The Impact of Digital Economy Development on Industrial Restructuring: Evidence from China

Abstract

This paper aims to analyze how the digital economy affects industrial restructuring and examine the mediating role of the relevant factors. Based on the analysis of the effect mechanism of the digital economy development on industrial restructuring, this paper constructs an improved measuring index system of digital economy covering digital infrastructure, digital industrialization, and industrial digitalization, and measures regional digital economic growth based on provincial panel data in China from 2013 to 2020 by using the entropy TOPSIS method, empirical analyses on the impact of the digital economy on industrial restructuring, and verifies the mediating effect of human capital, technological innovation, and financial development in this process. Research findings are as follows: (i) Digital economic growth demonstrates a considerable positive effect of the speed, efficiency of industrial restructuring, and industrial structure rationalization. (ii) Digital economy indirectly boosts industrial restructuring by affecting human capital, technological innovation, and financial development. The findings in this study are of theoretical significance to interpret the effect of digital economic growth on industrial restructuring, facilitating relevant economies in the decision-making of industrial restructuring and upgrading.

1. Introduction

In a world moving toward the era of the digital economy, the global economy has been transformed, shaking the traditional relationships between individuals, enterprises, and society, and digitalizing the mode of social production [1]. Global countries tend to focus on competing for digital resources rather than the production factors of the labor force and capital [2]. The digital economy, an emerging production factor, turns out to be a mighty force driving economic growth [1] as well as a promising engine boosting the economic growth of international countries. The new characteristics of the digital economy give it the ability to borrow digital technology to form new industries and new models, thus promoting the digital transformation of industries and the high-quality development of the economy [3]. However, using digital technology and allocating digital resources efficiently and rationally has become a major practical issue in promoting sustainable socio-economic development [4].

Previous studies on the impact of the digital economy on industrial development have agreed that the digital economy, as a more advanced and sustainable economic form, has been considered a powerful impetus to accelerate industrial restructuring and achieve sustainable and high-quality economic growth [5]. However, this part of the study analyzes the impact of digital technology on industrial structural transformation and economic growth from different perspectives from a national or single industry perspective. The mechanisms and processes of the impact are not analyzed in depth. Gault (2019) argued that developing the digital economy can support industrial structure upgrades by introducing technological innovations, production, business models, and industrial integration [6]. Hosan et al. (2022) pointed out that digital industrialization is the foundation and precondition to upgrading industrial structure [7]. This conclusion coincides with numerous scholars who found that the digital economy contributes to industrial structure upgrades [8,9]. A notable positive impact of digital economy development on rationalizing and advancing industrial structure is observed [10]. However, other minds criticized the heterogeneity of the digital economy despite its boosting effect on industrial structure upgrades. They argued that such a positive impact might be dampened by the dependence effects of digital technologies, digital monopoly, compound digital–technological talent shortage, and the absence of a digital public service system [11,12].

The above review indicates that the effect of the digital economy on industrial restructuring has not received sufficient empirical tests. More research works explore whether the digital economy contributes to or drags the speed of industrial restructuring, whether it improves the quality of industrial restructuring, what implications of the digital economy may emerge on the efficiency of industrial restructuring, and whether different regions are trapped in the heterogeneity when implementing digital economy-driven industrial restructuring. In view of that, we propose the following research question:

RQ: What kind of impact and role will the digital economy bring to industrial restructuring?

Addressing these problems is important to interpret the digital economy’s effect on industrial restructuring and improve theories regarding the digital economy. It is also conducive to boosting industrial transformation and high-quality economic growth. To this end, this study attempts to investigate the effect mechanism of the digital economy on industrial restructuring from the perspective of speed–quality–efficiency by measuring models based on regional digital economic growth computed from China’s provincial panel data from 2013 to 2020. We seek to reveal the effect of digital economic growth on the speed, quality, and efficiency of industrial restructuring to support high-quality regional development.

The contributions of this paper are as follows: (1) The index measuring digital economic growth is made more applicable as we constructed an improved measuring index system of digital economy covering digital infrastructure, digital industrialization, and industrial digitalization. (2) Existing theories might be developed as we empirically examine the effect of the digital economy on industrial restructuring in a mediating effect model with mediating variables (human capital, technological innovation, and financial development). (3) We innovatively measured industrial restructuring from the perspective of speed–quality–efficiency and proposed a feasible approach to lifting industrial restructuring by digital economy growth in this perspective. Our attempt is distinct from prior studies, which primarily focused on industrial structure supererogation and rationalization when exploring the effect of the digital economy on industrial structure optimization.

2. Literature Review

Traditional research on the digital economy is mainly divided into theoretical research and quantitative research.

Theoretical research on the digital economy focuses on the specific changes in economic growth, industrial turnover, and consumer behavior brought by the digital economy from neoclassical economics. Jiao and Sun (2021) explored digital economic development and its impact on economic growth, and they found that digital economic development in China has a positive effect on urban economic growth, and heterogeneity of effects exists between different cities; urban employment is the “effect mechanism” of digital economic growth on urban economic growth [13]. Li et al. (2023) used “Energy-Environmental-Economic” (3E) system to examine the relationship between the digital economy and 3E efficiency in EU countries, and they found that in terms of the relationship between the digital economy and 3E efficiency, the digital economy has direct and indirect (through economic growth) impacts on 3E efficiency; when GDP per capita exceeds EUR 15,580, the influence coefficient of the digital economy on 3E efficiency changes from negative to positive [14]. The digital economy accelerates the upgrading of traditional industrial infrastructure, promotes the construction of digital infrastructure, and then realizes the digital upgrade of traditional industries and improves the efficiency of industrial development [15]. The analysis of property rights theory and transaction cost theory from new institutional economics; and the research on changes in innovation subjects, technologies, and behavioral approaches from innovation theory [3]. Yu et al. (2023) found that the research on enterprise innovation behavior in the digital economy era has formed eight research directions, such as expertise, human capital FSA, integration in global value chains, financial innovation, fintech, people preference shift, internet of everything, and consumer co-creation [16].

The quantitative research on the digital economy focuses on the analysis of the drivers of the digital economy, the impact of the digital economy on high-quality economic growth, and the impact of the digital economy on industrial development [17]. The development of the digital economy can reduce environmental pollution and resource consumption, reduce the pressure on traditional economic models, increase market vitality, and accelerate the digital construction of cities to improve residents’ quality of life [18]. Zhang et al. (2022) noted industrial digitalization promotes industrial integration and encourages innovations, pushing forward industrial restructuring [19]. Wen et al. (2021) observed that industrial digitalization enhances industrial productivity and use values of products, leading to industrial structure supererogation [20]. The application of digital technology promotes the green technology innovation of resource-based enterprises by breaking the boundaries of enterprise innovation, reducing transaction costs, and improving the level of cooperative innovation [17]. The integrated development of industries, the optimization and upgrading of the industrial structure, the upgrading of the industry, and the innovation of green technology are all conducive to the green and high-quality development of the region. However, the excessive application of digital technology will lead to excessive industrial digitization, thus causing enterprises to face the situation of “information overload”, which will bring an adverse impact on decision-making mistakes [21].

3. Theoretical Basis and Research Hypothesis

3.1. Definition of Digital Economy

The digital economy is a new economic form and has become a new engine for sustainable economic growth [22], and the digital economy has always been the focus of scholars’ research. Don Tapscott officially proposed the concept of the digital economy in “The Digital Economy: Hope and Risks in the Era of Network Intelligence”, he proposed that the digital economy is a network system built by human beings through technology, which links knowledge, skills, and innovation to promote creative breakthroughs in wealth and social development [23]. In recent years, the OECD, the US Census Bureau, the G20, the UK, and other countries and institutions have defined the digital economy to varying degrees [8]. Although there is no consensus on the concept, the representative concept is the definition of the digital economy proposed by the G20, according to which the digital economy is a series of economic activities that use digital knowledge and information as key production factors, modern information networks as important carriers, and the effective use of information and communication technology as an important driving force for efficiency improvement and economic structure optimization [8]. According to the G20’s definition of the digital economy, the important carrier of modern information networks can be regarded as digital infrastructure. Digital knowledge and information become key production factors, which means the industrialization of digital. The use of information and communication technology to promote the optimization of economic structure is the ultimate realization of industrial digital transformation, that is, industrial digitalization. Therefore, this paper defines the digital economy as using the development of new-generation digital technology, supported by the construction of digital infrastructure, to promote the realization of digital industrialization and industrial digitization, and to drive the improvement of production efficiency and optimization of economic structure. This article also constructs an evaluation index system for the development of the digital economy based on the connotation of the digital economy.

3.2. Industrial Organization Theory

Industrial organization theory was developed by Marshall in 1881 on the basis of Adam Smith’s theory of competition, followed by the first climax after World War II, typically represented by the “structure-behavior-performance” model of the Harvard School and the Chicago School. After the 1970s, with the development of information technology, the second climax of industrial organization theory emerged, mainly based on game theory and information economics, with the emergence of empirical industrial organization theory, new institutional economics theory, and product differentiation theory. Modern industrial organization theory includes three aspects: market structure, market behavior, and market consequences, and the core problem of the theory is how to find a balance between market economic dynamics and scale economies.

Traditional industrial organization theory mainly adopts static analysis and deductive reasoning to analyze the issues of vendor games and industrial aggregation from the perspective of structure-behavior performance. However, in the era of the digital economy, the new technological models and commercialization methods accompanying digital technology have prompted traditional industries to develop their own level continuously in the face of new technologies to adapt to the challenges of new technologies, which in turn will generate a series of new industrial modes and new organizational forms, thus providing new research gaps in the connotation and research methods of traditional industries.

The traditional industrial organization mainly realizes industrial upgrading and creates new values through reorganizing production factors and optimizing production links. The development of digital technology has expanded the spatial scope and broadened the boundaries of traditional industry [24]. In the past, industries and enterprises within industries received the constraints of geographical proximity. The information exchange and resource exchange received certain restrictions. Digital technology has changed the constraint of geographical proximity so that industrial aggregation does not receive the constraints of geographical conditions and environment. Industrial synergy beyond the traditional spatial limits begins to emerge, which can better optimize traditional economic behavior and influence the change in industrial organization [25]. Digital technology and data itself are also a resource and a factor, and the development of digital technology generates massive amounts of data, including data, images, and information that can become elements to guide the competitive strategies of enterprises. The diversity and complexity of these factors present challenges to traditional industrial organization theory. Then, from the theoretical point of view, how to divide digital technology and through what path digital technology changes and optimizes the traditional industrial structure are the issues that need to be studied.

3.3. Research Hypothesis

To make up for the currently incomplete understanding of industrial restructuring and to truly achieve the goal of industrial restructuring, the speed of industrial restructuring must be accelerated, the quality of industrial restructuring must be improved, and the benefits of industrial restructuring must be given focus. The new industry, models, and ideas bred by the digital economy, combined with the faster pace of technological updates and shorter technological innovation cycles, will lead to accelerated optimization and upgrading of industries to adapt to the speed of technological development, fundamentally driving the optimization and adjustment of the industrial system [8]. Digital infrastructure, digital industrialization, and industrial digitalization are agreed upon by researchers as major indexes measuring the digital economy [16]. The digital economy leads to faster infrastructure upgrading in traditional industries and several digital infrastructures, thus digitalizing these industries to make industry development more efficient [15]. Digital infrastructure lifts the productivity of production factors, which improves industrial division and specialization to optimize industrial structure and causes a knowledge spillover effect [26].

Digitalization accompanied by information technology is a booster of industrial structure upgrading and rationalization. Given the existence of the external effects of the Internet and digital technology, the Internet platform can bring together all kinds of information, and the frequency of information and resource exchange between innovation subjects inside and outside the industry increases, effectively solving the structural contradiction of information not piling up and reducing the transaction cost of the market and making it possible to discover the problems and possible improvements of the industrial structure faster and to promote a more rational industrial structure by fixing the problems.

Thanks to digital industrialization and industrial digitalization, the digital economy empowers emerging industries, transforms traditional industries, and reshapes basic patterns of industrial structure to upgrade industrial structure [27]. As the foundation and precondition of industrial restructuring, digital industrialization derives from digital technologies; it enables traditional manufacturers to move faster in transforming toward medium- and high-end manufacturing and integrates with the service industry to push forward industrial restructuring [16]. As the focus of the digital economy, industrial digitalization underlines integration between traditional industries and digital technologies, digitally upgrading and transforming the industrial chain’s upstream and downstream total factors. Schumacher and Sihn (2020) noted industrial digitalization promotes industrial integration and encourages innovations, pushing forward industrial restructuring [28]. Wen et al. (2021) observed that industrial digitalization enhances industrial productivity and uses the values of products, leading to industrial structure supererogation [20]. Research hypotheses are presented:

Hypothesis 1a (H1a).

Digital economic growth positively affects the speed of industrial restructuring.

Hypothesis 1b (H1b).

Digital economic growth positively affects industrial structure supererogation.

Hypothesis 1c (H1c).

Digital economic growth positively affects industrial structure rationalization.

Hypothesis 1d (H1d).

Digital economic growth positively affects the efficiency of industrial restructuring.

Evolving digital technologies can result in a higher level of intelligence and automation. As jobs become more automated, demand for high-end workers or human capital is on the rise, causing higher production process efficiency [29]. Characterized by high growth, the digital economy requires a substantial input of technology and human capital to sustain its development; in other words, digital economy growth increases the scale of high-end factors like emerging technologies and their corresponding human capital [30]. An advanced new generation of information technologies lowers the cost of information access and provides various ways to improve human capital; meanwhile, it allows people to acquire knowledge regardless of time and space, in particular for students in remote areas, who are technologically accessible to tutoring from first-class teachers, to enhance the quality of human capital in different areas [31]. This improvement gives rise to a new impact on industrial restructuring. Zhao et al. (2022) concluded that, in the digital economy, human capital dividend plays a notable positive role in industrial structure supererogation, and this impact is compounded by increasing the quantity of data [32]. The following research hypothesis is then put forward:

Hypothesis 2 (H2).

Digital economic growth positively affects human capital; human capital delivers a mediating effect in the effect of the digital economy on industrial restructuring.

Researchers have agreed that the digital economy remarkably enhances regional innovation capacity [33,34,35]. The digital economy grows based on digital technological innovations and accelerates regional innovation [36]. With technological advancement, digitalized technologies transform traditional production modes, organization forms, business models, and innovation theories and serve as the essential factor in maintaining sustainable competitiveness and improving core competitiveness [35]. Digitalization, intelligence, and automation substantially enhance the overall effectiveness of the industrial chain [37]. Digital economic growth plays a positive role in the R&D input and innovation process. Its high penetration rate and high substitution rate increase technology input in the production process; such an R&D input intensity contributes to industrial restructuring [38]. Su et al. (2021) considered the digital economy as a new vehicle to boost industrial restructuring, in which a mediating effect is found in the heterogeneous technological innovation [8]. Acemoglu et al. (2018) pointed out that green technological innovation is an important transmission mechanism when the digital economy releases the dividend of industrial restructuring [39]. The following research hypothesis is then put forward:

Hypothesis 3 (H3).

Digital economic growth positively affects technological innovation; technological innovation exerts a mediating effect on the impact of the digital economy on industrial restructuring.

Relying on innovative technologies such as big data and cloud computing, internet finance has developed rapidly, such as the use of online payments, which has significantly improved the availability and convenience of financial services, especially for all those who previously did not have access to the financial market, and therefore, rapid digital economic growth enhances the accessibility and convenience of finance, contributing to achieving inclusive finance [40,41]. Internet finance is known for its low cost and no space restrictions [42]. It facilitates underdeveloped areas by providing accessible finance; meanwhile, digital currency expands finance coverage and reduces its cost [43]. The evolving digital economy further optimizes financial asset allocation and makes the finance sector more profitable [44]. Inclusive finance enables a faster flow of production factors and higher production effectiveness, particularly digital inclusive finance, which matches finance capital with real industry capital and thus enhances industrial restructuring [45]. Gao et al. (2022) found that inclusive digital finance, as a hub of talents, technologies, and information, can drive industrial restructuring by improving capital allocation; meanwhile, finance digitalization leads to lower information costs and more efficiently allocated production factors, thus rationalizing industrial structure [46]. In particular, it is noted that certain conditions are required to realize the digital economy to enhance access to finance and to facilitate the flow of funds to the relevant industries, especially in terms of the indirect role of financial development to promote the digital economy to enhance the industrial restructuring supererogation. The following research hypothesis is put forward:

Hypothesis 4 (H4).

Digital economic growth positively affects financial development, and financial development has a mediating effect on the impact of the digital economy on industrial restructuring.

The research framework of this article is shown in Figure 1.

4. Research Design

4.1. Measuring Model

Digital innovation can change the technological base and thus the scope of the production possibilities frontier, and some studies have shown that digital transformation has a positive impact on technological innovation. At the same time, the development of digital technology has increased the level of intelligence and automation, increasing the demand for high-end labor, i.e., human capital, in order to accommodate automated jobs [47]. With the development of the digital economy, the market-based allocation of factors will increasingly rely on digital means to achieve this, which means that factors such as capital, labor, technology, and data can flow freely and effectively to high-efficiency production sectors, thus adjusting the economic structure, improving economic efficiency, and promoting the transformation and upgrading of the industrial structure [48]. In summary, the impact of digitalization is an indirect influence on industrial restructuring through variables such as human capital, technological innovation, financial capital, and investment flows. In conclusion, the production function in the digital economy is very different from the production function in the traditional economy, and the digital economy opens up a production function that can be expressed between different states of technology. The production function in the digital economy era mainly relies on the application of information technology, and the digital production process can improve production efficiency and reduce production costs.

Therefore, to elaborate on the effect of digital economic growth on industrial restructuring, we constructed a regression model with industrial restructuring as the dependent variable and digital economic growth as the independent variable. The model is expressed as

$$is{a}_{it}=\alpha +{\beta }_{1}d{e}_{it}+{{\displaystyle \sum }}_m{x}_m+{\phi }_i+{\eta }_t+{\mu }_{it},$$

(1)

where $is{a}_{it}$ refers to the index of industrial restructuring of region i in year t; $d{e}_{it}$ represents the index of digital economic growth of region i in year t; $x$ denotes control variables, including opening-up, foreign direct investment, fiscal investment, and fixed asset investment;${\phi }_i$ is the entity fixed effect of region i; ${\eta }_t$ is the time-fixed effect of region i; and ${\mu }_{it}$ is the disturbance term.$\alpha$ represents the intercept term, and ${\beta }_1$ is the coefficient of digital economic growth, indicating its impact on industrial restructuring.

Inspired by the mediating effect model that Mackinnon et al. (1995) constructed [49], we adopted a stepwise regression method to empirically test whether the digital economy would affect industrial restructuring through human capital, technological innovation, and finance. The mediating effect model is expressed as

$${\pi }_{it}=\gamma +{\gamma }_{1}d{e}_{it}+{\displaystyle \sum }_k{x}_k+{\phi }_i+{\eta }_t+{\mu }_{it}$$

(2)

$$is{a}_{it}=\alpha +{\beta }_{1}d{e}_{it}++{\beta }_2{\pi }_{it}+{{\displaystyle \sum }}_m{x}_m+{\phi }_i+{\eta }_t+{\mu }_{it}.$$

(3)

4.2. Variables

4.2.1. Explained Variable

The explained variable in this study is the overall level of industrial restructuring. Based on the findings of Garonna and Sica (2000), the variable is measured from the perspective of speed, quality, and efficiency, in which quality is specified in terms of industrial structure supererogation and rationalization and efficiency refers to the economic and ecological benefits [50].

1.

Industrial restructuring speed. Industrial restructuring speed ($isas$) is a major factor in measuring changes in industrial restructuring. The speed of regional industrial restructuring is measured by a modified Lilien index. We computed the redistribution speed of the workforce in different divisions [50]. This process is denoted as

$$isa{s}_{it}=\sqrt{{{\displaystyle \sum }}_{n=1}^3{\theta }_{int}{\left[\ln \left(\frac{z_{int}}{z_{int-1}}\right)-\ln \left(\frac{Z_{it}}{Z_{it-1}}\right)\right]}^2}.$$

(4)

In Equation (4),$isa{s}_{it}$ represents the speed of industrial restructuring of region i in year t. ${\theta }_{int}$is the mean value of the proportion of workers employed in industry n in the total employment of region i in year t and year$t-1$.$z_{int}$refers to the number of employees in industry n in year t, $Z_{it}$ is the total national employment in year t, and n is the specific industry. In the modified Lilien index, the variable is set between period t and period $t-1$. The greater the index grows, the faster speed of industrial restructuring shows [50].

2.

Industrial restructuring quality. In the study, industrial restructuring quality is measured in terms of industrial structure supererogation and rationalization.

Industrial structure supererogation ($isah$) is a dynamic evolving process in which an industrial structure advance. Inspired by methods adopted by Li et al. (2021), this study considers a variety of layer coefficients and labor productivity of an industrial structure [51]. The equation is expressed as

$$isa{h}_{it}={{\displaystyle \sum }}_{n=1}^3\frac{Y_{int}}{Y_{it}}\times \frac{Y_{int}}{L_{int}}, n=1,2,3,$$

(5)

where $isa{h}_{it}$ represents the level of industrial structure supererogation of region i in year t. $Y_{int}$ denotes the added value generated by industry n of region i in year t,$Y_{it}$is the GDP of region i in year t,$L_{int}$ refers to the number of workers employed in industry n of region i in year t, and $\frac{Y_{int}}{L_{int}}$ is the labor productivity of industry n of region i in year t. A greater value means a higher level of industrial structure supererogation.

Industrial structure rationalization ($isar$) indicates inter-industry coordination and coupling. Referring to the methods in Blankmeyer [52], we employed the Theil index in this study to construct the following equation to measure industrial structure rationalization:

$$isa{r}_{it}={{\displaystyle \sum }}_{i=1}^3\left(\frac{Y_{int}}{Y_{it}}\right)ln\left(\frac{\raisebox{1ex}{$Y_{int}$}\!\left/ \!\raisebox{-1ex}{$Y_{it}$}\right.}{\raisebox{1ex}{$L_{int}$}\!\left/ \!\raisebox{-1ex}{$L_{it}$}\right.}\right), n=1,2,3$$

(6)

In Equation (6), $isa{r}_{it}$ represents industrial structure rationalization of region i in year t, $L_{it}$ is the total employment of region i in year t, and other variables are those in Equation (5). When $isar$ is 0, the industrial structure reaches equilibrium; when it is not, the greater the value grows, the more irrational the industrial structure is.

3.

Industrial restructuring efficiency. The industrial restructuring efficiency in this study should be examined economically and ecologically. Referring to the methods [53], we computed the efficiency based on an undesirable output-super efficiency SBM model based on the input, desirable, and undesirable output indexes. The equation is denoted as

$$\rho =\underset{\lambda ,\overline{x},y^g,y^b}{\min }\frac{{{\displaystyle \sum }}_{i=1}^m\frac{\overline{x_t}}{x_{i0}}}{\frac{1}{s_1+s_2}\left({{\displaystyle \sum }}_{r=1}^{s_1}\frac{\overline{y_r^g}}{y_{r0}^g}+{{\displaystyle \sum }}_{k=1}^{s_2}\frac{\overline{y_k^b}}{y_{k0}^b}+\right)}.$$

(7)

In Equation (7), $\overline{x}$, $\overline{y_r^g}$, and $y_k^b$ are the projection values or target values of input-output of the evaluation unit; $x_{i0}$, $y_{r0}^g$, and $y_{k0}^b$ represent the corresponding original value [53]. The input index covers labor, capital, energy, and pollution management. Specifically, the labor input is measured by year-end total employment; the capital input is denoted by fixed asset investment of the whole society; and the energy input refers to regional total energy consumption. Pollution management is measured by a completed investment in industrial pollution management. The desirable output index is denoted by regional GDP, and the undesirable output index includes industrial SO₂ emissions, industrial water waste discharge, and industrial dust emissions.

4.2.2. Core Explanatory Variable

The core explanatory variable of this study is digital economic growth. A measuring index system of digital economy measures regional digital economic growth in China. This system comprises three first-level indexes (digital infrastructure, digital industrialization, and industrial digitalization) and twelve second-level indexes listed in

Table 1. Measuring index system of digital economic growth.

First-Level	Second-Level Index	Unit
Digital infrastructure	Number of domain names	10,000 units
	Popularization rate of mobile telephone	Sets/100 persons
	Length of optical cable lines	km
	Base stations of mobile telephones	10,000 units
Digital industrialization	Broad band subscribers port of internet	10,000 ports
	Software income	10,000 yuan
	Business volume of telecommunication services	CNY 100 million
	Income from IT oncome	CNY 10,000
Industrial digitalization	Sales of E-commerce	CNY 100 million
	Purchases of E-commerce	CNY 100 million
	Enterprises with E-commerce transactions	Unit
	Websites per 100 enterprises	Unit

. Multi-criteria decision-making (MCDM) is one of the important contents of analytical decision-making theory [54]. Currently, decision-making methods are divided into two categories, namely, multiple-objective decision-making (MODM) models and multiple-attribute decision-making (MADM) [54]. One of the most recent multiple-criteria decision analysis methods is the ordinal priority approach. Ataei et al. (2020) proposed a new method called the ordinal priority approach (OPA) in multiple-attribute decision-making (MADM) [54]. Mahmoudi et al. (2023) proposed a novel ensemble ranking model based on the ordinal priority approach to solving multiple-criteria decision-making (MCDM) problems in the field of supply chain finance [55]. Guided by the theory of statistics and the ordinal priority approach (OPA), Mahmoudi et al. pioneer a probabilistic approach to supplier evaluation and selection under incomplete information using a novel confidence level measure [56]. Mahmoudi et al. (2022) solved the traditional problem using the group-weighted ordinal prioritization approach (GWOPA) model in multi-attribute decision-making (MADM) and determined the performance of the project [57].

In this paper, the entropy TOPSIS method is applied to determine the weights of each indicator in Table 1, and the specific steps are described below.

The first step is dimensionless processing. Because of the large variability of the dimensionality of the indicators in the digital economy development evaluation index system, this paper adopts Equation (8) to perform dimensionless processing of the original data.

$$x_{ij}^{\prime }=\frac{x_{ij}-\min (x_{ij})}{\max (x_{ij})-\min (x_{ij})}$$

(8)

where $x_{ij}$ denotes the j-th indicator in region i, $\max \left(x_{ij}\right)$ and $\min \left(x_{ij}\right)$ denote the maximum and minimum values of each indicator in all regions, and $x_{ij}^{\prime }$ denotes the indicator value after dimensionless processing of $x_{ij}$; the maximum value is 1 and the minimum value is 0. Therefore, in order to avoid dimensionless processing, the indicator takes the value of 0 after dimensionless treatment, which can be fine-tuned by the following equation:

$$x_{ij}^{″}=x_{ij}^{\prime }\times 0.99+0.01$$

(9)

In the second step, the entropy value of each indicator is calculated. The entropy value of the j-th indicator is calculated by the following equation:

$$e_j=-\frac{1}{\ln m}{\displaystyle \sum _{i=1}^m{p}_{ij}\ln p_{ij}}$$

(10)

where $p_{ij}=\raisebox{1ex}{$x_{ij}^{″}$}\!\left/ \!\raisebox{-1ex}{${\displaystyle {\sum }_{i=1}^m{x}_{ij}^{″}}$}\right.$.

In the third step, the indicator weights are calculated according to Equation (11).

$${\omega }_j=\frac{1-e_j}{{\displaystyle {\sum }_{j=1}^m(1-e_j)}}$$

(11)

In the fourth step, the level of digital economy development is calculated. The specific equation for calculating the level of digital economy development for region i is as follows:

$$D{e}_i={\displaystyle \sum _{j=1}^n({\omega }_j\times x_{ij}^{″})}$$

(12)

where $x_{ij}^{,,}$ represents the index value after dimensionless processing and fine-tuning.

4.2.3. Mediating Variables

According to previous analysis, we identified human capital, technological innovation, and financial development as mediating variables.

• Human capital (hum). Human capital continuously affects economic growth and industrial restructuring. Inspired by the methods adopted by Mandelman and Zlate (2012), human capital is represented by high-skilled talents to lower-skilled ratio, with the former referring to workers with a college degree or above and the latter meaning those with lower education [58].
• Technological innovation (innov). In light of innovation performance, patents are high-tech innovations; therefore, the ratio of regional patent grants to national patent grants is computed to measure regional technological innovation.
• Financial development (fina). Digital finance contributes to industrial restructuring. In this paper, digital finance refers to the new generation of financial services combined with the traditional financial services industry by means of Internet and information technology, mainly including Internet payment, mobile payment, online banking, financial services outsourcing and online loans, online insurance, online funds, and other services. The digital finance referred to in this paper is essentially the process of digitization of the financial industry with the new generation of information technology as the core and promotes the development of digital transformation in various fields. Therefore, this study uses the digital finance index released by the Institute of Digital Finance of Peking University to describe regional digital financial development.

4.2.4. Control Variables

In the regression model, factors affecting industrial restructuring are identified as control variables, which are opening-up (open, measured by the proportion of the total volume of imports and exports in GDP), fiscal investment (fe, measured by the proportion of local general public budget in GDP), foreign direct investment (fdi. measured by the proportion of foreign direct investment in GDP), and fixed asset investment (invest, measured by the proportion of fixed asset investment in GDP).

4.3. Data Sources

Data in this study are sourced from the China Statistical Yearbook, China Labor Statistics Yearbook, China City Statistical Yearbook, China Trade and External Economic Statistical Yearbook, China Statistical Yearbook on Science and Technology, and other statistical yearbooks of China’s provinces from 2013 to 2020. The present study focuses on 30 provinces in mainland China except for the Tibet autonomous region owing to its missing data. In particular, we use constant price data to assess the evolution of the industry’s value added over time.

Table 2. Descriptive statistics of variables.

Variable	Obs	Mean	Std. Dev.	Min	Max
de	240	0.62757	0.105016	0.518805	0.969663
isas	240	0.206254	0.097225	0.004847	0.396795
isah	240	12.88378	4.602434	6.491967	29.55351
isar	240	0.163906	0.116726	0.007897	0.702733
isae	240	0.337101	0.226313	0.139365	1
hum	240	0.514091	0.396202	0.161301	2.70621
innov	240	8.341179	1.388345	4.51086	11.16613
fina	240	253.8675	68.59175	118.01	431.93
open	240	0.253036	0.269189	0.007652	1.365595
fe	240	0.254992	0.103459	0.118807	0.643011
fdi	240	0.018826	0.014442	0.000103	0.079594
invest	240	0.822389	0.269454	0.20999	1.479562

shows descriptive statistics of variables.

5. Analyses of Empirical Tests Results

5.1. Analysis of Benchmark Regression Results

With the speed of industrial restructuring, industrial structure supererogation, industrial structure rationalization, and industrial restructuring efficiency of surveyed areas from 2013 to 2020 as dependent variables and digital economic growth of these regions as the independent variable, we used a fixed effect model to test the effect of digital economic growth on industrial restructuring empirically.

Table 3. Benchmark regression results.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	isas	isah	isar	isae	isas	isah	isar	isae
de	1.883 **	20.90 ***	−1.839 ***	0.953 ***	1.851 **	33.30	−1.875 ***	0.712 ***
	(2.29)	(13.10)	(−3.26)	(6.90)	(2.65)	(1.35)	(−3.29)	(8.20)
open					−0.233 **	−13.76 ***	0.0170	0.241 **
					(−2.26)	(−4.18)	(0.29)	(2.51)
fe					1.040 ***	15.03 *	−0.0908	1.020 ***
					(4.95)	(1.77)	(−0.46)	(12.33)
fdi					−1.750 **	−65.74 **	0.103	3.526 ***
					(−2.35)	(−2.45)	(0.34)	(4.12)
invest					−0.0584	−2.660 *	0.0208	−0.209 ***
					(−1.22)	(−1.94)	(0.75)	(−3.77)
_cons	−0.975 *	−0.251	1.318 ***	−0.262 ***	−1.081 **	−4.941	1.340 ***	−0.322 ***
	(−1.89)	(−0.59)	(3.72)	(−4.26)	(−2.42)	(−0.31)	(3.77)	(−4.58)
N	240	240	240	240	240	240	240	240

t statistics in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

shows the benchmark regression results before and after considering control variables. According to Table 3, after including control variables, the effect coefficients of digital economic growth on the speed of industrial restructuring are all positive and significant at the level of 5%. The same positive values are seen for the influence coefficients of the digital economy on the efficiency of industrial restructuring. Negative values are seen in the effect coefficients of industrial restructuring rationalization, which are significant at the 1% level. These figures demonstrate that digital economic growth contributes to the speed, efficiency, and rationalization of industrial restructuring. Hypotheses H1a, H1c, and H1d proposed in this study are therefore verified. Empirical results of the digital economy on industrial restructuring supererogation show that the significance level varies when control variables are introduced. Therefore, hypothesis H1b is partially verified, which might attribute to the fact that industrial restructuring supererogation is affected by various factors. Widely applied digital technologies enhance industrial structure upgrading; meanwhile, digital economic growth also gives rise to the digital gap. Together with other factors, the effect of the digital economy on industrial restructuring supererogation seems less noticeable.

As for control variables, the effect coefficient of fiscal investment is significantly positive, indicating that fiscal investment considerably improves the speed of industrial restructuring. The effect coefficients of opening-up and foreign direct investment are negative, suggesting that they drag the speed of industrial restructuring, which is also observed in the effect on industrial structure supererogation. It seems to be associated with China being in the middle and low end of the international industrial chain and its exports are labor-intensive products in large. No significant effect is seen from control variables on industrial restructuring rationalization. Opening-up, foreign direct investment, and fiscal investment all have a remarkable positive effect on the efficiency of industrial restructuring. The negative coefficient of fixed asset investment indicates a dampening effect, which means excessive fixed assets investment, particularly in real estate, reduces the efficiency of industrial restructuring.

5.2. Analysis of Regression Results of Mediating Effect

We tested the effect of digital economic growth on industrial restructuring regarding its speed, supererogation, rationalization, and efficiency by constructing a mediating effect model, in which human capital, technological innovation, and financial development are considered mediating variables. Regression results are shown in

Table 4. Regression results of mediating effect (hum).

	(1)	(2)	(3)	(4)	(5)
	hum	isas	isah	isar	isae
de	1.418 *	1.418 ***	13.30	−1.621 ***	0.881 ***
	(1.77)	(2.94)	(0.93)	(−3.64)	(4.81)
hum		0.305 ***	14.10 ***	−0.179 **	0.0696 *
		(3.60)	(7.10)	(−2.40)	(1.68)
open	−0.880 ***	0.0362	−1.340	−0.140 *	−0.0280
	(−4.92)	(0.44)	(−0.64)	(−1.76)	(−0.33)
fe	0.931 **	0.756 ***	1.899	0.0755	0.992 ***
	(2.34)	(3.39)	(0.29)	(0.33)	(6.38)
fdi	−3.796 ***	−0.591	−12.19	−0.575	1.879 **
	(−2.95)	(−0.97)	(−0.66)	(−1.26)	(1.99)
invest	−0.104	−0.0266	−1.191	0.00220	−0.344 ***
	(−1.67)	(−0.69)	(−1.31)	(0.08)	(−5.33)
_cons	−0.233	−1.009 ***	−1.652	1.298 ***	−0.250 *
	(−0.43)	(−3.16)	(−0.18)	(4.31)	(−1.71)
N	240	240	240	240	240

t statistics in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

,

Table 5. Regression results of mediating effect (innov).

	(1)	(2)	(3)	(4)	(5)
	innov	isas	isah	isar	isae
de	106716.5 ***	1.900 ***	34.97 *	−1.898 ***	0.970 ***
	(6.38)	(4.09)	(1.94)	(−3.59)	(6.31)
innov		0.00000631 ***	0.000212 ***	−0.00000299 **	−0.00000340
		(5.14)	(3.93)	(−2.69)	(−1.89)
open	3808.0 **	0.118	−1.973	−0.149	0.259 **
	(2.88)	(0.91)	(−0.40)	(−1.53)	(2.70)
fe	13702.3 ***	0.961 ***	12.38 *	−0.0534	1.043 ***
	(4.17)	(5.09)	(1.77)	(−0.26)	(12.19)
fdi	35504.0 **	−1.365 *	−52.79 **	−0.0798	3.561 ***
	(2.78)	(−1.95)	(−2.43)	(−0.23)	(4.14)
invest	−1853.8 **	−0.0726 *	−3.137 **	0.0275	−0.211 ***
	(−3.45)	(−1.81)	(−2.70)	(1.01)	(−3.75)
_cons	−61237.6 ***	−1.236 ***	−10.17	1.414 ***	−0.468 ***
	(−6.03)	(−3.85)	(−0.84)	(4.13)	(−5.43)
N	240	240	240	240	240

t statistics in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

and

Table 6. Regression results of mediating effect (fina).

	(1)	(2)	(3)	(4)	(5)
	fina	isas	isah	isar	isae
de	1715.9 **	0.427 *	0.566	−1.005 ***	0.653 ***
	(2.31)	(1.92)	(0.13)	(−3.79)	(3.80)
fina		0.000830 ***	0.0295 ***	−0.000507 ***	0.00100 ***
		(12.40)	(22.90)	(−4.61)	(6.29)
open	−312.3 ***	0.0266	−1.809 *	−0.141 **	0.0594
	(−3.61)	(0.63)	(−1.71)	(−2.66)	(0.83)
fe	824.7 ***	0.355 *	−9.339 ***	0.327	1.000 ***
	(3.77)	(2.01)	(−3.32)	(1.50)	(7.17)
fdi	−1824.3 **	−0.236	−11.71	−0.821 *	2.125 **
	(−2.61)	(−0.76)	(−1.22)	(−1.85)	(2.43)
invest	−60.42	−0.00824	−1.716 ***	−0.00980	−0.312 ***
	(−1.41)	(−0.29)	(−3.20)	(−0.37)	(−5.34)
_cons	−870.2 *	−0.358 **	9.519 ***	0.899 ***	−0.381 ***
	(−1.85)	(−2.45)	(3.31)	(4.84)	(−2.79)
N	240	240	240	240	240

t statistics in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

.

According to Table 4, with human capital as a mediating variable, the effect of digital economic growth on human capital shows a noticeable positive value, significant at the level of 10%, demonstrating that the digital economy enhances the quality of human capital. The influence coefficients of human capital and digital economic growth are positive on the speed and efficiency of industrial restructuring and are negative on the rationalization of industrial restructuring, suggesting that the digital economy indirectly improves the speed, rationalization, and efficiency of industrial restructuring by human capital. With other influence factors unchanged, when digital economic growth goes upward by one unit, the speed of industrial restructuring becomes faster by 1.418 units. Such an increase is seen in human capital, which indirectly drives the speed of industrial restructuring up by 0.432 units. The total effect, a combination of direct effect and indirect effect, reaches 1.850 units, with the indirect effect accounting for 23.4%. When digital economic growth increases by one unit, industrial structure rationalization directly increases by 1.621 units. Meanwhile, industrial structure rationalization affected by human capital indirectly improves by 0.254 units, with indirect effect accounting for 13.5%. When digital economic growth increases by one unit, 0.881 units of additional efficiency of industrial restructuring is generated. The efficiency of industrial restructuring affected by human capital indirectly improves by 0.099 units, with an indirect effect accounting for 10.1%. Unfortunately, the test of the effect of the digital economy on industrial structure supererogation by human capital fails. Therefore, the research hypothesis H2 is partially verified.

According to Table 5, when technological innovation is identified as a mediating variable, the effect of digital economic growth on technological innovation is positively valued, significant at the level of 1%, which indicates a boosting effect of digital economic growth on technological innovation. The effect coefficients of technological innovation and digital economic growth on the speed of industrial restructuring and industrial structure supererogation are all positive, yet negative values are seen when it comes to industrial structure rationalization, suggesting that the digital economy has an indirect positive effect on the speed of industrial restructuring, industrial restructuring supererogation and rationalization by technological innovation. When other affecting factors remain unchanged, one unit increase in the digital economic growth leads to 1.9 units up in the speed of industrial restructuring. Meanwhile, the speed of industrial restructuring affected by technological innovation indirectly accelerates by 0.673 units. The total effect hits 2.573 units, with an indirect effect accounting for 26.2%. One unit increase in digital economic growth also gives rise to 34.97 units up in industrial structure supererogation. An additional 22.62 units are seen in industrial structure supererogation indirectly affected by technology innovation, with a total effect of 57.59 units and an indirect effect accounting for 39.3%. One unit increase in digital economic growth equals a direct increase of 1.898 units in industrial structure rationalization. Technological innovation also indirectly improves rationalization by 0.319 units, with a total effect of 2.217 units and an indirect effect accounting for 14.4%. The test of the digital economy’s effect on industrial restructuring efficiency by technological innovation fails. Therefore, research hypothesis H3 is partially verified.

According to Table 6, when financial development is identified as a mediating variable, the effect of digital economic growth on financial development is positively valued, and its regression coefficient is significant at the level of 5%, indicating a boosting effect of digital economic growth on financial development. The effect of financial development and digital economic growth on the speed of industrial restructuring and the efficiency of industrial restructuring is also significantly positive. Yet, its regression coefficient becomes negative regarding industrial structure rationalization, suggesting that the digital economy indirectly enhances the speed of industrial restructuring, industrial restructuring supererogation, and rationalization by boosting financial development. When other affecting factors remain unchanged, one unit increase in digital economic growth leads to 0.427 units up in the speed of industrial restructuring. Also, the speed of industrial restructuring affecting by financial development indirectly accelerates by 1.424 units, with a total effect of 1.851 units and an indirect effect accounting for 76.9%. One unit increase in digital economic growth also gives rise to 1.005 units up in industrial structure rationalization. A 0.870 unit increase is seen in industrial structure rationalization indirectly affected by finance, with a total effect of 1.875 units and an indirect effect accounting for 46.4%. A 1 unit increase in digital economic growth means that the efficiency of industrial structuring directly climbs up by 0.653 units, together with a 1.716 unit increase indirectly caused by finance, with a total effect of 2.369 units and an indirect effect accounting for 72.5%. Test of the effect of the digital economy on industrial restructuring supererogation by finance fails. Therefore, research hypothesis H4 is partially verified.

6. Conclusions and Implications

6.1. Conclusions

From a global perspective, the digital economy has become a new force and engine to drive the economic development of relevant countries. Currently, relevant economies have reaped a batch of achievements in big data, AI, cloud computing, and electronic commerce. Digital economic growth has been essential for boosting industrial restructuring and high-quality economic growth. Under such a social context, this study measured regional digital economic growth by the entropy TOPSIS method based on a constructed measuring index system of the digital economy. We theoretically analyzed the effect mechanism of the digital economy on industrial restructuring in light of speed, quality, and efficiency. We also employed a fixed effect model to empirically test the impact of digital economic growth on the speed of industrial restructuring, industrial structure supererogation and rationalization, and the efficiency of industrial restructuring. Conclusions are made as follows:

First, digital economic growth has a direct positive impact on the speed and benefit of industrial restructuring. In contrast, the positive impact on the rationalization of the industrial structure needs to be realized through the level of human capital, technology innovation, and financial development. The effect of the digital economy on the speed of industrial restructuring is significant at the 5% level. All the effects on the benefits of industrial restructuring are significant at the 1% level. These findings are consistent with the findings of existing studies [7,8,13]. The main reason is that the digital economy is a new driving force for economic development. The development of the digital industry can accelerate the coupling effect and spillover effect between traditional industries and environment-friendly industries, hasten the mobility of factors between industries, and then reasonably optimize the value chain of traditional industries, improve process efficiency and product innovation, and promote the transformation of industrial structure from labor-intensive to environment-friendly with high technology content.

Second, fiscal investment has a significant role in promoting the speed of industrial restructuring, whereas the level of opening up and foreign direct investment have a suppressive effect on both the speed of industrial restructuring and the advanced industrial structure. The reason is that the opening-up level and FDI can better promote the growth of the regional economy, which is the same as the purpose of the speed of industrial restructuring and advanced industrial structure. In the framework of seeking the same goal, the level of opening up and foreign direct investment has a particular crowding-out effect on the speed and advancement of industrial restructuring. Fiscal investment, opening up, and foreign direct investment are important in promoting industrial restructuring benefits. The benefits of industrial restructuring mainly include economic and ecological benefits. Whether fiscal investment in the economy or opening up to the outside world, the ultimate goal is to promote regional economic development and benefit enhancement. At this time, the three goals are the same and can promote economic benefits for improvement. Fixed asset investment will restrict the benefits of industrial restructuring; the fundamental reason is that the real estate industry is an important field of fixed asset investment. Moreover, at this stage, China’s real estate industry has certain financial attributes, absorbing more asset investment, reducing the investment in new business fields and traditional non-real estate fields, and thus reducing the benefits of industrial restructuring. These findings are consistent with the findings of existing studies [40,41,42].

Third, in addition to directly affecting industrial restructuring, the digital economy also has an indirect influence by releasing human capital dividends, technological innovation, and financial development. Specifically, the digital economy indirectly promotes the speed and efficiency of industrial restructuring and industrial structure rationalization by human capital. Technological innovation caused indirect positive influence of the digital economy is found on the speed of industrial restructuring and industrial structure supererogation and rationalization. These findings are consistent with the findings of existing studies [16,45,47,51].

Finally, the digital economy also indirectly improves the speed and efficiency of industrial restructuring and industrial structure rationalization through finance. Moreover, the comparative analysis also reveals that the direct effect of the digital economy on industrial restructuring outperforms the indirect effect generated by human capital and technological innovation. However, the indirect effect of the digital economy on industrial restructuring by finance turns out to be more significant than the direct effect of the digital economy on industrial restructuring.

6.2. Implications

The above findings are of practical significance for relevant economies to push forward their industrial structure transformation and upgrading, accelerating high-quality economic growth in the long term. These conclusions facilitate the economies implementing industrial policies to promote regional industrial upgrading. Given these conclusions, we prescribe recommendations as follows:

Firstly, upgrading digital infrastructure. The study has confirmed that digital economic growth positively affects industrial restructuring from the perspective of speed–quality–efficiency. Digital infrastructure is the foundation of digital economic growth. In this regard, expanding the information network, including gigabit optical networks and 5G connection, developing 6G network technological reserves in advance, and accelerating the construction of satellite communication networks are recommended. Building nationwide integrated big data centers that coordinate computing power, algorithms, data, and applications is also suggested. More green digital centers should be transformed, which are energy-saving and powered by renewable energy. Infrastructure must be upgraded in terms of its network, intelligence, service, and coordinating capacity. Developing AI infrastructure is conducive to enhancing the capacity to empower intelligent plus industries.

Second, accelerating industrial digitalization. Industrial digitalization, a core issue in developing the digital economy, underscores an integrated development of digital technologies and traditional industries. Enterprises should speed up transformation by digitalizing their R&D, product designing, production, processing, management, marketing, and service. We should deepen digitalization in key industries. Traditional industries are expected to transform in an all-rounded way. Developing intelligent agriculture and the industrial Internet enhances industrial digitalization. Service industries such as commerce and trade, logistics, and finance are encouraged to transform digitally to boost industrial integration through digital technologies.

Third, enhancing digital industrialization. Digital industrialization derives from evolving digital technologies. It is the foundation and precondition of industrial restructuring and contributes to utilizing the digital economy’s positive effect on industrial restructuring. The focus should be given to strategic frontier fields, improving the fundamental R&D capacity of digital technologies. Moreover, joint innovation among enterprises in this field, platform enterprises, and enterprises specializing in digital technologies is suggested. A diversified innovation ecological system should be in place. It is expected to boost the intelligent economy based on digital technologies, intelligent products, and service operations. We also should accelerate resource sharing and open data to facilitate online-to-offline collaborative innovation.

Finally, utilizing mediating effect. This study reveals that the digital economy indirectly boosts industrial restructuring by mediating variables. As one of the mediating factors, digitalizing human capital is an important way to the overall situation of industrial restructuring. Specifically, a nationwide digital capacity and skill promotion campaign is advised. In other words, improving information technology courses in primary and middle schools, training more talents skilled in digital technologies in vocational colleges, building modern industrial schools jointly by enterprises and colleges, and cooperating with laboratories and practice bases are needed to roll out diversified training modes, such as a make-to-order and modern apprenticeship. In view of the mediating factor of technological innovation, we should speed up a wide and in-depth penetration of digital technologies in society and industries. Efforts also should be made to make innovations integrating digital technologies with application scenarios and business models. We strive to foster a new development pattern in which technological advancement promotes higher total factor productivity and field applications improve technological progress.

6.3. Future Directions and Limitations

Despite the findings of this study, future research is advised in the following respects. Considering the measuring index system of digital economy constructed in this study covers digital infrastructure, digital industrialization, and industrial digitalization, we suggest more works to enrich and improve this system based on the interpretation of digital economy, for instance, digital applications and digital governance might be added to measure digital economic growth. In addition, industrial restructuring seems to be distinct from region to region, which is associated with regional economic gaps. Therefore, different effects of the digital economy on industrial restructuring in different regions should be empirically tested, which may contribute to proposing region-specific strategies for different economies to boost industrial restructuring by developing the digital economy.