The Mechanism of Digital Business Environment Affecting the Sustainable Development of Enterprises

Abstract

The role of the digital business environment (DBE) in the sustainable development of enterprises (SDE) has received extensive attention. This study aims to discover what factors of DBE affect SDE. The empirical results based on China’s inter-provincial panel data from 2011 to 2021 show that DBE has a dual impact on the quantity and quality of enterprises from five dimensions, digital infrastructure, digital industrial environment, digital R&D support, digital talent supply, and digital market demand, among which the digital industrial environment, digital R&D support, and digital talent supply are more conducive to enterprise sustainability. The digital industry environment, digital R&D support and digital talent supply are more conducive to improving enterprise development quality. The results of the heterogeneity analysis further show that there is a significant difference in the regional effect of DBE on SDE. Enterprises in eastern, central, and western China show a tendency to differentiate the demand for different factor conditions, such as technology, industry, and talent, in the process of development. The research in this study provides new theoretical perspectives and empirical evidence for optimising the business environment and achieving SDE.

1. Introduction

In the context of sustainable economic development, the role of the business environment in enhancing the competitiveness and promoting the efficient development of enterprises has been highly valued and has taken on new meaning with the strengthening of digital technology. In July 2021, Chinese President Xi Jinping, in his speech at the Informal Meeting of APEC Leaders, explicitly proposed the concept of DBE, and pointed out that DBE, adhering to the dividends of information technology development, has a series of natural advantages, including ‘openness, fairness, and discrimination’, representing a new direction in the development of business environment.

DBE is new in the academic world. The impact of the mechanism on sustainable development is still not clear. Early studies tended to focus only on the role of hardware infrastructure for investment attraction. As research has deepened, the impact of soft elements of the business environment, such as institutions, on enterprises has also received more and more attention [1]. From the level of influence mechanism, the business environment affects the development of enterprises in two main ways: the internal governance level and the external business environment level.

At the level of internal corporate governance, the most direct effect of the business environment on enterprises is shown by a better business environment effectively extending the time that entrepreneurs engage in economic activities and improving corporate governance performance [2]. Evidence based on empirical analyses of listed companies shows that the impact of entrepreneurs on enterprise total factor productivity also exists through the ‘threshold effect’ of the business environment; that is, entrepreneurs can have a positive effect on enterprise total factor productivity only when they are combined with certain conditions of the business environment.

At the level of the external business environment, on the one hand, rent-seeking as informal compensation means and ‘relationship capital’, to a certain extent, have a distorted positive impact on the allocation of market resources. The optimisation of the business environment can significantly affect the relationship between enterprise rent seeking and the allocation of market resources, and has a positive effect on the elimination of rent-seeking influence and the promotion of the optimal allocation of resources [3], which helps to attenuate the negative effect of rent-seeking corruption on enterprise R&D investment [4,5]. On the other hand, the stability of the business environment also affects enterprise operations, and the uncertainty of the business environment may increase the unproductive expenditures and tax expenditures of the enterprise, thus crowding out the productive resources of private enterprises and reducing the vitality of private enterprises [6].

However, the core subjects of economic activities, whether DBE will effectively impact the development of enterprises, how to define the connotation of DBE corresponding to enterprises, and which elements of DBE will play a significant role in promoting SDE still need to be explored in depth.

In particular, the shortcoming of existing research is that researchers focus more on the construction of the evaluation system of DBE, adhering to a kind of ‘evaluation thinking’ rather than ‘correlation thinking’, and ‘theoretical thinking’ rather than ‘empirical thinking’. There is less involvement in the study of the relationship between the elements of DBE and the regional economy, enterprise development, and the construction of indicators, which also considers the elements of facilities and policies for the construction of DBE, necessary to build a specialised economic environment and industrial elements.

This study aims to promote SDE, relying on relevant theories and existing research, and defines DBE in five aspects: digital infrastructure, digital industrial environment, digital R&D capability, digital talent supply, and digital market demand. We use the fixed effects model and rely on panel data to empirically analyse the impact of DBE on SDE. This study will argue that DBE has a significant positive effect on SDE and explore the influence mechanism of DBE on SDE from both quantitative and qualitative perspectives. This will help deepen the theoretical understanding of DBE, improve the efficiency of DBE construction, and have more important reference significance to further promote the digital transformation of government governance and optimise the business environment.

2. Literature Review

2.1. Business Environment and Sustainable Development of Enterprises

The relationship between the business environment and enterprise development has been widely studied by the academic community. The most significant impact of the business environment on enterprises is reflected in the reduction of institutional costs. Relevant research shows that in recent years, in the process of constructing China’s rule of law government, institutional functions, organisational leadership, institutional construction, administrative decision-making, administrative law enforcement, supervision and accountability, information disclosure, dispute resolution, and other aspects of the reform, the institutional costs of enterprises were significantly reduced [7], releasing the energy efficiency of enterprise production. In particular, the optimisation of key indicators of the business environment, such as the degree of regional judicial justice and the level of property rights protection, has led to a better enhancement of enterprises‘ innovation capacity [8], enhanced product differentiation, dissolved excess capacity [9], stimulated the emergence of specialised and new small and medium-sized enterprises [8], and facilitated the enhancement of enterprises’ competitiveness, which at the same time can attract more labour [10] and significantly increase the share of enterprises’ labour income [11] and further promote common wealth [12].

In the process of the operation of foreign trade-oriented enterprises, the business environment system in customs and trade supervision efficiency, administrative approval efficiency, infrastructure bar, financing convenience, high-quality labour and other factors can significantly impede or promote the enhancement of enterprise export intensity, which in turn affects the efficiency of enterprise foreign trade activities [13]. The optimisation of DBE significantly enhances the competitiveness of the manufacturing export scale, effectively boosts the competitiveness of manufacturing export quality, and through the cost-saving effect and innovation enhancement effect, significantly promotes the competitiveness of manufacturing export ‘quantity and quality’ [9]. In recent years, the outward investment activities of Chinese enterprises also reflect the tendency of relevant enterprises in the business environment. The outward foreign direct investment of 49 representative countries along the Belt and Road shows that it is more convenient for the host country to set up a business and issue construction permits, which has a positive impact on the OFDI of Chinese enterprises [14].

2.2. New Opportunities and Challenges of the Business Environment

At the same time, the transformation of public management thinking also affects the change of governance thinking related to the business environment to a certain extent. From a broader global perspective, in the mid-to-late 1970s, Western governments were generally faced with economic, financial and trust crises. In this regard, the New School of Public Management put forward the slogan of creating a ‘competitive government’ to rectify the ‘closeness’ and ‘fragmentation’ of traditional public administration. It advocated that the public should be guided by social values and the actual needs of multiple subjects, and that the public should be provided with seamless and holistic services by eliminating the segregated and segmented administrative environment through vertical and horizontal coordination and integration of governmental organisations and departments [15,16,17]. Given the powerful convergence properties and synergistic capabilities of information technology, it even provides an ideal tool for this purpose by facilitating low-carbon transformation in a green-driven context [18] and, to a certain extent, weakening the negative impact of the ‘resource curse’ [19]. In this context, the construction of DBE has become an inevitable choice for governments to continuously optimise the business environment and establish an endogenous rigid demand for enterprises to improve quality and efficiency; thus, it has received extensive attention from governments and academics [20,21,22]. Meanwhile, in order to better measure the level of digital business, the World Bank designed the Digital Business Indicator (DBI) system in 2017, which contains a number of indicators, such as digital infrastructure, network connectivity, etc. The DBI has established a synergistic institutional framework among different countries [23]. The disadvantage of DBI is that a few cities in one country are selected, and the evaluation of the indicators is limited to the selection of information technology. The selection of indicators is limited to information infrastructure construction. With the deepening of research, more DBE evaluation systems suitable for Chinese characteristics have been established, and various factors, such as the government regulatory environment, the market environment, and the innovation environment, have been considered comprehensively [24], which provide valuable Chinese solutions for exploring the digital business model and the world’s business environment problems [2,25].

2.3. Comments

The potential marginal contributions of this study are primarily reflected in the following three dimensions:

• Advancing research on the influencing factors of DBE: The current DBE indicator system encompasses multiple factors, such as the government regulatory environment, the market environment, and the innovation environment. This study, however, has established an indicator system covering five dimensions—digital infrastructure, digital industrial environment, digital R&D support, digital talent supply, and digital market demand—providing a more comprehensive assessment of China’s DBE.
• Innovating research methodology: Considering that existing studies have overlooked the significant role of the degree of marketization in various Chinese cities in SDE, this study selects the degree of marketization (mai) as the control variable to more accurately analyze the impact of DBE on SDE.
• Expanding research content: Existing enterprises often explain their sustainable development from micro factors, such as profit margins, or use macro indicators, such as ESG. These indicators have difficulty in reflecting the comprehensive influence mechanism of DBE on SDE This article analyses the comprehensive mechanism of DBE on SDE from the two levels of enterprise quantity and enterprise quality, and through the dual macro- and micro-perspectives.

Overall, this study not only establishes a novel theoretical basis of DBE but also introduces fresh perspectives into SDE research by framing it in the context of the relationship between total quantity and quality. These findings deepen the understanding of the intrinsic linkages between DBE and SDE, offering theoretical support and empirical evidence for policy making and corporate practices, aiming to achieve the synergistic development of the digital economy and sustainable development.

3. Theoretical Mechanisms and Empirical Hypotheses on the Impact of DBE on SDE

DBE has both similarities and significant differences with the traditional business environment. In essence, regardless of the type of business environment, it is a product of the integration of technology, facilities and institutions. From the perspective of the traditional business environment, the role of the system is more prominent, and technology and facilities play a fundamental role in supporting the entire business environment. At the same time, it is precisely for this reason that the main body of the business environment is relatively limited, and the government is the absolute leader and builder.

The emergence of DBE has greatly changed this situation. On the supply side, in order to stimulate the enabling capacity of technology for the business environment, especially information technology, the main body of the supply of information technology has also been given more responsibility as a new subject involved in the construction of the business environment to provide a higher quality and a wider range of specialised services. For example, digital finance promotes enterprise financing by improving the accessibility and efficiency of financial services [26]. On the demand side, due to the characteristics of the node-based information technology system, information or institutional obstruction at a node will constrain the normal operation of the entire system, and DBE not only requires the government to actively participate in the construction of digitisation, but also requires enterprises to comprehensively carry out digital transformation, so as to form an effective internal and external synergy. In addition, the characteristics of information technology, such as a high level of difficulty, large investment and a long cycle, also determine that the construction of DBE cannot rely solely on the government’s investment, but should fully stimulate the market’s effective supply and effective demand, and rely on industrial support and market-oriented operations to achieve long-term development.

In short, compared with the traditional business environment, the construction of DBE requires the deep integration between the elements of technology, facilities and systems and is highly dependent on the extensive cooperation between the government, the market, research and development institutions, and other multi-disciplinary subjects [27]. This also means that, compared with the traditional business environment, DBE should pay more attention to the support of the industrial perspective to promote the transformation of the traditional business environment by the development of industrial technology and to promote the sustainability of DBE by the expansion of industrial demand.

Therefore, this study relies on the theory of collaborative governance, and, at the same time, from the industrial perspective, defines the elements of DBE and tries to supplement the insufficiency of the research of the institutional school and the facility school of the business environment.

This study argues that under the deep integration of information technology facilities, policies and institutions, and industrial conditions, DBE affects SDE in terms of both enterprise quantity and enterprise quality through the dimensions of digital infrastructure, digital industrial environment, digital research and development support, digital talent supply, and digital market demand.

Based on these considerations, we propose the following hypotheses:

Hypothesis 1. 

DBE significantly promotes SDE.

Hypothesis 2. 

DBE affects SDE at both quantitative and qualitative levels.

4. Research Design

4.1. Methods

This study focuses on the impact of DBE on SDE. Based on the hypotheses presented above, the model is set up as follows (see Equation (1)):

$$Y_{it}={\beta }_0+{\beta }_{1}Digf+{\beta }_{2}Digi+{\beta }_{3}Digd+{\beta }_{4}Digh+{\beta }_{5}Digm+Contro{l}_{it}+{\epsilon }_{it}$$

(1)

where $Y_{it}$ is the explanatory variable total number of enterprises in the province, i is the province area, and t is the year. digf, digi, digd, digh, and digm represent the five indicators of DBE, respectively, and are analysed by using the specific second-level indicators as substitutes in the regression process. Control is the control variable.

4.2. Construction of the Indicator System and Selection of Variables

4.2.1. Main Variables and Classification of DBE Indicators

According to the existing literature and the theoretical analyses in the previous article, main variables and the indicator system is now set up in five aspects as follows (see

Table 1. Main variables and classification of DBE indicators.

Tier 1 Indicators	Tier 2 Indicators	Abbreviations	Content
Sustainable Development of Enterprises (SDE)	Enterprise quantity	EQN	The number of enterprise units
	Enterprise quality	EQL	The gross regional product
Digital Infrastructure (Digf)	Use of computers	cpu	Internet users per 100 people
	Websites	web	Number of websites by province
Digital Industrial Environment (Digi)	IT industry	inf	Revenue from software operations
Digital R&D support (Digd)	Science and Technology R&D Support	tec	Percentage of investment in scientific research in government expenditures
Digital talent supply (Digh)	Information labour costs	lab	Average wages of workers in the private sector in the information transmission, software and information technology services sector
	Total information human resources	hums	Number of staff in information transmission, software and information technology services in the private sector
	Overall human capital level	huml	Number of graduates from tertiary education and above as a proportion of the total school population
Digital Market Needs (Digm)	Strength of demand in digital markets	mar	E-commerce sales
control variable (Control)	Marketability	mai	Sub-provincial marketisation index

).

4.2.2. Explained Variable

The explained variable is SDE. At the level of the regional economy, SDE is reflected in the growth of the number of enterprises and the improvement of quality [28]. Based on the relationship between total quantity and quality, the growth of total quantity may not necessarily improve the development scale. Thus, this study also selects the number and quality of enterprises as the explanatory variables to comprehensively reflect SDE. Specifically, the number of enterprise units (EQN) is selected as the indicator of enterprise quantity, and the gross regional product (EQL) is selected as the indicator of enterprise quality to measure the scale of enterprise development.

4.2.3. Explanatory Variables

The explanatory variables are selected by comprehensively drawing on the practices of scholars [29,30,31,32]. In view of the demand for industrialised support for DBE, this study takes into full consideration that the traditional business environment requires strong industrial technological capabilities, specialised talent teams, and strong market demand in the process of transitioning to DBE. The study innovatively adds first-level indicators, such as the digital industrial environment (Digi), the supply of digital talent (Digh), and the demand for the digital market (Digm), in the screening of the identified indicators. Among them, the digital industrial environment is expressed by the output value of the information technology industry (inf). The supply of digital talents needs to comprehensively consider the total number of information technology talents and their wages, and whether other types of human capital have the ability to produce sufficient synergies with them. Thus, three second-level indicators are selected for the cost of information labour (lab), the total number of information human resources (hums), and the overall level of human capital (huml). Digital market demand is represented by e-commerce sales as the strength of digital market demand, because this indicator can reflect the demand status of enterprises and the government for the digital market at the same time, and the demand status is directly related to the transitional impetus of DBE and the sustainability of development.

4.2.4. Control Variables

Since the degree of marketisation of a city also plays an important role in the establishment and development of enterprises, this study selects the degree of marketisation (mai) as a control variable, and the specific indexes are selected from the marketisation indexes of provinces in the Report on China’s Sub-Provincial Marketisation Indexes published by Wang Xiaolu, Hu Lipeng, and Fan Gang on an annual basis. In addition, existing research generally finds that the investment in fixed assets and the industrial structure of the city also affect the development of enterprises; therefore, this study selects the degree of marketisation as a control variable. In addition, existing studies generally find that investment in fixed assets and urban industrial structure also affect the development of enterprises; thus, this study uses the output value of the tertiary industry divided by the output value of the secondary industry in the China Statistical Yearbook to represent the level of industrial structure of a city (str), and uses the investment in fixed assets of the whole society (inv) as the control variable [33].

4.3. Sample Selection and Data Source

In this study, the 31 provinces in mainland China from 2011 to 2021 were used as the research sample, and the raw data, obtained from the China Statistical Yearbook, the China Internet Development Survey Statistical Report, the China Regional Economic Statistical Yearbook, the China Sub-Provincial Marketisation Index Report and the CSMAR database, and missing data are systematically filtered and cleaned to ensure accuracy and reliability.

5. Empirical Tests

5.1. Regression Analysis

To validate Hypothesis 1 and Hypothesis 2, this study separately regresses the number of enterprises and enterprise quality, and the regression results are detailed in columns (1) and (2) of

Table 2. Empirical analysis of the impact of DBE on SDE.

	(1)	(2)	(3)	(4)
VARIABLES	lnEQN	lnEQL	lnEQNc	lnEQLc
lncpu	0.188 ***	−0.0112	0.127 **	0.000297
	(0.0614)	(0.0371)	(0.0614)	(0.0402)
lnweb	0.242 ***	0.0325	0.225 ***	0.00680
	(0.0596)	(0.0360)	(0.0596)	(0.0390)
lninf	0.0603 ***	0.0909 ***	0.0459 **	0.0934 ***
	(0.0231)	(0.0140)	(0.0231)	(0.0151)
lntec	0.148 ***	0.159 ***	0.164 ***	0.145 ***
	(0.0408)	(0.0246)	(0.0408)	(0.0267)
lnhuml	0.669 ***	0.293 ***	0.828 ***	0.343 ***
	(0.121)	(0.0732)	(0.121)	(0.0793)
lnlab	0.431 ***	0.271 ***	0.555 ***	0.336 ***
	(0.0849)	(0.0513)	(0.0849)	(0.0556)
lnhums	−0.0692	0.130 ***	−0.0682	0.103 ***
	(0.0426)	(0.0257)	(0.0426)	(0.0279)
lnmar	0.0343 **	0.00712	0.0387 **	−0.00835
	(0.0152)	(0.00917)	(0.0152)	(0.00993)
lnmai	0.0749	0.219 ***	−0.194 *	0.132 *
	(0.117)	(0.0707)	(0.117)	(0.0766)
Constant	9.418 ***	7.266 ***	9.652 ***	7.090 ***
	(0.963)	(0.582)	(0.963)	(0.631)
Observations	330	330	330	330
R-squared	0.921	0.901	0.921	0.883

Note: ***, **, * represent 1 percent, 5 percent and 10 percent levels of significance, respectively.

.

In terms of the impact of DBE on the number of enterprises, the facilitating effect of digital infrastructure represented by computer indicators (cpu) and websites (web) on the growth of the total number of enterprises suggests that good information technology conditions are more conducive to the development and growth of enterprises. Indicators of the digital industrial environment and digital R&D support, including the development of the information technology industry (inf) and government support for science and technology R&D activities (tec), also contribute to the growth of the number of enterprises. In terms of digital talent supply, the overall human capital level of the city (huml) and the cost of information labour (lab) also have a positive and significant effect on the number of enterprises; however, the regression results of the total amount of human resource information (hums) is not significant, which suggests that in order to promote the growth of the number of enterprises in DBE, the accumulation of the total amount of human capital is insufficient. Instead, it is more important to pay attention to the quality of talent in order in order to improve efficiency and service quality. The digital market demand intensity indicator (mar) also contributes.

In terms of the impact of DBE on enterprise quality, the development of the information technology industry (inf), government support for scientific and technological R&D activities (tec), the overall level of human capital in the city (huml), the cost of informational labour (lab), and the total amount of informational human resources (hums) all play a significant role. In contrast to the regression results in column (1), it is no longer enough for enterprises to rely only on investment in facilities to achieve higher operating efficiency; they should also pay more attention to the level of information technology-related labour as well as the cost and rely on the extensive collaboration among the entire city’s information technology industry, the government’s investment in R&D, and the diversity of human resources. The empirical results above confirm Hypothesis 1 and Hypothesis 2.

Considering that the impact of DBE on enterprises has a certain lag, this study regresses the dependent variable with a one-year lag in columns (3) and (4) of Table 2. In the regression results, the indicators are basically consistent with the previous regression results, which, on the one hand, proves the robustness of the regression, and on the other hand, also indicates that the impact of DBE on enterprises has long-term effects.

5.2. Robustness Tests

To validate the robustness of the research findings and rule out the possibility that the conclusions were influenced by other unobservable factors, this study designed and implemented the following test.

To ensure that the estimation results are robust, this study conducts model testing by adding or removing control variables. Since this study considers the dual impact of DBE on the quantity and quality of enterprises at the same time, the two will be tested for robustness in

Table 3. Robustness test for the impact of DBE on enterprise quantity.

	(1)	(2)	(3)	(4)	(5)
VARIABLES	lnEQN	lnEQN	lnEQN	lnEQN	lnEQN
lncpu	0.188 ***	0.209 ***	0.203 ***	0.222 ***	0.226 ***
	(0.0614)	(0.0661)	(0.0622)	(0.0667)	(0.0659)
lnweb	0.242 ***	0.253 ***	0.231 ***	0.241 ***	0.240 ***
	(0.0596)	(0.0610)	(0.0601)	(0.0616)	(0.0614)
lninf	0.0603 ***	0.0579 **	0.0522 **	0.0500 **	0.0493 **
	(0.0231)	(0.0233)	(0.0238)	(0.0240)	(0.0239)
lntec	0.148 ***	0.137 ***	0.127 ***	0.117 **	0.116 **
	(0.0408)	(0.0429)	(0.0436)	(0.0455)	(0.0454)
lnhuml	0.669 ***	0.665 ***	0.635 ***	0.632 ***	0.642 ***
	(0.121)	(0.121)	(0.123)	(0.124)	(0.121)
lnlab	0.431 ***	0.437 ***	0.421 ***	0.427 ***	0.433 ***
	(0.0849)	(0.0852)	(0.0851)	(0.0854)	(0.0836)
lnhums	−0.0692	−0.0647	−0.0788 *	−0.0743 *	−0.0749 *
	(0.0426)	(0.0429)	(0.0430)	(0.0434)	(0.0433)
lnmar	0.0343 **	0.0368 **	0.0330 **	0.0354 **	0.0360 **
	(0.0152)	(0.0155)	(0.0152)	(0.0155)	(0.0154)
lnmai	0.0749	0.0593	0.0603	0.0457
	(0.117)	(0.119)	(0.117)	(0.119)
lnstr		−0.0626		−0.0595	−0.0640
		(0.0757)		(0.0757)	(0.0746)
lninv			0.0577	0.0567	0.0581
			(0.0415)	(0.0415)	(0.0413)
Constant	9.418 ***	9.241 ***	8.837 ***	8.679 ***	8.718 ***
	(0.963)	(0.987)	(1.049)	(1.068)	(1.062)
Observations	330	330	330	330	330
R-squared	0.921	0.921	0.922	0.922	0.922

Note: ***, **, * represent 1 percent, 5 percent and 10 percent levels of significance, respectively.

and

Table 4. Robustness test for the impact of DBE on enterprise quality.

	(1)	(2)	(3)	(4)	(5)
VARIABLES	lnEQL	lnEQL	lnEQL	lnEQL	lnEQL
lncpu	−0.0112	0.0668 *	0.0246	0.0992 ***	0.110 ***
	(0.0371)	(0.0380)	(0.0357)	(0.0363)	(0.0361)
lnweb	0.0325	0.0741 **	0.00499	0.0461	0.0423
	(0.0360)	(0.0351)	(0.0344)	(0.0335)	(0.0336)
lninf	0.0909 ***	0.0815 ***	0.0712 ***	0.0627 ***	0.0607 ***
	(0.0140)	(0.0134)	(0.0136)	(0.0130)	(0.0131)
lntec	0.159 ***	0.116 ***	0.107 ***	0.0666 ***	0.0659 ***
	(0.0246)	(0.0247)	(0.0250)	(0.0247)	(0.0249)
lnhuml	0.293 ***	0.277 ***	0.210 ***	0.197 ***	0.225 ***
	(0.0732)	(0.0698)	(0.0708)	(0.0673)	(0.0660)
lnlab	0.271 ***	0.292 ***	0.248 ***	0.269 ***	0.287 ***
	(0.0513)	(0.0490)	(0.0488)	(0.0465)	(0.0458)
lnhums	0.130 ***	0.148 ***	0.107 ***	0.125 ***	0.123 ***
	(0.0257)	(0.0247)	(0.0247)	(0.0236)	(0.0237)
lnmar	0.00712	0.0170 *	0.00394	0.0136	0.0153 *
	(0.00917)	(0.00891)	(0.00870)	(0.00844)	(0.00843)
lnmai	0.219 ***	0.158 **	0.183 ***	0.126 *
	(0.0707)	(0.0682)	(0.0673)	(0.0647)
lnstr		−0.242 ***		−0.235 ***	−0.247 ***
		(0.0436)		(0.0412)	(0.0409)
lninv			0.139 ***	0.135 ***	0.139 ***
			(0.0238)	(0.0226)	(0.0226)
Constant	7.266 ***	6.580 ***	5.862 ***	5.238 ***	5.344 ***
	(0.582)	(0.568)	(0.601)	(0.581)	(0.582)
Observations	330	330	330	330	330
R-squared	0.901	0.910	0.911	0.920	0.919

Note: ***, **, * represent 1 percent, 5 percent and 10 percent levels of significance, respectively.

, respectively.

After adjusting the indicator of the degree of marketisation (mai) and considering the industrial structure (str) and the investment in society’s fixed assets (inv), the regression results in Table 3 are still robust, which proves the credibility of the results of the empirical analysis. Therefore, the estimated coefficient of the impact of DBE on enterprise quantity remains significantly positive, further confirming the robustness of the research conclusions.

After adjusting the indicator of the degree of marketisation (mai) and considering the industrial structure (str) and the investment in society’s fixed assets (inv), the regression results in Table 4 are still robust, which proves the credibility of the results of the empirical analysis. Therefore, the estimated coefficient of the impact of DBE on enterprise quality remains significantly positive, further confirming the robustness of the research conclusions.

5.3. Heterogeneity Analysis

The impact of DBE on SDE varies across regions and has different characteristics. To investigate this, this study examined the heterogeneity of DBE’s effects on SDE from three perspectives, the eastern, central, and western regions of China. For this reason, this study still takes the number of enterprises and the quality of enterprises as the dependent variables, and adopts a partitioned regression to analyse heterogeneity. The regression analysis results in

Table 5. Zonal effects of the impact of DBE on the number and quality of firms.

	(1)	(2)	(3)	(4)	(5)	(6)
VARIABLES	lnEQN (Eastern Part)	lnEQN (Central Part)	lnEQN (Western Part)	lnEQL (Eastern Part)	lnEQL (Central Part)	lnEQL (Western Part)
lncpu	0.489 ***	0.0351	0.0985	0.684 ***	0.107	0.0502
	(0.119)	(0.127)	(0.0876)	(0.151)	(0.0840)	(0.0712)
lnweb	0.339 ***	0.571 ***	0.388 ***	−0.166 *	0.358 ***	0.261 ***
	(0.0830)	(0.0630)	(0.0870)	(0.0891)	(0.0416)	(0.0619)
lninf	0.113 **	−0.0883 ***	0.0228	0.0615	0.0933 ***	0.0220
	(0.0546)	(0.0261)	(0.0287)	(0.0588)	(0.0172)	(0.0236)
lntec	0.282 ***	0.451 ***	0.140 **	−0.0657	0.0465	0.0687
	(0.0966)	(0.0545)	(0.0655)	(0.113)	(0.0360)	(0.0554)
lnhuml	−0.160	0.133	0.816 ***	−0.903 ***	−0.663 ***	0.347 ***
	(0.277)	(0.193)	(0.150)	(0.153)	(0.127)	(0.115)
lnlab	0.118	0.126	0.265 **	−1.120 ***	−0.182	−0.0686
	(0.139)	(0.195)	(0.128)	(0.170)	(0.128)	(0.106)
lnhums	−0.160 *	−0.123	−0.202 ***	0.339 ***	0.0673	0.343 ***
	(0.0873)	(0.0871)	(0.0764)	(0.101)	(0.0575)	(0.0579)
lnmar	0.0204	0.167 ***	0.0650 ***	0.270 ***	0.0733 ***	0.0283 *
	(0.0470)	(0.0362)	(0.0178)	(0.0671)	(0.0239)	(0.0155)
lnmai	1.330 ***	0.0646	0.157	1.803 ***	0.639 ***	0.0230
	(0.304)	(0.281)	(0.142)	(0.343)	(0.185)	(0.119)
Constant	6.212 ***	11.95 ***	11.98 ***	10.31 ***	6.252 ***	10.74 ***
	(1.916)	(1.900)	(1.386)	(2.269)	(1.254)	(1.168)
Observations	121	88	121	121	88	121
R-squared	0.911	0.921	0.911	0.919	0.918	0.916

Note: ***, **, * represent 1 percent, 5 percent and 10 percent levels of significance, respectively.

show that the impact of DBE on enterprises in the eastern, central, and western regions of China shows more significant heterogeneity, and it is also manifested in the two dimensions of enterprise quantity and enterprise quality.

The reasons behind these differences are as follows: The eastern region of China generally exhibits higher levels of economic development. Enterprises in this region have higher requirements for the external development environment, enabling DBE to more effectively drive SDE. In contrast, the central and western regions have lower levels of economic development, and DBE’s effect on driving SDE is different from the eastern region of China. Considering the large gap between the level of infrastructure construction and economic development between regions in China, this study argues that the impact of DBE on enterprises also varies between regions.

In terms of the number of enterprises, the regression results show that the digital infrastructure, digital industrial environment and digital R&D support in the eastern region have created a favourable external environment for enterprises, which has played a significant role in promoting the growth of the number of enterprises. However, it should also be noted that the role of digital talent supply and digital market demand on the growth of the number of enterprises is not significant. This is due to the fact that DBE, compared with the traditional business environment, has a strong cross-domain spillover effect, and the inputs of local factors may also be transformed into foreign market demand and enterprise development results. The degree of marketisation (mai) is more significant among the control variables, which responds to this speculation to some extent. For the central region, digital infrastructure represented by websites, digital R&D support and digital market demand have a positive and significant impact on the number of enterprises, which indicates that there is still a greater potential for infrastructure and basic R&D inputs to promote the growth of the number of enterprises in the central region. However, the impact of the digital industrial environment is negative, which may be the threshold effect of the industry scale, because the information industry inputs are characterised by high costs and long cycles, and before the formation of scale effects, they may be transformed into the results of the development of the field and the market demand. Because information industry inputs is characterised by high costs and long cycles, before the scale effect is formed, it will instead constitute a larger cost burden on the regional economy or enterprises, and only after a certain threshold is reached can it play a sufficiently positive effect. The situation in the western region is similar to that in the central region, with the difference that the cost of information labour (lab) and the level of overall human capital (huml) also play a positive role in the growth of the number of enterprises, which to a certain extent reflects the weakness of the talent base in the western region as well as the sensitivity to costs in the development process. Total human capital information (hums) is significantly negative, similar to the situation in the digital industry environment in the central region.

In terms of the quality of enterprise development, the intensity of digital market demand (mar) plays a significant role in promoting enterprise development in all three regions, and the degree of marketisation plays a more significant role in the development of enterprises in the eastern and central regions (mai), which further verifies the supportive role of marketisation and industrialisation on DBE as well as enterprise development. From the specific regression situation, the eastern region has a higher demand for specialised human capital, i.e., total information human resources (hums), because of a more solid infrastructure and industrial foundation. It is expected to form a scale effect through the linkage of talent and industry. The central region has a higher demand for industry, and the growth of the information technology industry (INF) can significantly lead to the enhancement of the quality of enterprise development. The impact of talent on enterprise development quality is the most significant in the western region, with the degree of marketisation having the most significant impact on the quality of enterprise development. The most significant impact of talent on the quality of enterprise development in the western region includes not only general talent (huml) but also professional talents (hums) in order to form a good synergy effect and avoid the lack of a certain type of talent caused by the decline in the overall productivity of the enterprise.

6. Discussion and Recommendations

6.1. Research Limitations

This study, leveraging data from the 31 provinces in mainland China from 2011 to 2021 and utilising the fixed effect model, conducts an in-depth exploration of the impact of DBE on SDE, yielding several findings of significant theoretical and practical value. However, it is subject to the following limitations:

(1)

We have taken into account the issue of regional heterogeneity, but the size of enterprises may also have different demands for DBE. Generally speaking, large enterprises have stronger market power and may have less demand for DBE. The market power of small enterprises is relatively weak, and therefore, they may rely more on DBE.

(2)

Furthermore, the size of a city may also have heterogeneous impacts on SDE. The larger the scale of a city and the better the mobility of enterprises, the stronger the influence of DBE on SDE. Similarly, cities are small in scale, enterprises have relatively poor mobility, and the effect of DBE on SDE is relatively weak.

6.2. Policy Implications

First, accelerate the construction of DBE work systems, from digital infrastructure, the digital industrial environment, digital R&D support, digital talent supply, digital market demand and other five aspects of business, to promote the scientific evaluation system to push the digital business work, improving the ability to rise.

Second, the use of differentiated strategies to promote the incremental increase in enterprises and improve the quality of the enterprise, mainly focusing on incremental optimisation of the business environment with market-oriented thinking, from the business. Second, it adopts differentiated strategies to promote enterprise increment and quality improvement, especially focusing on optimising the business environment with market-oriented thinking incrementally, expanding the demand for digital markets from enterprises and the government, and deepening market-oriented reform in the qualitative aspect, starting from the reform of factor marketisation, the reform of the transaction system, and the industrialisation of data, so as to build a high-standard market system.

Finally, it distinguishes between and designs DBE working strategies in different regions and adopts a policy system focusing on R&D-driven, industry-driven, and talent-driven strategies in the eastern, central, and western regions.

7. Conclusions

Based on China’s inter-provincial panel data from 2011 to 2021, this study empirically examines the relationship between DBE and sustainable enterprise development. It is found that DBE affects enterprise development at both the quantitative and qualitative levels. From the perspective of total enterprise growth, digital infrastructure, digital industrial environment, digital R&D support, digital talent supply, and digital market demand all contribute positively to enterprise quantity growth. From the perspective of enterprise development quality, enterprises have higher requirements for DBE in terms of the digital industry environment, digital R&D support, and digital talent supply. They also have certain requirements for the degree of marketisation of the city where they are located, while the role of digital infrastructure in the process of enterprise scale growth and development quality enhancement is less significant in comparison. (See

Table 6. The main influence on the interpretation of the variables.

	SDE	Sustainable Development of Enterprises
DBE		Enterprise Quantity (EQN)	Enterprise Quality (EQL)
Digital Infrastructure (Digf)	cpu	Significant positive	Non-significant
	web	Significant positive	Non-significant
Digital Industrial Environment (Digi)	inf	Significant positive	Significant positive
Digital R&D support (Digd)	tec	Significant positive	Significant positive
Digital talent supply (Digh)	lab	Significant positive	Significant positive
	hums	Non-significant	Significant positive
	huml	Significant positive	Significant positive
Digital Market Needs (Digm)	mar	Significant positive	Non-significant
control variable (Control)	mai	Non-significant	Significant positive

).

Considering regional heterogeneity, it is found that in promoting the growth of the number of enterprises, the eastern region mainly relies on digital infrastructure, digital industrial environment and digital R&D support. The central region mainly relies on digital infrastructure represented by websites, digital R&D support and digital market demand. The western region is similar to the central region, with additional demand for the overall level of human capital in the city and the wage level of specialised talent. The eastern region is similar to the central region. In terms of improving enterprise quality, the eastern region has a higher demand for the total amount of specialised talent (i.e., information human resources), the central region has a higher demand for industry, the growth of the information technology industry can significantly lead to the improvement of the quality of enterprise development, and the western region has the most significant impact of talent on the quality of enterprise development, where talent includes both generalist and specialist talent.