), while in Phase Two it was positively significant on
granted utility model patents (β = .487, p<.001) (as highlighted in the table 2). The
comparison of coefficients confirmed the difference in impact between the two phases. Hence
the positive impact during the whole period may be mainly influenced by the effect in Phase
Two.
Industrial enterprises: The impact of IE differed between the two phases as well. In Phase
One, the estimated coefficient was only negatively significant on granted invention patents
= -3.669, p<.001). In Phase Two, it was positively significant on overall applications
= .202, p<.001), but negatively significant on overall granted patents (β = -.263, p<.001).
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Comparing its effect during the whole period, the significantly negative impact on granted
invention patents was mainly from the impact in Phase One, while the positive impact on
overall applications and negative impact on overall granted patents in Phase Two were
weakened in the long term.
Testing hypothesis 2: the role of financial input through science and technology funding
Science & Technology fund: The estimated results show science and technology effort was
only significant and positive on granted invention patents (β = 3.465, p<.001) in Phase One
and none of the effects were significant in Phase Two.
Testing hypothesis 3: the role of human capital through scientists and engineers and
employment rate
Scientists and engineers employed full time: According to the results shown in Table 2,
differences exist in the impact of scientists and engineers employed full time. In Phase One,
all the coefficients were positive and it was significant on overall granted patents (β = .436,
p<.001) and granted utility model patents (β = .426, p<.001). In Phase Two, it was only
positively significant on granted invention patents (β = 1.152, p<.001). The results indicated
skilled labor drove incremental innovations in Phase One, but radical innovations in Phase
Two.
Employment rate: The estimated coefficients of employment were only negatively significant
on overall granted patents (β = -.753, p<.001) in Phase One and were positively significant on
granted utility model patents (β = .656, p<.001) in Phase Two.
Testing hypothesis 4: the role of inward FDI
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FDI: The impact of FDI was quite different between the two phases. In Phase One, FDI was
significantly and negatively related to all four DVs (DV1 = β = -.112, p<.001; DV2 = β = -
.141, p<.001; DV3 = β = -2.069, p<.001; DV4 = β = -.167, p<.001). In Phase Two, the effect
became significantly positive on overall granted patents (β = .135, p<.001). Therefore, the
strong negative impact over the whole period was mainly because of Phase One.
Testing hypothesis 5: the role of international trade through import-export
International trade: The impact of international trade was greater in Phase Two than in Phase
One. The estimated results showed the impact of international trade was negative and
significant on granted invention patents (β = -2.804, p<.001), but was positive and significant
on granted utility model patents (β = .238, p<.001) in Phase One. In Phase Two, the
coefficients were positive and significant across all four DVs (DV1 = β = .319, p<.001; DV2
= β = .411, p<.001; DV3 = β = .603, p<.001; DV4 = β = .467, p<.001). This indicates the
negative effect of international trade on granted invention patents over the whole period was
mainly during Phase One, and the strategy of enhancing international trade worked better in
Phase Two than in Phase One.
Testing hypothesis 6: the role of domestic technology transfer
Domestic technology transfer: The impact of domestic technology transfer was weaker than
the impact of international interactions in both phases. In Phase One the estimated
coefficients were positive and significant on overall applications (β = .040, p<.001) and
granted invention patents (β = .890, p<.001), but are negatively significant on granted utility
model patents (β = -.049, p<.001). However, in Phase Two, they are positive and significant
on overall granted patents (β = .108, p<.001) and granted utility model patents (β = .082,
p<.001).
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8. Discussion and conclusion
Our study elaborates on the role of drivers in increasing innovation activities within
China via fixed effect panel data modeled across two phases. The results in this part revealed
the impacts of the drivers changed over time, as did the interactive effects between science
and technology investment and interactions.
We found that both higher education institutes and industrial enterprises showed a
negative impact in Phase One, while in Phase Two higher education institutes exerted a
positive effect on incremental innovations and industrial enterprises exerted a positive effect
on applications but a negative impact on overall granted patents. The different impact of the
two phases may be explained by the reform of higher education institutes in China. The
reform of higher education institutes started with the open door policy in 1978. With the
reform of science and technology system, the importance of higher education institutes in
science and technology development was re-emphasized. The progress of the reform led to
higher education institutes having different impacts on national innovation capability, as
shown in the analysis. In Phase One the national government asserted that, other than the
responsibility of educating, higher education institutes should expand their role in improving
science and technology by putting more effort into applied research (Zhou 2009). Although
they made great achievements in technology innovation during 1991 and 1998, most
achievements were in theoretical research, which largely resulted in published papers rather
than patent counts. Therefore, analyzing the impact on patent counts would impair the impact
of higher education institutes on the improvement of overall China’s innovation. However,
with the progress of reform, higher education institutes engaged more in technology
innovation and industry development in Phase Two than in Phase One (Zhou 2009), which
makes patent counts a better proxy of innovation capability than in Phase One. Hence, the
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impact of higher education institutes on patent counts better reflects the impact of higher
education institutions on China’s ability to innovate in Phase Two.
The different impact of industrial enterprises between the two phases was closely
related to the progress of enterprise system reform, especially the reform on enterprises with
funds from government, which include state-owned, collective-owned, state joint ownership
and collective joint ownership enterprises). Although the government funded enterprises
were given more autonomy for operations, to a great extent they still relied on the order of
government directive (Zheng 2004) and had low incentives to initiate innovation activities.
With the deepening of reform, the ownership structure of the government funded enterprises
has been changing and most small and medium sized government funded enterprises have
been privatized (Zheng 2004). With the change of ownership structure, the government
funded enterprises may have improved their incentives to innovate (Li and Zhou 2008), and
the innovation capability of industrial enterprises has improved (Ye 2009), which can be seen
from the positive impact on overall applications in Phase Two. Although the impact of
industrial enterprises on overall granted patents was significantly negative, the change of the
impact between the two phases showed signs of improvement of contribution of industrial
enterprises to innovation development in China.
Financial capital and human capital did not display a great impact on overall
innovation activities in either phase. Considering the strong effect across the whole period, it
suggests financial and human capitals have an accumulated effect in the long term. Our study
also illustrates that FDI negatively influenced innovation activities in Phase One and exerted
a positive effect in Phase Two, while the impact of international trade depended on the type
of innovations in Phase One and it was positive on overall innovation activities in Phase Two.
These results imply that, with the improvement of innovation activities, domestic innovators
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benefit more from international interactions, and the positive effect of the strategies to attract
FDI and enhance international trade emerges.
Overall, it is useful information for government and other key stakeholders to
understand the key determinants of innovation in building national capability and how the
strategies related to that factor worked in the past prior to developing new strategies or
adjusting policies to improve its innovation capability. The changing impact also indicates
governments should modulate innovation strategies and policies in terms of the change of the
time and impact. Specifically, the impact of higher education sector in the two phases
suggests it is good for governments to continue encouraging universities to take advantage of
the research resources they have and exert spin-offs to serve the development of industries.
Developing further incentives for enterprises to put more effort into innovation activities will
help achieve the objective of developing an enterprise-oriented innovation system. The
accumulative impact of our proposed key determinants implies that to improve China’s
innovation capability, financial inputs need to be increased and human capital investment is
also required. The improved impact of FDI in the second stage suggests that, in addition to
putting effort into enhancing inward FDI, governments also need to improve absorptive
capability and other conditions to gain more positive spillovers.
9. Limitations
Even though the study was carefully designed, there are some inevitable limitations.
The first limitation relates to the data source. When using secondary data, the quality of the
data cannot be controlled (Bryman and Bell 2003), and measures of the variables have to be
adjusted if the information available does not meet the requirements of the study (Emory and
Cooper 1991). This being said, the fit of the data to the research question is a common
concern (Saunders, Lewis and Thornhill 2003). The next limitation refers to counted patents
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as the measure of innovation capability. However, not all innovations are patentable
(Griliches 1990) and patents are not the ultimate goal of enterprise and higher education
institutes (Bai and Li 2011). Thus, patents at most can be used as a proxy of research and
development capability. Besides, a large number of patents are applied for and granted to
individuals in China, which measures the innovation capability of individual residents within
a region, but the financial and human resource inputs for those activities are unknown. Our
study did not consider the ownership of patents, which may have led to some biases in the
results. To overcome the disadvantages of secondary data in the future, researchers can
design studies, collecting primary data to meet their specific needs and controlling the quality
of the data. To better measure national innovation system, alternative indicators can be
employed in future research, such as the number of new products (Fritsch 2002), new product
sales (Liu and White 1997), and literature-based innovation counts (Acs, Anselin and Varga
2002).
In sum, our study illustrates the roles of the main drivers that can influence innovation
capability in China during the transitional phases, which are divided into two phases
according to the reform process of innovation system. The differences found between the
two phases reveal significant impact of institutional environment on innovation capability
through changing key innovation drivers. The change of impact in two phases provides
indications that the alignment between government innovation policies and innovation drivers
could be the key to improving China’s innovation capability.
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10. References
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Table 1: The comparison of key drivers of innovation during the two critical periods of innovation policies during the China’s reform (between
1991-1998 and 1999-2005) from 33 administrative regions.
Period One
(1991-1998)
Period Two
(1999-2005)
Key drivers
Mean
Mean
No. of higher education institution
2.981
3.070
No. of industrial enterprises
1.200
1.230
No. of scientists and engineers (fulltime)
3.084
3.16
Employment rate
0.517
0.520
Funding for science and technology
1.193
1.25
Inward FDI
1.302
1.30
International trades
2.202
2.21
Domestic technology transfer
0.556
0.620
Note: To ensure distributions are approximately normal, the logarithm transformation of most metric variables was used.
26
Table 2: The main effects between key innovation drivers and patents as a proxy for innovation capabilities from 33 administrative regions.
Overall patent
applications (DV1)
Overall granted patents
(DV 2)
Granted invention
patents (DV3)
Granted utility model
patents (DV 4)
Phrase
One
Phase
Two
Phrase
One
Phase
Two
Phrase
One
Phase
Two
Phrase
One
Phase
Two
No. of higher education institution (HEI)
β
-0.046
-0.117
-.378***
0.05
-3.466**
-0.103
-.325***
.487***
SE
-0.09
-0.104
-0.14
-0.184
-1.704)
-0.268
-0.12
-0.117
No. of industrial enterprises (IE)
β
-0.167
.202***
-0.132
-.263**
-3.669*
0.207
-0.053
-0.05
SE
-0.107
-0.064
-0.165
-0.113
-2.017
-0.164
-0.142
-0.072
No. of scientists and engineers (SCI-ENG)
β
0.117
0.126
.436**
-0.012
2.173
1.152***
.426***
-0.043
SE
-0.117
-0.091
-0.182
-0.162
-2.218
-0.236
-0.156
-0.103
Employment rate (EMPLOY)
β
0.017
0.093
-.753*
0.05
-0.921
0.955
-0.436
.656*
SE
-0.246
-0.349
-0.381
-0.618
-4.65
-0.899
-0.326
-0.394
Funding for S&T (FUND)
β
0.048
0.083
-0.113
-0.038
3.465**
0.224
-0.074
0.023
SE
-0.076
-0.066
-0.118
-0.116
-1.434
-0.169
-0.101
-0.074
Domestic technology transfer (TECH)
β
.040* 0.037 -0.034 .108** .890** -0.029 -.049* .082**
SE
-0.021
-0.028
-0.033
-0.05
-0.399
-0.073
-0.028
-0.032
Inward FDI (FDI)
β
-.112***
0.028
-.141***
.135**
-2.069***
-0.021
-.167***
0.054
SE
-0.023
-0.035
-0.036
-0.062
-0.438
-0.09
-0.031
-0.039
International trades (TRADE)
β
-0.034
.319***
0.135
.411***
-2.804***
.603***
.238***
.467***
SE
-0.053
-0.08
-0.083
-0.141
-1.012
-0.205
-0.071
-0.09
_cons
0.059
-2.770***
-1.470**
-3.330***
-34.514***
-11.343***
-1.459**
-4.792***
se
-0.422
-0.251
-0.655
-0.445
-7.99
-0.647
-0.561
-0.283
Within R-sq
0.4892 0.7883 0.6983 0.6291 0.6799 0.7938 0.6711 0.8645
Note: β = Standardize coefficient, SE = Standard errors, ***p<0.001, **p<0.01, *p<0.05
27
Figure 1: A conceptual model for innovation capabilities in China
... Regional policies embed reform and deliver disruptive (hence economic) capabilities and capacities for improvements. In literature, capabilities tend to represent the most essential determinant in institutional innovation (Sawang et al. 2017), and even though this determinant spans different categories of determinants in literature as shown in Table 4, the reform and improvement category heightens the role of capabilities. Insights from policy making and the development of technical standards advance this line of reasoning (Smink et al. 2015) as policy makers wrestle with necessary blends of heterogeneous actions (radical or incremental) (Turker and Vural 2017;Ventura et al. 2020), agents (internal or external) (Villavicencio et al. 2015), and arrangements (formal or informal) (Fischer and Tello-Gamarra 2017) for successful innovations. ...
... (Mutenje et al. 2016;Hinings et al. 2018) Actors(de Zubielqui et al. 2015;van Wijk et al. 2019;Wu and Park 2019) Partnership(Whitley 2014;Petruzzelli and Rotolo 2015) Strategy(Drenth 1996;Unger and Zagler 2003;Wu 2013;Sartor and Beamish 2014) Networking (Van Bockhaven et al. 2015;Smith and Thomas 2015) ReputationStrategy(Pesti et al. 2019;Kalkabayeva et al. 2021) Process(Hargrave and Van De Ven 2006; Ottenbacher and Harrington 2009logic (Cestino and Berndt 2017; Xie et al. 2019) Network engagement and relationship (Kwan and Chiu 2015; Yang 2016) Capabilities (Goldsmith 1988; Woodhill 2010; Ito et al. 2016; Barasa et al. 2017) Knowledge (De Laurentis 2006; de Zubielqui et al. 2015; Kwan and Chiu 2015; Boudreau and Lakhani 2016) Strategy and strategic leadership (Koh 2006; Wallman 2009; Jayabalan et al. 2021) Output Partnership (Krishnan and Jha 2012; Azadegan et al. 2013; Rosário et al. 2013; Robin and Schubert 2013; Kolk and Lenfant 2015; Pesti et al. 2019) Ownership (Kim et al. 2019; Didenko et al. 2020) Governance (de la Mothe 2004; Wallman 2009; Hu 2014; Vecchi et al. 2015; Oppong 2016) Technology (Wiskerke and Roep 2007; Quiroga and Martin 2017; Markey-Towler 2020; Mohsen et al. 2021) Economic condition (Kochhar and David 1996; Haggard and Zheng 2013; Corsi and Prencipe 2019; Kim et al. 2019) Capabilities (Oi 2004; Kwan and Chiu 2015; Styhre and Remneland-Wikhamn 2016; Cestino and Berndt 2017; Gehman and Höllerer 2019; Yu et al. 2020; Khan and Gulati 2021condition (Patel and Burra 1994; Kasper 1994; Simpson 2005; Hoque et al. 2011; McCarthy et al. 2014; Zhang and Putzel 2016; Khan and Gulati 2021) Technology(Regan 1993;Rasiah 1996;Clark 2002;Sandberg 2007) Strategic leadership and orientation(Wang and Swanson 2007;Reed and Wallace 2015; van der Krabben and Lenferink 2018; Jensen and Fersch 2019; Widyani 2019) Logic and institutional forms(Nolan and Xiaoqiang 1999;Lazer et al. 2011;Waldorff 2013;Jansson et al. 2013;Huang and Ding 2016; Kooijman et al. 2017; Perry 2021) Governance(Shang and Fagan 2006;Yoshikawa et al. 2007;Pascucci and De Magistris 2011; Karaulova et al. 2017;Yang and Al-Sayed 2021) ImprovementCapabilities and externalities(Drew 1995;Lee and Yoo 2008;Woodhill 2010; Karaulova et al. 2017;Sawang et al. 2017; Antonova and Lomakina 2020) Governance(Lane 1986;Nakamura and Born 1993;Lee and Yoo 2008; Ransdell 2019) Knowledge(Clark 2002;Velho 2004) Process(Fullerton 1986;Drew 1995;Irlenbusch et al. 2003;De Jong and Woolthuis 2008;Tao and Jinchuan 2008;Larsen et al. 2011) Strategy(Villavicencio et al. 2015;Smink et al. 2015;Jayabalan et al. 2021) Constraints(Hung 2000;Niosi 2010;van Dijk et al. 2011;McCarthy et al. 2014;Turker and Vural 2017;Onsongo 2019;Ananthram and Chan 2021) ...
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