Research on the Impact of Digital Innovation Ecosystem Niche Suitability for High-Quality Economic Development

Abstract

This paper selects the relevant data of 30 provinces and cities in China from 2010 to 2022 as samples. By constructing a digital innovation ecosystem (DIES) niche suitability model, the overall and regional differences in China’s DIES niche suitability were analyzed. On this basis, static and dynamic panel data models were established to empirically analyze the influence of DIES niche suitability on high-quality economic development. The results showed that (1) the overall level of niche suitability for China’s digital innovation ecosystem is relatively stable, with unbalanced development among regions, but plentiful space for development. (2) The DIES niche suitability plays a significant role in promoting high-quality economic development. Regions with a high degree of DIES niche suitability have more active innovation activities and a higher original accumulation of innovation. (3) The positive effect of DIES niche suitability for high-quality economic development is sustainable. The improvement in regional economic development quality needs to gradually emerge with the continuous improvement in and optimization of the DIES.

1. Introduction

With the advent of the digital economy era, the application of digital technology, the penetration of data elements, and the intervention of digital platforms have changed the operation mode of the traditional innovation ecosystem. It has aroused scholars’ thinking and attention to the digital innovation ecosystem (DIES) Zhang, 2023 [1,2]. The DIES is a complex system formed by the digital transformation of the main body, structure, and function of the innovation ecosystem based on digital technology, digital infrastructure, and other digital resources, between different innovation subjects and innovation environments in the system [3]. In addition to the innovation ecosystem’s complexity, openness, and transformation, the DIES also has the characteristics of the virtualization of participants, the ambiguity of system boundaries, and the digitalization of elements [4]. As a complex ecosystem, the DIES has an interaction process among the subjects, resources, and environment within the system, and ecological niches are formed in this process. The niche suitability of the DIES refers to the close degree between the actual value and the optimal value of the ecological factor, which can reflect the living state of each innovation subject in the entire innovation ecosystem and their adaptation to the environment. In the DIES, the innovation subject dynamically matches the innovation environment and continuously adjusts its ecological niche. The goal is to find the appropriate space for innovation and development, efficiently acquire and utilize heterogeneous innovation resources [5], and then lay the foundation for promoting the high-quality development of the regional economy.

At present, the study of the DIES is concentrated in theory construction, value co-creation, governance mechanism, and evolution. Most existing studies focus on a niche suitability evaluation of the innovation ecosystem and its impact on innovation performance and innovation efficiency, while few studies explore the impact of DIES niche suitability on high-quality economic (HQ economic) development. It is important to scientifically determine the niche suitability of China’s DIES and explore its impact on high-quality economic development. This is very helpful for the innovation-driving effect; a sustainable and healthy development of the regional economy will follow. Existing research for this paper laid a solid theoretical foundation, but there is still a need to expand the space in question. This paper tries to make contributions from the following aspects: First, the DIES niche suitability evaluation index system is constructed to scientifically measure the DIES niche suitability of 30 provinces in China from 2010 to 2022. In addition, this paper analyzes the niche suitability of China’s DIES from two dimensions of time and space, and it reveals the overall and regional differences. It provides a theoretical decision-making basis for optimizing regional innovation resource allocation and building a collaborative and symbiotic DIES. Secondly, through the construction of an econometric model, it explores the impact mechanism of the niche suitability of the DIES on high-quality economic development. It not only enriches relevant research on the DIES and high-quality economic development but also provides a theoretical basis for enhancing regional economic development by improving the niche suitability of the DIES.

The remainder of this paper is organized as follows. Section 2 is the literature review and theoretical analysis. Section 3 is the variable selection and model construction. Section 4 is the empirical analysis. Section 5 is the conclusion and discussion. Section 6 is about research limitations and prospects.

2. Literature Review and Theoretical Analysis

2.1. Literature Review

The combination of an ecosystem and an innovation system forms the innovation ecosystem. Therefore, the theoretical source and foundation of innovation ecosystem research are ecological theory and innovation system theory. Moore [6] applied the ecological theory to the competitive strategy of enterprises and proposed the concept of “enterprise ecosystem” for the first time. Many subsequent studies were carried out based on Moore’s theory. Adner [7] formally defines what an innovation ecosystem is from a micro-perspective. With the deepening of the research, Adner and Kapoor [8] once again point out that enterprises are no longer independent innovators but a broader part of the innovation ecosystem. So far, the innovation ecosystem has stimulated a new round of research in the academic circle. Authors may define the concept of the innovation ecosystem from the perspective of the niche and network [9], discuss the components of the innovation ecosystem [10], explore the value co-creation mechanism of ecosystem participants [11], or explore the evolutionary mechanism of the innovation ecosystem [12]. An innovation ecosystem is a complex dynamic system composed of multiple participants. The various subjects in the system depend on each other and work together to promote the occurrence and development of innovation. The innovation ecosystem can be roughly divided into a national, regional, industrial, and enterprise innovation ecosystem according to the macro-, meso-, and micro-levels.

The digital age has given a new connotation to the innovation ecosystem, and the academic research on the innovation ecosystem has gradually turned to the field of the DIES. At present, the research on the DIES is still in the initial stage, which mainly focuses on theoretical construction, value co-creation, governance mechanisms, and evolution. In terms of the theoretical construction of the DIES, Zhang [13] takes the development of digital innovation and digital empowerment as the starting point of analysis and divides the DIES into an innovation-oriented digital ecosystem and a digital-empowered innovation ecosystem. In terms of value co-creation in the DIES, Suseno [14] asserts that the value of the digital innovation ecosystem comes from the interaction between consumers and professional stakeholders in the system. In terms of the DIES governance mechanism, Wei [15] built an innovation ecosystem governance mechanism based on the construction of digital platforms, the application of digital technology, and the collaboration of digital resources. In addition, Ning [16] presented a study of a DIES simulation analysis by building a dynamic evolution model.

In the digital innovation ecosystem, natural resources, labor, knowledge, capital, technology, and other factors are involved in the innovation flow and overflow among innovation entities. The results are newly formed ecological niches, causing the concept of niche suitability to come into view. Li [17] and Li [18] developed the concept of niche suitability, believing that niche suitability comes closest to matching the real niche and the optimal niche of a species’ habitat. At present, there is no research on the niche suitability of a DIES, and scholars mainly pay attention to the niche suitability of innovation ecosystems, mainly at the regional and industrial levels. Qin [19] focused on the development state of a regional innovation ecosystem and evaluated it with the theory of niche suitability. Lei [20] used the niche suitability model to measure the development potential of a high-tech industrial innovation ecosystem. In addition, some scholars have gradually studied the impact of niche suitability for an innovation ecosystem on other economic and innovation indicators. Liu [21] studied the positive promoting effect of niche suitability for regional innovation ecosystems on GDP and the output value of high-tech industries. Walrave [22] believes that the niche suitability of an innovation ecosystem plays a significant role in promoting improvements in innovation efficiency. Yin [23] focuses on the innovation performance of high-tech industries and analyzes the spatial influence and spatial spillover effect of system niche suitability on innovation performance.

2.2. Theoretical Analysis

The DIES is a complex ecosystem, which contains different innovation subjects and rich innovation resources, as shown in Figure 1. The innovation subject is affected by the internal and external environment of the system, and there is a resource interaction process between the innovation subject and the innovation environment. According to the principle of ecological niche, different innovation subjects in the system have their ecological niche [24]. Similarly to the natural ecosystem, the niche in the DIES is also composed of a variety of ecological factors, which exist in the environment of the system and interact with the innovation environment, ultimately affecting the health and development of the system.

The niche suitability of the DIES refers to the close degree between the actual value of ecological factors and the most appropriate value. It can better reflect the living state of each innovation subject in the whole system and adaptability to the environment, and its value is generally between 0 and 1. The greater the niche suitability, the higher the degree of ecological factors to meet ecological needs, and the better the survival status and adaptability of a species to the environment. The niche suitability of the DIES reflects its role and potential in promoting HQ economic development, mainly reflected in the following aspects.

First of all, it promotes HQ economic development by improving the efficiency of resource allocation. In a DIES with high niche suitability, resource allocation efficiency is significantly improved. The system realizes the efficient allocation of innovation resources such as capital, talent, and technology by constructing a dynamic and coordinated resource integration network. By giving full play to the potential of innovation resources, an effective solution is provided for the resource mismatch and island effect in the system, so as to significantly improve innovation efficiency and economic benefits [25].

Secondly, it promotes HQ economic development by strengthening the innovation-driven effect. Innovation entities in the DIES, including digital enterprises and scientific research institutions, can rapidly gather in an environment with high niche suitability. This clustering effect not only promotes the exchange and integration of knowledge and technology but also promotes the accelerated development of technological innovation. As the core driving force of economic growth, technological innovation can generate new industrial chains and value chains and then promote industrial upgrading and high-quality economic development [26].

Finally, it promotes HQ economic development by speeding up market response. When the niche suitability of the DIES is high, the market responds more quickly to technological innovation and needs are more diversified. This rapid market response mechanism is conducive to the rapid industrialization of innovation results, thus accelerating the transformation process of innovation results from the laboratory to the market. Diversified market demand has further stimulated the innovation impetus of innovation entities, promoted the continuous optimization and upgrade of the DIES, and finally achieved a virtuous circle of economic growth. Therefore, this paper proposes the following hypothesis.

Hypothesis 1: 

Digital innovation ecosystem niche suitability promotes high-quality economic development.

The impact of the niche suitability of the DIES on HQ economic development usually has a lag effect, which is mainly reflected in the following aspects: First of all, the lag effect of technological innovation. Although the development and application of digital technology can significantly improve the competitiveness of enterprises in a short period of time, its role in promoting high-quality economic development often takes a certain amount of time to emerge [27]. The cycle of technological innovation is relatively long, and the whole process from research and development, testing, to the final realization of market application requires a lot of time and resources. This makes it difficult to fully demonstrate the impact on high-quality economic development in the short term.

Secondly, the lag effect of talent and capital. An environment with high niche suitability can certainly attract talented people and capital inflows. However, the cultivation and reserve of talents as well as the accumulation and allocation of capital are long-term and gradual processes. Especially in the field of emerging digital technologies, technical barriers are high and market risks are large, which makes the inflow of capital and talent often lag behind the actual demand for technological innovation.

Finally, the lag effect of industrial transformation. The digital innovation ecosystem can effectively promote the transformation of traditional industries toward a digital and intelligent direction, but this transformation process is usually gradual and will be restricted by many factors such as the industrial structure, labor market, policy environment, and so on. Therefore, there is an obvious lag in the driving effect of niche suitability on industrial structure adjustments and economic model transformations. It is necessary to gradually achieve high-quality economic development under the synergistic effect of policy guidance, market incentives, and technological innovation. Therefore, this paper proposes the following hypothesis.

Hypothesis 2: 

The impact of the niche suitability of the DIES on high-quality economic development is continuous.

3. Variable Selection and Model Construction

3.1. Variable Selection

3.1.1. Independent Variable: Niche Suitability of DIES

The niche suitability of the DIES is a comprehensive index. To measure it, the niche suitability evaluation indicator system of the DIES is constructed, as shown in

Table 1. Evaluation indicator system of niche suitability of DIES.

Evaluation Objective	Primary Indicator	Secondary Indicator	Explanation of Indicators	Unit of Indicator
Niche suitability of DIES	Innovation subjects	Digital enterprise	Number of digital enterprises	units
		Colleges and universities	Number of colleges and universities	units
		Scientific research institutions	Number of scientific research institutions	units
	Innovation resources	Digital infrastructure	Long-distance cable line length	km
			Internet broadband access ports	10,000 unit
			Number of mobile phone base stations	units
		Digital technology	Internet penetration rate	percentage
			Mobile phone penetration rate	sets/100 people
		Digital talent	People employed in the ICT industry	10,000 people
	Innovation environment	Economic environment	Per capita GDP of each region	yuan
			Per capita disposable income of urban and rural residents	yuan
			Per capita consumption expenditure of urban and rural residents	yuan
		Policy environment	Local government expenditure on science and technology	billions of yuan
			Local fiscal expenditure on education	billions of yuan
		Cultural environment	Number of public libraries	units
		Technical environment	Number of three major patent applications	item
			Technical contract turnover	billions of yuan

. Based on the structural similarity between the DIES and natural ecosystem, and combined with the research of Qin [19] and Liu [28], the evaluation indicator system consists of three parts: the innovation subject, innovation resource, and innovation environment.

Innovation subjects include digital enterprises, colleges and universities, and scientific research institutions. Digital enterprise [29] is the main body of digital innovation and an important component of the DIES, and it uses digital technology to create new products or provide new services. Colleges, universities, and scientific research institutions are important participants in the DIES, providing theoretical and talent support to enterprises and society. It is an important embodiment of system innovation ability [16]. Innovation resources include digital technology, digital talent, and digital infrastructure. Digital technology refers to emerging technologies such as artificial intelligence, big data analytics, Internet of Things, and blockchain. It is a technological means to achieve innovation [30]. Digital infrastructure refers to the hardware, network facilities, and computing resources that support the operation of digital technologies. It provides hardware and network support for system innovation activities [31]. The quantity and quality of digital talents determine the efficiency of innovation activities [32]. The innovation environment refers to all environmental factors related to the development of the DIES, including the economic environment, policy environment, cultural environment, and technological environment [33]. The innovation environment provides the necessary resource environment and positive atmosphere for innovation activities, which are important guarantees for improving the innovation ability of the DIES [34]. It also affects the innovation behavior of innovation subjects in the system, and innovation subjects constantly adapt to the environment in the interaction with the innovation environment, which makes the innovation ecosystem of the digital industry sustainable and have healthy development.

Assuming that there are m DIESs and n ecological factors, x_ij (i = 1, 2, …, m; j = 1, 2, …, n) represents the data observation value of the i DIES on the j indicators. The niche suitability model is as follows.

Step 1: the dimensionless normalization process is used for each data entry and is expressed as follows:

$$x_{ij}^{\prime }={\displaystyle \frac{x_{ij}-x_{jmin}}{x_{jmax}-x_{jmin}}}$$

(1)

where x_jmin represents the minimum value of the j indicator sequence, and x_jmax represents the maximum value of the j indicator sequence.

Step 2: the optimum ecological niche (x_aj) of the indicator is calculated by the following:

$$x_{aj}=\max \left(x_{ij}^{\prime }\right)$$

(2)

Step 3: to calculate the niche suitability of the DIES, the following formula is given:

$$Suit{a}_i=\sum _{j=1}^n{w}_j{\displaystyle \frac{min\left\{\left|x_{ij}^{\prime }-x_{aj}\right|\right\}+\epsilon max\left\{\left|x_{ij}^{\prime }-x_{aj}\right|\right\}}{\left|x_{ij}^{\prime }-x_{aj}\right|+\epsilon max\left\{\left|x_{ij}^{\prime }-x_{aj}\right|\right\}}}$$

(3)

In (3), w_j represents the weight of the indicator j, indicating the importance of this indicator in the ecosystem. This paper calculates it by the entropy weight method, which is expressed as follows:

$$w_j={\displaystyle \frac{1-H_j}{n-\sum H_j}}$$

(4)

where $H_j=-k\sum _{i=1}^m{f}_{ij}ln{f}_{ij}$, $k={\displaystyle \frac{1}{lnm}},f_{ij}=r_{ij}/(\sum _{i=1}^m{r}_{ij})$, $f_{ij}=0,f_{ij}ln{f}_{ij}=0$.

In (3), ε is the model parameter based on Suita_i = 0.5, expressed as follows:

$$Suit{a}_i={\displaystyle \frac{min\left\{\left|x_{ij}^{\prime }-x_{aj}\right|\right\}+\epsilon max\left\{\left|x_{ij}^{\prime }-x_{aj}\right|\right\}}{\frac{1}{mn}\sum _{i=1}^n\sum _{j=1}^n\left(\left|x_{ij}^{\prime }-x_{aj}\right|\right)+\epsilon max\left\{\left|x_{ij}^{\prime }-x_{aj}\right|\right\}}}=0.5$$

(5)

Step 4: The evolution momentum model of the DIES is given as follows:

$$E{M}_i=\sqrt{{\displaystyle \frac{\sum _{j=1}^n\left\{\left|x_{ij}^{\prime }-x_{aj}\right|\right\}}{n}}}$$

(6)

where EM_i represents the evolution momentum of the i DIES, and the greater the EM_i, the greater the development space of the DIES.

3.1.2. Dependent Variable: High-Quality Economic Development

At present, there is no complete consensus on how to measure high-quality economic development. Some scholars choose a single indicator [35] or comprehensive evaluation indicator [36,37] to measure the level of HQ economic development. Some scholars generally believe that improving economic efficiency is an important means to achieve HQ economic development, which is mainly measured by the total factor productivity [38] or labor productivity [39]. The difference between the factor productivity and the total labor productivity is that the total factor productivity can measure the economic efficiency of a country more comprehensively and objectively. Therefore, this paper uses total factor productivity to measure HQ economic development [40]. The Solow residual method is the most popular method in calculating total factor productivity, and this paper also uses it for estimations. The calculation formula is as follows:

$$TFP=Y/K^{\alpha }{L}^{1-\alpha }\hspace{1em}\hspace{1em}\hspace{1em}0<\alpha <1$$

(7)

where Y is the total output, expressed by the total output GDP of each province and city; L is the labor input, represented by the number of employees at the end of each province and city; α is the elasticity of the capital output; and K is the capital input. The continuous inventory method is used to estimate the physical capital stock of each country over the years for measurement [41], and the formula is as follows:

$$\mathrm{K}=\mathrm{I}/\mathrm{P}+(1-\mathsf{\delta })/{\mathrm{K}}_0$$

(8)

In (8), K is the fixed capital stock of the current period; I is the nominal fixed capital formation for the period; P is the price index of the fixed asset investment; δ is the depreciation rate, which is 9.6%; and K₀ is the fixed capital stock of the previous period.

3.1.3. Control Variable

Considering that HQ economic development is not only affected by the niche suitability of the DIES but is also affected by other factors, if these factors are not controlled, the analysis results may be affected. By introducing control variables, the influence of other factors on the results can be eliminated, so the influence of the independent variables on the dependent variables can be more accurately judged. Thus, the open level (open) [42], foreign trade level (ftrade) [43], fixed asset investment (fas) [34], urbanization (urb) [44], and financial development (fin) [45] are introduced as control variables. Among them, the level of opening-up promotes regional innovation and economic growth through international exchanges, foreign investment, technical cooperation, and talent accumulation. The level of foreign trade affects the level of high-quality regional economic development through a scale effect and a competition effect. As infrastructure support, fixed asset investment directly affects the efficiency of technology applications and has an impact on the regional economy. Urbanization brings an agglomeration economic effect, which has an impact on high-quality economic development through the concentration of innovation resources. The level of financial development affects regional economic growth by controlling capital flows, financing costs, and innovation risks. The level of opening-up is measured by the proportion of foreign direct investment actually utilized to GDP. The level of foreign trade is usually measured by the total value of imports and exports as a share of gross domestic product (GDP). Fixed asset investment is expressed as the proportion of total fixed asset investment to GDP. The level of urbanization is measured by the proportion of the urban population to the total population of the region, and the level of financial development is expressed by the ratio of loans outstanding by financial institutions to GDP per capita at the end of the year.

3.2. Model Construction

This paper constructs an econometric model of the DIES niche suitability effects on HQ economic development, as shown by the following:

$$\begin{gathered} H{Q}_{it}={\alpha }_0+{\beta }_{1}Suit{a}_{it}+{\beta }_2{open}_{it}+{\beta }_3{ftrade}_{it}{+\beta }_4{fas}_{it}+{\beta }_5{urb}_{it} \\ {+\beta }_6{fin}_{it}+u_i+v_t+{\epsilon }_{it} \end{gathered}$$

(9)

HQ_it and Suita_it are, respectively, the HQ economic development level of a province like i and in t years where the ecological niche suitability of the DIES open_it, ftrade_it, fas_it, urb_it, and fin_it represent the opening level of the control variables, foreign trade level, fixed asset investment, urbanization, and financial development level, respectively. u_i and v_t are the control region and time effects, respectively, and ɛ_it is the unobservable disturbance term.

In addition, this paper adds a term lagging behind the level of HQ economic development shown in (9), and it uses the dynamic panel model to estimate the effect level. The dynamic panel model in this paper is as follows:

$$\begin{gathered} {HQ}_{it}={\alpha }_0+{\beta }_0{HQ}_{it-1}+{\beta }_{1}Suit{a}_{it}+{\beta }_2{open}_{it}{\beta }_3{ftrade}_{it}{+\beta }_4{fas}_{it} \\ +{\beta }_5{urb}_{it}+{\beta }_6{fin}_{it}+u_i+v_t+{\epsilon }_{it} \end{gathered}$$

(10)

3.3. Data Source

In this paper, 30 provinces or cities in China are selected as research objects (some data in Tibet have been missing for many years, so they are excluded), and 2010–2022 is selected as the research period. The research data came from the China Statistical Yearbook, the China Science and Technology Statistical Yearbook, the China Regional Innovation Capability Evaluation Report, and the websites of the Bureau of Statistics and the Bureau of Science and Technology. Because there are missing values in some provinces, the linear interpolation method is used for processing.

4. Empirical Analyses

4.1. Niche Suitability Analysis of DIES

4.1.1. Overall Analysis of Niche Suitability on DIES

Based on the above niche suitability and evolutionary momentum model, the level of DIES niche suitability and the evolutionary momentum in China during 2010–2022 are calculated in Figure 2. The results show that from 2010 to 2022 the overall level of DIES niche suitability in China was relatively stable, with an average slightly more than 0.5 and a variation range that was small each year, indicating the stable and orderly development of the system. The evolutionary momentum of the DIES in China was more than 0.8, and it increased slightly each year, indicating a positive annual system development potential. Among them, from 2019 to 2021, due to the impact of the pandemic, the suitability level of the DIES is low, showing a continuous downward trend. With the positive results of epidemic prevention and control, all provinces started and resumed work in an orderly manner, and the suitability level of the DIES also began to rise in 2021.

4.1.2. Analysis of Regional Difference in Niche Suitability of DIES

To analyze the DIES niche suitability and evolutionary momentum compliance with the distribution rules of each province and city, both standards were averaged in each province. The results are shown in Figure 3 and

Table 2. Niche suitability and ranking of digital innovation ecosystem.

District	Niche Suitability	Ranking	District	Niche Suitability	Ranking
Beijing	0.6807	2	Fujian	0.4925	10
Jiangsu	0.6804	3	Shandong	0.5536	6
Hebei	0.4803	13	Tianjin	0.4720	15
Jiangxi	0.4642	16	Hainan	0.4299	29
Guangdong	0.8203	1	Chongqing	0.4555	23
Liaoning	0.4842	12	Sichuan	0.5115	7
Shanghai	0.5830	5	Ningxia	0.4286	30
Shanxi	0.4571	22	Guangxi	0.4597	19
Heilongjiang	0.4578	20	Guizhou	0.4470	26
Anhui	0.4852	11	Hunan	0.4642	17
Neimeng	0.4613	18	Shaanxi	0.4726	14
Zhejiang	0.5906	4	Gansu	0.4415	27
Qinghai	0.4310	28	Xinjiang	0.4486	24
Hubei	0.4951	9	Jilin	0.4470	26
Henan	0.4976	8	Yunnan	0.4576	21

. From the perspective of DIES niche suitability, the development of different regions was unbalanced and the gap was obvious. Guangdong ranked first at 0.8203, and Ningxia ranked last at 0.4286. The average niche suitability of the DIES in China is 0.5017, and only seven provinces and cities had niche suitability values higher than the national average level. The top five provinces and cities are Guangdong, Beijing, Jiangsu, Zhejiang, and Shanghai in that order. These advancements provided a good innovation environment for the development of the DIES. The last five provinces are Guizhou, Gansu, Qinghai, Hainan, and Ningxia. These provinces are in remote areas and were unable to meet the capital, technology, and other requirements of DIES development.

Beijing is the capital of China, as well as a primary economic, cultural, and transportation center; hence, its participation in the DIES came at the right time and place for DIES development. Good policy conditions provide an institutional guarantee for its innovation participants to achieve digital transformation. Zhongguan Village is a high-tech birthplace northwest of Beijing that provides advanced technical support. Beijing also has a large number of “double first-class” universities and numerous scientific research institutes, which provide a large number of technical and management talent. The last five provinces are Guizhou, Gansu, Qinghai, Hainan, and Ningxia. These provinces are in remote areas and were unable to meet the capital, technology, and other requirements for DIES development.

The results are shown in Figure 3 and

Table 3. Evolution momentum and ranking of digital innovation ecosystem.

District	Evolutionary Momentum	Ranking	District	Evolutionary Momentum	Ranking
Beijing	0.6383	28	Fujian	0.8271	23
Jiangsu	0.6270	29	Shandong	0.7532	25
Hebei	0.8605	19	Tianjin	0.8740	17
Jiangxi	0.8956	13	Hainan	0.9647	2
Guangdong	0.4994	30	Chongqing	0.9104	9
Liaoning	0.8332	22	Sichuan	0.8200	24
Shanghai	0.7107	26	Ningxia	0.9656	1
Shanxi	0.8961	12	Guangxi	0.9025	10
Heilongjiang	0.8988	11	Guizhou	0.9189	6
Anhui	0.8664	18	Hunan	0.8956	14
Neimeng	0.8888	15	Shaanxi	0.8791	16
Zhejiang	0.6954	27	Gansu	0.9414	4
Qinghai	0.9599	3	Xinjiang	0.9164	7
Hubei	0.8420	21	Jilin	0.9262	5
Henan	0.8448	20	Yunnan	0.9104	8

. From the perspective of the evolution momentum of the DIES, the average level of China is 0.8454, and 19 provinces and cities are above the average level, indicating that China’s overall DIES is in a stage of rapid development, with a large space for development available. From the general trend, the provinces and cities with a large DIES niche suitability value have a relatively small evolutionary momentum because these provinces or cities have the first-mover advantage and take the lead in developing the digital economy, which has a certain foundation, and in the future, more of them will shift from the high-growth development model to the high-quality digital development model. On the other hand, provinces or cities with a relatively small DIES niche suitability value will have opportunities to engage in a relatively large evolutionary momentum, which is often a part of digital innovation’s high-speed development stage. They will also have relatively fast development speed and large development space.

4.2. The Impact of DIES Niche Suitability on HQ Economic Development

4.2.1. Descriptive Statistics of Variables

The descriptive statistics and correlation coefficient analysis results of variables are shown in

Table 4. Descriptive statistics and correlation figures.

Code	Mean	Sd	HQ	Suita	open	ftrade	fas	urb	fin
HQ	0.7537	0.1525	1
Suita	0.5024	0.0879	0.439 ***	1
open	0.2640	0.2551	0.219 ***	0.431 ***	1
ftrade	0.2071	0.2400	0.467 ***	0.374 ***	0.385 ***	1
fas	0.3406	0.2530	−0.117 **	0.187 ***	−0.286 ***	−0.200 ***	1
urb	0.5949	0.1239	0.525 ***	0.410 ***	0.442 ***	0.428 ***	0.056	1
fin	1.4777	0.4495	−0.207 ***	−0.065	−0.052	−0.265 ***	0.457 ***	−0.462 ***	1

Note: ** p < 0.01, *** p < 0.001.

. Here, the average value of HQ economic development is 0.7537, the maximum value is 1.000, and the minimum value is 0.3830, indicating that the development level of HQ economic development among different regions is unbalanced, and the gap is obvious. The maximum variance expansion factor of each regression model is less than 10, indicating that there is no multicollinearity interference between the variables.

4.2.2. Static Regression Results Analysis

In this paper, panel data are selected and a Hausman test is used to determine the choice between fixed effect model and random effect model. The test results show that at the significance level of 1%, the null hypothesis is rejected, that is, the random effect model does not apply. Therefore, it is more appropriate to choose the fixed effect model for regression analysis. The regression results of DIES niche suitability by degrees represents HQ economic development as shown in

Table 5. Static regression results.

Variables	HQ
	(1)	(2)	(3)	(4)
Suita	0.626 *** (0.0353)	0.483 *** (0.0963)	0.534 *** (0.0946)	0.609 *** (0.0727)
open		0.151 * (0.0670)	0.192 * (0.0748)	0.269 ** (0.126)
ftrade		0.380 *** (0.0634)	0.429 *** (0.0660)	0.273 *** (0.0749)
fas		−0.109 (0.0614)	−0.184 * (0.0921)	−0.242 * (0.0648)
urb		0.534 *** (0.0603)	1.574 *** (0.244)	0.492 ** (0.149)
fin		−0.161 *** (0.0420)	−0.325 *** (0.0928)	−0.354 *** (0.0811)
_cons	0.159 *** (0.0141)	0.105 *** (0.0263)	0.124 *** (0.0360)	0.207 *** (0.0497)
Province control	NO	NO	YES	YES
Time control	NO	NO	NO	YES
N	390	390	390	390
R-squared	0.5466	0.6337	0.7365	0.8446

Note: * p < 0.05, ** p < 0.01, *** p < 0.001.

.

In the regression results of Table 5, Column (1) is used as the baseline regression without adding control variables, and Columns (2) to (4) list the test results of the relationship between DIES niche suitability and HQ economic development after adding control variables. Comparing the regression coefficients of the individual fixed effect (3) and bidirectional fixed effect (4), the coefficients of the core explanatory variables are all significantly positive, and the original hypothesis without the time effect is rejected by the time joint significance test, indicating that the high-quality development is affected by the time trend. Therefore, it is more accurate to use the two-way fixed effect regression results to explain the DIES niche suitability effects on HQ economic development. The results show that the estimated coefficient of the DIES niche suitability (Suita) core explanatory variable is significantly positive at the 5% level, indicating that DIES niche suitability promotes HQ economic development, and hypothesis 1 is verified.

Generally speaking, regions with high DIES niche suitability have more active innovation activities and a higher original accumulation of innovation. When serving as the “preferential mechanism” of innovation factors, more advantageous resources are attracted to the inflow and new establishment of enterprises. This promotes the formation of a healthy and suitable innovation environment that promotes regional economic development, which in turn further promotes the regional DIES. Nevertheless, regions with poor DIES niche suitability are less attractive to the high-tech-skill-based talent and resources. This will hinder the economic development of technologically underdeveloped regions.

About control variables, the estimated coefficient of an open level (open) is significantly positive at the 5% level, indicating that the level of this parameter directly relates to HQ economic development. Moreover, the improvement that an open level promotes by the accumulation of regional talents, the inflow of capital, and the acceleration of innovation output is crucial to the high-quality increases in economic development. The estimated coefficient of the foreign trade level (ftrade) is significantly positive at the 1% level. Foreign trade affects the high-quality development level of the regional economy through the technology spillover effect, scale effect, and competition effect. The estimated coefficient of a fixed asset investment (fas) is significantly negative, which indicates that the fixed asset investment has a certain inhibitory effect on HQ economic development. It may be that there have been blind investments and expansions of the fixed assets when the utilization rate is not high, and the role of fixed assets is not fully played. At the 5% level, the estimated coefficient of urbanization (urb) is significantly positive. With the expansion of city size and an improved urbanization level, technology and resources are satisfied, which means the economic level has continuously improved. The estimated level of the financial development coefficient (fin) is significantly negative at the 1% level, indicating that the current financial environment does not meet the needs of innovative activities and has failed to provide security for economic development.

4.2.3. Analysis of Dynamic Regression Results

By adding the lag phase of HQ economic development to Equation (7), a dynamic panel model is constructed to analyze the lag effect of the suitability of the DIES on high-quality economic development. Equation (8) includes HQ-1 with a lag of one phase. The lag term is correlated with an individual effect or error term, and there may be a two-way causal relationship between HQ and Suita_it. That is to say, the improvement in the HQ economic development level is in turn conducive to promoting the suitability of the DIES, so the model inevitably has endogenous problems. The ordinary least square (OLS) and fixed effect (FE) methods lead to a model estimation bias, but the dynamic GMM estimation can overcome the endogenous problem of model estimation. Differential GMM estimation and system GMM estimation are two important methods for dynamic GMM estimation. Differential GMM eliminates individual fixed effects by first-order difference and uses lag variables as instrumental variables to solve endogenous problems. The system GMM combines the difference equation and the horizontal equation, and the estimation efficiency is higher. Therefore, in this paper, the system GMM and differential GMM estimation methods are used to estimate the model. At the same time, in order to eliminate the impact of heteroscedasticity on the model, this paper adopts robust standard error processing. The results are shown in

Table 6. Dynamic regression results.

Variables	(5)	(6)	(7)	(8)
	System GMM	Differential GMM	OLS	FE
HQ-1	0.724 *** (0.125)	0.651 *** (0.0561)	0.750 *** (0.1484)	0.658 *** (0.0852)
Suita	0.627 *** (0.0952)	0.563 *** (0.0441)	0.628 *** (0.0831)	0.616 *** (0.0770)
open	0.260 ** (0.116)	0.324 * (0.119)	0.233 ** (0.0840)	0.193 ** (0.0726)
ftrade	0.268 *** (0.108)	0.173 * (0.02134)	0.368 *** (0.0517)	0.178 ** (0.0544)
fas	−0.150 * (0.0622)	−0.0494 * (0.0192)	−0.0813 * (0.0360)	−0.0485 * (0.0476)
urb	0.246 ** (0.0973)	0.255 ** (0.0728)	0.253 *** (0.0519)	0.244 *** (0.0468)
fin	−0.1259 ** (0.0552)	−0.211 ** (0.0188)	−0.0816 * (0.0350)	−0.241 *** (0.0625)
_cons	0.226 * (0.0398)	0.171 * (0.0334)	0.305 *** (0.0625)	0.162 * (0.0767)
AR (1)	0.044	0.064
AR (2)	0.456	0.594
N	360	360	360	360
Sargan Test	0.228	0.235

Note: * p < 0.05, ** p < 0.01, *** p < 0.001.

.

From the results in Table 6, the p-values of the Sargan test for the differential GMM and system GMM are 0.235 and 0.228, respectively, both of which are greater than 0.05, and both models pass the over-recognition test. The p-values of AR (2) were 0.594 and 0.456, respectively, both greater than 0.05, and all models passed the autocorrelation test. Therefore, the validity of the model’s instrumental variables is confirmed, and there is no autocorrelation of the random disturbance terms. The system GMM estimation method is proposed on the basis of the differential GMM estimation method, which has a wider application range and higher estimation efficiency. Therefore, this paper selects the estimated results of the system GMM for the following analysis.

The results show that the estimated coefficients of the lagged term of HQ economic development are significantly positive, which indicates that HQ economic development has the characteristics of accumulation and continuity. The results further indicate that it is necessary to construct a dynamic panel model for analysis. According to the estimation results of (5), the estimation coefficient of the core explanatory variable DIES niche suitability (Suita) is significantly positive at the 1% level. This shows that long-term DIES niche suitability has a positive promoting effect on HQ economic development, and hypothesis 2 is verified. Compared with the regression coefficient of the static regression model, the regression coefficient of the dynamic model is larger, which indicates that DIES niche suitability has a stronger effect on high-quality, long-term economic development.

This is mainly because a short-term DIES promotes regional economic development through the input and output of talents and resources in the innovation ecosystem. This makes the short-term DIES effect more intuitive and obvious, while the “quality” of regional economic development improvement will gradually appear with the continuous optimization of the DIES; however, the improvement process takes some time. Therefore, it is necessary to pay attention to the optimization and healthy development of the DIES from a long-term perspective, which requires getting rid of the simple resource patchwork model, focusing on the “ecological” development of the innovative ecosystem, improving the level of DIES niche suitability, and implementing the driving effect of a DIES.

4.3. Robustness Test

To ensure the robustness and reliability of the empirical results, different methods were adopted in this paper, such as cross-sectional OLS robust regression, the panel fixed effect model, and the dynamic panel model. Although the coefficient sizes of the test results were slightly different, the coefficient symbols remained unchanged (Table 5 and Table 6), indicating that the research conclusion was robust and DIES niche suitability had a significant positive effect on HQ economic development. Furthermore, to solve the endogeneity problem, all control variables were also subjected to one-stage lag processing. The results of the endogeneity test were consistent with the results of the main test, which shows that the research conclusions of this research are robust to a certain extent. This is not shown in the table due to space limitations.

5. Conclusion and Discussion

5.1. Theoretical Contributions

The theoretical contribution of this study is mainly reflected in the following three aspects. Firstly, based on the DIES niche suitability model, this paper scientifically measures the DIES niche suitability of each province in China and deeply analyzes its overall and regional differences. This study points out that the variation range of DIES niche suitability in China was small each year, and the overall level was relatively stable, but the development of different regions was unbalanced, and the regional DIES progress gap was obvious. From the perspective of evolutionary momentum, the DIES has a large space for development. This study provides theoretical guidance for optimizing regional innovation resource allocation and building a collaborative and symbiotic DIES.

Secondly, the static econometric model is used to quantitatively analyze the influence mechanism of the niche suitability of the DIES on high-quality economic development. This study points out that DIES niche suitability plays a significant role in promoting high-quality economic development. Regions with high suitability for the digital innovation ecosystem have more active innovation activities and higher original accumulation of innovation. Under the role of the “preferential mechanism” of innovation elements, more advantageous resources are attracted to the inflow and new establishment of enterprises. This promotes the formation of a healthy and suitable innovation ecosystem, which in turn drives regional economic development. This study broadens the scope of DIES research.

Thirdly, the dynamic econometric model is used to further explore the continuity of the impact of the niche suitability of the DIES on high-quality economic development. This study points out that the positive effect of DIES niche suitability on high-quality economic development is sustainable. Through the input and output of talents and resources in the innovation ecosystem, the DIES promotes regional economic development, and its approach is more intuitive and obvious. Meanwhile, the improvement in regional economic development quality needs to gradually emerge with the continuous improvement and optimization of the DIES. This research result broadens the depth of DIES research and also provides a theoretical basis for promoting high-quality economic development in China.

5.2. Practical Implications

The results of this study also provide some management implications for managers and decision-makers. Firstly, all regions are actively exploring strategies to enhance the suitability of the digital innovation ecosystem, which provides sufficient momentum for high-quality regional economic development. For example, based on the weak scientific and technological resources and economic foundation of Gansu and Guizhou provinces, it is necessary to increase directional financial support at the government level and establish special innovation support funds. It is also important to attract the gathering of young talents and leading talents through subsidies and entrepreneurial funding. Provinces with a high niche suitability of a DIES such as Beijing and Shanghai, while maintaining their advantages, have targeted long-term strategies for the development of regional DIESs. By stimulating the role of data as an engine of key new elements of innovation, high-quality regional economic development is therefore enabled.

Secondly, it is necessary to break resource barriers, explore the collaborative development mechanism of innovation subjects, and stimulate the vitality of systematic innovation. Innovation subjects should break resource barriers, promote the deep integration of innovation resources, and maximize the use of resources. On the other hand, this will promote the formation of a collaborative development mechanism between enterprises in the DIES and universities and research institutes. This mechanism is complementary, risk-sharing, and benefit-sharing. As auxiliary innovation bodies, research institutes and universities carry out R&D activities with core enterprises within the system to rapidly improve innovation capabilities.

Thirdly, the government needs to speed up wise constructions and create a good environment for ecological innovation, giving play to the role of the government for diversified subject cooperation guidance. It is important to deepen wise government construction, the application of large data, artificial intelligence, the Internet, and other new technology to improve government management’s scientific, refinement, and intelligence levels. On the other hand, starting from the top-level design, it is necessary to carry out a good job in big data planning and overall design, promote the application of big data in government affairs, and create a good innovative ecological environment. In addition, through the introduction of guidance policies, the construction of a unified and open public service platform is required to to mobilize the enthusiasm of enterprises and multi-subject collaborative innovation.

6. Limitations and Future Research

As a new topic, the DIES has attracted the attention of many scholars. This paper focuses on the overall level and regional differences in DIES niche suitability in China and analyzes the DIES niche suitability effects on high-quality economic development from both static and dynamic aspects. Finding a beneficial DIES in this field is not easy due to the many shortcomings of current digital technology development. First, the future development level of a DIES must be analyzed from more critical perspectives, and the sources of differences in development levels must be studied. Secondly, sensitivity analysis can be added to enrich and enhance the robustness of the research results. Thirdly, this paper only empirically analyzed the influence of DIES niche suitability on high-quality economic development. In follow-up research, in-depth analysis and research should be conducted from the perspective of development paths and DIES-driving mechanisms.