The Role of Business Environment and Digital Government in Mitigating Supply Chain Vulnerability—Evidence from the COVID-19 Shock

Abstract

In recent years, the continuous spread of the COVID-19 epidemic has impacted the supply chain of enterprises. Mitigating the supply chain’s vulnerability has great significance for the survival and development of enterprises. Optimizing the business environment and building a digital government will help improve the external environment for enterprise development. However, its impact on the vulnerability of the enterprise supply chain has yet to be studied. Taking the impact of COVID-19 as an example, this paper uses the survey data of nearly 40,000 enterprises of the National Federation of Industry and Commerce in 2020 and “10,000 private enterprises evaluating the business environment”, to conduct systematic empirical research and fill the research gap in this area. The study indicates that the business environment and digital government can significantly mitigate the impact of COVID-19 on the supply chain. This conclusion is still valid after a series of robustness tests. Mechanism analysis demonstrates that the business environment and digital government can prompt the government to introduce effective mitigation measures promptly, better guarantee production factors and logistics, and thus improve the vulnerability of the enterprise supply chain. This study deepens our understanding of the economic outcome of the business environment and digital government and also sheds new light on supply chain management.

1. Introduction

COVID-19 has had a severe impact on enterprises. The supply chain of enterprises has been significantly negatively affected by both supply and demand [1]. However, the damage degree of different enterprises in the impact is quite different. Some enterprises can rapidly recover from the crisis and maintain the stability of the supply chain, while others risk supply chain interruption [2]. The characteristics of enterprises that are seriously exposed to risks and difficulties to recover quickly are considered the supply chain’s vulnerability [3]. Ensuring the supply chain’s stability is crucial for enterprises’ survival and development. If the enterprise can effectively identify and manage the weakness of the supply chain in advance, it can effectively reduce the possibility of supply chain disruption and subsequent impact [4]. Therefore, academia has conducted in-depth research on the supply chain’s vulnerability and achieved a series of valuable results. Among them, the impact of the external environment on the supply chain has produced a series of meaningful research results, but there is still room for further discussion [5].

The business environment is the comprehensive external environment for the development of enterprises and an essential component of the soft power of national and regional economic development. Optimizing the business environment is also crucial to deepening reform and attracting investment from local governments in recent years. The government work report of the State Council in 2022 specifically pointed out that we should “deepen reform and expand opening up, and continue to improve the business environment.” The government has introduced several reform measures to improve the business environment. According to the World Bank’s Global Business Environment Report 2020, China’s business environment has risen significantly to 31st in the world, and remarkable achievements have been made in business environment construction. Digital government is a new form of government based on the new generation of information and communication technology, which is conducive to scientific government decision-making, accurate social governance, and effective public services. The “Fourteenth Five Year Plan” points out that it “strengthens the construction of digital society and digital government, and improves the digital intelligence level of public services and social governance.” In recent years, China’s digital government has also made considerable achievements. According to the 2022 UN E-government Survey Report, China’s e-government level ranks 43rd in the world, reaching the highest level in history, and is one of the countries with the highest growth rate in the world.

Optimizing the business environment and building a digital government provide a good external environment for enterprise development. Scholars worldwide have conducted much valuable research on this, but there is still room for further research. Whether the business environment and digital government can relieve the supply chain vulnerability and mitigate the COVID-19 epidemic’s impact is still unknown in the academic community. From the perspective of economic logic, it is easier for enterprises to obtain the policies needed to deal with risks from a good business environment. The digital government also helps reduce the cost of enterprises to comply with epidemic prevention measures and achieve accurate epidemic prevention.

This paper aims to investigate whether the business environment and digital government can mitigate enterprises’ supply chain vulnerability subject to the COVID-19 epidemic’s shock. We conducted a systematic empirical study based on the survey data of nearly 40,000 enterprises of the National Federation of Industry and Commerce in 2020, “10,000 private enterprises evaluating the business environment,” to verify this conjecture and fill the gap in the academic circle. Our results show that the business environment and digital government can mitigate the impact of the COVID-19 epidemic and improve the vulnerability of the enterprise supply chain, and this conclusion is robust. Further mechanism analysis implies that the business environment and digital government can speed up the introduction of effective bail-out policies and protect production factors and logistics, thus mitigating the impact of the COVID-19 epidemic and the supply chain’s vulnerability. Currently, most countries have abandoned the “Zero COVID-19” strategy. Under the “New Normal” coexisting with COVID-19, strengthening the construction of the business environment and the digital government is crucial to the sustainable development of enterprises.

Compared with the previous literature, this paper has three main contributions. First, most previous studies on the business environment and the digital government used provincial or municipal data. Most enterprise supply chains used questionnaires with a sample size of hundreds of enterprises. This study uses large-scale survey data to measure the business environment and digital government by enterprise score, combining the advantages of the two research methods. Second, this paper uses COVID-19 in 2020 as an exogenous shock, which can effectively mitigate the endogenous problems commonly existing in the research of business environment, digital government, and enterprises, and the research conclusions are robust. Third, this paper explores the channels through which the business environment and digital government influence the vulnerability of enterprise supply chains, deepening our understanding of the relationship between the external environment and firm performance.

2. Literature Review

2.1. Supply Chain Vulnerability

The supply chain refers to the enterprise network organized by enterprises to meet customer needs, including suppliers, manufacturers, distributors, and retailers [6]. In supply chain management, vulnerability is associated with risk, uncertainty, and resilience. Different scholars have different definitions of the above concepts. Ho et al. (2015) [7] define supply chain risk as “unexpected macro and micro events that may have adverse effects on any part of the supply chain, thus leading to enterprise failures or violations”. Wang and Jie (2020) [8] believe that uncertainty and risk are closely linked, uncertainty increases the possibility of risk occurrence, and risk is the result of uncertainty. Vulnerability is a prerequisite for risk and is affected by various supply chain decisions [9]. Resilience and vulnerability are widely used in biophysics and society, with a strong correlation [10]. Resilience is the ability of an enterprise to reorganize resources to cope with risks and changes in the external environment and to recover to the original or better state [11]. In contrast to resilience, vulnerability may weaken or limit the ability to withstand, handle, and survive threats and destructive events from inside and outside the system boundary [12]. Resilience and vulnerability are two characteristics of an enterprise when dealing with risks. Factors that affect resilience often also affect vulnerability. Compared with resilience, academic articles on vulnerability are relatively limited [13]. However, with the development of economic globalization, the supply chain has become more complex, and the risk and vulnerability have increased [14]. Therefore, vulnerability has essential research value.

The research of different scholars on the vulnerability of enterprise supply chains can be divided into three categories. First, they define the connotation of supply chain vulnerability and explore the source of vulnerability. Ho et al. (2015) [7] summarized the existing literature. They divided vulnerability into two categories: Operation and interruption, according to the severity of consequences, or into three categories: Enterprise internal, supply chain network, and external environment, according to the source of risk. Pettit et al. (2010) [15] divided the sources of vulnerability into seven factors: Turbulent environment, deliberate attack, external pressure, limited sources, sensitivity, dependence, and supply/ demand interruption. Grosse Ruyken et al. (2012) [16] believed that the most significant source of vulnerability was the concentration of global procurement and suppliers, and 73.3% and 72.5% of the surveyed enterprises had these problems, respectively. Second, they build an indicator system to measure the vulnerability level of the supply chain. Sharma et al. (2021) [17] designed a questionnaire to evaluate the supply chain vulnerability of manufacturing enterprises through 4 categories of 26 driving factors. Blackhurst et al. (2018) [18] designed a visualization method so managers can directly assess potential vulnerabilities in the supply chain. The third is to evaluate the impact of supply chain vulnerability on enterprises. Ali et al. (2021) [19] found that managing the supply chain’s vulnerability can effectively control the supply chain risk, and then the enterprise performance can be optimized. Wieland and Wallenburg (2012) [20] also reached a similar conclusion.

2.2. COVID-19 and Supply Chain

The COVID-19 epidemic is a sudden, unpredictable event with various risks in a broad sense. It has affected both ends of the supply chain simultaneously. On the supply side, the epidemic caused a labor shortage, transportation disruption, and the corresponding shortage of enterprise output [21]. On the demand side, some industries’ demand surged, such as medicine [22] and food [23], while most industries’ demand plummeted, such as tourism [24] and transportation [25]. Moreover, the epidemic’s impact has led to a sharp decline in residents’ income and an increase in uncertainty. Residents increase savings with less consumption and unnecessary expenditures due to their risk aversion, which may harm the supply chain of relevant industries [26]. The prices and demand for necessary products increase, while the prices and demand for non-necessary products decrease. Overall, the literature on the COVID-19 epidemic and supply chain vulnerability mainly focuses on case studies and specific industries, while empirical studies systematically exploring their relationships across various industries are limited.

2.3. Definition of the Business Environment and Its Impact on Enterprises

The business environment is the sum of all external environments for market entities’ operation and development, involving public services, labor, markets, innovation, finance, the rule of law, and government affairs. It comprehensively impacts market entities such as enterprises [27,28]. Previous studies have evaluated the business environment through objective indicators and subjective evaluation methods. The objective indicator method selects economic indicators to weigh the business environment. In contrast, the subjective evaluation method issues questionnaires to local enterprises and measures the business environment according to the respondents’ answers [29]. In recent years, scholars have conducted a series of research on the impact of the business environment on enterprises. First, the business environment significantly affects entrepreneurs’ confidence and behavior: A good business environment can significantly improve entrepreneurs’ confidence and promote the high-quality development of enterprises [30]. Optimize the business environment, encourage entrepreneurs to allocate more time to production and operation, and reduce non-productive time such as business entertainment [31]. Establishing government affairs centers can reduce private dealings with government officials and force private entrepreneurs to participate in “glorious undertakings” and charitable donations [32]. The business environment can give full play to entrepreneurs’ talents [33] and support entrepreneurs to start businesses [34]. Second, a good business environment can significantly improve the export performance of enterprises [35,36]. Third, the business environment can reduce enterprises’ credit risk and improve internal control quality, thereby reducing the financing cost [37,38] and institutional transaction cost [39]. Fourth, the government’s policy of optimizing the business environment can significantly improve enterprise performance [40]. Fifth, a good business environment can encourage enterprises to increase their innovation investment [41] and enhance private enterprises’ competitiveness [42].

2.4. Definition of Digital Government and Its Impact on Enterprises

Different scholars have different definitions of digital government, most of which are explained in the concept of an electronic government proposed by UNESCO [43]. Lee (2010) [44] believes that digitalization improves entity attributes by combining information, computing, and communication technologies. Accordingly, digital government is a process in which the government allocates information more effectively through digital technology and reshapes the organizational structure through digital infrastructure [45]. Digital government can reduce the time for enterprises to engage in administrative affairs and effectively reduce institutional costs [46].

2.5. Business Environment and the Mitigation Effect of Digital Government on Epidemic Impact

Optimizing the business environment and digital government can improve the enterprises’ external environment, thereby mitigating the epidemic’s impact. Through economic analysis, the business environment and digital government may relieve the vulnerability of enterprise supply chains through the following mechanisms. First, they strengthen the supply of production factors. Affected by COVID-19, the supply of enterprise production factors is severely challenged [47]. Enterprises in better business environments are more likely to obtain necessary production resources, so the supply chain is relatively stable with strong resilience [48]. Second, the logistics are smooth. Enterprises need to obtain upstream raw materials and intermediate products through logistics and send products downstream. It is more likely to ensure smooth logistics, which is conducive to strengthening the relationship between enterprises, suppliers, and customers, thus ensuring the stability of the supply chain [49,50]. A good business environment is more likely to provide good logistics support. A high degree of digital government achieves precise prevention and control and reduces the negative impact of the epidemic on logistics. Finally, they support external rescue. Insurance and rescue measures can significantly mitigate the impact of disasters and accelerate the recovery of enterprises [51]. The government has a stronger sense of service and security capabilities in areas with a better business environment and digital government. It is more likely to issue effective rescue policies promptly.

Based on the above literature, scholars have performed much valuable research on the business environment and digital government. However, only a few studies have discussed its role in mitigating the epidemic’s impact on the enterprise supply chain. Under the “New Normal” in which most countries worldwide choose to coexist with COVID-19, there is excellent research value in exploring the impact of the business environment and digital government on the supply chain vulnerability of enterprises.

3. Data, Variables, and Models

3.1. Data Sources

The primary data source of this paper is the survey data of “10,000 private enterprises evaluating business environment” conducted by the All-China Federation of Industry and Commerce in 2020. The survey evaluates the business environment and the characteristics and operating conditions of the enterprises. In 2020, the survey covered 355 municipal administrative units nationwide, and 40,216 valid responses were recovered. Excluding the samples missing information on the region of enterprise and the main business, this paper obtained 39,389 valid samples. The data source of urban-level control variables is the China Urban Statistical Yearbook.

3.2. Variable Description

3.2.1. Explained Variable

Before discussing the supply chain vulnerability, we need to define the scope of the supply chain accurately. The supply chain is a company network that participates in the upstream and downstream flow of products, services, funds, and information from the initial supplier to the final customer [15]. Previous studies primarily divided the methods for evaluating supply chain vulnerability into two categories. First, they investigated the characteristics of all aspects of the supply chain and evaluated them by issuing questionnaires to managers [52]. Secondly, they considered the vulnerability as the probability and severity of supply chain disruption [53] and evaluated the supply chain vulnerability according to the impact. We adopted the second evaluation method. Therefore, this paper uses the supply chain difficulties encountered by enterprises to measure the supply chain’s vulnerability. In the questionnaire, there were three answers to the question, “What are the difficulties in your company’s operation at present.” “Insufficient supply of raw materials,” “increase in accounts receivable,” and “insufficient recovery of the upstream and downstream capacity of the industrial chain” are related to the vulnerability of the supply chain. In this paper, we set the dummy variable as vulnerability. For each answer, the enterprise has a supply chain difficulty, indicating a vulnerability in the supply chain, therefore 1 point was scored. If there was no supply chain difficulty, the value was 0; if there were three, the value was 3.

3.2.2. Explanatory Variable

This paper discusses how the business environment and digital government can mitigate the epidemic’s impact on enterprises. As of 30 June 2020, the cumulative confirmed cases of COVID-19 at the municipal level indicate the epidemic’s severity. After adding 1, they were logarithmically treated and recorded as Lncase. In this paper, the time point of cumulative confirmed cases was cut to June 30, matching the explained variable. The measurement of the business environment is the score of enterprises on the overall environment of the business environment. The measurement of digital government is the score of enterprises on the digital application of epidemic prevention and control. The answers of the two were a score of 1–5 points. The average score of all enterprises at the municipal level was taken as the city’s business environment and digital government score, assigned to all enterprises in the city and recorded as Environment and Digital. This paper used the average value at the city level rather than the individual score of enterprises as the explanatory variable because the business environment at the city level and the level of digital government should be identical and objective. However, the answers of enterprises are subjective and differentiated, and averaging can reduce the errors caused by the subjective perception of enterprises [54]. In order to measure the mitigation degree of the business environment and digital government to the impact of the epidemic, this paper multiplied them with Lncase, respectively, and recorded them as Xlncase1 and Xlncase2.

3.2.3. Control Variable

First, we had the enterprise-level control variable, including (1) revenue. Wagner and Neshat (2011) [55] found that the supply chain of large-scale enterprises is more complex and fragile. In this paper, we used two methods to measure the size of an enterprise: Business revenue and the number of staff. According to the Standard Regulations on the Classification of Small and Medium-sized Enterprises formulated by the National Bureau of Statistics, combined with the questionnaire option settings, the total annual revenue of enterprises was divided into four levels: Less than 5 million yuan, 5–30 million yuan, 30–100 million yuan, and more than 100 million yuan, generating corresponding dummy variables. The second control variable was (2) the number of staff in the enterprise (Staff). According to the number of staff of the enterprise in 2019, the number of staff of the enterprise is divided into three levels: Less than 100, 100–300, and more than 300, and corresponding dummy variables are generated. Thirdly, we considered (3) whether it is a foreign-trade or a going-global enterprise (Trade). On the one hand, export diversification can smooth the demand fluctuation in the external market, help reduce the demand side change, and enhance the ability of enterprises to withstand risks [56]. On the other hand, the external environment of export-oriented enterprises is more complex and may be subject to greater exogenous impact [57]. This paper sets a dummy variable to measure the export characteristics of enterprises. If the enterprise is a foreign-trade enterprise, a “going global” enterprise, or both, it was assigned a value of 1; otherwise, it was assigned a value of 0. Next, we evaluated the (4) R&D investment proportion (Rd). Parast et al. (2019) [58] found that enterprise innovation will enhance the ability to cope with supply chain disruption. In 2019, the proportion of enterprise R&D investment in total revenue was “below 1%”, “1.1–5%”, “5.1–7%”, “7.1–10%,” and “above 10.1%”. This paper set corresponding dummy variables.

Secondly, we assessed the urban-level control variables, including (1) population size (Lnpop). There is a positive correlation between city size and urban resilience [59], and enterprises in high-resilience cities may also be less affected by the impact of COVID-19. This paper controlled the average population of the city in 2019 and conducted logarithmic processing. Next, we considered (2) economic level (Averagegdp). There is also a positive correlation between the urban economic level and urban resilience [60]. This paper measures the level of urban economic development by the per capita GDP of the city where the enterprise was located. (3) Fiscal expenditure level (Fiscal). The level of fiscal expenditure reflects the activity of local governments in economic activities. Governments with high expenditure levels may be more proactive in preventing and controlling epidemics and rescuing enterprises. This paper measures the level of fiscal expenditure by the proportion of fiscal expenditure to GDP. Furthermore, we evaluated (4) urban prevention and control capability (Doctor). Medical resources affect the ability of epidemic prevention and control. This paper used the number of doctors per 10,000 people to express the city’s epidemic prevention and control ability. We used the urban-level control variables in 2019 instead of those in 2020. On the one hand, COVID-19 broke out in January 2020, which is closer to the end of 2019 in terms of time. On the other hand, the COVID-19 epidemic also affects the control variables in 2020.

In addition, this paper also controlled the fixed effect of the province where the city is located and the fixed effect of the industry to which the enterprise’s primary business belongs, which are recorded as (Province) and (Industry), respectively.

3.2.4. Descriptive Analysis

Table 1. Descriptive statistics.

Variables	Obs.	Mean	SD	Min	Max
Lncase	39,389	1.67	0.80	0	4.70
Environment	39,389	4.03	0.27	1	5
Digital	39,389	3.90	1.03	1	5
Xlncase1	39,389	6.76	3.25	0	17.23
Xlncase2	39,389	6.52	3.58	0	23.51
Vulnerability	39,389	−0.53	0.72	−3	0
Staff1	39,389	0.62	0.49	0	1
Staff2	39,389	0.21	0.41	0	1
Staff3	39,389	0.17	0.38	0	1
Revenue1	39,389	0.39	0.49	0	1
Revenue2	39,389	0.31	0.46	0	1
Revenue3	39,389	0.17	0.37	0	1
Revenue4	39,389	0.14	0.35	0	1
Rd1	39,389	0.36	0.48	0	1
Rd2	39,389	0.32	0.47	0	1
Rd3	39,389	0.18	0.38	0	1
Rd4	39,389	0.08	0.27	0	1
Rd5	39,389	0.06	0.24	0	1
Lnpop	37,018	6.29	0.77	3.16	8.04
Averagegdp	37,018	78,359.19	42,539.55	14,746.00	203,489.00
Fiscal	37,018	20.84	11.63	6.62	148.75
Doctor	37,018	30.22	8.74	13.24	64.24

shows the descriptive statistics. The average score of the business environment faced by the sample enterprises is 4.03 points, and the average score of the digital government is 3.90 points, which reflects the effectiveness of China’s governments at all levels in building the business environment and digital government in recent years. This sample covers enterprises of all sizes, which can better reflect the overall situation of enterprises in China.

3.3. Empirical Model

According to the value characteristics of variables, this paper primarily uses the ordered Probit model for empirical analysis. The specific settings of the model are as follows:

$$Vulnerabilit{y}_{ij}={\beta }_0+{\beta }_{1}Lncas{e}_j+{\beta }_{2}Xlncase{1}_j+{\beta }_{3}Xlncase{2}_j+{\phi }^{\prime }{X}_{ij}+Industr{y}_i+Provinc{e}_j+{\epsilon }_{ij}$$

(1)

$$Vulnerabilit{y}_{ij}=\{\begin{array}{c}0 Vulnerabilit{y}_{ij}^{*}\le a_1\\ 1 a_1<Vulnerabilit{y}_{ij}^{*}\le a_2\\ 2 a_2<Vulnerabilit{y}_{ij}^{*}\le a_3\\ 3 a_3<Vulnerabilit{y}_{ij}^{*}\end{array}$$

$Vulnerabilit{y}_{ij}^{*}$ is a potential variable that cannot be directly observed. $Vulnerabilit{y}_{ij}$ represents the supply chain vulnerability of enterprise i, located in city j. $Lncas{e}_j$ represents the cumulative number of confirmed cases in the city where the enterprise is located as of 30 June 2020. $Xlncase{1}_j$ represents the cross-multiplying term of the city-level average business environment score and Lncase. $Xlncase{2}_j$ represents the cross-multiplying term of the city-level average digital government score and Lncase. $X_{ij}$ represents a series of control variables. $Industr{y}_i$ represents the fixed effect of the industry of the primary business of enterprise i. $Provinc{e}_j$ represents the fixed effect of the province where city j is located. ${\epsilon }_{ij}$ is the residual item. In the following section, we use data to conduct a regression analysis on the above models.

4. Results and Discussion

4.1. Benchmark Regression

Table 2 suggests that the coefficient of logarithmic confirmed cases is positive, indicating that the impact of COVID-19 significantly increases the supply chain’s vulnerability. Cross-multiplying terms are negative, which demonstrates that the business environment and digital government significantly mitigate the COVID-19 epidemic’s impact on supply chain vulnerability. In the control variables, the supply chain of foreign-trade enterprises is more vulnerable. The supply chain of enterprises with annual operating revenue between 5 million yuan and 100 million yuan is more vulnerable than those with fewer than 5 million yuan. The supply chain of small and medium-sized enterprises is more complex than that of micro-enterprises, which is more vulnerable to the overall impact and more challenging to recover. The local government will prioritize large enterprises with annual revenue of more than 100 million yuan, which is less prone to supply chain difficulties. In terms of the number of staff, the higher the number of staff, the stronger the supply chain’s vulnerability. There is a significant positive correlation between R&D investment intensity and supply chain vulnerability.

Table 2. Benchmark regression.

	(1)	(2)	(3)
Variables	Vulnerability	Vulnerability	Vulnerability
Lncase	0.973 ***	0.837 ***	0.972 ***
	(0.0732)	(0.0651)	(0.0733)
Xlncase1	−0.235 ***		−0.154 ***
	(0.0182)		(0.0385)
Xlncase2		−0.210 ***	−0.0852 **
		(0.0168)	(0.0355)
Trade	0.291 ***	0.295 ***	0.291 ***
	(0.0156)	(0.0156)	(0.0156)
Staff2	0.105 ***	0.105 ***	0.105 ***
	(0.0174)	(0.0174)	(0.0174)
Staff3	0.161 ***	0.163 ***	0.161 ***
	(0.0201)	(0.0201)	(0.0201)
Revenue2	0.153 ***	0.150 ***	0.152 ***
	(0.0161)	(0.0161)	(0.0161)
Revenue3	0.0664 ***	0.0625 ***	0.0657 ***
	(0.0203)	(0.0203)	(0.0203)
Revenue4	−0.0344	−0.0404 *	−0.0352
	(0.0237)	(0.0237)	(0.0237)
RD2	0.0452 ***	0.0448 ***	0.0451 ***
	(0.0160)	(0.0160)	(0.0160)
RD3	0.0873 ***	0.0885 ***	0.0880 ***
	(0.0188)	(0.0188)	(0.0188)
RD4	0.0797 ***	0.0836 ***	0.0815 ***
	(0.0255)	(0.0255)	(0.0255)
RD5	0.0712 **	0.0737 ***	0.0724 **
	(0.0283)	(0.0283)	(0.0283)
Urban control variables	yes	yes	yes
Provincial fixed effect	yes	yes	yes
Industry fixed effect	yes	yes	yes
Observations	36,827	36,827	36,827

Note: Figures in brackets are robust standard errors; *, **, and *** are significant at 10%, 5%, and 1%, respectively, and Table 3, Table 4, Table 5, Table 6, Table 7 and Table 8 are the same.

Table 2. Benchmark regression.

	(1)	(2)	(3)
Variables	Vulnerability	Vulnerability	Vulnerability
Lncase	0.973 ***	0.837 ***	0.972 ***
	(0.0732)	(0.0651)	(0.0733)
Xlncase1	−0.235 ***		−0.154 ***
	(0.0182)		(0.0385)
Xlncase2		−0.210 ***	−0.0852 **
		(0.0168)	(0.0355)
Trade	0.291 ***	0.295 ***	0.291 ***
	(0.0156)	(0.0156)	(0.0156)
Staff2	0.105 ***	0.105 ***	0.105 ***
	(0.0174)	(0.0174)	(0.0174)
Staff3	0.161 ***	0.163 ***	0.161 ***
	(0.0201)	(0.0201)	(0.0201)
Revenue2	0.153 ***	0.150 ***	0.152 ***
	(0.0161)	(0.0161)	(0.0161)
Revenue3	0.0664 ***	0.0625 ***	0.0657 ***
	(0.0203)	(0.0203)	(0.0203)
Revenue4	−0.0344	−0.0404 *	−0.0352
	(0.0237)	(0.0237)	(0.0237)
RD2	0.0452 ***	0.0448 ***	0.0451 ***
	(0.0160)	(0.0160)	(0.0160)
RD3	0.0873 ***	0.0885 ***	0.0880 ***
	(0.0188)	(0.0188)	(0.0188)
RD4	0.0797 ***	0.0836 ***	0.0815 ***
	(0.0255)	(0.0255)	(0.0255)
RD5	0.0712 **	0.0737 ***	0.0724 **
	(0.0283)	(0.0283)	(0.0283)
Urban control variables	yes	yes	yes
Provincial fixed effect	yes	yes	yes
Industry fixed effect	yes	yes	yes
Observations	36,827	36,827	36,827

Note: Figures in brackets are robust standard errors; *, **, and *** are significant at 10%, 5%, and 1%, respectively, and Table 3, Table 4, Table 5, Table 6, Table 7 and Table 8 are the same.

4.2. Marginal Effect Analysis

The coefficients obtained through the regression analysis of the ordered Probit model cannot directly explain the magnitude of the impact [61]. Further marginal effect analysis is needed to calculate the business environment’s and digital government’s marginal effect on mitigating the epidemic’s impact on the supply chain.

The coefficients of logarithmic confirmed cases in columns (1)–(3) of Table 3 are significantly positive, and the coefficient in column (4) is significantly negative. Specifically, when other variables are in the mean value, the probability of the enterprise supply chain facing three, two, and one difficulty increases by 4.04, 11.2, and 19.7 percentage points, respectively. For each 1% increase in urban confirmed cases, the probability of no supply chain difficulties decreases by 34.9 percentage points. The results imply that the epidemic’s shock increases the probability of enterprises facing supply chain difficulties and vulnerability. The more serious the epidemic is, the more stringent the prevention measures taken by the government, and the more likely the enterprises will face supply chain crises.

Table 3. Marginal effect analysis.

	(1)	(2)	(3)	(4)
Variables	Vulnerability = 3	Vulnerability = 2	Vulnerability = 1	Vulnerability = 0
Lncase	0.0404 ***	0.112 ***	0.197 ***	−0.349 ***
	(0.00326)	(0.00838)	(0.0146)	(0.0257)
Xlncase1	−0.00862 ***	−0.0238 ***	−0.0420 ***	0.0744 ***
	(0.000800)	(0.00210)	(0.00366)	(0.00647)
Xlncase2	−0.00122 ***	−0.00338 ***	−0.00597 ***	0.0106 ***
	(0.000139)	(0.000371)	(0.000651)	(0.00115)
Enterprise control variables	yes	yes	yes	yes
Urban control variables	yes	yes	yes	yes
Provincial fixed effect	yes	yes	yes	yes
Industry fixed effect	yes	yes	yes	yes
Observations	36,827	36,827	36,827	36,827

Table 3. Marginal effect analysis.

	(1)	(2)	(3)	(4)
Variables	Vulnerability = 3	Vulnerability = 2	Vulnerability = 1	Vulnerability = 0
Lncase	0.0404 ***	0.112 ***	0.197 ***	−0.349 ***
	(0.00326)	(0.00838)	(0.0146)	(0.0257)
Xlncase1	−0.00862 ***	−0.0238 ***	−0.0420 ***	0.0744 ***
	(0.000800)	(0.00210)	(0.00366)	(0.00647)
Xlncase2	−0.00122 ***	−0.00338 ***	−0.00597 ***	0.0106 ***
	(0.000139)	(0.000371)	(0.000651)	(0.00115)
Enterprise control variables	yes	yes	yes	yes
Urban control variables	yes	yes	yes	yes
Provincial fixed effect	yes	yes	yes	yes
Industry fixed effect	yes	yes	yes	yes
Observations	36,827	36,827	36,827	36,827

The coefficients of cross-multiplying terms in columns (1)–(3) are significantly negative, and column (4) is positive. When other variables are in the mean value, the probability of the enterprise supply chain facing three, two, and one difficulty decreases by 0.86, 2.38, and 4.2 percentage points, respectively. When the urban business environment and the logarithm of confirmed cases increased by 1, the probability of no supply chain difficulties increased by 7.44 percentage points. When other variables are in the mean value, the probability of the enterprise supply chain facing three, two, and one difficulty will decrease by 0.12, 0.34, and 0.6 percentage points, respectively. The probability of the absence of supply chain difficulties will increase by 1.06 percentage points for each increase in the cross multiplier between digital government and the logarithmic of confirmed cases. The results indicate that the business environment and digital government have a mitigating effect on the epidemic’s impact, reducing the probability of difficulties in the enterprise supply chain and relieving the supply chain’s vulnerability.

4.3. Robustness Test

4.3.1. Replacing Supply Chain Vulnerability Variables

In the benchmark regression, this paper measures the supply chain vulnerability by the number of difficulties faced by the enterprise supply chain. In the robustness test, we measure the vulnerability of the supply chain based on whether the enterprise has supply chain difficulties, which is recorded as Vulnerability2. We conduct the regression, and the results are robust.

Table 4. Robustness test (1).

	(1)	(2)	(3)
Variables	Vulnerability2	Vulnerability2	Vulnerability2
Lncase	0.819 ***	0.700 ***	0.821 ***
	(0.0810)	(0.0723)	(0.0811)
Xlncase1	−0.198 ***		−0.137 ***
	(0.0201)		(0.0419)
Xlncase2		−0.175 ***	−0.0641 *
		(0.0186)	(0.0387)
Enterprise control variables	yes	yes	yes
Urban control variables	yes	yes	yes
Provincial fixed effect	yes	yes	yes
Industry fixed effect	yes	yes	yes
Observations	36,827	36,827	36,827

Table 4. Robustness test (1).

	(1)	(2)	(3)
Variables	Vulnerability2	Vulnerability2	Vulnerability2
Lncase	0.819 ***	0.700 ***	0.821 ***
	(0.0810)	(0.0723)	(0.0811)
Xlncase1	−0.198 ***		−0.137 ***
	(0.0201)		(0.0419)
Xlncase2		−0.175 ***	−0.0641 *
		(0.0186)	(0.0387)
Enterprise control variables	yes	yes	yes
Urban control variables	yes	yes	yes
Provincial fixed effect	yes	yes	yes
Industry fixed effect	yes	yes	yes
Observations	36,827	36,827	36,827

4.3.2. Replacing Epidemic Variables

The COVID-19 epidemic’s impact on the economy includes two aspects. On the one hand, the epidemic impacts residents’ health, affecting labor supply, enterprise production, and consumption. On the other hand, the government will take corresponding prevention and control measures to prevent the spread of the epidemic, which will also harm the economy. In the robustness test, this paper measures the impact of the epidemic situation by the number of days of emergency response to major public health events [62]. According to the National Overall Emergency Response Plan for Public Emergencies, China has four emergency response levels for major public health events. The response intensity has decreased from Level I (especially urgent) to Level IV (general). On 23 January 2020, 31 provinces in China launched a Level I response to major public health events, and local governments lowered their response to Level II and Level III, respectively, after a while. Under the level III emergency response, the epidemic prevention and control measures have a limited impact on economic activities. This paper considers the Level I response and the Level II response. By 30 June 2020, the number of Level I response days experienced by the enterprise location is multiplied by two and the sum of the Level II response days and then divided by 159 (159 days from January 23, when the emergency response started in each province to June 30). We define the cross-multiplying term between the alternative measure and the business environment as Xemergency1 and define the cross-multiplying term between the alternative measure and the digital government as Xemergency2. The regression is conducted again; the results are shown in Table 5 and remain stable.

Table 5. Robustness test (2).

	(1)	(2)	(3)
Variables	Vulnerability	Vulnerability	Vulnerability
Emergency	11.27 ***	0.243	11.57 ***
	(1.540)	(0.640)	(1.543)
Xemergency1	−2.656 ***		−2.370 ***
	(0.292)		(0.294)
Xemergency2		−0.427 ***	−0.380 ***
		(0.0473)	(0.0477)
Enterprise control variables	yes	yes	yes
Urban control variables	yes	yes	yes
Provincial fixed effect	yes	yes	yes
Industry fixed effect	yes	yes	yes
Observations	36,827	36,827	36,827

Table 5. Robustness test (2).

	(1)	(2)	(3)
Variables	Vulnerability	Vulnerability	Vulnerability
Emergency	11.27 ***	0.243	11.57 ***
	(1.540)	(0.640)	(1.543)
Xemergency1	−2.656 ***		−2.370 ***
	(0.292)		(0.294)
Xemergency2		−0.427 ***	−0.380 ***
		(0.0473)	(0.0477)
Enterprise control variables	yes	yes	yes
Urban control variables	yes	yes	yes
Provincial fixed effect	yes	yes	yes
Industry fixed effect	yes	yes	yes
Observations	36,827	36,827	36,827

4.3.3. Removing Hubei Sample

Hubei Province is the initial outbreak location of the epidemic, with the number of confirmed cases and the closure time far exceeding the national average. The impact of the epidemic is the largest. In order to exclude the specific impact of Hubei Province, this paper removed the sample of Hubei Province and regresses again, and the results remained stable.

Table 6. Robustness test (3).

	(1)	(2)	(3)
Variables	Vulnerability	Vulnerability	Vulnerability
Lncase	1.081 ***	0.992 ***	1.117 ***
	(0.0895)	(0.0831)	(0.0903)
Xlncase1	−0.258 ***		−0.147 ***
	(0.0215)		(0.0413)
Xlncase2		−0.246 ***	−0.126 ***
		(0.0207)	(0.0398)
Enterprise control variables	yes	yes	yes
Urban control variables	yes	yes	yes
Provincial fixed effect	yes	yes	yes
Industry fixed effect	yes	yes	yes
Observations	35,155	35,155	35,155

Table 6. Robustness test (3).

	(1)	(2)	(3)
Variables	Vulnerability	Vulnerability	Vulnerability
Lncase	1.081 ***	0.992 ***	1.117 ***
	(0.0895)	(0.0831)	(0.0903)
Xlncase1	−0.258 ***		−0.147 ***
	(0.0215)		(0.0413)
Xlncase2		−0.246 ***	−0.126 ***
		(0.0207)	(0.0398)
Enterprise control variables	yes	yes	yes
Urban control variables	yes	yes	yes
Provincial fixed effect	yes	yes	yes
Industry fixed effect	yes	yes	yes
Observations	35,155	35,155	35,155

4.3.4. Removing Cities with Less than 50 Enterprise Samples

In order to avoid the impact of extreme values of individual enterprises, this paper removed cities with less than 50 enterprises, and the results after re-regression remained stable.

Table 7. Robustness test (4).

	(1)	(2)	(3)
Variables	Vulnerability	Vulnerability	Vulnerability
Lncase	0.960 ***	0.828 ***	0.956 ***
	(0.0754)	(0.0672)	(0.0755)
Xlncase1	−0.235 ***		−0.152 ***
	(0.0187)		(0.0410)
Xlncase2		−0.210 ***	−0.0854 **
		(0.0173)	(0.0378)
Enterprise control variables	yes	yes	yes
Urban control variables	yes	yes	yes
Provincial fixed effect	yes	yes	yes
Industry fixed effect	yes	yes	yes
Observations	35,195	35,195	35,195

Table 7. Robustness test (4).

	(1)	(2)	(3)
Variables	Vulnerability	Vulnerability	Vulnerability
Lncase	0.960 ***	0.828 ***	0.956 ***
	(0.0754)	(0.0672)	(0.0755)
Xlncase1	−0.235 ***		−0.152 ***
	(0.0187)		(0.0410)
Xlncase2		−0.210 ***	−0.0854 **
		(0.0173)	(0.0378)
Enterprise control variables	yes	yes	yes
Urban control variables	yes	yes	yes
Provincial fixed effect	yes	yes	yes
Industry fixed effect	yes	yes	yes
Observations	35,195	35,195	35,195

4.4. Mechanism Analysis

4.4.1. Promptly Introducing Effective Rescue Policies

At the beginning of the outbreak of the COVID-19 epidemic, enterprise production was significantly impacted. Governments at all levels introduced a series of bail-out policies to help enterprises cope with the epidemic’s impact. In areas with a good business environment, the government has a good sense of service, and administrative efficiency is often higher, so it is possible to introduce and implement effective support measures more quickly, thus relieving the impact of the COVID-19 epidemic. This paper constructed efficiency and timeliness dummy variables to measure the bail-out policy based on the answers of enterprises regarding the efficiency and timeliness of rescue support policies. The value range is 1–5 points, and a higher score represents a more effective and timely rescue support measure. Using those variables as the explained variable, the regression results are shown in Table 8. The impact of the epidemic situation could be more conducive to the timely introduction of effective bail-out policies by local governments. In contrast, the business environment and digital government can mitigate this problem, accelerate the government’s response, introduce effective policies as soon as possible, and mitigate the vulnerability of enterprise supply chains.

4.4.2. Providing Better Production Guarantee

First of all, the epidemic situation caused a shortage of enterprise production factors, and the supply chain was impacted [63]. Secondly, the epidemic situation dramatically weakened the transportation capacity of the logistics industry, hindering enterprises from obtaining raw materials and selling products. In areas with a good business environment, the government has a stronger sense of service, which can better ensure the smooth flow of production factors and logistics of enterprises, thus mitigating enterprises’ vulnerability. Cities with solid digital government capabilities can reduce the impact of epidemic prevention measures through health codes, big data, and other technologies to protect the enterprise supply chain better. This paper measures the ability of factor and logistics guarantee through the satisfaction of enterprises with relevant questions. The dummy variables Factorsupply and Logistics were set, with the value range of 1–5 points. A higher score represents a higher guaranteed level. Using those variables as the explained variable, the regression results are shown in Table 8. The epidemic’s impact has significantly damaged enterprises’ ability to ensure factors and logistics. At the same time, the business environment and digital government can significantly mitigate the epidemic impact and the vulnerability of enterprise supply chains.

Table 8. Mechanism analysis.

	Rescue Policy		Ensure Smooth Production Factors and Logistics of Enterprises
Variables	Efficiency	Timeliness	Factorsupply	Logistics
Lncase	−1.690 ***	−1.895 ***	−2.041 ***	−1.752 ***
	(0.0757)	(0.0772)	(0.0769)	(0.0757)
Xlncase1	0.205 ***	0.262 ***	0.303 ***	0.252 ***
	(0.0183)	(0.0185)	(0.0186)	(0.0184)
Xlncase2	0.232 ***	0.230 ***	0.226 ***	0.211 ***
	(0.00422)	(0.00431)	(0.00424)	(0.00408)
Enterprise control variables	yes	yes	yes	yes
Urban control variables	yes	yes	yes	yes
Provincial fixed effect	yes	yes	yes	yes
Industry fixed effect	yes	yes	yes	yes
Observations	36,827	36,827	36,827	36,827

Table 8. Mechanism analysis.

	Rescue Policy		Ensure Smooth Production Factors and Logistics of Enterprises
Variables	Efficiency	Timeliness	Factorsupply	Logistics
Lncase	−1.690 ***	−1.895 ***	−2.041 ***	−1.752 ***
	(0.0757)	(0.0772)	(0.0769)	(0.0757)
Xlncase1	0.205 ***	0.262 ***	0.303 ***	0.252 ***
	(0.0183)	(0.0185)	(0.0186)	(0.0184)
Xlncase2	0.232 ***	0.230 ***	0.226 ***	0.211 ***
	(0.00422)	(0.00431)	(0.00424)	(0.00408)
Enterprise control variables	yes	yes	yes	yes
Urban control variables	yes	yes	yes	yes
Provincial fixed effect	yes	yes	yes	yes
Industry fixed effect	yes	yes	yes	yes
Observations	36,827	36,827	36,827	36,827

5. Conclusions, Policy Implications, and Future Research

The COVID-19 epidemic has brought many difficulties and challenges to the supply chain of enterprises, which largely determines the safety and prosperity of the economy. Using large-scale enterprise survey data to conduct systematic empirical research, this paper finds that the business environment and digital government can mitigate the negative impact of the COVID-19 epidemic on the enterprise supply chain. The conclusion remains stable after a series of tests, filling the gap in relevant research. In addition, we also propose and verify the mechanism of digital government and business environment to mitigate the epidemic’s impact, that is, introducing effective mitigation policies and providing better product guarantees in a timely manner. By linking the macro-business environment and digital government with the micro-enterprise supply chain, this paper has essential research significance. From the perspective of enterprises, overcoming the supply chain vulnerability caused by COVID-19 is conducive to forming a competitive advantage. From the perspective of local governments, creating a good business environment contributes to regional economic recovery, forming supply chain advantages, and attracting enterprises to settle in.

Based on the research conclusion, this paper puts forward the following policy recommendations to improve enterprise supply chain stability. First, the positive impact of the business environment and government digitization on the supply chain deserves attention. Developing a good business environment and accelerating digital construction is a potential way to improve the stability of the supply chain and regional economy and to reestablish economic competitiveness for local government in the “post-COVID-19 era”. Second, the timely release of relief policies to ensure the stability of production factor supply and logistics can effectively mitigate the epidemic’s impact on the enterprise supply chain in the context of the rapid rise of confirmed cases.

This paper provides evidence that the business environment and digital government can benefit enterprises by relieving the COVID-19 shock on supply chain stability. However, the supply chain is only one of the critical elements of enterprise development, while other factors still deserve more attention in future research. For example, whether the business environment and digital government can enhance enterprises’ resilience to COVID-19 is an issue of practical significance Furthermore, the relationship between the business environment, digital government, and enterprises’ innovation strategy has great research potential.