1. Introduction
In 2019, the COVID-19 pandemic quickly swept the world. While having a huge impact on the economy and society, it also provides new opportunities for developing the digital economy. In recent years, China’s digital economy has maintained rapid growth in the face of adversity, becoming an important force in coping with the difficult and complex international economic situation and ensuring the steady progress of the domestic economy toward a stage of high-quality development. As a new growth point for global economic development and an important engine of technological innovation, the digital economy has attracted great attention from all countries. In 2023, the Digital Decade Policy Program 2030 proposed by the EU was implemented. Japan also started strategically planning consecutive, large-scale data centers in multiple locations. Furthermore, “the America Rescue Plan” aims to provide more reliable and affordable high-speed Internet services. Similarly, the development of the digital economy has been elevated to a national strategy in China. This shows that the Central Committee Party has fully realized that the digital economy, as a new driving force, is of great significance in breaking the shackles and constraints on the road of scientific and technological innovation.
As one of the pillar industries of China’s economy, the manufacturing sector occupies an important position globally in terms of scale and output. In 2022, the added value of China’s manufacturing sector accounted for 28% of the country’s GDP, the most heavily weighted among the 20 sectors of the national economy. In recent years, major breakthroughs have been made in information technology, biotechnology, and new energy industries. Generally speaking, most breakthroughs are still in the field of manufacturing. Regarding important points in the development of human society, the world has experienced three waves of modernization. The manufacturing industry has always been at the forefront of the scientific and technological revolution, providing the material and technological basis for the economy and society in the context of a new round of industrial change. According to the statistics of the Organization for Economic Cooperation and Development (OECD) regarding the sub-industry share of enterprise R&D expenditures in major countries, the manufacturing industry of the United States accounted for 57%. In contrast, the share of R&D expenditures of the manufacturing industry in Japan, Germany, and China for all enterprises was 87%, 85%, and 88%, respectively. Thus, the manufacturing industry is significant to the country’s scientific and technological innovation. Since the 18th Party Congress, China’s scientific and technological innovation and technological research and development capabilities have improved significantly. Simultaneously, global high-tech competition has become increasingly intense. In this context, how to use the digital economy as a new type of grip to accelerate manufacturing innovation and maintain its competitive advantage in the international market is a topic worthy of in-depth discussion.
There is no doubt that the booming digital economy will greatly affect innovation. Many studies based on different topics and different perspectives have confirmed this. Through data analysis and case studies, He et al. pointed out that prioritizing the development of digital technology can enhance the competitiveness of the manufacturing industry, thus achieving sustainable economic expansion and technological progress [
1]. Li et al. found that the digital economy in the era of big data has a significant role in promoting the institutional innovation of the manufacturing industry [
2]. Hui et al. pointed out that the development of the digital economy has significantly improved the innovation efficiency of the manufacturing industry, and the deep upgrading of industrial structure and technology spillover are two effective paths [
3]. Cyrielle Gaglio et al. (2022) used small- and medium-sized manufacturing enterprises (MSEs) in middle-income countries in South Africa as samples to study the relationship between the use of digital communication technology, innovation performance, and productivity. The results show that digital communication technologies (including the use of social media and business mobile internet) have a positive impact on enterprise innovation [
4]. Träskman et al. pointed out that digital infrastructure constitutes the cornerstone of modern enterprises. Improvement in digital infrastructure can not only provide more advanced services for enterprises but also help to achieve more efficient production, which is crucial for promoting technological innovation [
5]. Mukesh conducted a fuzzy set qualitative comparative analysis of samples from 55 countries. The findings indicate that digital transformation fuels business model innovation in specific and entrepreneurial innovation, in general, at the country level [
6]. Lanzolla et al. found that digital transformation may introduce a more extensive knowledge search and more effective knowledge reorganization. Based on this logic, they believe digital transformation can promote developmental innovation [
7,
8]. In addition, Bigliardi pointed out, from the perspective of entrepreneurial ideas, that with the emergence of new technologies, such as digital technology and the Internet of Things, open innovation will also become more pervasive and comprehensive [
9]. Fernández et al. (2021) indicated that the eco-product innovation of companies could be promoted by mining data collected from external stakeholders [
10]. Based on the perspective of dynamic capabilities, Hoang et al. explored the impact of digital capabilities on the performance and innovation capabilities of manufacturing SMEs in emerging markets in Vietnam. The results showed that digital capabilities play a significant role in promoting process and product innovation capabilities [
11]. Mubarak et al. found that Industry 4.0 technologies can improve information dissemination and knowledge accumulation in enterprises, motivating enterprises to implement open innovation, and thereby enhance green innovation [
12]. By combing the above literature, it can be seen that scholars have carried out many investigations on the topic of the digital economy affecting innovation, which provides a solid theoretical reference for our research. Based on this, this study aimed to explore the impact of the digital economy on the innovation of manufacturing enterprises from the perspectives of “scale expansion” and “quality improvement” so as to enrich the relevant theoretical results.
After the international financial crisis, the global manufacturing industry was key in reshaping the development concept, adjusting the unbalanced structure, and reshaping the competitive advantage. Integrating the digital economy and real economy is a key choice to seize a new round of industrial transformation and development opportunities and spur high-quality development. As an integrated economy, the digital economy uses digital knowledge and information as key production factors. Through integration and development with the real economy, it can give full play to the activation, innovation, and empowerment of the digital economy. Its core lies in applying new-generation information technology in the real economy [
13]. The accelerated integration of digital technology and manufacturing technology has introduced subversive changes to the production mode and development model of the manufacturing industry. With the emergence of “ABCD” technologies such as artificial intelligence, blockchain, cloud computing, and data analysis, digital technology is increasingly embedded in the production and operation process of enterprises, thus changing the traditional way of combining production factors and technology [
14,
15]. According to the Research Report on China’s Digital Economy Development released by the China Academy of Information and Communications Technology, the penetration rate of China’s digital economy in the secondary industry reached 24% in 2022. The digital economy is moving deeper into “reality” at an unprecedented scale and speed [
16]. Digital technologies are constantly changing and converging in the process of innovation. The resulting integrated innovation model of “data + algorithm + computing power” will inevitably lead to the transformation of industrial technology in the production process of the traditional manufacturing industry to intelligent technology. It has gradually become a “digital engine” for the transformation and overall upgrading of the real economy. Given this, we will comprehensively explore the role and impact mechanism of regional digital economy development on manufacturing innovation from the two dimensions of quantity and quality. An in-depth exploration of the above issues can support manufacturing enterprises in striving for maximum innovation dividends in the era of rapid digital economy development.
The contributions of this study are mainly reflected in the following aspects: (1) We combined the academic literature and a series of policy documents issued by the China Institute of Information and Communications to build a feature lexicon of the digital economy. The word frequency counted in the government work report of each region in the research interval was used as the proxy variable of the comprehensive development index of the digital economy for subsequent empirical analysis. (2) We measured manufacturing innovation based on the dual dimensions of quantity and quality and comprehensively investigated the relationship between the development of the digital economy and manufacturing innovation. This is a useful supplement to existing research results. (3) We examined the mechanism of the digital economy in promoting innovation from manufacturing enterprises at the macro-regional level and micro-enterprise levels, respectively, by more comprehensively tapping the “black box” of the digital economy and determining innovation dividends. (4) The heterogeneity of the enterprise samples with different growth stages, property rights, and technology endowments is further discussed to provide targeted suggestions for comprehensively stimulating the innovation vitality of the digital economy.
The rest of this article is arranged as follows:
Section 2 summarizes the relevant literature and puts forward the research hypotheses;
Section 3 shows the research design, including sample selection and data sources, variable descriptions and measurements, empirical models, and descriptive statistics;
Section 4 provides the main regression results and discussion; and
Section 5 expounds the practical implications and research limitations of this study.
4. Empirical Testing
4.1. Distribution Characteristics of Regional Digital Economy
Figure 2 shows a three-dimensional scatter plot of the total index and sub-index of the digital economy, which more intuitively shows the distribution characteristics of inter-regional digital economy development. First, the total index shows a clear ladder-like distribution characteristic of decreasing from the eastern coastal areas to the western inland areas, which is generally consistent with the objective law of the current development of China’s digital economy. Among them, Guangdong, Chongqing, Shanghai, Zhejiang, Hunan, Anhui, Beijing, and Guizhou are in the first echelon, which has obvious advantages in developing the digital economy. Specifically, Guangdong, Shanghai, and Zhejiang are important ‘growth poles’ for the development of digital economy in the eastern coastal areas. Chongqing and Hunan are the digital engines of the western and central regions, respectively. The seven provinces of Jilin, Shaanxi, Inner Mongolia, Xinjiang, Qinghai, Ningxia, and Heilongjiang belong to the third echelon, and the development stage of the digital economy in these regions is relatively lagging. Specifically, in addition to Jilin and Heilongjiang, other provinces belong to the western region. In addition, the remaining 15 provinces are in the catch-up stage and belong to the second echelon. Second, there are differences in the development speed of the sub-index. The development level of digital infrastructure is relatively backward, followed by data elements, and the development level of digital technology is the highest.
4.2. Gray Correlation Coefficient Analysis
Before the regression analysis, we first constructed a gray correlation degree model to preliminarily explore whether there was a correlation between
DE and
Quantity and
Quality and the strength of the effect. In exploring the degree of correlation among factors, a gray correlation degree model can compare and obtain the changing trend of data series among multiple factors [
62]. The specific steps are as follows:
First, calculate the difference sequence and maximum and minimum difference.
where Δi(k) is the difference sequence, M is the maximum difference, m is the minimum difference, and x0 ′(k) and xi′ (k) are the sequence of the coupling coordination degree and the sequence of influencing factors after standardization, respectively.
The second step is to calculate the correlation coefficient and gray correlation degree.
where ξi (k) is the correlation coefficient; ρ is the resolution coefficient; and ri is the gray correlation coefficient, and its value range is [0, 1].
Figure 3 shows a line chart of the gray correlation coefficients of
DE,
Data,
DT,
DI, and
Quantity. The correlation between
DE,
Data,
DT,
DI, and
Quantity is consistent but generally shows a fluctuating upward trend. In addition, from 2012 to 2022, the average gray correlation coefficient between
DE and
Quantity was 0.68. The average gray correlation coefficient between the other three sub-indicators and
Quantity was also greater than 0.65. That is to say, there is a positive correlation between
DE and
Quantity.
Figure 4 shows a line chart of the gray correlation coefficients of
DE,
Data,
DT,
DI, and
Quality, respectively. The correlation between
DE,
Data,
DT,
DI, and
Quality also has strong consistency and generally presents a “w” type of development law. In addition, from 2012 to 2022, the average gray correlation coefficient between
DE and
Quality was 0.71. The average gray correlation coefficient between the other three sub-indicators and
Quality was also close to 0.7. Thus, the correlation between
DE and
Quality is more significant.
4.3. Benchmark Regression Results
Table 3 shows the benchmark regression results of the digital economy’s impact on manufacturing firm innovation. In order to exclude the influence of other potential factors, we controlled for time and industry effects, respectively. Columns (1)–(4), respectively, present the estimated results of the impact of
DE,
Data,
DT, and
DI on
Quantity. Columns (5)–(8), respectively, present the estimated results of the impact of
DE,
Data,
DT, and
DI on
Quality. Except for model (3), all other variables related to the digital economy are significantly positive at the 1% level. In particular, the digital economy’s role in promoting the quality of innovation is more obvious. Under the constraints of the dual goals of “high-quality development” and “China creation”, China is undergoing a transformation from “large-scale” innovation to “high-quality” innovation. In particular, the application of digital technology has increased the survival pressure of manufacturing enterprises. In order to ensure their own competitive advantage, enterprises are more willing to carry out breakthrough innovation. The increasingly fierce market competition has increased the concentration of R&D elements of high technological innovation, which has a more significant role in promoting the quality of innovation. According to the above results, H1, H1a, H1b, and H1c are verified.
4.4. Robustness Test and Endogenous Treatment
In order to ensure the reliability of the conclusions, a series of robustness tests were conducted, the results of which are shown in
Table 4. First, considering that compared with other manufacturing enterprises, digital technology-related industries have obvious platform advantages and are more likely to absorb the innovation dividends generated by the digital economy. In order to avoid the interference of the reverse causality problem on the research results, we eliminated the samples of computer, communication, and other electronic equipment manufacturers and re-performed the regression analysis. The regression results are shown in columns (1) and (2). The coefficient of the core explanatory variable is significantly positive, proving that the conclusion is robust. Second, considering that the promotion effect of the digital economy on enterprise innovation may be sustainable, we used the lag term of the digital economy to regress again. The results are shown in columns (3)–(6). We found that the digital economy lagging one period has a positive impact on the quantity of enterprise innovation, but it does not pass the significance test. In contrast, the elasticity coefficient of the digital economy on innovation quality with a three-period lag is still significantly positive. This suggests that the digital economy drives firms’ innovation quality more persistently. Meanwhile, the regression results again prove that the conclusion is robust. Finally, the 19th National Congress of the Communist Party of China (CPC), as well as the 2017 government work report, emphasized the importance of developing the digital economy. In order to avoid policy-oriented errors in the regression results, which may lead to a “false enhancement” of firms’ innovative capacity, we eliminated the data from 2017 and 2018 and conducted regression analysis again. The results are shown in columns (7) and (8). The regression coefficient of
DE is still significantly positive.
There may be reverse causality between the explanatory variable and the explained variable, which affects the robustness of the conclusion. Therefore, we used the instrumental variable method to re-estimate. Based on previous studies, we learned that many scholars use hysteresis as an instrumental variable [
63,
64]. Therefore, this study used the lag term of
DE as
iv to deal with the endogeneity problem. In addition, the correlation test of instrumental variables showed that
iv did not have the problems of weak instrumental variables and insufficient recognition, which further indicated the feasibility of the instrumental variables.
Table 5 shows the results of the regression of the instrumental variables. After considering endogeneity, the promoting effect of
DE on
Quantity and
Quality also passed the significance test.
4.5. Heterogeneity Analysis
The different situations of the enterprises themselves will have an impact on the effect of the digital economy in promoting manufacturing innovation. In order to explore the phased characteristics of the digital economy in promoting manufacturing innovation, this study included a heterogeneity test regarding three aspects: the enterprise development stage, property right nature, and technology endowment type.
4.5.1. Regressions Based on Groups of Enterprises at Different Stages of Development
In order to investigate whether the innovation-driven effect of the digital economy is different for enterprises at different stages of development, we referred to the research by Dickinson et al. and used the cash flow model to divide the enterprises’ life cycle into three stages: growth, maturity, and decline [
65].
Table 6 shows the regression results. Columns (1)–(3) present the regression results of
DE on
Quantity using enterprise samples in the growth, maturity, and decline stages, respectively. Columns (4)–(6) present the regression results of
DE on
Quality using enterprise samples in the growth, maturity, and decline stages, respectively. When comparing the
DE regression coefficient,
DE has an innovation-driven effect on
Quantity and
Quality at different stages of development. Regarding
Quantity,
DE only has a significant driving effect on the enterprises in the growth stage. In terms of
Quality,
DE has a significant driving effect on enterprises in the growth and the decline stages. Generally, enterprises in the growth stage often have strong innovation ability and creative potential. However, the demand for capital is also strong, and the innovation process often faces serious financing constraints. Overall, the digital transformation of enterprises and the wide application of digital technology can help enterprises broaden financing channels and improve the efficiency of capital allocation. This provides an effective way for enterprises to solve the “financing dilemma” and further stimulate their innovation vitality.
4.5.2. Regression Analysis Based on Enterprise with Different Property Rights
We divided the samples into non-state-owned and state-owned enterprises and further explored whether there were differences in the innovation-driven effects of the digital economy on enterprises with different property rights. Columns (1) and (2) present the regression results of
DE on
Quantity in non-state-owned and state-owned enterprise samples, respectively.
Table 7 shows the regression results. Columns (3) and (4) present the regression results of
DE on
Quality in non-state-owned and state-owned enterprise samples, respectively. According to model (2), the elasticity coefficient of
DE is 0.334 and passes the significance test of 1%; that is,
DE has a significant promoting effect on the
Quantity of state-owned enterprises. According to the results of models (3) and (4), the elasticity coefficient of
DE to
Quality is significantly positive in the samples of state-owned and non-state-owned enterprises. However,
DE has a stronger innovation-driven effect on state-owned enterprises.
4.5.3. Regression Analysis Based on Different Technology Endowment Enterprises
We divided the enterprise samples into advanced manufacturing and general manufacturing enterprises and analyzed whether the innovation-driven effect of the digital economy on enterprises with different technology endowments is different [
66]. The regression results are shown in
Table 8. Columns (1) and (2) present the regression results of
DE on
Quantity in general manufacturing and advanced manufacturing samples, respectively. Columns (3) and (4) present the regression results of
DE on
Quality in general manufacturing and advanced manufacturing samples, respectively. According to the results, regardless of
Quantity or
Quality, the digital economy only has a significant innovation-driving effect on the sample of advanced manufacturing enterprises. As the most innovative and fruitful field in the manufacturing industry, advanced manufacturing enterprises urgently need to realize the extensive application and iterative upgrading of advanced and cutting-edge technologies. The innovation dividend introduced by the digital economy is the core engine to promote the innovation and development of advanced manufacturing enterprises at this stage.
4.6. Intermediary Mechanism Tests
Table 9 shows the regression results of the macro-level mechanisms of action. Models (1) and (2) indicate the regression results of R&D personnel allocation as a mechanism variable. The cross-term between
L and the
DE in column (1) is 0.183 but does not pass the significance test. In column (2), the cross-term elastic coefficient is 0.984, which passes the 1% significance test. Models (3) and (4) show the regression results of R&D fund allocation as a mechanism variable. In column (3), the crossover between
K and
DE is 0.089 but does not pass the significance test. In column (4), the cross-term elastic coefficient is 0.371, which passes the 1% significance test. Obviously,
DE can improve
Quality through the effective allocation of R&D personnel and R&D funds. In summary, H2a and H2b are verified.
Table 10 shows the regression results of the mechanism of action at the micro-level. Columns (1) and (2) show the regression results of the factor-integration degree as a mechanism variable. In column (1), the cross-term between
FI and
DE is 0.022 and passes the 5% significance test. In column (2), the cross-term elastic coefficient is 0.108, which passes the 1% significance test. The factor integration effect is an effective path for
DE to improve
Quantity and
Quality. Columns (3) and (4) show the regression results of the information optimization ability as a mechanism variable. In column (3), the cross-term between
IO and
DE is 0.031 but does not pass the significance test. In column (4), the cross-term elastic coefficient is 0.04, which passes the 10% significance test. Overall,
DE can promote
Quality by improving information transparency. According to the above results, H3a and H3b are verified.
4.7. Threshold Regression Results
This study analyzed the direct and indirect effects of the digital economy on the innovation of manufacturing enterprises. However, whether the digital economy can fully release the innovation dividend is closely related to the development level of its internal subsystems. Therefore, we used data elements, digital technologies, and digital infrastructure as threshold variables to further identify the nonlinear effects of the digital economy on innovation in manufacturing firms.
Table 11 shows the threshold existence test results. After repeated sampling 300 times through bootstrapping, the results show that when
Quanlity is used as the explained variable,
Data,
DT, and
DI all pass the single-threshold test. When
Quality is the explained variable,
DT passes the double threshold test. On this basis, we used the threshold regression model for testing, and the results are shown in
Table 12.
Column (1) shows the regression results of using data elements as threshold variables. With the expansion of the scale of data elements, the promotion effect of DE on Quantity shows a nonlinear law of marginal decline. The results of column (2) show a significant inhibitory effect of DE on Quantity before the level of digital technology development crosses the threshold value. When DT crosses the threshold value, DE still has an inhibitory effect on Quantity, but it does not pass the significance test. Column (3) shows the regression results of digital infrastructure as a threshold variable. With the gradual improvement in the overall layout of digital infrastructure, the promotion effect of DE on Quantity also shows a nonlinear characteristic of marginal decline. Column (4) shows the regression result of digital technology as a threshold variable. As the development of digital technology becomes more mature, DT shows a marginal increasing nonlinear effect on Quality. Furthermore, the elasticity coefficient of DE is significantly positive when DT crosses the first threshold.
4.8. Discussion
Manufacturing innovation is the core layout of the digital economy era. As a product of the deep integration of the new generation of information technology and economic society, the digital economy can provide a strong enabling force for the manufacturing industry and help it achieve high-quality development. Based on the background of the booming digital economy, this study deeply explored the “digital dividend” absorbed in the innovation and development of China’s manufacturing industry and its action mechanism. This helps to fully release the potential energy of innovation based on a comprehensive understanding of the characteristics of digital economy development. In fact, many studies have confirmed the positive impact of the digital economy on manufacturing innovation from different perspectives, such as open innovation [
67], green innovation [
68], technological innovation efficiency [
69], and business model innovation [
70]. However, there is a relative lack of research on the impact of the digital economy on the quantity and quality of innovation in manufacturing enterprises. This study aimed to further enrich the driving force system of manufacturing innovation.
First, we used text analysis to measure the comprehensive development index of the digital economy. On this basis, the regional distribution characteristics of the digital economy were analyzed. The overall performance is a ladder-like distribution that decreases from the east to the west, which is highly consistent with the objective law of the current development of China ‘s digital economy. It is worth noting that among the top five provinces in the digital economy composite index, Chongqing and Hunan belong to the western and central regions, respectively. In 2022, the multi-sectoral joint layout of the “East Number West Calculation“ project aimed to build a national integrated computing power network and realized the dynamic and coordinated development of the digital economy in the regions. However, overall, the western region is still in a backward position, which should attract the attention of local government departments.
Second, the empirical results show that
DE,
Data,
DT, and
DI can promote
Quantity and
Quality. However, the driving effect on
Quality is more significant. Some scholars have also studied the impact of the digital economy on manufacturing innovation from the perspectives of
Data,
DT, and
DI. The market-oriented allocation of data elements can expand the innovation boundary of enterprises, thus having a positive impact on innovation and development. The use of digital technology can significantly improve the productivity of employees [
71]. Automating repetitive tasks and implementing digital collaboration tools allow staff to focus on higher-value activities, thus stimulating innovation and creativity within the organization [
72]. The digital infrastructure allows for real-time data sharing between regions. Innovators across regions can access the latest progress of product innovation in time through data sharing, thus accelerating the innovation process [
33].
Finally, the results of threshold regression are also worthy of further consideration. With the growth of the scale of data elements, the innovation-driven effect of the digital economy is characterized by marginal diminishing. At present, data elements have become an important source of value creation that cannot be ignored, but they face great challenges in data confirmation and accounting. Therefore, it is necessary to further improve the digital regulatory system and remove the evolutionary obstacles of the digital economy. With the development of digital technology, the digital economy has different effects on Quantity and Quality. This result shows that digital technology can amplify the role of the digital economy in promoting innovation quality. Therefore, enterprises should seize digital opportunities and prioritize innovation quality. When digital infrastructure is used as a threshold variable, the impact of the digital economy on Quantity also shows a marginally decreasing promotion effect. In recent years, China has been committed to promoting the planning and deployment of digital infrastructure. However, in this process, different regions should correspondingly improve the level of digital infrastructure construction according to the development stage of their digital economy in order to fully ensure that the scale of digital infrastructure can support the orderly development of the digital economy.
5. Conclusions and Policy Implications
5.1. Main Conclusions
This study took Shanghai and Shenzhen A-share manufacturing enterprises from 2012 to 2022 as research samples and examined the impact of the digital economy on the innovation of manufacturing enterprises from two aspects: Quantity and Quality. The empirical analysis was based on the logic of the mechanism of action–nonlinear action’s direct influence via heterogeneity analysis. The main conclusions are as follows.
First, as an important component of the digital economy, data elements, digital technology and digital infrastructure all contribute to manufacturing innovation. Specifically, data elements play the most significant role in driving innovation (H1, H1a–H1c are supported).
Second, there are significant differences in the release of digital economy innovation dividends among enterprises at different growth stages and with different property rights and technology endowments.
Third, the mechanism test results at the macro-regional level show that the rational allocation of R&D personnel and R&D funds plays an intermediary role between DE and Quality (H2a and H2b are supported). The results of mechanism testing at the micro-enterprise level show that the factor-combination effect is an effective path for DE to improve Quantity and Quality (H3a is supported). The information-optimization effect plays an intermediary role between DE and Quality (H3b is supported).
Finally, the impact of DE on manufacturing innovation shows nonlinear characteristics with the change in the internal composition. Specifically, with the development of Data, DT, and DI, the impact of DE on Quantity has a single-threshold effect. However, when DT is used as a threshold variable, DE has a double-threshold effect on Quality.
5.2. Policy Implications
Based on these conclusions, the following practical insights are offered.
First, government departments should fully recognize the importance of the integrated development of the digital economy and the real economy. In particular, enterprises should focus on grasping the innovation force of “data elements + digital technology + digital infrastructure” formed in the process of digital economy development. On the one hand, enterprises should use real digital technology to develop a closer coupling relationship in the “virtual” space; form a multi-chain collaborative management model, such as organization, logistics, and information chains; and realize the integration and unification of innovative resources in the “data and data dialog” environment. On the other hand, local governments should promote the digital transformation of traditional industries based on policy guidance. They should also actively deploy high-end CNC machine tools, industrial robots, intelligent sensing and control, and other intelligent manufacturing equipment to help enterprises achieve digital intelligent upgrading. Lastly, they should form an intelligent manufacturing system composed of a digital workshop, intelligent production lines, flexible manufacturing, etc., and realize technological iteration and upgrading in the production process.
Second, enterprises should pay attention to the channel effect of the digital economy to drive innovation and development. Generally, the core of the mechanism test, whether at the macro-regional level or the micro-enterprise level, is the expansion of the enterprise innovation boundary. From the perspective of external environment, we should standardize the circulation mechanism of R&D personnel and R&D capital and break down local protection and regional barriers. We should also build a national unified market for the flow of innovation factors and fully realize the important role of the market in the effective allocation of innovation factors. From the perspective of internal development, enterprises should promote the exchange and flow of digital information based on the strong externality of data elements and reduce information asymmetry so as to enhance the elasticity of collaborative innovation networks and activate the innovation potential of vulnerable SMEs. It is necessary to speed up the digitization process of traditional factors, encourage the diversified development of different production factor combinations, and achieve “twice the result with half the effort” in enterprise innovation through factor-integration and information-optimization effects at the micro-level.
Third, the government should adapt to local conditions and provide differentiated policy support for developing the digital economy. Due to the differences in strategic planning, industry attributes, and technology endowments of different companies, the innovation-driven effects of the digital economy are also different. Therefore, the government should formulate multi-level and targeted support policies in combination with the specific situation of the enterprise’s business model and development potential. For example, the government can increase support for companies that are slow to undergo digital transformation and are less competitive. This can avoid the phenomenon of “innovation islands” caused by the polarization effect of the “digital divide”. In addition, enterprises should fully consider that the release of innovation dividends in the digital economy is a dynamic and complex process and should cultivate new growth poles based on consolidating existing competitive advantages to avoid homogeneous competition.
5.3. Limitations and Future Research
There are still some limitations in this study, which should be fully considered in future research. Firstly, the feature thesaurus needs to be further refined. Based on the internal composition, this study constructed a digital economy feature lexicon with three sub-dimensions—data element, digital technology, and digital infrastructure—considering that a good and orderly institutional environment has an important impact on the sustainable development of the digital economy. Therefore, we will choose the institutional environment as the fourth sub-index to further improve the digital economy feature lexicon in order to obtain a more accurate and reasonable digital economy comprehensive development index. Secondly, innovation is a dynamic and complex process. Based on the perspective of output, this study examined the effect of the digital economy in empowering manufacturing innovation. However, the impact of the digital economy on the innovation process of enterprises is also worth studying. Therefore, in future research, we will examine the actual effects of the digital economy on the three innovation stages of enterprise technology research and development, achievement transformation, and technology diffusion. Finally, the spatial effect is an important direction for further research. Against the background of the integration and development of the digital technology and the real economy, the rise in network virtual space has greatly expanded the concept and connotation of “space”, weakening the constraints of the traditional geographical distance on innovation activities to a certain extent. Considering the spatial imbalance of the development level of the regional digital economy, we will use a spatial econometric model to further supplement the spatial spillover effect of the digital economy regarding innovation and development in the future.
