1. Introduction
Innovation is the first driving force, and leads development [
1]. The main subjects responsible for technological innovation are universities, public research organizations, and enterprises [
2]. In addition, there are cooperative relationships between these organizations to achieve technological progress through innovative interaction. The most typical partnership is university–enterprise collaborative innovation [
3], in which university–enterprise knowledge flow plays an important role as a driving force [
1]. Specifically, the role of universities in the national innovation system is to create new knowledge, and enterprises obtain economic benefits through the application of this new knowledge. To support universities to carry out innovative activities, enterprises provide universities with funds and other innovative resources for scientific research [
1,
4]. In university–enterprise knowledge flow, local government behavior has become the best candidate to regulate and strengthen this partnership, and its importance has become increasingly prominent [
5]. The alliance between universities, enterprises, and local governments has been confirmed and supported by the triple helix theory [
6].
However, unlike the traditional triple helix framework, which exhibits the features of blurry boundaries in the university–enterprise–government relationship, this study attempts to take the government as an external control parameter and examine its influence on university–enterprise knowledge flow. Therefore, we need to find a suitable research context to satisfy this research framework. The literature indicates that China’s institutional system is both unique and universal compared to other countries. Specifically, in contrast to a centralized political system, China has implemented a market economy [
7]. The political and economic environment provides the Chinese government with strong institutional power and flexible market power in innovation policy implementation. On the one hand, universities and enterprises in China choose their innovation partners mainly because they match their mutual research goals and conditions [
8]. On the other hand, the Chinese government provides a relatively well-developed innovation policy environment and grants financial subsidies for university–enterprise knowledge flow [
9]. Therefore, the Chinese institutional context and university–enterprise–government relations align with the research framework of this study. Using China as a case study, the findings can provide policy implications for other developing countries and emerging economies.
In China, the importance of university–enterprise knowledge flow in constructing the national innovation system has always been emphasized, becoming a key support project of the Chinese government [
10,
11]. On the basis of the perspective of knowledge flow, universities are undoubtedly evolving into knowledge exporters and creators, while enterprises become knowledge importers and users. According to the duality of the innovation value chain theory, the process of knowledge flow involves knowledge creation and knowledge transfer [
1,
12,
13]. The local government can formulate policies to encourage universities and enterprises to organize innovative cooperation activities and expand university–enterprise knowledge flow through financial investment [
9].
Following reform of China’s tax-sharing system, local governments have gradually gained fiscal revenues and control power [
14]. Corresponding to China’s fiscal decentralization reform is administrative centralization [
15]. Such a political and economic environment has fundamentally changed how local governments participate in innovation activities. In order to fully implement the national strategic policy of innovation-driven development, the central government has put forward rigid technological innovation requirements for local governments and incorporated indicators such as technological innovation performance into the local official assessment system [
16]. In this context, local governments must improve their competitiveness in order to obtain more innovation-related resources and attract innovation factors. University–enterprise knowledge flow plays a primary role in improving regional innovation capacity [
17]. Therefore, promoting university–enterprise knowledge flow becomes one of the goals of intergovernmental competition. Along with intensification of the degree of intergovernmental competition, the impact of government support on university–enterprise knowledge flow may be influenced by different moderating sources.
Local governments centrally control most local innovation resources. The concentration of scientific management power of local governments provides rent-seeking space. In the absence of effective supervision, the rent-seeking space is even greater [
18]. Fiscal transparency can curb budget violations by helping to ensure the public’s right to know, participate, and supervise [
19]. Fiscal transparency impacts scientific technology investment, including for university–enterprise knowledge flow, reflecting the public’s restriction of local governments. Fiscal transparency can constrain the direction and extent of local government competition [
18]. Specifically, fiscal transparency may affect the degree of influence of intergovernmental competition on the relationship between government support and university–enterprise knowledge flow to a certain extent [
17].
In this study, we first measured knowledge creation efficiency (KCE) and knowledge transfer efficiency (KTE) and found that the average value of KCE is higher than KTE. Furthermore, the regression results indicate that government support has a nonlinear effect on KCE and a positive impact on KTE. We discovered that intergovernmental competition has a positive moderating effect in the relationship between government support and the dual efficiency of university–enterprise knowledge flow, as well as that fiscal transparency can enhance the moderating effect of intergovernmental competition.
The contributions of our study are as follows. First, in previous studies of university–enterprise cooperation most scholars have focused on university–enterprise cooperative behavior or single-dimensional university–enterprise cooperative innovation performance [
20]. We divided university–enterprise knowledge flow into dual stages, namely, knowledge creation and knowledge transfer, which further promote the research progress of university–enterprise cooperation in the field of knowledge management.
Second, while many studies have shown that government support has a significant impact on university innovation performance or enterprise innovation ability [
16,
21], there is no unified answer as to whether local government behavior can play a role in university–enterprise cooperation. We found heterogeneous effects of government support on university–enterprise knowledge creation and knowledge transfer.
Third, different competition objectives may cause variance in the influence of government financial support on university–enterprise knowledge flow. Meanwhile, fiscal transparency, as one of the public’s supervisory modes for government management, can affect local government behavior, including intergovernmental competition [
15]. Therefore, we studied the joint moderating effect of intergovernmental competition and fiscal transparency within the impact mechanism of government support and university–enterprise knowledge flow.
Finally, considering that innovation subjects have knowledge spillovers in their spatial location [
2], the spatial correlation and temporal dependence of panel data may be ignored when using the multiple linear regression method. To reduce the estimation error in empirical research, we adopt the Dynamic Generalized Spatial Model (DGSM) method.
3. Materials and Methods
3.1. Model
According to the first law of geography, provincial university–enterprise cooperation depends on economic development and human capital factors. It is affected by university–enterprise cooperation in the surrounding provinces [
39]. Therefore, spatial effects cannot be ignored in research on the efficiency of university–enterprise knowledge flow. In addition, the efficiency of university–enterprise knowledge flow in a certain province is usually related to the previous period. More precisely, the efficiency of university–enterprise knowledge flow has a spatial spillover effect and shows a temporal dynamic effect. As a powerful tool for studying spatial economics, the DGSM method can meet the needs of this study. The DGSM can explain the spatial spillover effect of the efficiency of university–enterprise knowledge flow in surrounding provinces in the local area and resolve the temporal dynamic effect of the efficiency [
40]. The equation expression of DGSM is:
Here, Yi,jt is the dependent variable, i,j = 1, 2, …, N (N = 30) reflect 30 provinces in China, t = 1, 2, …, T (T = 14) reflect 14 years of data, Yit−1 is the dependent variable that lags one year, α is the constant term, δ refers to the coefficient of the efficiency that lags one year, Xit denotes the independent variables, ρ is the regressive spatial coefficient, λ is the spatial autocorrelation coefficient, β is the coefficient of the independent variables, Wij denotes the spatial weight matrix, ηi refers to the province effect, νt denotes the time effect, µi,jt reflects the spatial fixed effects, and εit is the error term.
On this basis, the equation expression to test the inverted U-shaped influence of government support on the efficiency of university–enterprise knowledge flow is as follows:
In Formula (2), lnKCE and lnKTEi,jt are the dependent variables in this study, representing the efficiency of university–enterprise knowledge flow, while lnKCE and lnKTEit−1 are the efficiencies of university–enterprise knowledge flow that lags one year; lnGSTit is the core independent variable in this study, representing government support, lnGST2it is the quadratic term of government support, Xit denotes the control variables selected in this study, and β1 and β2 are the regression coefficients that are the main focus of this paper. Among them, when β1 is significantly positive and β2 is significantly negative, it indicates that there is an inverted U-shaped relationship between government support and the efficiency of university–enterprise knowledge flow.
The equation expression to test the moderating effect of intergovernmental competition on the relationship between government support and the efficiency of university–enterprise knowledge flow is as follows:
In Formula (3), lnIGCit is the moderating variable in this study, representing intergovernmental competition, while β4 and β5 are the regression coefficients that are our main focus. Among them, if β1 is significantly positive and β2 is significantly negative in Formula (2), and if β5 is significantly positive here, this indicates that intergovernmental competition plays a positive moderating role on the inverted U-shaped relationship between government support and the efficiency of university–enterprise knowledge flow. Moreover, if β1 passes the significant test, β2 is not significant in Formula (2), and β4 is significantly positive here, this indicates that intergovernmental competition plays a positive moderating effect on the positive relations between the government support and the efficiency of university–enterprise knowledge flow.
The equation expression to test the moderating effect of fiscal transparency on the moderating role of intergovernmental competition is as follows:
In Formula (4), lnFTYit is the moderating variable in this study, representing the fiscal transparency, while β10 and β11 are the regression coefficients that are our main focus. Among them, if β1 is significantly positive and β2 is significantly negative in Formula (2), β5 is significantly positive in Formula (3), and β11 is significantly positive here, this indicates that fiscal transparency has a positive moderating effect on the moderating role of intergovernmental competition. Moreover, if β1 passes the significant test, β2 is not significant in the Formula (2), β4 is significantly positive in Formula (3), and β10 is significantly positive here, this indicates that fiscal transparency has a positive moderating effect on the moderating role of intergovernmental competition as well.
3.2. Variables
3.2.1. Dependent Variables
The dependent variables are the efficiency of university–enterprise knowledge flow, namely, KCE and KTE as measured with the super-efficiency DEA model. Referring to the index selection of past studies [
1,
20,
41,
42,
43,
44], the evaluation index system of KCE and KTE in this study is shown in
Table 1. It is worth mentioning that our reason for choosing the number of R&D technology transfer contracts and total funds to measure KTE is that technology transfer is one of the main pathways by which universities can transfer their research outputs to the industrial sector [
42,
43,
44,
45,
46,
47].
3.2.2. Core Independent Variables
The core independent variables are government support (GST) and its quadratic term (GST2). As mentioned above, fiscal expenditure is the most basic way for local governments to support regional innovative activities [
21]. It is reasonable to use government fiscal expenditure to reflect government support for university–enterprise knowledge flow [
33]. Therefore, we adopted the proportion of scientific and technological expenditure in local government fiscal expenditure to express the degree of GST.
3.2.3. Moderating Variables
Based on theoretical analysis, we believe that the degree of intergovernmental competition and fiscal transparency play a joint moderating role in the influence mechanism of GST on KCE and KTE. Therefore, we chose intergovernmental competition (IGC) and fiscal transparency (FTY) as the moderating variables in this study.
Intergovernmental competition mainly refers to the economic behavior of local governments in attracting liquidity elements and serving their jurisdictions through taxation, environmental policies, and welfare [
48]. In fact, due to the restrictions of the registration system and the financial system, the liquidity elements of labor and capital are often insufficient [
27]. Therefore, as a liquidity element with significant economic benefits and spillover effects, scholars have often favored FDI to measure the degree of local government competition in past studies [
14,
19]. Thus, we used each province’s ratio of FDI to GDP to define IGC.
Fiscal transparency reflects the extent to which taxpayers can participate in fiscal activities and the degree of fiscal democracy [
49]. The “Report on China’s Fiscal Transparency” issued by the Public Policy Research Center of Shanghai University of Finance and Economics is an authoritative earlier assessment of fiscal transparency in China. The report takes 31 provinces in China as its survey objects. It uses various methods, such as information disclosure applications, online searches, and document collection (ranging from public budgets, fund budgets, financial account management capital budgets, state-owned capital operating budgets, government asset debts, departmental budgets, social insurance fund budgets, state-owned enterprises, and the attitude and sense of responsibility of those surveyed) to examine the degree of openness of provincial fiscal information and calculate the provincial fiscal transparency index. The value range of the index is 0–100, with larger values indicating higher fiscal transparency in government. We used this index to measure the FTY of each province.
3.2.4. Control Variables
In order to eliminate the error in the regression results, we selected control variables according to previous studies [
1,
16], including the level of human capital (HCL), industrial structure (ILS), infrastructure (INF), and marketization (MAR). Among these, we used the number of college students per 10,000 people to measure the HCL variable and the ratio of the added value of tertiary industry to GDP to measure the ILS variable. The INF variable was measured by the per capita road area, while the MAR variable was characterized by the proportion of non-state investment in total regional investment.
3.3. Data Sources
Based on the principle of data availability, the sample in this study contained 30 provinces, autonomous regions, and municipalities in China, excepting Tibet, Taiwan, Hong Kong, and Macau. The data were derived from the Compilation of Science and Technology Statistics of Institutions of Higher Learning, the China Statistical Yearbook, and the Easy Professional Superior (EPS) data platform. The sample period was from 2007 to 2020. We used the linear interpolation method to adjust and supplement the data and adopted the logarithmic form of all the variables in this paper (In order to avoid negative values after taking the logarithm of values less than 0, we follow the method of adding 1 to the value of all variables and then taking the logarithm.). The symbols and descriptive analysis results of the variables are shown in
Table 2.
5. Discussion and Conclusions
Based on dual knowledge flow, this study divides university–enterprise knowledge flow into dual stages, namely, knowledge creation and knowledge transfer, and uses the super-efficiency DEA model to measure the dual efficiency. By adopting the DGSM method, we find that government support has an asymmetric effect on the dual efficiency of university–enterprise knowledge flow. Specifically, government support has an inverted U-shaped effect on knowledge creation efficiency while positively impacting knowledge transfer efficiency. In addition, intergovernmental competition plays a moderating role in the relationship between government support and dual efficiency. Moreover, we incorporate fiscal transparency into the theoretical and empirical framework, revealing that fiscal transparency can enhance the moderating effect of intergovernmental competition.
5.1. Theoretical Implications
This study provides theoretical implications for deeper understanding of the mechanism of local government behavior on university–enterprise cooperation. First, from the perspective of knowledge flow, this study reveals the asymmetric mechanism of government support on knowledge creation efficiency and knowledge transfer efficiency. Our findings can help to further understanding of the relationship between government financial investment and university–enterprise knowledge flow, and can enrich the theory of the university–enterprise–government triple helix from the field of knowledge management.
Second, this study verifies the effect of intergovernmental competition on the process of university–enterprise collaborative innovation when using government financial support to improve knowledge flow efficiency. Our findings clarify the moderating role of intergovernmental competition in the relationship between government support and university–enterprise knowledge flow, providing new evidence for the “competition for innovation” view of Chinese local government. These findings help to understand the mechanism of local government behavior and university–enterprise knowledge flow.
Third, this study analyzes the complexity of the joint moderating mechanism of financial transparency and intergovernmental competition as boundary conditions. By revealing the joint moderating effect of fiscal transparency and intergovernmental competition on the impact of government support on knowledge creation efficiency and knowledge transfer efficiency, these findings can help to expand research into the boundary conditions of the influencing mechanism of local government behavior on university–enterprise knowledge flow.
5.2. Policy and Managerial Implications
Based on our research findings, we propose the following practical implications. First, government financial investment in science and technology is a vital booster in improving university–enterprise cooperation. In particular, to promote the improvement of university–enterprise cooperation, local governments should effectively distinguish the different stages of knowledge creation and knowledge transfer and use R&D subsidies rationally in the different stages. Compared to university–enterprise knowledge transfer, local governments should be more cautious in using financial investment in order to avoid potential negative effects on knowledge creation.
Second, local governments should pay special attention to controlling the degree of intergovernmental competition within a reasonable range. While advancing the reform of the fiscal decentralization system, the central government should effectively restrain the self-interested investment preferences of local governments and should adopt a more diversified official evaluation system in order to improve local officials’ implementation of central innovation policy. These measures can guide the innovation preferences of local government competition, thereby strengthening the effect of government support on university–enterprise knowledge flow.
Third, governments should pay attention to the different meanings of financial investment in science and technology for regional university–enterprise knowledge flow under conditions of different fiscal transparency. The specific suggested approach is to raise the level of public participation in supervision of government fiscal investment and increase the transparency of government fiscal expenditures. Meanwhile, governments should actively engage in fiscal transparency by guiding intergovernmental competition, thus improving university–enterprise knowledge flow.
5.3. Limitations and Future Research
This study has several limitations that can offer scope for future research. First, university–enterprise knowledge flow is divided into knowledge creation and knowledge transfer stages in this paper, meaning that we had to choose more than one index in order to measure knowledge creation efficiency and knowledge transfer efficiency. Due to limited data availability, we only chose the secondary index mentioned above. Therefore, it would be possible to choose more indicators in future research in order to re-measure the dual efficiency. Second, because the sample in this study was 30 provinces in China, the research scope is relatively macro-level and insufficiently focused. I would be possible to lower the research scope to the city level and conduct an empirical study using urban panel data to reveal the influence mechanism of local government behavior on university–enterprise knowledge flow in future research. Finally, in the theoretical framework of this paper, fiscal transparency was used as a moderating variable on intergovernmental competition as a way to test its indirect moderating effect. However, fiscal transparency has a direct and significant impact on government fiscal investment. Therefore, in future research, we intend to further investigate its direct moderating effect on government fiscal investment and the efficiency of university–enterprise knowledge flow.