How to Improve the Synergetic Development Capabilities of the Innovation Ecosystems of High-Tech Industries in China: An fsQCA Analysis Based on the TOE Framework

Abstract

This study explores the synergetic development capability of China’s high-tech industry innovation ecosystem and the diversification path necessary to improve it. Based on the four-spiral perspective, the system synergetic development index was constructed, and the composite system collaborative degree model was used to measure the synergetic development capability of the high-tech industry innovation ecosystem across 30 provinces and cities in China from 2012 to 2020. The influencing factor model of improving the system’s synergetic development capability was constructed under the technology–organization–environment (TOE) framework, and a fuzzy set qualitative comparative analysis (fsQCA) method was used to explore the multiple paths available to improve the synergetic development ability of the system. The results show that the four-helix system’s synergetic development capability is poor and needs to be improved. The “technology–organization–environment” conditions cannot be used alone as the necessary conditions for the improvement in the system’s synergetic development capability but need to exert a joint, matching effect through the combination of different factors. In addition, market openness is crucial on the path toward the highly synergetic development of the system. The research results not only provide a theoretical basis for comprehensively improving the system’s synergetic development capability but also provide a practical reference for the differentiation path being revealed. In short, this study has important theoretical and practical significance to promoting the synergetic development and benign evolution of the high-tech industry innovation ecosystem.

1. Introduction

As the backbone industry of the rapid growth of the national economy, the high-tech industry promotes the transformation and upgrade of the industrial structure and the high-quality development of the national economy. As a knowledge- and technology-intensive industry, the high-tech industry inhabits the middle- and high-end part of the industrial value chain and has gradually become the support point and focus of the new momentum of national economic development. In the 14th Five-Year Plan, China once again emphasized the tremendous role of high-tech industries in promoting economic development. According to data from the National Bureau of Statistics, the main business income of China’s high-tech industry is displaying a growth trend. By 2021, the main business income of the high-tech industry was CNY 20,989.6 billion, displaying an increase of nearly 20 times compared with that of 2000, but its growth rate is gradually decreasing. The number of high-tech enterprises in the industry as a whole is also growing: the number of high-tech enterprises in 2000 was 9758, while in 2021, this number reached 45,646, indicating an increase of about 3.7 times compared with 2000. Similar to the overall development trend regarding the number of enterprises, the number of employees in the high-tech industry is also increasing year by year, but this growth rate is relatively small. From the overall trend, the overall scale of China’s high-tech industry is increasing, and its status and contribution in the national economy are becoming more and more prominent.

With the continuous expansion of the connotations of innovation, the innovation paradigm continues to evolve. The evolution process of the innovation paradigm can be divided into three stages: the linear paradigm (innovation paradigm 1.0), the innovation system (innovation paradigm 2.0), and the innovation ecosystem (innovation paradigm 3.0). The innovation ecosystem is a new innovation research paradigm in the context of globalization. The evolution of the innovation paradigm has promoted the continuous evolution and development of the corresponding innovation-driven model, from the double spiral based on “demand + research” to the triple spiral of “demand + research + competition” (government + enterprise + academic research), which then evolved into the four-spiral model of “demand + research + competition + symbiosis” (government + enterprise + academic research + public, etc.) [1].

The innovation ecosystem is essentially the organic combination of an ecosystem and an innovation system, so it has the dual characteristics of an ecosystem and an innovation system, including openness, diversity, dynamics, and symbiosis. The United States PCAST proposed the concept of the innovation ecosystem for the first time in 2004, and then Adner enriched and improved the connotations of the innovation ecosystem, believing that the innovation ecosystem involves the interaction and exchange of knowledge, technology, resources, and energy between multiple stakeholders to achieve the common goal of innovation and continuously achieve capability improvement, value creation, and acquisition in the process of cooperation and competition [2]. Some scholars also believe that the innovation ecosystem realizes the exchange of matter, energy, and information among species, communities, and the external environment through material flow (human capital, physical capital, etc.), energy flow (knowledge capital, financial capital, etc.), and information flow (policy, market information, etc.) within the system, forming an open, complex system of symbiosis, competition, cooperation, and dynamic evolution [3]. From the initial conceptual connotations [2,4], composition, and operation mechanism [5,6,7], research on the innovation ecosystem has gradually expanded to explore the construction of the system [8,9,10], its influencing factors [5], and its evaluation [11,12]. From the perspective of research, it gradually extends from the macro national level to the meso regional and industrial level, to the micro enterprise level: in other words, from the national innovation ecosystem [13,14] to the regional [15,16,17] and industrial innovation ecosystem [5,18,19,20,21], to the enterprise innovation ecosystem [22,23,24]. Among them, the enterprise innovation ecosystem extends into the open innovation ecosystem [25,26,27,28]. With the wide application of digital technology, digital transformation promotes the continuous evolution and development of the innovation ecosystem, and the digital innovation ecosystem has become the focus of current research [1,29,30,31,32].

The innovation ecosystem is a new innovation paradigm emerging in the context of the continuous advancement of global innovation. The industrial innovation ecosystem is an important part of national innovation-driven development strategy. The escalating trade frictions between China and America, the prevalence of anti-globalization, and the huge impact of COVID-19 have compounded into a fatal blow to the innovation ecosystems of various industries across many countries [33]. Against the complex and ever-changing global landscape, China is accelerating its innovation-driven development strategies to build and improve the national innovation ecosystem. As the core of China’s innovation-driven strategies, the high-tech industry is a major driving force to facilitate the structural optimization and upgrade of the manufacturing industry and advance supply-side reform. It is also a key field for international economic and technological competition [34]. Therefore, promoting the benign evolution and development of the high-tech industrial innovation ecosystem, and creating a sound innovation ecosystem have become a “ballast stone” for improving the national innovation ecosystem and expediting the development of a powerful manufacturing and technological giant. Under the background of profound changes in the international landscape and the restructuring of global value chains, how to improve the synergetic development capability to enable the high-tech industrial innovation ecosystem, so as to realize the stable development of China’s high-tech industry and achieve high-quality economic development is a very important theoretical and practical issue.

Moore first proposed the concept of the “business ecosystem” in 1993, and the industrial innovation ecosystem was born [35]. Subsequently, Dhanaraj et al. [18] found that leading enterprises are the key coordinator of industrial innovation networks and value acquisition. Martin [19] built an ecological model to study the elements of innovation ecosystem in the information and communication industry. Ritala et al. [20] analyzed the value acquisition mechanism of the industrial innovation ecosystem. Ander et al. [5] have verified the promoting effect of the industrial innovation ecosystem on the formation of inter-industry linkages, thus promoting technological innovation and industrial development and realizing the process of value creation. Dougherty et al. [21] proposed that the establishment of an innovation ecosystem can provide a breeding environment for cooperation, and cooperative innovation was conducive to the synergetic development of various industries. As an important branch of the industrial innovation ecosystem, the high-tech industry innovation ecosystem was developing gradually. In recent years, scholars at home and abroad have focused on perspectives such as the systematic structure [36,37], mechanism [38,39], evolution [40] and evaluation [41,42,43,44,45,46,47] of the high-tech industrial innovation ecosystem. Some scholars proposed the composition and model of coupling strategies for the high-tech industrial innovation ecosystem [36], and empirically studied the asymmetric coupling relationship between upstream and downstream technological groups of the system [37]. Other scholars established an operating mechanism of the high-tech industrial innovation ecosystem from the composition and characteristics of the system [38,39], and analyzed the evolution path and stable conditions from the perspective of constructing a platform for the high-tech industrial technology services [40]. Specifically, evaluation-related studies mainly include system synergy, sustainability, and ecological suitability. Some researchers have built classification and evaluation systems for the synergy of the high-tech industrial innovation ecosystem [41], constructed a model for the system-level core capabilities [42], and evaluated the systems’ health by adopting improved entropy, DEMATEL (Decision Making Trial and Evaluation Laboratory) and ISM (Interpretative Structural Modeling) [43] or the tripartite evolutionary game model [44]. Some scholars constructed an ecological suitability model to evaluate the evolution and evolutionary potential of the systems [45], and based on the quadruple helix model, measured the overall synergy and dynamic sustainable development ability of the systems [46]. Other academics created an evaluation index system including the system’s dynamic evolution, sustainable innovation and openness [47], and analyzed the symbiotic efficiency of the systems and its influencing factors by introducing the ecologically symbiotic model [48]. Moreover, some scholars have explored the influence of the industrial development environment on high-tech industrial innovation efficiency based on the system framework [49].

To sum up, research on the high-tech industry innovation ecosystem has made some achievements, but there are some limitations. Among them, although some scholars identified and put forward the influencing factors of synergetic development from different perspectives, they lacked the theoretical research framework to systematically analyze the key influencing factors of synergetic development. Secondly, most scholars used mathematical models to quantitatively analyze the system synergy, but did not deeply explore the synergistic development among multiple factors and their combined effects on the system synergy development capability. Therefore, the TOE framework for improving the synergetic development capability of high-tech industrial innovation ecosystem was constructed in this paper, and the linkage and matching effects of five antecedent conditions were identified from the three levels of “technology–organization–environment”, which provided a theoretical basis for comprehensively improving the synergetic development capability of high-tech industrial innovation ecosystem. Secondly, the fuzzy set qualitative comparative analysis method (fsQCA) from the perspective of configuration was introduced to deeply analyze the multiple concurrent causal relationships and joint effects among various factors of the high-tech industrial innovation ecosystem’s synergetic development, accurately located the regional cases covered by each equivalent path, and conducted an in-depth analysis of the system synergetic development capability of different regions. Therefore, it provides a practical reference for revealing the differentiated paths for systematic synergetic development capability enhancement. In short, this paper has important theoretical and practical significance for promoting the synergetic development and benign evolution of the high-tech industrial innovation ecosystem.

The synergetic in this paper is the interaction between the different subsystems and their main elements in the system, which promotes the organic combination of each subsystem, forms a self-organizing structure with certain functions, and realizes the orderly state of the overall macrostructure of the system. The synergetic development of the system refers to the dynamic development process of mutual influence, interaction and interdependence formed by the constant exchange and interaction between the main elements and subsystems of the systems, and between the subsystems and the external environment, in order to achieve the stable and orderly and benign evolution of the system. Synergetic development capability is used to characterize the degree of consistency of synergetic development among subsystems in a system.

The rest of this article Is organized as follows. Section 2 Introduces the theoretical basis of this paper and constructs the research framework. In Section 3, the research methods and models of this study are designed, and variable selection and data sources are clarified. Section 4 provides empirical analysis and discussion. Finally, Section 5 is the conclusions and implications.

2. Theoretical Foundation and Research Framework

2.1. Theoretical Foundation

The innovation ecosystem is a system of cross-organizational, political, economic, environmental and technological subsystems that interact with each other to create an enabling environment for innovation that catalyzes and promotes sustainable business growth. An innovation ecosystem is a network connected by various relationships. According to some related studies [3,37,38,39,40,41,42,43,50], the high-tech industrial innovation ecosystem was regarded as a composite system consisting of multiple innovation participants. Participants are not isolated and static in their innovation activities. Instead, they interact and lead each other under the influence of innovative elements such as technology, talent, and capital. In addition, each subsystem has special functions and roles. All subsystems interact with each other and form a whole system. The synergetic development of the high-tech industrial innovation ecosystem is a joint result of corporate operations and management, technological research and development (R&D) of academic and research institutions, government financial investments, institutional innovation service-related support, and diversified social participation [46]. The synergistic interaction between the components in the quadruple helix model affects the sound operation and sustainable development of the high-tech industrial innovation ecosystem. Hence, based on the “university–industry–government–social public” quadruple helix theory, this paper divides the high-tech industrial innovation ecosystem into five subsystems (enterprise operation, research and development, mediation service, government support, and social participation), and constructs the high-tech industrial innovation ecosystem quadruple helix model, as shown in Figure 1.

The enterprise operation subsystem is a subsystem under the industrial spiral, taking high-tech enterprises as the main body. In the process of production and operation, it constantly coordinates and interacts with various innovation subjects, grasps the market demand and innovation concepts in time, creates valuable scientific and technological products or services, develops core technologies in high-tech fields, and ensures the orderly conduct of innovation activities under the guidance of government policies [51]. The research and development subsystem is a subsystem under the spiral of universities. It takes universities and research institutes as the main body, creates basic innovative knowledge and cutting-edge professional technology through scientific research projects, constantly delivers talents and innovative achievements, and realizes synergetic interaction among innovation subjects through industry–university–research cooperation, so as to provide the foundation for the healthy and sustainable development of high-tech industries [46]. The mediation service subsystem is an intermediary platform under the four spirals. It realizes the flow of resources, knowledge interaction and sharing among various of innovation subjects through investment institutions, science and technology business incubators and intermediary platforms, so as to form diversified innovation services to support the rapid growth and sustainable development of high-tech industries. At the same time, the integration of knowledge and human resources provides a convenient way to establish extensive cooperative relations [52]. The government-driven subsystem is a subsystem under the government spiral. As an innovator of the system, the government provides R&D performance and risk avoidance for innovative activities through financial support and policy assistance [53]. Some government policy guidance can promote synergetic development and interaction among innovation subjects, so as to ensure the healthy and sustainable development of high-tech industries. The social participation subsystem is a subsystem under the social public spiral. The innovative product experiencers from the public participate in communication and interaction through online and offline means, which realizes both scientific and technological cognitive sharing and demand feedback, as well as knowledge exchange and interaction with various innovation subjects, and helps to promote the coordinated and sustainable development of the system. Among various scientific and technological exchange activities, the communication of innovation participants’ diversified demands and the exchange of information from multiple sources under the quadruple helix model make R&D and innovation activities more market-oriented, which is conducive to improving the conversion rate of achievements [54].

2.2. Research Framework

The TOE theory framework was proposed by Tornatizky and Fleischer in 1990 based on the diffusion of innovation theory and the technology acceptance model. It is a comprehensive framework suitable for analyzing the factors influencing the adoption of innovations in the context of multi-level technology application [55]. The framework mainly includes three application dimensions: technology, organization and environment. The technology dimension involves elements such as technical resources or capabilities, highlighting the coupling and matching relationships between technology and organizational structure, as well as resultant potential benefits [56]. The organization dimension generally includes elements such as organizational scale and related institutional arrangements, and pays more attention to matching with technology or subjective initiative of participants [57]. The environment dimension refers to elements influenced by various external positive or negative factors, as well as the ecological environment created for technology application [58]. The TOE theory was initially applied to analyze the scientific and technological innovation activities of micro-organizations [55], but the TOE theory does not specify the specific variables of technology, organization and environment, so it has strong flexibility and operability in practice and theoretical application [59]. According to the practical scenarios and experiences of the research, scholars have continuously expanded the TOE theoretical framework, and gradually extended its application scenario from a single specific micro-organization to a complex and changeable macro-economic system, revealing the mechanism of the matching and linkage of multi-factors on economic activities [60]. Some scholars have studied the impact of stakeholder cooperation strategies on the risk prevention performance in digital innovation ecosystem based on the TOE theoretical framework [61]. Through the flow of technology, information, talent and other elements in the network system, sustainable co-creation value can be achieved [62,63]. As an industrialization innovation ecosystem, the high-tech innovation ecosystem is also characterized by complex diversity, self-organization evolution, open collaboration, etc., and various elements in the system constantly communicate and interact with each other to achieve the synergetic development of the system [4,64,65]. The synergistic development of the innovation ecosystem is mainly related to technological, organizational, and environmental factors, but also includes other influencing factors such as public participation, R&D taxation, and intellectual property protection.

Therefore, based on the development direction of the TOE theoretical framework mentioned above, and combined with the characteristics of the high-tech industry innovation ecosystem. The five most important influencing factors affecting the synergetic development capability of the system are finally screened out, as shown in Figure 2.

2.2.1. Technology Dimension

As the innovation ecosystem is a complex system based on technological innovation, the complexity of technology itself and the emergence generated by the adaptation of technology to the environment will lead to the uncertainty of technological evolution, and this uncertainty will make the individual associations more diversified and further improve the complexity of the system [60,66]. On the one hand, the R&D personnel input is crucial In the process of the creation of new knowledge by technology and an improvement in innovative capabilities. R&D personnel input affects innovation output to some extent, which facilitates the synergetic development of industrial innovation ecosystems. On the other hand, product technology innovation aims at creation of new product technologies by enterprises and reflects the degree of fit between existing technical resources, capabilities and innovation activities of the industry, encouraging users to participate in product innovation activities [67]. Therefore, through identification and screening, this paper divides the influence of technology on the synergetic development capability of high-tech industrial innovation ecosystem into two secondary factors: R&D personnel input and product technology innovation.

2.2.2. Organization Dimension

The management of innovation ecosystem as a complex system requires the combination of government functions and market functions [68]. As an important organization in economic and social management, the government’s decisions not only directly affect the behaviors of various subjects in the system, but also affect the system’s operating environment and technological innovation [69]. Firstly, the synergetic development of the high-tech industrial innovation ecosystem has a close correlation with government support. The development of the industry and its innovation ecosystem require financial support from governments and policy-based or mechanism-based regulation. Government support has become an essential strategic resource [55]. Secondly, the size of industrial entities also influences synergetic development. In the innovation ecosystems of high-tech industries, R&D institutions and enterprises, as the major units, are a core for promoting the systems’ synergetic development. A considerable number of R&D institutions and enterprises will accelerate industrial agglomeration and layout, as well as the systems’ synergetic development. Therefore, through identification and screening, this paper takes government support and the size of industrial entities as two secondary elements at the organizational level.

2.2.3. Environment Dimension

An innovation ecosystem reflects that subject activities and organizational behaviors in complex systems are closely related to the openness and dynamics of the environment. Complex system management research attaches great importance to the influence and role of the environment on organizational decision-making goals and value preferences [70]. Market openness degree has a positive effect on the synergetic development of industrial innovation ecosystems. The resource dependence theory and the innovation ecosystem theory discuss a synergetic relationship between organizations and external environment from different perspectives. The innovation environment mainly refers to an atmosphere of market openness degree that benefits innovation [71]. The export volume of new products reflects the external competitiveness of high-tech products. This paper combines the existing studies to include it as a key factor in the innovation and development of high-tech enterprises. Through identification and screening, this paper selects the secondary factor of market openness degree from the environmental level.

3. Study Design

3.1. Research Methods

This study first identified the synergetic development index system of the high-tech industrial innovation ecosystem through the preliminary screening. Then, this study mainly used the collaborative degree model of the composite system to measure the synergetic development capability of the high-tech industrial innovation ecosystem. And this paper built a framework for the synergetic development capability improvement in the high-tech industry innovation ecosystem, which included three levels of technology, organization and environment and five influencing factors. Among them, the five influencing factors under the TOE framework were screened by identification. Similar to the construction of the above order parameter evaluation index system, firstly, by comparing and summarizing the influencing factors of the synergetic development capability of the initial selection system, using the combination screening method of group decision feature root method and Pearson correlation coefficient, the above 15 relevant experts and senior managers in the field of high-tech industry and innovation ecosystem were invited again to conduct expert scores on the primary indicators. Matlab software (R2018 a version) was used to calculate the correlation results of the maximum feature root and the index system, and the indexes whose key was less than 0.48 and correlation was less than 0.75 were eliminated. On this basis, the fuzzy set qualitative comparative analysis (fsQCA) method was used to study multiple paths to improve the synergetic development capability of the system. Among them, in the process of using the fsQCA method to conduct empirical research, sensitivity analysis, prediction validity analysis and post-analysis were, respectively, used to test the robustness of the results.

3.1.1. The Index Construction of System Synergetic Development

Based on the existing research on the index system of China’s regional innovation ecosystem, we compared and discussed the selection and dimension of the index system proposed in this paper with those in the previous literature. Among them, Zhou et al. constructed a suitability evaluation index for regional technological innovation ecosystems, which mainly includes four dimensions: innovation community, innovation resources, economic environment and technological environment [72], and Liu et al. constructed a regional innovation ecosystem suitability evaluation index system from three dimensions: innovation community, innovation resources and innovation environment [73]. The two scholars, respectively, refined the measurement factors and indicators of different dimensions, and the two have some similarities in the selection of measurement indicators, including enterprises, research institutions, colleges and universities, innovation funds, residents’ income, transportation conditions, and the number of innovation achievements, etc. However, the difference lies in that Liu et al. considered the influence of cultural and educational environment. Indicators such as public libraries and book stocks were introduced. Li et al. believed that the system was a symbiosis and constructed a regional innovation ecosystem evaluation index system including five symbiotic elements, including symbiotic units, symbiotic environment, symbiotic interface, symbiotic matrices and symbiotic networks [74]. Some scholars measured the level of system development from three dimensions: the level of system production and operation, the level of R&D and innovation activities and the level of fixed asset investment, including production and operation scale, production and operation quality, R&D and innovation personnel, R&D and innovation funds, R&D and innovation technology, R&D and innovation organization, basic investment, fixed assets and other factor indicators [41]. On this basis, Wu et al. considered industrial operation investment, R&D innovation capability, innovation capital, policy-driven and financial support, foreign investment and innovation and entrepreneurship incubation innovation service support, and introduced the main elements of social public participation from the perspective of online and offline participation to build the organic evaluation index system of high-tech industrial innovation ecosystem dominated by the main elements [46]. Liao et al. constructed a systematic evaluation index system from the two dimensions of innovation ecology subject and environment. In this system, 27 indicators were selected from the three dimensions of producer, consumer and disintegrator, mainly including enterprises, scientific research institutions, colleges and universities, other organizations, technology market, product market, government and intermediary market. The innovation ecological environment system is characterized by six dimensions: market environment, institutional environment, factor environment, credit environment, cultural environment and service environment [75].

This paper followed the principles of scientificity, systematization and comparability, and drew on the above research results [41,46,72,73,74,75] to analyze from the perspective of the main elements of the innovation ecosystem participation, and constructed an indicator system for the synergetic development of the high-tech industrial innovation ecosystem from five dimensions, including enterprise operation, academic research and development, intermediary services, government-driven and social participation. Full consideration has been given to key factors such as enterprise operation and market liquidity, R&D and innovation ability, R&D investment capital, innovation platform support, government funding support, and public online and offline participation. Among them, it mainly drew on the research of Wu Feifei et al., which was the same as dividing the innovation ecosystem according to the diversified characteristics of the main elements, and selecting representative indicators according to the characteristics of the main elements of each subsystem. The main difference was that this paper considered the influential factors in the consumer technology market and reflects the market liquidity [75]. The selection of indicators in this paper was based on existing research results, which were highly accessible and representative, but the selection of indicators was also redundant and subjective. Therefore, through comparison and induction, the order parameter evaluation index was initially constructed. By using the combination screening method of group decision characteristic root method and Pearson correlation coefficient, 15 experts and senior managers in the field of high-tech industry and innovation ecosystem were invited to score the primary index. Matlab software (R2018 a version) was used to calculate the maximum characteristic root and the correlation results of the index system. Indexes with key values less than 0.46 and correlation less than 0.75 were eliminated, and an order parameter index system consisting of 18 representative indexes was finally screened out, as shown in

Table 1. Indicator system for the synergetic development of high-tech industrial innovation ecosystem.

The System Structure	The Dominant Factor	Order Parameter/Measure Factor/Niche Index	Symbol	Unit
Enterprise operation subsystem	Enterprise operation capability	Number of enterprises with R&D activities	X11	Piece
		Revenue from new product sales	X12	CNY ten thousand
	Market liquidity	Amount of technology contract inflow	X13	CNY one hundred million
		Amount of technology contract outflow	X14	CNY one hundred million
Research and development subsystem	R&D innovation ability	Number of R&D Institutions	X21	Piece
		R&D personnel equivalent to full-time	X22	One year
		Number of green invention patent applications	X23	Piece
	R&D investment	R&D investment intensity	X24	%
		Expenditure for technological improvement and upgrading	X25	CNY ten thousand
Mediation service subsystem	Innovative talents support	Number of incubated business mentors	X31	People
	Platform financial support	Total investment in public technology platform of science and technology business incubator	X32	CNY one thousand
		Accumulated venture capital investment of incubated enterprises	X33	CNY one thousand
Government-driven subsystem	R&D financial input	R&D is funded by the government	X41	CNY ten thousand
		Science and technology funds of higher education and government funds	X42	CNY ten thousand
	Financial input for platform innovation	Financial support for productivity promotion center	X43	CNY one thousand
Social participation subsystem	Public offline participation	The number of visitors to science museums that year	X51	Thousands of people
		Number of participants in popular science activities	X52	Thousands of people
	Public online participation	High-tech Baidu search index overall daily average	X53	—

.

3.1.2. Synergetic Development Capability Measurement Model

At present, there is much research on systematic synergetic development, among which some scholars have used the collaborative degree model to measure the synergetic development level of technological innovation system in China’s high-tech industries [76]. Some scholars have also used the collaboration degree model to construct a synergetic evaluation model of high-tech industrial innovation ecosystem under the four-spiral model [46]. Based on some scholars’ studies [77], this study constructed a model to measure the synergetic development capability of high-tech industrial innovation ecosystem. The high-tech industrial innovation ecosystem is divided into five subsystems, including enterprise operation, research and development, and mediation service, denoted as: $S_i=(i=1,2,\cdots ,5)$; subsystem order parameters are: $x_i=(x_{i1},x_{i2},\cdots ,x_{in}),j=1,2,\cdots ,n,$ which denotes that the $i-th$ subsystem has $n$ order parameters, and satisfies ${\alpha }_{ij}\le x_{ij}\le {\beta }_{ij}$; ${\beta }_{ij}$ and ${\alpha }_{ij}$ represent the upper and lower limits of the order parameter component $x_{ij}$. The order degree of order parameters can be calculated as:

$$u_i(x_{ij})=\left\{\begin{array}{l}\frac{x_{ij}-{\alpha }_{ij}}{{\beta }_{ij}-{\alpha }_{ij}},j=1,2,\cdots ,l_i\\ \frac{{\beta }_{ij}-x_{ij}}{{\beta }_{ij}-{\alpha }_{ij}},j=l_i+1,l_i+2,\cdots ,n_i\end{array}\right.$$

(1)

In Formula (1), $u_i(x_{ij})\in \left[0,1\right]$, there is a positive relationship between $u_i(x_{ij})$ and subsystem order degree; that is, the larger the value is, the higher the subsystem order degree is. Otherwise, the order degree of subsystem is lower. In this study, linear weighting method is adopted to integrate the order degree of order parameters of each subsystem of high-tech industrial innovation ecosystem, and the formula for calculating the subsystem order degree is as follows:

$$u_i(x_i)={\displaystyle \sum _{j=1}^{n_i}{\lambda }_{ij}{u}_i}(x_{ij}),i=1,2,\cdots ,5$$

(2)

In Formula (2), ${\lambda }_{ij}$ represents the weight value of the $j-th$ order parameter of the $i-th$ innovation ecological subsystem, and ${\lambda }_{ij}\in \left[0,1\right]$. $u_i(x_i)$ represents the order degree of the $i-th$ innovation ecological subsystem, and $u_i(x_i)\in \left[0,1\right]$. The larger $u_i(x_i)$ is, the more ideal the orderly development of the subsystem is.

Suppose that the order degree of the subsystem at the initial moment $t_0$ is $u_i^0(x_i)$ and the order degree at the moment $t_k$ is $u_i^k(x_i)$; then, the overall synergy degree formula of the innovation ecosystem of high-tech industry at the moment t_k is:

$$C_k=\frac{\underset{i}{\min }\left[u_i^k(x_i)-u_i^0(x_i)\ne 0\right]}{\left|\underset{i}{\min }\left[u_i^k(x_i)-u_i^0(x_i)\ne 0\right]\right|}\sqrt[5]{{\displaystyle \prod _{i=1}^5\left[\left|u_i^k(x_i)-u_i^0(x_i)\right|\right]}},i=1,2,\cdots ,5$$

(3)

In Formula (3), if $C_k\in \left[-1,1\right]$, the larger the $C_k$ value is, the higher the level of synergy among the subsystems of high-tech industrial innovation ecosystem and the stronger the system synergetic development capability; in contrast, the lower the level of synergy, the weaker the system synergetic development capability.

3.1.3. Fuzzy Set Qualitative Comparative Analysis (fsQCA)

The fsQCA method is based on Boolean algebra and set theory. Unlike the traditional QCA method, it studies a combination of multiple antecedent conditions that lead to an outcome [78,79]. At present, the fsQCA method has been widely used in various fields, among which the innovation-ecosystem-related studies have also extensively used this method. Based on the fsQCA method, some scholars have compared the causal and compound mechanism of different national innovation ecosystems driving national talent competitiveness [80]. Based on the symbiosis theoretical framework of “agent–network–environment”, some scholars have adopted the fsQCA method to explore the specific paths of innovation ecosystems to achieve knowledge integration with technology-oriented and market-oriented value propositions [81]. The configuration path of regional digital innovation ecosystem development was analyzed based on fsQCA method [82]. Therefore, the fsQCA method proposed by Ragin [78] was adopted to explore the paths for enhancing the synergetic development capability of high-tech industrial innovation ecosystems. This is based on the following considerations. Firstly, the synergetic development of high-tech industrial innovation ecosystems is a consequence of multiple concurrent causes, mainly including five factors related to the dimensions of technology, organization and environment. The configuration-based fsQCA method can test the coupling and matching effects of various elements, identify multiple equivalent paths for the synergetic development of high-tech industrial innovation ecosystems, and discover the substitutable and complementary relationships between various elements [83]. Secondly, the fsQCA method could accurately locate the regional cases covered by each equivalent path and further analyse the synergetic development capabilities in different regions, thereby revealing the differentiated paths to improve the systems’ synergetic development capability [83,84]. Furthermore, fsQCA follows the asymmetric hypotheses on causality, and based on the measurement results, it can identify the differences in factor combinations between low and high levels of synergistic development. Thirdly, the fsQCA method is suitable for different data volumes and types, including small-scale with 10 samples or fewer than 15 samples, large-scale with more than 100 samples, and medium-scale quantitative and qualitative data [78,84,85]. In this study, 30 samples were used, which corresponded to a medium sample size. Typically, a single condition variable should be tested to see if it is necessary for the result before a truth table standard analysis. Consistency is an important standard for testing necessary conditions. When the consistency level is greater than 0.9, the condition can be regarded as a necessary condition for results [86]. In this paper, we used the fsQCA3.0 software to test whether a single condition (including its non-set) was a necessary condition for the high (non-high) synergetic development of the system.

3.1.4. Sensitivity Analysis

Sensitivity analysis aims to check whether the research results are robust when the conditions of other measurement criteria are used [79]. It is conducted by adjusting the anchor point system of data calibration, methods for data calibration, or the cutoff values of the number of cases and consistency. Based on existing studies [71,87,88], this paper developed the sensitivity analysis of antecedent configurations for the system’s synergetic development by adjusting the PRI consistency threshold and modifying calibration standards.

3.1.5. Predictive Validity Analysis

Predictive validity analysis aims to verify the capability of the acquired configuration models to predict outcome variables in the case of different data sets [89,90]. After random selection, original samples are divided into two subgroups of samples with an approximately equal size, including a modeling subgroup (subgroup 1) and a validation subgroup (subgroup 2). For subgroup 1, fsQCA is carried out by using the same cutoff values of the number of cases and consistency with the main analysis. Then, the configuration model generated from subgroup 1 is used for subgroup 2 to check whether the acquired consistency and coverage of subgroup 2 are similar to those of subgroup 1 [91].

3.1.6. Post Hoc Analysis

Post hoc analysis aims to introduce the solution obtained by fsQCA into the regression analysis framework through Tobit regression analysis (transform combinations of related conditions obtained by fsQCA into independent variables), and could provide supplementary and additional insights into target phenomena [92]. Therefore, it is also considered part of robustness testing.

3.2. Variable Measurement and Data Sources

3.2.1. Variable Selection and Measurement

• Outcome variable

The synergetic development capability of innovation ecosystems of high-tech industries reflects the level of complementarity and synergy between diverse innovation participants in the industrial innovation ecosystems. In this study, a composite model for synergy is used to measure the synergetic development capability of innovation ecosystems of high-tech industries. The indicator system for evaluating the synergetic development capability includes enterprise operation, research and development, mediation service, government support and social participation (See Table 1).

• Condition variables

1.

Technology-related condition variables

The full-time equivalent of R&D personnel is adopted for measuring the R&D personnel input, which reflects the capability of technological innovation from the perspective of investment. The international community usually uses the full-time equivalent of R&D personnel as a major indicator for measuring the input of human resources. It can measure the investment of R&D human resources more accurately, compared with the number of R&D personnel [93]. The proportion of R&D projects to the total number of projects for developing new products is adopted to measure the product technology innovation. This indicator reflects the level of technological innovation.

2.

Organization-related condition variables

Government support refers to financial support, tax incentives, intellectual property protection, etc. Financial support is a more direct reflection of the level of government support [94]. Hence, based on existing studies [94,95], the proportion of government funds to internal R&D expenditures is adopted as an indicator for measuring government support. The proportion of institutions and enterprises with R&D activities to high-tech enterprises is used to measure the size of industrial entities. R&D institutions and enterprises, as major units of high-tech industries, can reflect the size of industrial entities more precisely.

3.

Environment-related condition variable

The ratio of export volumes of new products to sales revenues of new products is utilized to measure market openness degree. Market openness degree reflects the exchange of innovation ecosystems of high-tech industries with foreign new technologies and new products in the export process. It reveals the international competitiveness of high-tech products [94].

3.2.2. Data Sources

Firstly, to ensure the availability, continuity and integrity of the data, this paper used the high-tech industrial innovation ecosystem of 30 provincial administrative regions in China (except Tibet Autonomous Region, Hong Kong Special Administrative Region, Macao Special Administrative Region and Taiwan Province) as the research object. The panel data of system synergistic development measurement indicators from 2012 to 2020 were obtained from China Statistical Yearbook on High Technology Industry, China Statistical Yearbook on Science and Technology, China Torch Statistical Yearbook, and the Baidu Index search website platform, and the synergistic development capability of high-tech industry innovation ecosystem was measured by using the composite system synergy degree model. Secondly, the fsQCA method was used to analyze the diversified paths of system synergistic development. Considering that the factor inputs of the high-tech industry innovation ecosystem have a certain time lag effect on the system synergistic development, this paper referred to the relevant research [95] to determine the time lag as two years; that is, the outcome variable adopts the measurement results of the system synergistic development capability in 2020. The five condition variables were based on the data of 2018, and the specific measurement indicators of the condition variables include the full-time equivalent of R&D personnel, the proportion of R&D projects in new product development projects, the proportion of government funds in the internal expenditure of R&D funds, the proportion of high-tech enterprises with R&D institutions, and the proportion of new product exports in new product sales revenue. The index data mainly came from China Statistical Yearbook on High Technology Industry and China Statistical Yearbook on Science and Technology.

3.3. Data Calibration

Direct calibration was adopted to transform original data into values of fuzzy set membership [78]. Since the indicators of technology–organization–environment and system synergetic development capability selected in this study are all newly published measurements, and there are no relevant theoretical standards to base them on. Therefore, based on existing studies [79], at the upper quartile (75%), the median (50%) and the lower quartile (25%), the values of 5 condition variables and 1 outcome variable were defined as “full membership”, “the crossover point” and “full non-membership”, respectively. Then, the calibrated values of the raw variables membership between 0 and 1 were obtained. The calibration of the highly synergetic development capability was achieved by obtaining the non-set of the non-high synergetic development capability. The specific calibration anchors for the condition variables and outcome variable are shown in

Table 2. Calibration of condition and outcome variables.

Results and Conditions	Goal Set	The Calibration
		Full Membership	The Crossover Point	Full Non-Membership
System synergetic development capability	High synergetic development capability	$0.9142$	$0.8348$	$0.6885$
R&D personnel input	Sufficient R&D personnel investment	$0.0745$	$0.0420$	$0.0069$
Product technology innovation	Rich product technology innovation	$0.4166$	$0.3398$	$0.2202$
Government support	Strong government support	$0.2830$	$0.1265$	$0.0597$
Size of industrial entities	Good industrial entity scale	$0.3698$	$0.1608$	$0.1256$
Market openness degree	High market openness	$0.3978$	$0.1991$	$0.0899$

.

4. Analysis of Empirical Results

4.1. Measurement Results of Synergetic Development Capability

The raw data of each province and city were obtained for dimensionless quantification, and the system synergy degrees of each region from 2013 to 2020 were obtained through Equations (1)–(3), as shown in

Table 3. Measurement results of synergetic development capability of system in China.

Provinces	Year
	2013	2014	2015	2016	2017	2018	2019	2020
Beijing	$-0.0264$	$-0.0079$	$-0.0085$	$-0.0134$	$-0.0171$	$-0.0174$	$0.0183$	$-0.0207$
Tianjin	$-0.0243$	$-0.0160$	$-0.0198$	$-0.0103$	$-0.0134$	$-0.0063$	$-0.0131$	$-0.0146$
Hebei	$-0.0073$	$-0.0102$	$-0.0082$	$-0.0053$	$0.0052$	$-0.0069$	$-0.0063$	$-0.0067$
Shanxi	$0.0130$	$0.0088$	$-0.0209$	$-0.0037$	$-0.0159$	$-0.0085$	$-0.0087$	$-0.0057$
Inner Mongolia	$-0.0412$	$-0.0140$	$-0.0197$	$-0.0072$	$-0.0047$	$0.0118$	$0.0105$	$-0.0050$
Liaoning	$-0.0114$	$-0.0145$	$-0.0121$	$-0.0045$	$-0.0012$	$-0.0021$	$-0.0058$	$-0.0048$
Jilin	$0.0132$	$-0.0100$	$-0.0113$	$-0.0095$	$-0.0121$	$-0.0101$	$-0.0049$	$-0.0042$
Heilongjiang	$0.0126$	$-0.0130$	$-0.0098$	$-0.0055$	$-0.0068$	$-0.0058$	$-0.0093$	$-0.0055$
Shanghai	$-0.0173$	$-0.0136$	$-0.0219$	$-0.0064$	$-0.0053$	$0.0047$	$0.0017$	$-0.0063$
Jiangsu	$0.0082$	$-0.0028$	$-0.0111$	$-0.0023$	$-0.0047$	$0.0058$	$-0.0063$	$-0.0035$
Zhejiang	$-0.0132$	$-0.0066$	$-0.0108$	$-0.0058$	$0.0057$	$-0.0095$	$-0.0060$	$-0.0154$
Anhui	$0.0125$	$-0.0086$	$-0.0063$	$-0.0014$	$0.0063$	$-0.0057$	$-0.0078$	$-0.0045$
Fujian	$0.0112$	$-0.0075$	$-0.0181$	$-0.0074$	$-0.0061$	$-0.0037$	$-0.0066$	$-0.0102$
Jiangxi	$-0.0140$	$-0.0028$	$-0.0236$	$-0.0048$	$-0.0087$	$0.0099$	$0.0141$	$-0.0194$
Shandong	$0.0144$	$0.0106$	$-0.0126$	$-0.0058$	$-0.0024$	$-0.0032$	$-0.0045$	$-0.0087$
Henan	$0.0191$	$-0.0115$	$-0.0128$	$-0.0055$	$0.0069$	$-0.0133$	$-0.0124$	$-0.0043$
Hubei	$-0.0068$	$0.0115$	$-0.0139$	$-0.0116$	$-0.0101$	$0.0093$	$-0.0095$	$-0.0098$
Hunan	$0.0116$	$-0.0078$	$-0.0176$	$-0.0073$	$-0.0106$	$-0.0052$	$0.0067$	$-0.0083$
Guangdong	$0.0163$	$-0.0081$	$-0.0193$	$-0.0042$	$0.0030$	$0.0077$	$-0.0039$	$-0.0087$
Guangxi	$-0.0254$	$-0.0074$	$-0.0190$	$-0.0104$	$-0.0071$	$-0.0091$	$-0.0022$	$-0.0063$
Hainan	$-0.0117$	$-0.0099$	$-0.0119$	$-0.0083$	$-0.0031$	$0.0076$	$-0.0063$	$-0.0026$
Chongqing	$-0.0102$	$-0.0199$	$-0.0221$	$-0.0072$	$-0.0011$	$-0.0098$	$-0.0095$	$-0.0105$
Sichuan	$0.0145$	$-0.0129$	$-0.0229$	$-0.0055$	$-0.0163$	$-0.0061$	$-0.0057$	$-0.0069$
Guizhou	$-0.0047$	$0.0204$	$-0.0124$	$-0.0041$	$-0.0150$	$0.0019$	$-0.0077$	$-0.0059$
Yunnan	$0.0116$	$-0.0043$	$-0.0201$	$-0.0024$	$-0.0024$	$0.0071$	$-0.0014$	$-0.0298$
Shaanxi	$0.0119$	$0.0132$	$-0.0212$	$-0.0052$	$-0.0067$	$-0.0018$	$-0.0029$	$-0.0023$
Gansu	$-0.0117$	$0.0115$	$-0.0165$	$-0.0073$	$-0.0087$	$0.0042$	$-0.0022$	$-0.0029$
Qinghai	$0.0095$	$-0.0078$	$-0.0231$	$-0.0210$	$-0.0093$	$-0.0045$	$-0.0017$	$-0.0036$
Ningxia	$-0.0092$	$0.0031$	$-0.0082$	$-0.0103$	$-0.0135$	$-0.0054$	$-0.0011$	$-0.0087$
Xinjiang	$-0.0123$	$-0.0063$	$-0.0156$	$-0.0087$	$-0.0022$	$-0.0023$	$-0.0028$	$-0.0104$

. As can be seen from Table 3, the systematic synergy degrees of each province and city in 2013–2020 were mostly negative, and the system synergy level was generally low. In addition, the trend in system synergy varied greatly among provinces and cities. Among them, the system synergetic development capability of most provinces and cities in 2013–2018 showed an overall increasing trend, while the system synergetic development capability of most provinces and cities in 2019–2020 showed an overall decreasing trend. But the fluctuation of each year was drastic, and the instability was obvious, indicating that the synergetic development capability of China’s high-tech industrial innovation ecosystem was poor under the quadruple helix perspective.

From the time series results, the main reason for this development trend is that the global coronavirus outbreak during 2019–2020 caused a huge impact on the high-tech industry, and the synergetic capability of system innovation entities is poor, which is reflected in the decline in the level of system synergy. From the overall results, the reasons for this development trend are: First, China’s high-tech industry innovation development momentum is insufficient, and the basic resources are weak and unevenly distributed. Second, the lack of information exchange and interaction between innovation subjects, the mismatch between the level of social participation and awareness, and the level of industrial development affect the promotion of scientific and technological achievements and the industrialization process. This makes synergetic development difficult.

Figure 3 shows the geographical distribution trend in the system synergetic development capability of provinces and cities during 2013–2020. As can be seen from Table 3 and Figure 3a, the spatial distribution of system synergetic development capability among provinces and cities showed obvious regional differences in 2013. Sichuan, Shandong, Guangdong and Henan had strong system synergetic development capability, while Inner Mongolia and Guangxi had poor system synergetic development capability. As can be seen from Table 3 and Figure 3b, the spatial distribution of system synergetic development capability among provinces and cities in 2014 also presented obvious regional differences. Shandong, Gansu, Shaanxi, Shanxi and Guizhou had stronger system synergetic development capability, while Chongqing, Sichuan, Inner Mongolia, Heilongjiang and Liaoning had weaker system synergetic development capability. As can be seen from Table 3 and Figure 3c, the spatial distribution difference of system synergetic development capability among provinces and cities in 2015 was relatively insignificant, showing a pattern of high in the east and low in the west. Among them, Beijing, Hebei, Heilongjiang and Anhui had higher system synergetic development capability, while Qinghai, Sichuan, Chongqing and Jiangxi had lower system synergetic development capability. Similarly, the geographical distribution trend in the system synergetic development capability of provinces and cities from 2016 to 2020 can be analyzed by comparison. Among them, the spatial distribution difference of the system synergetic development capability of provinces and cities in 2019 was more significant. The low system synergetic development capability of Hubei, Henan, Chongqing, Shanxi and Heilongjiang was mainly attributed to the severe obstruction of innovation-driven development caused by the huge impact of the COVID-19 epidemic in these provinces and cities, and it was difficult to maintain the system synergetic development because all the main elements in the system were unable to take care of themselves. Hubei and Heilongjiang were typical examples. In 2020, there was no significant difference in the spatial distribution of synergetic development capability among provinces and cities, but the overall synergetic development capability of the whole country was poor, mainly due to the spread of the novel coronavirus epidemic and its impact on provinces and cities across the country, high-tech enterprises, research institutes, universities and governments were greatly affected by the impact, and the synergetic development capability of the system continues to be weakened.

4.2. Analysis of Combinations of Key Factors for Synergetic Development

4.2.1. Univariate Necessity Analysis

The results are shown in

Table 4. Univariate necessity analysis results.

Condition Variable	High Synergetic Development Capability		Non-High Synergetic Development Capability
	Consistency	Coverage	Consistency	Coverage
R&D personnel input (R&D PI)	$0.6698$	$0.6576$	$0.4278$	$0.4139$
~R&D personnel input (~R&D PI)	$0.4030$	$0.4168$	$0.6461$	$0.6585$
Product technology innovation (PTI)	$0.6175$	$0.6395$	$0.4090$	$0.4174$
~Product technology innovation (~PTI)	$0.4375$	$0.4289$	$0.6467$	$0.6249$
Government support (GS)	$0.5493$	$0.5732$	$0.4654$	$0.4786$
~Government support (~GS)	$0.5003$	$0.4871$	$0.5849$	$0.5612$
Size of industrial entities (SIE)	$0.6287$	$0.6747$	$0.3935$	$0.4162$
~Size of industrial entities (~SIE)	$0.4560$	$0.4328$	$0.6924$	$0.6476$
Market openness degree (MOD)	$0.6036$	$0.6213$	$0.4835$	$0.4905$
~Market openness degree (~MOD)	$0.5050$	$0.4980$	$0.6266$	$0.6090$

Note: “~” indicates the logical operation “not”.

. From the results, the consistency level of all condition variables is lower than 0.9, indicating that all conditions are not necessary for high (non-high) synergetic development capability of the system. This reflects that the synergetic development of the system is influenced by many factors rather than determined by a single factor. To improve the synergetic development capability of high-tech industrial innovation ecosystem, we should analyze the joint matching effect of multiple conditions from a holistic perspective based on three levels of technology, organization and environment.

4.2.2. Results of Configuration Analysis

Based on existing research [79,83,84] and considering the characteristics of data and sample size in this study, the threshold of original consistency was defined to be 0.80, the threshold of case frequency was set to be 1, and the threshold of PRI consistency was designed to be 0.70. In the counterfactual analysis, since relevant theories and bases were created to determine the direction of the conditions that affect results, this paper assumed that a single condition variable will promote synergetic development. After standard analysis, parsimonious solutions, intermediate solutions and complex solutions were obtained. Then, core conditions and edge conditions were determined according to the intermediate solutions and parsimonious solutions [79,84]. Finally, four configurations for highly synergetic development were identified according to complex solutions. The results of specific configuration analysis are shown in

Table 5. Configuration results of system high synergetic development.

Conditional Variable	High Synergetic Development Configuration
	Configuration 1	Configuration 2	Configuration 3	Configuration 4
R&D personnel input (R&D PI)	⬤	⬤	⊗	⊗
Product technology innovation (PTI)	⬤		⊗	⬤
Government support (GS)	⊗	⬤	⊗	⬤
Size of industrial entities (SIE)	⊗	⬤	⬤	⊗
Market openness degree (MOD)		⬤	⬤	⬤
Consistency	$0.9190$	$0.9380$	$0.9051$	$0.8456$
Raw coverage	$0.1816$	$0.2317$	$0.0863$	$0.1677$
Unique coverage	$0.1113$	$0.1308$	$0.0397$	$0.0828$
Cover typical cases	Henan, Shandong	Beijing, Anhui, Hunan	Hebei	Guangxi, Xinjiang
Overall solution consistency	$0.8922$
Overall solution coverage	$0.4781$

Notes: ⬤ indicates the existence of the core condition; ⊗ indicates the absence of the core conditions; ⬤ indicates the existence edge conditions; ⊗ indicates the absence of edge condition; blank spaces indicate do-not-care conditions.

. These four configurations represent diverse paths for highly synergetic development (configuration 1–configuration 4). The values of consistency of individual paths were 0.9190, 0.9380, 0.9051, and 0.8456, respectively, and the value of overall consistency is 0.8922. All these values were higher than the threshold of consistency (0.80), revealing that the above four paths are sufficient conditions for highly synergetic development. The overall coverage was 0.4781, indicating that these four paths covered 47.8% of highly synergetic development cases.

4.2.3. Results of Sensitivity Analysis, Predictive Validity Analysis and Post Hoc Analysis

In the sensitivity analysis, the threshold of PRI consistency was first increased from 0.70 to 0.80, and the resultant configurations for highly synergistic development and non-high synergistic development were perfect subsets of original configurations. Then, three anchor points for variable calibration were adjusted in the interval of −25%~25%, and the levels of full membership, the crossover point and full non-membership at all were changed from 75%, 50% and 25% to 78.75%, 52.50% and 26.25%. According to the results, except for a slight change in the result of one configuration, there was no significant change in the results of analyzing other configurations of conditions.

As the results of predictive validity analysis show, for subsample 1, fsQCA was carried out by using the cutoff value (1) of the number of cases and the cutoff value (0.8) of consistency. Then, a configuration model generated from subsample 1 was used for subsample 2, and the acquired consistency and coverage of subsample 2 were similar to those of subsample 1. Then, configuration 1 was adopted for this testing (See Figure 4). Consequently, the acquired configuration models were highly capable of predicting outcomes in the case of different data sets.

According to the results of post hoc analysis, configuration 1 had a significant influence on the highly synergetic development (b = 0.7428, p = 0.0011). Configuration 2 produced a more significant influence (b = 1.3903, p = 0.0000). Configuration 4 generated a remarkable influence on the highly synergetic development to some extent (b = 0.5276, p = 0.0926), while configuration 3 had an insignificant effect (b = 0.9636, p = 0.1109) (See

Table 6. Regression analysis results of high synergetic development of the system.

Independent Variable	Coefficient	Std. Error	z-Statistic	Prob.
Dependent variable: ~Z
Configuration 1	$0.7428$	$0.2307$	$3.2103$	$0.0011$
Configuration 2	$1.3903$	$0.2865$	$4.8486$	$0.0000$
Configuration 3	$0.9636$	$0.6053$	$1.5927$	$0.1109$
Configuration 4	$0.5276$	$0.3148$	$1.6788$	$0.0926$

). Despite the insignificant influence of configuration 3 shown in the Tobit regression analysis, we still have reasons to believe that configuration 3 is a supportive path for highly synergetic development in the fsQCA analysis. Therefore, these results are basically consistent with the fsQCA analysis. The above analysis generally shows that the results in this study are highly robust.

4.3. Analysis of the Paths for the System’s High Synergetic Development Capability

According to the results of configurations for highly synergetic development, diverse paths for improving the synergetic development capability can be identified. Specifically, there are four paths, which can be classified into three types. Furthermore, proper propositions were produced according to the analysis of such paths (See Table 7).

4.3.1. “Technology”-Led Type

Configuration 1: R&D PI*PTI*~GS*~SIE. In this path, R&D personnel input is the core condition, and product technology innovation is the edge condition. This path stresses the core role of technology in the highly synergetic development, thus winning the title of the “technology”-led type. It shows that despite insufficient government support and unfavorable scale of industrial players, adequate R&D personnel input and abundant innovations of product technologies allow the highly synergetic development and prevent the systems from being affected by the market openness degree. This means that the high-tech industrial innovation ecosystem pays high attention to the technical level and realizes the technological innovation output by encouraging technological innovation input, so as to promote the synergetic development of the high-tech industrial innovation ecosystem and further realize the rapid and high-quality development of the industry. This configuration covers about 18.2% of cases characterized by highly synergetic development, such as Henan and Shandong. Shandong pays great attention to high-tech industries. It continuously increases the R&D personnel input and facilitates the output of innovations of product technologies. In 2018, there were 49,617 full-time equivalents of R&D personnel in Shandong, and the funds for developing new products reached CNY 21.83 billion. Both indicators ranked fourth nationwide, only next to Guangdong, Jiangsu and Zhejiang. However, there are still gaps between Shandong and other provinces in terms of revenues of main business, number of enterprises, export volume, etc.

4.3.2. “Technology–Organization–Environment” Synergy

Configuration 2: R&D PI*GS*SIE*MOD. In this path, government support, the scale of industrial entities and market openness degree are the core conditions, and R&D personnel input is the marginal conditions. It emphasizes the synergy of technology, organization and environment, so it is named the “technology–organization–environment” synergy type. It means that the highly synergetic development can be realized by sufficient government support, favorable scale of industrial players, high market openness degree, and adequate R&D personnel input, without the influence of innovations of product technologies. This shows that the high-tech industry innovation ecosystem pays attention to the synergetic development of various elements of the system. While expanding the scale of industrial entities and improving government support, the high-tech industry innovation ecosystem pays attention to the input of technology elements and creates a good ecological environment for high-tech industry innovation, so as to promote the synergetic development of the high-tech industry innovation ecosystem. This configuration covers about 23.2% of cases characterized by highly synergetic development, such as Beijing, Anhui and Hunan. Beijing is a pioneering demonstration area for high-tech industries. Its high-tech industry expands rapidly, thanks to the promotion by Zhongguancun High-tech Industrial Park, China’s first state-level industrial park. During the “13th Five-Year Plan” period, Beijing took active actions to build the “National Science and Technology Innovation Center”, issued favorable policies, and provided a vast amount of funds. The number of high-tech enterprises was at a stable level. Governments also attracted a large number of high-tech talents, enhanced export volumes, and encouraged exchanges about foreign technologies. Hence, its high-tech industry developed stably.

Configuration 4: ~R&D PI*PTI*GS*~SIE*MOD. In this path, product technology innovation, government support and market openness degree are the core conditions, while R&D personnel input is the marginal condition. It also emphasizes the synergy of technology, organization and environment, so it is also named the “technology–organization–environment” synergy type. It indicates that despite the insufficient R&D personnel input and a small scale of industrial players, the system’s highly synergetic development can also be achieved by abundant innovations of product technologies, sufficient government support and a high market openness degree. This shows that the high-tech industry innovation ecosystem attaches importance to technological innovation output, increases government support, creates a good open market environment, and takes into account the input of R&D personnel, so as to improve the synergetic development capability of the high-tech industry innovation ecosystem. This configuration covers about 16.8% of cases characterized by highly synergetic development, such as Guangxi and Xinjiang. Promoted by policies of “one area, two belts” and “deep integration of informatization and industrialization”, Guangxi formulated and implemented a series of strategies to encourage high-tech industries. In order to prevent the unreasonable expansion of high-tech enterprises, it limited the number of high-tech enterprises. In spite of a smaller scale of industrial players year by year and insufficient scientific and technological talents, there has been an increase in the output of technological innovation and export value.

Both configuration 2 and configuration 4 emphasize the coupling and matching of three dimensions: technology, organization and environment. These dimensions play an important role in achieving highly synergetic development. Therefore, these two paths win the title of “technology–organization– environment” synergy.

4.3.3. “Organization–Environment” Balanced Synergy

Configuration 3: ~R&D PI*~PTI*~GS*SIE*MOD. In this path, the scale of industrial entities and market openness degree are the core conditions. This path highlights the key effect of coupling and matching of elements related to organization and environment on the highly synergetic development, thus winning the title of “organization–environment” balanced synergy. It shows that despite the low R&D personnel input, inadequate innovations of product technologies and insufficient government support, the favorable scale of industrial players and a high degree of market openness can achieve highly synergetic development. It can be seen that the high-tech industry innovation ecosystem focuses on supporting the cultivation of organizational elements, while simultaneously creating a good environmental atmosphere. By increasing the scale of industrial entities and improving the market openness degree, the high-tech industry innovation ecosystem can enhance the synergetic development capability. This configuration covers about 8.6% of cases characterized by highly synergetic development, such as Hebei. Being adjacent to Beijing and Tianjin benefits the high-tech industry in Hebei. In recent years, there has been a rapid growth of high-tech industrial parks in Hebei, including the China-Japan Tangshan Caofeidian Industrial Park, Tianshan Science and Technology Industrial Park, etc. The scale of high-tech enterprises is relatively large. However, the unbalanced industrial layout and the low-level development of emerging industries have led to insufficient R&D investments (such as technological talents and financial support) and a lack of key technology-oriented innovation output.

In summary, there is a substitutional relationship between conditions of different configurations to some extent. A comparison of configuration 1 and configuration 2 reveals that in regions with sufficient investments in R&D personnel, abundant innovations of product technologies can be replaced by a combination of conditions including strong government support, a favorable scale of industrial players and a high degree of market openness, and vice versa. Similarly, as for configuration 1 and configuration 4, configuration 2 and configuration 4, and configuration 3 and configuration 4, there is also a substitutable relationship between combinations of conditions. Such substitutable relationship reflects the synergetic interaction, coupling and matching between innovation ecosystems of high-tech industries in the dimensions of technology, organization and environment to realize highly synergetic development. In addition, market openness degree is very important in the paths for the highly synergetic development of ecosystems. Among the four paths for highly synergetic development, three paths have the core condition: market openness degree.

Table 7. Path map and research proposition of highly synergetic development of the system.

Type of Highly Synergetic Development Path	Path Map	Research Propositions
“Technology”-led type		Proposition 1
“Technology–organization–environment” synergy		Proposition 2
		Proposition 3
“Organization–environment” balanced synergy		Proposition 4

Notes: The gray ellipse in the path map indicates the presence of the core condition, while the white ellipse indicates the absence of the core condition. A gray rectangle indicates the presence of the edge condition, and a white rectangle indicates the absence of the edge condition.

Table 7. Path map and research proposition of highly synergetic development of the system.

Type of Highly Synergetic Development Path	Path Map	Research Propositions
“Technology”-led type		Proposition 1
“Technology–organization–environment” synergy		Proposition 2
		Proposition 3
“Organization–environment” balanced synergy		Proposition 4

Notes: The gray ellipse in the path map indicates the presence of the core condition, while the white ellipse indicates the absence of the core condition. A gray rectangle indicates the presence of the edge condition, and a white rectangle indicates the absence of the edge condition.

Proposition 1.

Technological innovation plays a core role in the highly synergetic development of the high-tech industry innovation ecosystem. With sufficient input of R&D personnel and abundant output of product technological innovation, the system can achieve highly synergetic development even if the industrial entities is weak and the government is insufficient.

Proposition 2.

The government should attach importance to the development of high-tech industry, increase regional capital input, encourage the expansion of industrial entities, create a good market opening environment, ensure sufficient R&D and innovation personnel input, and finally promote the highly synergetic development of the system.

Proposition 3.

In the absence of technological innovation input and output in high-tech industries, even if government support is insufficient, as long as the size of industrial entities continues to expand and the market openness degree continues to increase, it will make up for the impact of the lack of other conditions and achieve highly synergetic development of the system.

Proposition 4.

Under the strong support of government departments, if the output of innovative products of high-tech enterprises increases, the export scale and market openness degree of new products will also increase, which will not only improve the competitiveness of the international market, but also weaken the disadvantages of the lack of R&D personnel and the poor size of industrial entities, so as to realize the highly synergetic development of the system.

5. Conclusions and Implications

5.1. Conclusions

Relying on the quadruple helix model, an indicator system for the synergetic development of the high-tech industrial innovation ecosystem was constructed. A composite model for synergy was adopted to measure the synergetic development capability of innovation ecosystems in 30 provinces and cities between 2012 and 2020. A TOE framework for improving synergetic development capability was built. Based on identified five conditions related to technology, organization and environment, the fsQCA method was used to study diverse paths for improving synergetic development capability. The research results are stated as follows: (1) The synergetic development capability of high-tech industrial innovation ecosystem is poor from the perspective of the quadruple helix, which needs to be improved. (2) None of the “technology–organization–environment” conditions can be used alone as the necessary conditions for improving the system’s synergetic development capability, but need to exert a joint, matching effect through the combination of different factors. (3) There are four diverse paths for achieving the highly synergetic development of innovation ecosystems: the “technology”-led type with R&D personnel input as the core condition and product technology innovation as the edge condition; the “technology–organization–environment” synergy type with government support, size of industrial entities and market openness degree as the core roles, and R&D personnel input as the auxiliary role; the “technology–organization–environment” synergy type with product technology innovation, government support and market openness degree as the core conditions, and R&D personnel input as the edge condition; and the “organization–environment” balanced type with the size of industrial entities and market openness degree as the core conditions. (4) There is a substitutable relationship among the elements of different configurations. The synergy and matching between technology, organization and environment help to realize the highly synergetic development of the ecosystems. (5) Market openness degree is very important in the paths for the highly synergetic development of ecosystems. Among the four paths for highly synergetic development, three paths have the core condition: market openness degree.

5.2. Theoretical and Practical Implications

This study constructed the TOE theoretical framework and used the fsQCA method to explore the improvement path of synergetic development capability of high-tech industry innovation ecosystem from three levels of technology, organization and environment, which had important theoretical and practical significance for promoting the synergetic development and orderly evolution of the system.

This study has important theoretical implications. Compared with the previous single research methods, the TOE theoretical framework is innovatively extended to the research system of synergetic development capability of high-tech industry innovation ecosystem in this study, and the application of this research framework is helpful for each system to better grasp the theoretical logic of high-tech industry innovation ecosystem development. In addition, the synergetic degree model and the fsQCA method are used to empirically study the synergetic development capability of the system and the diversified path of high synergetic development of systems, and further expand the application of the fsQCA research method to explain complex causality in an innovation ecosystem. Through the study of the concurrent synergistic effect among the diversified paths, the “black box” of the application of “technology–organization–environment” condition in the high-tech industry innovation ecosystem is revealed, so as to study the influence mechanism of the synergetic development of a high-tech industry innovation ecosystem.

This study also has important practical implications. This study aims to improve the methodology of the research framework system of innovation ecosystems and provide direction and strategies for improving the synergistic development capability of industrial innovation ecosystems. In addition, this study also teaches some lessons, specifically as follows. (1) All provinces and cities should strengthen the synergy and matching between conditions in technology, organization and environment; highlight the input of multiple innovation elements and the synergetic development of elements; and prevent the cask effect and “imbalance” between ecosystems. With reference to the high synergetic development configuration path, local advantages should be considered when formulating effective strategies for the synergetic development of innovation ecosystems of high-tech industries. A proper path for synergetic development that is consistent with regional elements and suitable for local industrial structure should be selected to narrow the gap between regions. (2) All provinces and cities should pay more attention to environment-related conditions, such as the continuous enhancement of market openness degree, the promotion of outbound investment, the all-round opening-up of high-tech parks and economic free trade zones, the expansion of the export scale of high-tech products, and an improvement in the international trade environment. Furthermore, a platform for the development and sharing of innovative resources needs to be built to facilitate the interaction between regional resources in the process of input, conversion and output. Emerging technologies such as big data and AI should be utilized for the co-construction and sharing of infrastructure and public services. It is necessary to develop a sound and open regional innovation environment. (3) Various innovation participants should give full play to their major roles. The innovation participants in the quadruple helix model need to interact with each other to maximize their driving effect on the synergetic development of innovation ecosystems. The complementary or substitutable relationship between innovation elements that affect the synergetic development of innovation ecosystems should be considered to effectively control and adjust the input and output of each key element. Governments should provide greater financial support for high-tech enterprises. Enterprises need to expand their scale. R&D institutions should introduce more technological and innovative talents. The public need to take an active part in scientific and technological activities and knowledge exchanges. Thus, innovation participants in the quadruple helix model jointly encourage the healthy and sustainable development of innovation ecosystems of high-tech industries.

5.3. Limitations and Prospects

There are some limitations in this study, which need to be further improved in future studies. (1) Based on the TOE framework and considering existing studies, this paper selects five condition variables related to technology, organization and environment. However, it fails to cover all the conditions that influence the synergetic development of innovation ecosystems, especially environment-related factors such as participation of the public, R&D taxation, intellectual property protection, etc. Further studies may focus on the important effect of these factors on the synergetic development of innovation ecosystems. (2) Due to the availability and timeliness of data, high-tech industries in only 30 provinces and cities are selected for research, which limits the coverage of typical cases to a certain extent. In the future, data about high-tech industries from prefecture-level cities can be added, or samples may be divided, such as the adoption of data about high-tech zones. In this way, the universality and generalizability of research results can be improved by expanding the sample size. (3) The fsQCA method used in this study could analyze the diverse paths for realizing the synergetic development of innovation ecosystems of high-tech industries only from a static perspective. However, synergetic development is a dynamic process. Subsequently, panel data may be collected, and the qualitative comparative analysis (QCA) method based on multiple time periods and multiple linear growths may be used to study how the change “trajectory” of multiple key elements affects the “trajectory” of the synergetic development of innovation ecosystems over time.