Impact of Transnational Research Collaboration on Universities’ Innovation Performance: Panel Data Research of 64 Chinese Universities from 2009 to 2019

Abstract

Recently, China launched policies to further internationalize Chinese universities, including the “First-Class Universities and First-Class Disciplines Project” (Double First-Class Project), which highlights the importance of increasing transnational research collaboration activities. However, little is known about the actual impacts of these national initiatives on universities’ transnational research collaboration activities. Research on the impact of the involvement of transnational research collaboration on universities’ innovation performance is lacking. The purpose of this study was (1) to further understand the link between the involvement of transnational research collaboration and the innovation performance of universities and (2) to examine the relation between the “Double First-Class Project” and transnational research collaboration in Chinese universities. Through collecting and analyzing 576 panel data (a combination of cross-sectional series data and time series data) on the involvement of transnational research collaboration and the innovation performance of 64 universities from 2009 to 2019, the study manifested a positive correlation between the involvement of transnational research collaboration and the innovation performance of universities. The study further indicated the national key university initiative had a direct positive impact. More meso-level studies and a more open international mindset from policymakers to maintain the sustainable development of research and innovation globally are needed.

Full text

Citation: Zhong, Z.; Zheng, G.; Wang,
Y. Impact of Transnational Research
Collaboration on Universities’
Innovation Performance: Panel Data
Research of 64 Chinese Universities
from 2009 to 2019. Sustainability 2023,
15, 83. https://doi.org/10.3390/
su15010083
Academic Editors: Yuzhuo Cai,
Jinyuan Ma and Qiongqiong Chen
Received: 11 November 2022
Revised: 16 December 2022
Accepted: 17 December 2022
Published: 21 December 2022
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
Impact of Transnational Research Collaboration on Universities’
Innovation Performance: Panel Data Research of 64 Chinese
Universities from 2009 to 2019
Zhiyang Zhong, Gaoming Zheng and Yan Wang *
Institute of Higher Education, Tongji University, Shanghai 200092, China
*Correspondence: wylady@tongji.edu.cn
Abstract:
Recently, China launched policies to further internationalize Chinese universities, including
the “First-Class Universities and First-Class Disciplines Project” (Double First-Class Project), which
highlights the importance of increasing transnational research collaboration activities. However,
little is known about the actual impacts of these national initiatives on universities’ transnational
research collaboration activities. Research on the impact of the involvement of transnational research
collaboration on universities’ innovation performance is lacking. The purpose of this study was (1) to
further understand the link between the involvement of transnational research collaboration and the
innovation performance of universities and (2) to examine the relation between the “Double First-
Class Project” and transnational research collaboration in Chinese universities. Through collecting
and analyzing 576 panel data (a combination of cross-sectional series data and time series data)
on the involvement of transnational research collaboration and the innovation performance of
64 universities from 2009 to 2019, the study manifested a positive correlation between the involvement
of transnational research collaboration and the innovation performance of universities. The study
further indicated the national key university initiative had a direct positive impact. More meso-
level studies and a more open international mindset from policymakers to maintain the sustainable
development of research and innovation globally are needed.
Keywords:
transnational research collaboration; international academic mobility; innovation perfor-
mance; “Double First-Class” Project; China
1. Introduction
Energy shortages and environmental pollution make the traditional economic de-
velopment model unsustainable. Innovation has gradually become a powerful engine of
national and regional economic sustainable development. With the rapid development of a
knowledge economy, universities have become an important source of knowledge flow in-
novation systems [
1
]. Universities provide critical infrastructure for scientific research and
technological innovation [
2
], which positions them at the forefront of scientific research and
innovation. Furthermore, universities, which are actors in the connotative development of
higher education, enable significant and competitive enhancement of a country’s national
science and technology [3].
International scientific research cooperation by universities has played an important
role [
4
]. The knowledge flow of scientific research innovation across national borders
vigorously promotes scientific research and industrial transformation worldwide. Through
integrating the global advanced science and technology resources, international scientific
research cooperation, usually in combination with international mobility, can effectively
solve the frontiers of scientific research problems and address complex challenges in in-
terdisciplinary research [
5
]. Moreover, it can further provide different cooperation paths
and modes, owing to the diversity of cooperation partners’ positive impacts on innovation
performance [
6
]. Recognizing the importance of international research cooperation for
Sustainability 2023,15, 83. https://doi.org/10.3390/su15010083 https://www.mdpi.com/journal/sustainability

Sustainability 2023,15, 83 2 of 16
university research and innovation performance, past research on higher education and
scientific research management has explored the influencing factors and enabling mech-
anisms of university innovation performance and points to the impacts of government
policy support.
In the Chinese context, past studies have drawn our attention to the impacts of policies
of world-class universities. For instance, Dong and Liu (2017) analyzed the evolution
of institutional cooperation networks in Chinese universities and found that first-class
universities in developed countries have long been at the forefront of the institutional
cooperation network, and top institutions are likelier to form large-scale agglomerative
subgroups [
7
]. Hong and Gao (2020) compared the performance and characteristics of
first-class universities in China and the U.S. regarding the co-authorship network [8].
To avoid repetition, we further illustrate the previous findings from the literature in
our next section. Although various studies have explored the characteristics of scientific
research cooperation networks and the influencing factors of scientific research innovation
performance, few studies have explored the influence of international scientific research
cooperation on innovation performance in China. Therefore, it is of theoretical and practical
significance to identify the key factors influencing the scientific research performance of
universities through international scientific research cooperation, particularly in the context
of the implementation of the “Double First-Classes Project” in China.
We took 576 pieces of panel data from universities directly under the jurisdiction of
the Ministry of Education in China from 2009 to 2019 to analyze the correlation between in-
ternational scientific research cooperation intensity and the overall innovation performance
of universities at the organizational level. This paper uses the dynamic panel data model of
system generalized moment (Sys-GMM) to conduct an empirical study of the innovation
performance growth of comprehensive universities and professional universities.
This paper aims to study the impact of China’s university transnational research
cooperation on the overall innovation performance and the moderating effect of university
types in the impact mechanism. The subsequent parts of the paper are organized as follows.
Section 2reviews the literature on the impact of transnational research collaboration on
universities’ innovation performance and puts forward the research hypothesis. Research
methods and variable refinement are discussed in Section 3, followed by Section 4, which
presents the findings and their analysis. The conclusion is proposed in Section 5. The main
contribution of this paper is that it reveals the correlation between transnational research
collaboration and universities’ research performance in China’s context.
2. Theoretical Background and Hypotheses
Since the 1990s, the rapid development of the internet has promoted the globaliza-
tion of information and ideas. Universities around the world have been involved in an
expanding global knowledge production network. According to the literature, existing
research focuses more on the impact of the strength of an international scientific research
cooperation activity on a certain innovation index. For example, Barjak (2008) pointed out
that international collaboration has a positive impact on the quality and quantity of the
scientific research outputs of EU research teams [
9
]. Goldfinch et al. (2003), through the
investigation and analysis of nine national research institutions in New Zealand, concluded
that a large number of countries, authors, and institutions involved in international cooper-
ation has a positive impact on the citation of a paper [
10
]. Frederiksen (2004), who analyzed
the impact of international scientific research cooperation among different disciplines and
the number of paper collaborators on the research citation of Danish industrial institutions,
found that the papers produced by international scientific research cooperation have a
significant impact on the citation rate of papers [11].
Research on Chinese universities found similar conclusions. Xiaolin (2019), from
an overall analysis of the citation influence of various discipline papers during the 12th
Five Year Plan period, discovered that the citation influence of international cooperation
papers is significantly higher than that of discipline papers, which means the influence of

Sustainability 2023,15, 83 3 of 16
international cooperation on disciplines is significantly beneficial [
12
]. Miao Yajun et al.
(2014) took the highly cited papers of 985 universities in China included in the Web of
Science database as their sample [
13
]. After a scientific econometric analysis, they concluded
that the international scientific research cooperation of universities has a significant positive
correlation with the number and influence of papers.
However, at present, communication between different disciplines is becoming stronger.
Some studies have found that when a university as a whole is analyzed at the organiza-
tional level, the results are controversial. Admas et al. (2005) conducted research based
on literature data published by the top 110 universities in the United States. Their results
showed that the international collaboration of research institutions has a positive impact on
the frequency of paper citations, but there is a negative correlation with scientific produc-
tivity [
14
]. In related research of the industry, it has also been found that too much scientific
research collaboration hinders an organization’s absorptive capacity and independent inno-
vation ability and damages the accumulation and creation of internal knowledge [
15
]. The
interaction of these different effects of research collaboration on the innovation performance
of an institution makes it have a non-linear impact on the improvement of innovation per-
formance. There is an inverted U-shaped relationship between enterprise scientific research
collaboration and performance [
16
]. International cooperation and exchange are no longer
carried out in a single discipline or by a single team, but via big projects amid globalization.
The study of international scientific research collaboration activities in this article
not only focuses on micro-team collaboration but also increases the perspective of orga-
nizational measurement through statistics and analysis of an organization’s overall R&D
investment and innovation performance data. Based on the current research conclusions of
universities and enterprises, hypothesis H1 is as follows:
H1.
The intensity of international research cooperation of a university has an inverted U-shaped
impact on its innovation performance.
The characteristics of colleges and universities will have a certain impact on the
scientific research activities of colleges and universities. Pan et al. (2009), studying the
classification of international education standards after analyzing the actual situation of
China’s higher education, showed that different types of universities carry out international
scientific research cooperation with different objects, modes, intensities, and frequencies,
and the corresponding innovation performance also differs [17].
Evans et al. (2011) showed that the research field, the identity of the scholars, and
the location of institutions all have an impact on cooperation in the field of economy and
management [
18
]. In addition, Huang (2018) pointed out that the higher the number of
high-level personnel in a university, the stronger the innovation ability and the higher the
efficiency of scientific research [19].
Since 2017, the Chinese government has established global first-class universities and
provided 42 universities in China with special financial support for each period of five
years. These universities, which have received special financial funds, represent the highest
level of Chinese universities and drive in-depth scientific research collaboration and equal
dialogue with other high-level international scientific research institutions. They can also
gain complementary advantages through international scientific research collaboration and
resource sharing. For example, Wang et al. (2019) analyzed 41,104 articles in 137 natural
science journals and 16,383 articles in 104 social science journals in the Chinese national
knowledge infrastructure database and found that the number of high-quality research
results issued by “Double First-Class Universities” in the fields of natural science and social
science is much higher compared to other types of universities [20].
In this paper, the types of universities are added as a regulatory variable when con-
structing a model of the impact mechanism of international scientific research cooperation
and innovation performance of colleges and universities. The sample colleges and universi-
ties participating in the research are divided into two categories: those that have obtained
the construction support of the “Double First-Class Project” and those that have not.

Sustainability 2023,15, 83 4 of 16
This paper studies the moderating effects of different types of colleges and univer-
sities on the overall scientific research performance of colleges and universities. These
assumptions are part of hypothesis H2.
H2.
University type has a significant moderating effect on the relationship between international
research collaboration personnel exchange and the innovation performance of a university.
The conceptual framework of the study and hypotheses are depicted below in Figure 1.
Sustainability 2023, 14, x FOR PEER REVIEW 4 of 16
research results issued by “Double First-Class Universities” in the fields of natural science
and social science is much higher compared to other types of universities [20].
In this paper, the types of universities are added as a regulatory variable when con-
structing a model of the impact mechanism of international scientific research cooperation
and innovation performance of colleges and universities. The sample colleges and univer-
sities participating in the research are divided into two categories: those that have ob-
tained the construction support of the “Double First-Class Project” and those that have
not.
This paper studies the moderating effects of different types of colleges and universi-
ties on the overall scientific research performance of colleges and universities. These as-
sumptions are part of hypothesis H2.
H2. University type has a significant moderating effect on the relationship between international
research collaboration personnel exchange and the innovation performance of a university.
The conceptual framework of the study and hypotheses are depicted below in Figure
1.
Figure 1. Conceptual Framework.
3. Data and Methodology
3.1. Empirical Data and Sample Selection
This study aims to determine the factors affecting the innovation performance of uni-
versities in China. There are currently 64 universities under the direct management of the
Ministry of Education in China. As representatives of Chinese universities, they have
played an exemplary role in teaching, scientific research, and social services. In this study,
we used secondary data based on a deductive methodology from 2009 to 2019 from 64
Chinese universities administrated by the Ministry of Education of China in STATA 15.
The sample data were collected from the “Compilation of Science and Technology Statis-
tics of Higher Education Institutions” published by the Ministry of Education of China
from 2010 to 2020, which provides detailed information on the scientific research human
resource, funding, institutions, projects, international communication, achievements, and
technology transfer of universities in China from 2009 to 2019. According to the research
needs and the availability of data, 704 panel data from 64 colleges and universities directly
under the Ministry of Education in China in the past 11 years were selected as observation
samples.
International
Conference,
IAC
Personnel
Exchange, PE
Control Variables
⚫Research Funds, RF
⚫
Number of Scientific Researchers, SR
⚫Year
The intensity of Inter-
national Research Co-
operation, IIC
University Type,
UT
Innovation Per-
formance of Uni-
versities, IPU
H1
H2
Figure 1. Conceptual Framework.
3. Data and Methodology
3.1. Empirical Data and Sample Selection
This study aims to determine the factors affecting the innovation performance of
universities in China. There are currently 64 universities under the direct management of
the Ministry of Education in China. As representatives of Chinese universities, they have
played an exemplary role in teaching, scientific research, and social services. In this study,
we used secondary data based on a deductive methodology from 2009 to 2019 from 64
Chinese universities administrated by the Ministry of Education of China in STATA 15. The
sample data were collected from the “Compilation of Science and Technology Statistics of
Higher Education Institutions” published by the Ministry of Education of China from 2010
to 2020, which provides detailed information on the scientific research human resource,
funding, institutions, projects, international communication, achievements, and technology
transfer of universities in China from 2009 to 2019. According to the research needs and
the availability of data, 704 panel data from 64 colleges and universities directly under the
Ministry of Education in China in the past 11 years were selected as observation samples.
The dependent variable is the innovation performance of a university (IPU). Guler
(2010) used the number of patents and papers as indicators to measure the country’s inno-
vation performance [
21
], and Bozeman and Lee (2005) used the total number of academic
papers published annually to measure the innovation performance of universities [
22
]. The
indicators that the Ministry of Education of China considers for the statistics of scientific
and technological achievements of its universities each year include the publication of
scientific and technological works, academic papers, national-level research projects, patent
status, and other intellectual property rights [23].
In this paper, we selected the number of scientific and technological works, published
academic papers, and national-level research projects per year of a university, and inte-
grated them into a comprehensive indicator by principal component analysis (PCA) to
observe the innovation performance of the university.

Sustainability 2023,15, 83 5 of 16
The process of international scientific research collaboration is a process of sharing
resources, which includes personnel dispatch and exchanges, international academic con-
ferences, collaborative construction of research platforms, technology introduction, and
collaborative publications. In essence, collaborative publications and the collaborative con-
struction of research centers are often the results of the work of university researchers under
certain circumstances (such as academic visits and international conferences). In some
studies, indicators such as the number of international collaborative papers are considered
the result of collaboration to measure collaboration performance [24].
In this paper, we consider the intensity of international research collaboration of uni-
versities (IIC) from two dimensions: personnel exchanges (PE) and international academic
conferences (IAC). According to the statistics of the Ministry of Education of China, the
annual number of personnel dispatched abroad, personnel receiving visits from abroad,
and personnel participating in international academic conferences are selected to reflect the
independent variables of PE. Meanwhile, the number of international conferences spon-
sored by universities each year was selected as the variable of IAC. Therefore, based on
hypothesis H1, we propose hypothesis H1a, which is that international personnel exchange
has an inverted U-shaped impact on the innovation performance of universities, and H1b,
which is that university international academic conferences have an inverted U-shaped
impact on the innovation performance of universities.
The moderating variable is the type of university (UT), which is divided into two cate-
gories according to whether it is supported by special financial funds for the construction
of global first-class universities. The construction plan started in 2017, and the panel data
analyzed in this study is from 2009. In fact, the Chinese government’s special construction
plans for these universities have been in existence since the 1990s. Among them, the “985
Project” started in 1998, and the current construction plan “Double First-Class Project”
is considered a continuation of the “985 Project”: both of which are national strategies
proposed by the central government to enhance comprehensive strength; the universities
supported by these two plans are almost the same and the 39 universities listed in the “985
Project” have also been supported by the construction of the “Double First-Class Project”.
To discuss the impact of international cooperation on the innovation performance
of universities under different university types, we divided the 64 universities directly
under the Ministry of Education into two categories according to whether they are cur-
rently supported by the “Double First-Class Project” construction plan. Therefore, based
on hypothesis H2, we propose the alternative hypothesis H2a, which is that university
type has a significant moderating effect between international personnel exchange and the
innovation performance of universities, and H2b, which is that university type has a signif-
icant moderating effect between international academic conferences and the innovation
performance of universities. The classification of 64 universities directly under the Ministry
of Education of China is shown in Table 1.
Controlling factors such as research funding (RF), number of scientific researchers (SR),
and time also have an impact on the innovation performance of universities. Therefore,
the above three variables are included in the model as control variables. Research funding
for a Chinese university mainly includes government funds, enterprises and institutions
entrusted funds, and other funds. “Government funds” refers to the scientific research
funds allocated by the government to all kinds of universities at all levels. The entrusted
funds of enterprises and institutions are obtained by universities from foreign enterprises
and institutions, which include the funds allocated by the research institutes of the Chinese
Academy of Sciences. Other funds are obtained through other channels for research,
development, and scientific and technological services in the current year.

Sustainability 2023,15, 83 6 of 16
Table 1.
List of two types of universities directly administrated by the Ministry of Education of China.
University Type Number of Samples List of Universities
The selected first-class universities
in the “Double First-Class Project”
(referred to as “First-class
Universities” in short afterward)
33
Peking University, Renmin University of China, Tsinghua
University, China Agricultural University, Beijing Normal
University, Nankai University, Tianjin University, Dalian
University of Technology, Northeast University, Jilin University,
Northeast Forestry University, Fudan University, Tongji
University, Shanghai Jiaotong University, East China Normal
University, Nanjing University, Southeast University, Zhejiang
University, Xiamen University, Shandong University Ocean
University of China, Wuhan University, Huazhong University of
Science and Technology, Hunan University, Central South
University, Sun Yat-sen University, South China University of
Technology, Chongqing University, Sichuan University,
University of Electronic Science and Technology, Xi‘an Jiaotong
University, Northwest University of Agriculture and Forestry
Science and Technology, Lanzhou University
Other universities 31
Beijing Jiaotong University, Beijing University of Science and
Technology, Beijing University of Chemical Technology, Beijing
University of Posts and Telecommunications, Beijing Forestry
University, Beijing University of Traditional Chinese Medicine,
Communication University of China, China University of
Political Science and Law, North China Electric Power University,
China University of Mining and Technology (Beijing), China
University of Petroleum (Beijing), China University of
Geosciences (Beijing), Northeast Normal University, East China
University of Technology, Donghua University, China University
of Mining and Technology, Hehai University, Jiangnan University,
Nanjing Agricultural University, China Pharmaceutical
University, Hefei University of Technology, China University of
Petroleum (East China), China University of Geosciences
(Wuhan), Wuhan University of Technology, Central China
Agricultural University, Central China Normal University,
Southwest University, Southwest Jiaotong University, Xi’an
University of Electronic Science and Technology, Chang’an
University and Shaanxi Normal University
Because the amount of research funding has a certain impact on the output of innova-
tion performance, it is included as a control variable in the model. However, due to the
large number of scientific research funds, we used Zhang’s (2018) method to process the
funds’ data and changed the unit of scientific research funds from yuan to 10,000 yuan in
order to avoid the large difference between the control variable and other variables, which
was likely to result in the change of the stability of the data [25].
The indicator of the number of scientific researchers was selected from the statistics
of the Ministry of Education for the number of full-time personnel in R&D. Meanwhile,
during the 2009 to 2019 period under study, China’s economic, social, and technological
development underwent tremendous changes, and academic research and innovation
output has also been affected by changes over time.
It is necessary to control the impact of time on the model. We use years as the unit
of time and calculate it in the form of dummy variables. The definition and basis of each
variable are shown in Table 2.

Sustainability 2023,15, 83 7 of 16
Table 2. Operational definitions of variables and measurements.
Variable Type Variable Variable Content
Dependent Variable Innovation Performance of Universities,
IPU
The number of scientific and technological works, the
number of published academic papers, and the number
of national research projects each year of the university
Independent Variable The intensity of International Research
Cooperation, IIC
Personnel Exchange, PE: Number of dispatched
personnel, number of accepted personnel, and number
of personnel attending international conferences.
International Conference, IAC: the number of
international academic conferences held by the
university every year
Moderator University Type, UT Whether to obtain special financial support
Control Variable
Research Funds, RF
The sum of government funds, enterprises and
institutions entrusted funds, and other funds received
by the university every year.
Number of Scientific Researchers, SR The full-time equivalent of R&D personnel
Year Year dummies during 2009–2019
3.2. Model Specification
Panel data are a combination of cross-sectional series data and time series data. Build-
ing a dynamic panel model based on panel data can solve the endogenous problems caused
by missing variables, measurement errors, or the model’s own causes. It can effectively
avoid biased and non-uniform problems caused by random effects or OLS fixed-effect
methods [23].
The panel data used in this paper came from 64 universities in China from 2009 to
2019. Since the level of innovation capacity is a dynamic evolution process, it is suitable
to use a dynamic panel estimation model for quantitative dynamic analysis. The GMM
model is often used in the dynamic panel estimation model. The GMM model includes the
differential generalized method of moments (DIF-GMM) model and the system generalized
method of moments (Sys-GMM) model. DIF-GMM removes the effects of individual effects
by making first-order differences in the equations. It eliminates the problem of incomplete
estimation caused by variables that do not change over time [
26
]. Sys-GMM has high
estimation efficiency and retains variable coefficients that do not change over time. In
addition, it can flexibly select instrumental variables so that the estimation results have less
bias [
27
]. A basis criterion for Sys-GMM application is N (the number of cross-sections) > T
(period), which was fulfilled by the data of this study, where T = 11 and N = 64 (N > T).
To ensure the reliability and accuracy of the regression results, we need to test the
quality of the selected data for multicollinearity and stationarity when using panel data
for econometric demonstration. Specifically, we mainly tested the stability of panel data
and the collinearity before variables. The multicollinearity test is used to test whether the
autocorrelation problem exists in the disturbance term, and the stationarity test is used to
test whether the data samples tend to be stationary [28].
Second, the over-identification constraint test and the autocorrelation of residual
sequence are carried out, including the over-identification constraint test to test the ef-
fectiveness of the instrumental variables selected in the model and the residual sequence
autocorrelation test to test the overall robustness of the model.
The empirical analysis part of this paper first tests the overall data and then carries out
descriptive statistics and regression analysis. It verifies whether the international research
collaboration activities of universities directly under the Ministry of Education of China
have a direct impact on the innovation performance of universities and whether the types
of universities have a moderating effect.

Sustainability 2023,15, 83 8 of 16
The general form of the dynamic panel model is as follows:
yit =αyit−1+βXit +ui+εit
where idenotes the cross-sectional units, of which there are 64 in our sample, and t
expresses time, which is 11 years in our sample; y
it−1
is the first-order lag variable of the
explained variable, reflecting the influence of historical behavior on current behavior. X
it
is
the explanatory variable, uiis the misspecific variable, and εit is the random error term.
In order to test the non-linear hypotheses H1a and H1b, we constructed non-linear
dynamic panel models of the intensity of university international research cooperation
innovation performance of universities from two aspects: personnel exchange and interna-
tional academic conferences. The non-linear dynamic models of this study can be written
as follows:
IPUit =α0+IPUi,t−1+α2PEit +α3PEit 2+α4RFit +α5SRit +α6year +εit (1)
IPUit =α0+IPUi,t−1+α2IACit +α3IACit 2+α4RFit +α5SRit +α6year +εit (2)
where IPU represents the innovation performance of universities, PE represents the person-
nel exchange, IAC represents the international academic conferences, RF,SR, and year are
the control variables representing research funding, scientific researcher, and time respec-
tively. If
α2
is positive and
α3
is negative, it indicates that PE and IAC have an inverted
U-shaped relationship with IPU, respectively, and hypotheses H1a and H1b can be verified.
Moreover, in order to discuss the moderating effect of the type of university on the
relationship between the international research collaboration intensity and the innovation
performance of the university, and to test hypotheses H2a and H2b, the interactions between
moderator and independent variables are introduced into the models. The moderating
impact of university-type interaction terms can be written as follows:
IPUit =α0+IPUi,t−1+α2PEit +α3PEit 2+α4PEit ×UT +α5PEit2×UT +α6UT +α7RFit +α8SRit +α9year +εit (3)
IPUit =α0+IPUi,t−1+α2IACit +α3IACit 2+α4IACit ×UT +α5EIACit2×UT +α6UT +α7RFit +α8SRit +α9year +εit (4)
where UT represents the types of universities, PE*UT and PE
2
*UT are the interaction terms
between personnel exchange and the university types, whereas IAC*UT and IAC
2
*UT
refer to the interaction terms between international academic conferences and university
types. If the coefficients of interaction terms
α4
and
α5
are significant, it indicates that the
moderating effect of university types is significant, and hypotheses H2a and H2b can be
verified.
4. Data Analysis and Results
4.1. Preliminary Analysis
Table 3shows the descriptive statistics of the selected dependent and independent
variables and presents the summary statistics in terms of the mean, standard deviation,
minimum and maximum values, skewness, Kurtosis, and observation count. All variables
are based on 704 observations from 64 universities over 11 years from 2009 to 2019.
Table 3. Descriptive statistics.
Variables Obs Mean Std. Dev. Min Max Skew Kurt
IPU 704 4383 3711.0 27 31,156 1.90 8.83
PE 704 471.5 621.5 0 4530 2.99 14.40
IAC 704 15.81 20.18 0 160 2.98 14.63
RF 704 918.7 913.1 0.124 6588 2.23 9.59
SR 704 858.4 770.2 9 5659 2.50 11.80
UT 704 1.5 0.2 1 2 0.06 1.00

Sustainability 2023,15, 83 9 of 16
Table 3shows the pairwise correlation between the variables. The results of first-class
universities show that PE and IAC have a robust positive correlation with the innova-
tion performance of universities at a 1% significance level. Innovation performance of
universities increases as the concentration of PE and IAC increases. However, control
variables RF and SR have a highly significant and positive relationship with the innovation
performance of universities at a 1% significance level. The moderating variable ST also
positively correlates with the innovation performance of universities at a 1% significance
level. All independent variables showed a 1% significance level. In this study, a significant
correlation exists between the innovation performance of universities, and the explanatory
findings have strongly supported our research objective and hypotheses.
In order to ensure the reliability of the Sys-GMM estimation results and avoid the
problem of “spurious regression,” it is necessary to test the stationarity of the panel residuals
of the Sys-GMM estimation results. If it is stationary, the results are reliable; otherwise,
they are not reliable. Table 4demonstrates the stationary data through LLC and IPS unit
root methods. Stationary test results show that all variables are stationary at a significance
at the 1% level, rejecting the null hypothesis of “panel residuals are not stationary”, thus
indicating that the results of the dynamic panel data model estimated by Sys-GMM in this
paper are valid.
Table 4. Pairwise correlations.
IPU PE IAC RF SR
IPU 1
PE 0.505 *** 1
IAC 0.585 *** 0.553 *** 1
RF 0.659 *** 0.532 *** 0.661 *** 1
SR 0.614 *** 0.431 *** 0.568 *** 0.632 *** 1
Note: *** p< 0.01 indicate significance at the 1% level.
This paper also pays attention to possible multicollinearity and uses Stata15SE for
panel data testing. Strictly collinear variables will be automatically eliminated by Stata.
The calculated value of variance inflation factors (VIF) between the explanatory variables
of the models are all less than 10, and the mean VIF of the models is 1.86. Moreover,
from the test results shown in Tables 5and 6, the t-tests of the core variables were found
to be significant and in line with economic expectations. Chen (2016) [
29
] believes that
if multicollinearity affects the significance of the core variables, it should be dealt with.
However, if multicollinearity does not affect the significance of the core variables, it is
not necessary to deal with it, because if there is no multicollinearity, the coefficient of the
variable will only be more significant.
Table 5. Results of unit root tests.
Variables LLC Test IPS Test
IPU −55.6977 *** −9.5298 ***
PE −14.9319 *** −5.3408 **
IAC −12.5696 *** −6.0785 **
RF −24.1457 *** −4.1222 ***
SR −9.4475 *** −4.2760 ***
Note: ** and *** indicate significance at the level of %5 and 1%.

Sustainability 2023,15, 83 10 of 16
Table 6. Sys GMM regression analysis.
Direct Impact Models Moderating Effects Models
Variables Model 1 Model 2 Model 3 Model 4
L.IPU 0.333 *** 0.346 *** 0.155 *** 0.211 ***
(0.0385) (0.0282) (0.0165) (0.00777)
RF 0.503 ** 0.267 ** 1.156 *** 1.656 ***
(0.200) (0.135) (0.0739) (0.0600)
SR 1.609 *** 1.269 *** 1.034 *** 1.027 ***
(0.112) (0.0693) (0.0831) (0.0398)
PE 2.491 *** 2.482 ***
(0.590) (0.281)
PE2 −0.000563 *** −0.000460 ***
(0.000197) (8.80 ×10−5)
PE*UT 1.429 *
(0.736)
PE2*UT −0.000492 ***
(0.000176)
IAC 82.48 *** 2.697 *
(7.645) (1.429)
IAC2 −0.399 *** 0.130 ***
(0.0459) (0.0156)
IAC*UT 78.33 **
(35.02)
IAC2*UT −2.064 **
(0.897)
Constant 6162 *** 5101 *** 8215 *** 5151 ***
(527.0) (1626) (2159) (1016)
Observations 640 640 640 640
AR(1) test 0.000718 0.000339 0.00129 0.00205
AR1 (p-value) test 0.00213 0.00561 0.00444 0.000128
AR(2) test 0.177 0.525 0.131 0.750
AR2 (pvalue) test 0.149 0.226 0.243 0.208
Hansen test 46.18 35.98 25.45 47.71
Hansen (p-value) test 0.239 0.194 0.274 0.216
No. of Instruments-j stat. 62 50 44 64
Wald Test-Chi2560601 11822 1856 7.350 ×106
Chi2(p-value) test 0 0 0 0
Number of universities 64 64 64 64
Note: *** p< 0.01, ** p< 0.05 and * p< 0.10. Figure in parentheses (.) indicates robust standard errors.
4.2. Direct Impact of Factors Affecting Innovation Performance
The purpose of this study is to examine the link between the involvement of transna-
tional research collaboration and the innovation performance of universities. Meanwhile,
as the key universities policy has been playing a key role in Chinese higher education and
innovation development, the study tried to contribute to the examination of its impacts
on the transnational research collaboration in Chinese universities. The results of regres-
sion analysis on the dynamic panel data of 64 universities directly under the Ministry of
Education in 11 years are shown in Table 6, and columns 1 and 2, showing the results of
Model 1 and Model 2, respectively, indicate the direct impacts of personnel exchange and
international conferences affecting the innovation performance of Chinese universities.
In general, all explanatory variables were statistically significant at the 5% level or better.
The results show that the impact of personnel involved in international cooperation and
exchanges, international conferences, annual research funding, and R&D personnel on
the innovation performance of the universities is positive. This means that these factors
contribute to improving universities’ innovation performance levels.
More specifically, according to the estimated results of Model 1, personnel exchange
(PE) in international cooperation has become the primary factor to promote the innovation

Sustainability 2023,15, 83 11 of 16
performance of Chinese universities (IPU), and the estimated coefficient for PE is positive
whereas the square of PE is negative, and they are statistically significant at the 1% level.
Moreover, using these two coefficients, it can be estimated that IPU reaches the extreme
value when PE is 2754, and the extreme value point is within the value range.
These results indicate that there is an inverted U-shaped relationship between PE
and IPU. In other words, with the increase in the number of personnel participating
in international collaboration, the innovation performance of a university will increase
accordingly; when the number of personnel participating in international collaboration
exceeds 2754, the innovation performance of a university will decrease with the increase in
the number of personnel, as expressed in hypothesis H1a. Since the square coefficient of PE
is small, the decline in IPU after the turning point is also small, and the trend is slow.
The impact of international conferences hosted by the university (IAC) on its innova-
tion performance (IPU) is similar. The estimated results of Model 2 show that the estimated
coefficient for IAC is positive whereas the square of IAC is negative, and they are statis-
tically significant at the 1% level. Moreover, IPU reaches an extreme value when IAC is
67, which is also within the value range. These results indicate that there is an inverted
U-shaped relationship between IAC and IPU. Therefore, when the number of international
conferences hosted in one year is less than 67, it will promote a university’s innovation
performance, and once it exceeds 67, it may inhibit the improvement of a university’s
innovation performance, as expressed in hypothesis H1b.
The results from the Arellano–Bond test and the Hansen test are also presented in
Table 6. The Arellano–Bond AR (1) p-values of the two models are 0.00213 and 0.00561,
respectively, which are less than 5%, indicating the autocorrelation and serial correlation
in first-order difference. The AR (2) p-values are 0.149 and 0.149, respectively, which are
greater than 5%. This means the null hypothesis cannot be rejected, and there is no second-
order correlation in the disturbance term. Furthermore, the Hansen test indicates that
over-identifying restrictions are valid. The Hansen p-values of the two models are 0.239
and 0.194, which are between 0.10 and 0.25, indicating that there is no over-identification
of instrumental variables. The numbers of instruments are 62 and 50, which are less than
the number of groups, 64, and fail to reject the null hypothesis. The direct detailed results
of Table 5are reported below in Table 6. According to the descriptive data, most Chinese
universities are in the rising stage and have not yet reached an extreme point.
4.3. Moderating Effect of Factors Affecting Innovation Performance
Table 6also reports the results of the models with multiple interaction terms (Model 3
and Model 4), which indicate the moderating effect on innovation performance and rela-
tionships with independent factors. We found that the results are fairly similar, especially
regarding the impact of personnel involved in international cooperation and exchanges,
international conferences, annual research funding, and R&D personnel on the innovation
performance of the universities. In addition, all of these variables are also statistically
significant at the conventional level. Nonetheless, the number of international conferences
seems slightly responsive to a change in the innovation performance of the universities, as
the variable is only statistically significant at the 10% level.
Furthermore, the four interaction terms (PE*UST), (PE2*UT), (IAC*UT), and (IAC2*UT)
are also significant at the 5%-or-less level, as shown in Table 5. Column 3 shows the person-
nel exchange in the international cooperation and university-type interaction term model,
which shows the positive and statistically significant impact of personnel exchange and
university type. The regression results are shown in column 4 of international conferences’
interaction with the moderating variable university type, which also positively impacts
the innovation performance of the universities. The estimated results indicate that for
different types of universities, the intensity of international cooperation represented by the
exchange frequency of international cooperation personnel and the number of international
conferences has different effects on the innovation performance level of universities, as
expressed in hypotheses H2a and H2b.

Sustainability 2023,15, 83 12 of 16
In addition, comparing the coefficients of the quadratic term and its interaction term,
it can be found that the coefficients of PE2 and PE2*UT are both negative. This means
that in first-class universities, the inverted U-shaped relationship between international
cooperation personnel exchanges and innovation performance is further strengthened by
Haans and Pieters [
30
], who indicated that the number of personnel exchanges that exceed
the extreme point will weaken the innovation performance of first-class universities faster.
Conversely, the coefficient of IAC2 is positive and the coefficient of IAC2*UT is negative,
indicating that the inverted U-shaped relationship between international conferences and
innovation performance is weakened in first-class universities.
According to the regression data, the relationship curve between the number of
personnel exchanges in international cooperation and the innovation performance of
universities under different types of universities is fitted (Figure 2, left), as well as the
relationship curve between the number of international conferences and the innovation
performance under different types of universities (Figure 2, right). It can be seen from
Figure 2that, compared with other universities, the innovation performance of first-class
universities increases faster with the increase in personnel exchange and international
conferences; it can reach a higher extreme value of innovation performance.
Sustainability 2023, 14, x FOR PEER REVIEW 12 of 16
4.3. Moderating Effect of Factors Affecting Innovation Performance
Table 6 also reports the results of the models with multiple interaction terms (Model
3 and Model 4), which indicate the moderating effect on innovation performance and re-
lationships with independent factors. We found that the results are fairly similar, espe-
cially regarding the impact of personnel involved in international cooperation and ex-
changes, international conferences, annual research funding, and R&D personnel on the
innovation performance of the universities. In addition, all of these variables are also sta-
tistically significant at the conventional level. Nonetheless, the number of international
conferences seems slightly responsive to a change in the innovation performance of the
universities, as the variable is only statistically significant at the 10% level.
Furthermore, the four interaction terms (PE*UST), (PE2*UT), (IAC*UT), and
(IAC2*UT) are also significant at the 5%-or-less level, as shown in Table 5. Column 3
shows the personnel exchange in the international cooperation and university-type inter-
action term model, which shows the positive and statistically significant impact of per-
sonnel exchange and university type. The regression results are shown in column 4 of
international conferences’ interaction with the moderating variable university type, which
also positively impacts the innovation performance of the universities. The estimated re-
sults indicate that for different types of universities, the intensity of international cooper-
ation represented by the exchange frequency of international cooperation personnel and
the number of international conferences has different effects on the innovation perfor-
mance level of universities, as expressed in hypotheses H2a and H2b.
In addition, comparing the coefficients of the quadratic term and its interaction term,
it can be found that the coefficients of PE2 and PE2*UT are both negative. This means that
in first-class universities, the inverted U-shaped relationship between international coop-
eration personnel exchanges and innovation performance is further strengthened by
Haans and Pieters [30], who indicated that the number of personnel exchanges that exceed
the extreme point will weaken the innovation performance of first-class universities faster.
Conversely, the coefficient of IAC2 is positive and the coefficient of IAC2*UT is negative,
indicating that the inverted U-shaped relationship between international conferences and
innovation performance is weakened in first-class universities.
According to the regression data, the relationship curve between the number of per-
sonnel exchanges in international cooperation and the innovation performance of univer-
sities under different types of universities is fitted (Figure 2, left), as well as the relation-
ship curve between the number of international conferences and the innovation perfor-
mance under different types of universities (Figure 2, right). It can be seen from Figure 2
that, compared with other universities, the innovation performance of first-class univer-
sities increases faster with the increase in personnel exchange and international confer-
ences; it can reach a higher extreme value of innovation performance.
Figure 2. The moderating effect of university type on the relationship between IIC and IPU.
Figure 2. The moderating effect of university type on the relationship between IIC and IPU.
Meanwhile, the model’s results show that the AR (1) p-value is less than 5%, which
indicates that autocorrelation and serial correlation in first-order difference and the AR (2) p-
value are greater than 5%, which means that the null hypothesis of second-order difference
is not accepted as per the established standards as well-reported and recommended by
Ulah et al. (2021) [
31
]. Moreover, the Hansen test indicates that overidentifying restrictions
are valid where the Hansen p-value supports instrument reliability and fails to reject the
null hypothesis. Therefore, system GMM is a valid and excellent technique to apply, as our
sample number of cross-sections group “N,” 64 countries, is greater than the period “T,”
which is 11 years in this study, and instruments are less than the number of groups, as per
the system GMM-required standards.
5. Discussion and Conclusions
Our study aims to determine the correlation between transnational research collabora-
tion and universities’ research performance in China’s context. Considering the current
regulative environment of the Chinese higher educational system, the study also tried
to examine the impacts of the ongoing “Double First-Class Project” on the outcomes of
transnational research collaboration in China, which may or may not lead to changes in
universities’ innovation performance.
In the study, the dependent variables regarding the research and innovation perfor-
mance of universities include the number of produced academic monographs, the number
of published academic articles, and the number of state-funded research projects. The
independent variables related to transnational research collaboration activities cover the
number of participants involved in international joint research projects and international

Sustainability 2023,15, 83 13 of 16
conferences and the number of international conferences hosted by Chinese universities.
The World-Class University Policy is considered a regulating variable, whose impacts are
assessed through the amount of research funding received from the state government, local
governments, and other related actors. Control factors include the population of full-time
researchers and the impacts of the timing (by year) of data collected. Throughout the study,
we employed the GMM methods to examine the working hypotheses and a robust check of
results with stationary and panel cointegration tests.
The analysis results point to the following three major findings: First, there is a
positive correlation between the intensity of transnational research collaboration and the
innovation performance of universities. The more actively Chinese universities participate
in transnational research collaboration, the more advantageously they perform in the
productivity of research and innovation. This echoes previous findings on the positive
impacts of international collaboration on organizational innovation performance, such as
firms [32], and contributes to the literature with new evidence of Chinese universities.
Second, as the relationship between transnational research cooperation and the in-
novation performance of Chinese universities is an inverted U-shaped relationship, the
correlative growth between both sides will reach its peak at some point and may decline
slowly afterward. The analysis found that except for Peking University, Tsinghua Unaiey,
and Zhejiang University, whose development is close to the peak point, the majority of
Chinese universities are still in the climbing stage of the inverted U-shaped curve. For these
universities, at this stage, increasing the intensity of international research cooperation can
enhance their innovation performance effectively.
Third, the analysis results reveal a positive moderating impact of the national policy
(World-Class University Policy) on the relationship between transitional research collabora-
tion and innovation performance. Particularly, in terms of hosting international conferences,
China’s world-class policy-funded universities were found to outperform other Chinese
universities substantially. Previously, researchers criticized that the world-class university
policy in China has been narrowly defined by the Chinese government, which overempha-
sized the international dimensions rather than national development [
33
]. To some extent,
this study verifies their argument. It, however, presents a bright side of the policy emphasis
if one considers Chinese universities’ contributions to the global innovation network, global
knowledge, and society development positively.
Interestingly, the analysis suggests the impact of increasing the intensity of transna-
tional research collaboration on universities’ innovation performance is statically more
significant on universities in the list of world-class policy-funded universities. The anal-
ysis suggests that these universities have higher performance under the same intensity
of international cooperation and can achieve higher performance at the peak point of the
inverted-U shape. These universities are usually at the frontier of university rankings and
enjoy a higher degree of internationalization. However, this finding does not mean that
investment in other universities with lower internationalization should be discouraged.
The investment in transnational research collaboration in the universities that are not on
the list may receive other payoffs, for instance, in joint educational provisions, which have
not yet been covered in this study. Nevertheless, a growing disparity in the performance
of research and innovation between the two groups of universities is becoming obvious.
In this sense, further efforts would be needed from different stakeholders, including the
Chinese government, at different levels and universities, to balance the need of pursuing
excellence and equality in knowledge production.
Nevertheless, this study has some limitations. First, the data of the study can be
manipulated, thus not revealing the real picture of universities’ performance. For instance,
the indicator of hosting international conferences is used in the assessment of the imple-
mentation of first-class universities’ policies in universities. Universities may increase the
number of hosting international conferences intentionally to comply with the assessment
requirement and neglect other aspects of transnational research collaboration. Panel data
documented yearly can also be influenced by data from previous years. Second, in terms

Sustainability 2023,15, 83 14 of 16
of research methods, the measurement of the innovation performance of universities in
this study does not consider indicators such as technology transfer, educational provision,
or social services, which will limit our knowledge of these aspects. Third, findings on
universities that do not receive world-class policy funding are limited. These universities
are strategically positioned by the Chinese government in other aspects such as industrial
talent provision. The assessment of their innovation performance should also be examined
by considering these key aspects, which are not included in this study.
Overall, the study contributes to the literature on universities’ innovation performance
with new evidence and perspectives. Not focusing on universities, past studies on the
innovation of firms already manifested that intensity and diversity of collaboration can
have positive impacts on the innovation performance of organizations [
34
]. This argument
has been partly examined by higher education researchers by studying the impacts of
increasing the diversity of collaboration between different sectors, i.e., industry–university
collaboration [35].
This study sheds light on a new perspective on this investigation by revealing the
positive impacts of increasing the intensity of collaboration through transnational research
collaboration on universities’ innovation performance. It will be interesting for future
research to examine the impacts of transnational industry–university research collaboration
on universities’ innovation performance, as the necessity of developing transnational
innovation ecosystems through promoting the synergy between transnational industry
cooperation (TIC) and transnational university cooperation (TUC) has already been pointed
out in a previous study [36].
Given the above discussion, the study provides solid support for the promotion
of transnational innovation ecosystems [
36
,
37
]. We should utilize the important role
of universities and the positive impacts of transnational research collaboration for the
sustainable development of the transnational innovation ecosystem.
Author Contributions:
Conceptualization, Z.Z., Y.W., and G.Z.; methodology, Z.Z.; software, Z.Z.;
validation, Z.Z.; formal analysis, Z.Z., Y.W., and G.Z.; investigation, Z.Z.; resources, Z.Z.; data
curation, Z.Z.; writing—original draft preparation, Z.Z., G.Z., and Y.W.; writing—review and editing,
G.Z.; supervision, Y.W.; project administration, Y.W.; funding acquisition, Y.W. All authors have read
and agreed to the published version of the manuscript.
Funding:
This work was financially supported by the Humanities and Social Science Fund Project
of the Ministry of Education of China under Grant No. 21YJC880108, by the National Education
Sciences Planning under Grant No. BDA190067, by Association of Chinese Graduate Schools under
the project Study on the Long-term Mechanism of High-quality Interdisciplinary Construction.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
We would like to express our sincerest gratitude to the editors and reviewers for
their constructive and insightful comments which have helped us improve and deepen the study.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Hu, M.C.; Mathews, J.A. Estimating the innovation effects of university–industry–government linkages: The case of Taiwan. J.
Manag. Organ. 2009,15, 138–154. [CrossRef]
2. Nelson, R.R. National Innovation Systems: A Comparative Analysis; Oxford University Press: New York, NY, USA, 1993.
3.
Olechnicka, A.; Ploszaj, A.; Celinska-Janowicz, D. The Geography of Scientific Collaboration; Routledge: London, UK; New York, NY,
USA, 2019.
4.
Wu, D.; Li, R. Research on the network structure and evolution law of institutional cooperation in the field of Management
Science in China. China Manag. Sci. 2017,25, 168–177.

Sustainability 2023,15, 83 15 of 16
5.
Chung, E.; Kwon, N.; Lee, J. Understanding Scientific Collaboration in the Research Life Cycle: Bio-and Nano scientists’
Motivations, Information-Sharing and Communication Practices and Barriers to Collaboration. J. Assoc. Inf. Sci. Technol.
2016
,67,
1836–1848. [CrossRef]
6.
Beers, C.; Zand, F. R&D Cooperation, Partner Diversity and Innovation Performance: An Empirical Analysis. J. Prod. Innov.
Manag. 2014,2, 292–312.
7.
Dong, Y.; Liu, L. Analysis on the evolution of institutional cooperation network of high-level papers in Colleges and universities
in China: Taking the cooperative papers of nature and science from 1978 to 2017 as an example. J. Inf. 2019,38, 138–144157.
8.
Hong, Y.; Gao, Y. Research on scientific research cooperation and innovation mechanism of China US high-level universities:
Social network analysis based on wos database Paper Co authorship. Jiangsu High. Educ. 2020,2, 36–41.
9.
Barjak, F.; Robinson, S. International collaboration, mobility and team diversity in the life sciences: Impact on research performance.
Soc. Geogr. 2008,3, 23–36. [CrossRef]
10.
Goldfinch, S.; Dale, T.; DeRouen, K. Science from the Periphery: Collaboration, Networks and Periphery Effects’ in the Citation of
New Zealand Crown Research Institutes Articles, 1995–2000. Scientometrics 2003,57, 321–337. [CrossRef]
11.
Frederiksen, L.F. Disciplinary Determinants of Bibliometric Impact in Danish Industrial Research: Collaboration and Visibility.
Scientometrics 2004,61, 253–270. [CrossRef]
12.
Zhou, X.; Ren, X.; Zhang, Z. Analysis of the current situation of China’s international scientific research cooperation and its
inspiration—Based on the perspective of bibliometric analysis. Inf. Eng. 2019,5, 86–98.
13.
Miao, Y.; Sun, J.; Qi, W. Characteristics of excellent academic cooperation: Model, intensity and influence perspective. Sci. Res.
Manag. 2014,35, 106–114.
14.
Adams, J.D.; Black, G.C.; Clemmons, J.R.; Stephan, P.E. Scientific Teams and Institution Collaborations: Evidence from U.S.
Universities, 1981-1999. Soc. Sci. Electron. Publ. 2005,34, 259–285.
15.
Berchicci, L. Towards an open R&D system: Internal R&D investment, external knowledge acquisition and innovative perfor-
mance. Res. Policy 2013,42, 117–127.
16.
Xu, X. Corporate technology in draught, property and inverted U-shaped performance: An empirical research on Chinese listed
companies. Sci. Res. Manag. 2015,36, 45–54.
17.
Pan, M.; Dong, L. Discussion on the Classification, Positioning and Characteristic Development of Colleges and Universities.
Educ. Res. 2009,2, 33–38.
18. Evans, T.S.; Lambiotte, R.; Panzarasa, P. Community Structure and Patterns of Scientific Collaboration in Business and Manage-
ment. Scientometrics 2011,89, 381–396. [CrossRef]
19.
Huang, X.; Liu, G.; Liu, X. Ability of Scientific and Technological Innovation in Regional Colleges and Universities Under the
Dual First-rate Background: Performance Evaluation and Improving Approaches. J. Jiangxi Norm. Univ. (Philos. Soc. Sci. Ed.)
2018,51, 93–102.
20.
Wang, Z.; Liu, H.; Wang, S. University scientific research cooperation in the construction of “birds of a feather flock together and
schools are divided by groups”—Based on the perspective of literature analysis. Manag. Rev. 2019,31, 99–109.
21.
Guler, I.; Guillen, M. Institutions and the Internationalization of the US Venture Capital Firms. Soc. Sci. Electron. Publ.
2010
,41,
185–205. [CrossRef]
22. Bozeman, B.; Lee, S. The Impact of research Collaboration on Scientific Productivity. Soc. Stud. Sci. 2005,35, 673–702.
23.
Compilation of Scientific and Technological Statistics of Colleges and Universities in 2017. Available online: http://www.moe.
gov.cn/s78/A16/A16_tjdc/201805/t20180522_336767.html (accessed on 6 October 2021).
24.
Abramo, G.; D’Angelo, C.A.; Solazzi, M. Are Researchers that Collaborate More at the International Level Top Performers? An
Investigation on the Italian University System; Elsevier: Amsterdam, The Netherlands, 2011.
25.
Zhang, B. Research on the Influence of University-Industry Collaboration on Research Performance of Chinese Universities. Ph.D.
Thesis, Harbin Institute of Technology, Harbin, China, 2018.
26.
Arellano, M.; Bond, S. Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment
Equation. Rev. Econ. Stud. 1991,58, 277–297. [CrossRef]
27.
Blundell, R.; Bond, S. Initial Conditions and Moment Restrictions in Dynamic Panel Data Models. J. Econom.
1998
,87, 115–143.
[CrossRef]
28.
Bond, S.R. Dynamic Panel Data Models: A Guide to Micro Data Methods and Practice. Port. Econ. J.
2002
,1, 141–162. [CrossRef]
29. Chen, Q. Application of Stata in Econometrics; Higher Education Press: Beijing, China, 2016.
30.
Haans, R.; Pieters, C.; He, Z.L. Thinking about u: Theorizing and testing u- and inverted u-shaped relationships in strategy
research. Strateg. Manag. J. 2016,37, 1177–1195. [CrossRef]
31.
Ullah, A.; Zhao, K.; Ullah, S.; Chen, P.; Khan, S. Sustainable Utilization of Financial and Institutional Resources in Reducing
Income Inequality and Poverty. Sustainability 2021,13, 1038. [CrossRef]
32.
Mario, I.; Kafouros, J.; Buckley, P.A.; Sharp, J.; Wang, C. The role of internationalization in explaining innovation performance.
Technovation 2008,28, 63–74. [CrossRef]
33.
Kim, D.; Song, Q.; Liu, J.; Liu, Q.; Grimm, A. Building world class universities in China: Exploring faculty’s perceptions,
interpretations of and struggles with global forces in higher education. Comp. J. Comp. Int. Educ. 2018,48, 92–109. [CrossRef]
34.
Hsu, C.-W.; Lien, Y.-C.; Chen, H. R&D internationalization and innovation performance. Int. Bus. Rev.
2015
,24, 187–195.
[CrossRef]

Sustainability 2023,15, 83 16 of 16
35.
Tseng, F.C.; Huang, M.H.; Chen, D.Z. Factors of university–industry collaboration affecting university innovation performance. J.
Technol. Transf. 2020,45, 560–577. [CrossRef]
36.
Cai, Y.; Ferrer, B.R.; Lastra, J.M. Building University-Industry Co-Innovation Networks in Transnational Innovation Ecosystems:
Towards a Transdisciplinary Approach of Integrating Social Sciences and Artificial Intelligence. Sustainability
2019
,11, 4633.
[CrossRef]
37. Cai, Y.; Ma, J.; Chen, Q. Higher Education in Innovation Ecosystems. Sustainability 2020,12, 4376. [CrossRef]
Disclaimer/Publisher’s Note:
The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.