The Value of Internal Control during a Crisis: Evidence from Enterprise Resilience

Abstract

Internal control is an important internal governance mechanism of enterprises and plays an important role in preventing and controlling corporate risks. This paper utilizes COVID-19 shocks and uses data from listed companies in China for 2019–2021 in order to study the impact of internal control on enterprise resilience and its functioning mechanism. The findings show that internal control significantly improves enterprise resilience during a crisis. By using firm characteristic quantile regressions, it is found that under a crisis, larger firms with sufficient cash flow from operating activities are more protected by internal control and more resilient. Mechanistic analysis suggests that internal control further increases enterprise resilience by improving resource allocation efficiency, reducing operating risk, and increasing innovation output. Further analysis shows that government support can enhance the resilience of firms during crises through tax and fiscal policies; a better business environment enhances firms’ ability to withstand risks in crisis situations and helps them gain a competitive advantage in crisis situations. Based on this, this paper provides empirical evidence for revising and improving the internal control system of enterprises to reduce the negative impact of public health emergencies in the context of epidemics.

1. Introduction

In recent years, with the frequent occurrence of black swan and gray rhinoceros events, such as COVID-19, which has spread globally since 2020, economic activities in China, as well as globally, have brought certain shocks. The increasing uncertainty of the external production and business environment has made the importance of enterprise resilience to crisis increasingly prominent, and improving enterprise resilience to the crisis has become an important topic in corporate management practice [1,2]. Moreover, internal control, as a systematic internal governance mechanism, can effectively regulate business activities and ensure the authenticity, integrity, and legitimacy of financial information. Therefore, enterprises can maintain a favorable position in competition in an uncertain external environment, especially in crisis situations [3]. In this context it is necessary to further consider whether internal control can improve the ability of enterprises to cope with crises and how it can be useful during crises as the internal control system is increasingly developed.

Internal control is an institutional arrangement that enables corporate behavior to comply with legal norms, ensures reliable corporate financial reporting, and ultimately achieves efficiency and effectiveness of business activities. From the perspective of institutional design, sound and perfect internal control is a fundamental guarantee for improving the quality of corporate information disclosure and achieving corporate governance, which helps management to make scientific and rational decisions. Studies have shown that internal control has the ability to allocate resources and control risks, can effectively compensate for the incompleteness of corporate contracts, can effectively reduce information asymmetry, alleviate financing constraints and agency conflicts, and plays an important role in preventing and controlling corporate risks [4,5,6]. Most of the current literature on internal control has focused on exploring the role of internal control in improving innovation performance; surplus value; investment efficiency, and thus firm value; and in improving firm performance by reducing financing costs, audit costs, cost of capital, and operational risk [6,7,8]. However, relatively few studies have been conducted on the role of internal control during crises. Only Zhu and Song (2021) studied the role of internal control in firms’ response to the impact of COVID-19 and concluded that internal control could mitigate the negative impact of the epidemic on firms’ performance; the higher the quality of firms’ internal control, the better the firm’s financial performance in the epidemic journals [9]. Rikhardsson et al. (2021) used the 2008 financial crisis to study the banking industry and found that internal control systems are the key to effective crisis response [10]. Therefore, this paper attempts to examine the value of internal control during crises using the exogenous shock of COVID-19.

Enterprise resilience reflects a corporation’s ability to reconfigure, regroup, and restructure its resources in response to changes in the external environment [11], more stable stakeholder relationships (e.g., employees, shareholders, and upstream and downstream supply chains) [12], and more proactive risk management capabilities [13], which can enhance the ability of enterprises to withstand external environmental disruptions and help them recover quickly from crises. In the current VUCA (VUCA is an acronym for volatility, uncertainty, complexity, and ambiguity) era of the global economy, the ability of enterprises to develop resilience during adverse events, and to reflect and improve on them, becomes the key to their survival, transformation, and even leveraging future development. Enterprise resilience is the micro-foundation of a country’s economic resilience, a key indicator of a country’s high-quality economic development, and an important guarantee of sustainable economic development [14].

In view of this, this study uses the exogenous shock event of COVID-19 to assess the impact of internal control on enterprise resilience and its functioning mechanism. The main contributions are as follows. First, the paper expands the research related to internal control, while the existing literature on the effect of internal control mainly focuses on the financial performance and decision-making of enterprises in normal times. This article focuses on the value of internal control during a crisis and can further enrich the study of internal control. Second, this paper enriches the study of factors influencing enterprise resilience. Most of the current literature on enterprise resilience focuses on the impact of objective conditions on enterprise resilience, including investor protection systems [15], human resources [16], corporate social responsibility [17], and innovation levels [18]. While this paper further studies the determinants of enterprise resilience during COVID-19 from the perspective of internal control and expands on the influencing factors of enterprise performance during the crisis. Third, the study further explores the mechanism of the role of internal control in influencing enterprise resilience and explores possible path mechanisms in terms of resource allocation efficiency, operational risk, and innovation output, providing a theoretical basis for the feasibility of improving enterprise resilience. Fourth, the findings of the study can further develop the dynamic capability theory, which emphasizes that in an uncertain environment, internal control can continuously build, adapt, and reorganize its internal and external resources, thereby gaining a competitive advantage and improving enterprise resilience. The dynamic capability theory is enriched by explaining the mechanism by which internal control affects the formation of enterprise resilience.

2. Theoretical Basis and Research Hypothesis

2.1. Internal Control and Enterprise Resilience

In terms of direct effect analysis, internal control is the sum of a series of management activities of an enterprise, which is a dynamic management activity that is constantly evolving. According to the dynamic capability theory, an enterprise with a high level of internal control will have a greater advantage during a crisis [19], which can improve the enterprise’s resistance and recovery ability. Specifically, the crisis is often accompanied by a market downturn, and the financial problem is the key to the enterprise’s recovery ability. The system of internal control effectively protects the company’s financial resources during the crisis, acting as an “organizational shock absorber” that effectively cushions the impact of external shocks and increases the company’s resistance [20]. Moreover, the financial reserves accumulated by a high level of internal control not only maintain a commitment to employees but also facilitate rapid adjustment and recovery after a crisis [16]. A high level of internal control facilitates accurate identification of share price information by stakeholders, prevents long-term deviation of share price from corporate value, and helps to enhance corporate recovery after a crisis. Therefore, internal control can effectively develop the role of enhancing enterprise resistance and recovery. Enterprise resistance and recovery is a visual demonstration of enterprise resilience, so internal control can enhance enterprise resilience.

2.2. Mechanisms of Internal Control Impact on Enterprise Resilience

From the analysis of indirect effects, the crisis tested the effectiveness of corporate governance, and internal control, as an important governance mechanism within the enterprise, plays an important role in preventing risks as well as improving operational efficiency, which can enhance resource allocation efficiency, reduce operating risks and improve innovation output. Further, a high level of resource allocation efficiency can effectively reduce the losses caused by the crisis, low operational risk means a stronger business capability, and innovation output can win core competitiveness for the enterprise in times of crisis. All these are important conditions for enhancing enterprise resilience. The specific analysis is as follows.

First, internal control can improve the efficiency of business operations and managers’ decision-making. Internal control, as an important governance mechanism, runs through the whole process of production and operation of enterprises, which can not only effectively solve the conflict of interests and information asymmetry conflicts brought by the separation of two powers but also effectively alleviate the opportunistic behavior of management and improve the resource allocation efficiency [4,21]. According to resource-based theory, managing and reallocating resources to respond to changing environments is critical to firm survival and growth, especially in dynamic environments where the reallocation of resources allows firms to develop adaptive capabilities that mitigate the impact of external shocks [22,23]. In addition, resource allocation efficiency can take advantage of synergies and economies of scope to increase enterprise resilience to risk [24]. Thus, internal control further enhances enterprise resilience by improving resource allocation efficiency.

Second, internal control helps firms proactively identify changes in the external environment, proactively manage risks, and improve risk-taking capacity, thereby increasing enterprise resilience [25]. On the one hand, the higher the quality of internal control, the stronger the ability to cope with changes in ecological factors or market factors, the more effective it is in weakening the influence of external factors, and the lower the risk of business operating [26,27]. On the other hand, internal control regulates the workflow through a perfect system and strengthens the responsibility awareness of relevant personnel to strictly control the business operating risks [5,28]. Lower operating risks imply that a company has a mature organizational structure, smooth supply chain lines, stable employees, and relatively adequate capital flow, which are all important conditions for a company to withstand crises and recover quickly [29]. Thus, internal control further enhances enterprise resilience by reducing operational risk.

Third, internal control improves corporate innovation output through effective institutional arrangements, which is an important support for enterprise resilience in times of crisis. On the one hand, well-developed internal control can help enterprises continuously optimize their organizational structure, which can avoid insufficient innovation investment due to agency problems, information asymmetry, and opportunism and also effectively reduce the risk of failure of innovation activities [30,31]. On the other hand, well-developed internal control can provide a positive environmental climate for corporate innovation, which can create a corporate culture of acute innovation and contribute to the innovative ability and motivation of employees [32,33]. Innovation is an important route for firms to gain a competitive advantage [34]. Alghanmi (2020) [35] and Chatzoglou and Chatzoudes (2018) [36] demonstrated that innovation significantly and positively affects firms’ competitive advantage. It is not difficult to understand that the agility of firms’ innovation and innovation output are core competencies for firms to respond positively to external shocks, and firms with higher levels of innovation will have a greater advantage during crises [16]. Thus, internal control further enhances enterprise resilience by improving innovation output.

In summary, the following research hypotheses can be formulated:

Hypothesis 1 H1.

Other things being equal, internal control makes firms operate in better conditions during a crisis, which enhances resilience.

Hypothesis 2 H2.

Other things being equal, internal control enhances enterprise resilience during crises by improving resource allocation efficiency, reducing business risks, and increasing innovation output.

2.3. Moderating Effect of Government Support and Business Environment

Under the shock of COVID-19 and the impact of trade friction, enterprises’ own operational capacity and efficiency are affected. In this situation, not only the establishment of sound internal control systems but also strong external support is needed to help enterprises break free from the mire, and government support can enhance the resistance and recovery of enterprises after the crisis through tax policies and fiscal policies [37]. Government support for enterprises releases the government’s policy signal to ensure the survival and development of enterprises, which can enhance the confidence and, thus, the cohesiveness of employees after the crisis [38]. The policy support and financial support provided by the government can accelerate the post-crisis resource reserves of firms, which can help them to improve their responsiveness and recover their business quickly [39]. For example, under the Sino–US trade friction scenario, the government has introduced a number of policy measures related to reducing labor costs, easing the operational burden of enterprises, increasing financial support, and improving government services to help enterprises cope with the shock; therefore, this study argues that government support can strengthen the positive impact of internal control on enterprise resilience.

Hypothesis 3 H3.

Other things being equal, government support positively promotes the impact of internal controls on enterprise resilience.

The improvement of the institutional environment enhances the ability of enterprises to resist risks in crisis situations and helps them gain a competitive advantage in crisis situations [40]. The business environment is the most direct external factor facing business operations, and it runs through every process of business development, so the business environment deeply affects the resilience of enterprises. First, a level playing field can improve the efficiency of resource allocation, and the continuous promotion of market-oriented reforms can enhance the power of enterprises to enter and exit the market freely and improve the level of financial services, which provide sufficient guarantees for enterprises to resume production [41]. Further, according to the system critical hypothesis, with the continuous improvement of the market system, capital, talent, and technology will flow in the direction of higher efficiency, investment efficiency, and resource allocation efficiency will be further improved, and enterprises with better internal control will unleash greater advantages with the support of the high-quality business environment, which plays an important role in the resistance and resilience of enterprises after the crisis [42].

Hypothesis 4 H4.

Other things being equal, the business environment positively promotes the impact of internal controls on enterprise resilience.

3. Model and Data

3.1. Sample Selection and Data Sources

By starting from the above analysis, this study selects the Wuhan city closure on 23 January 2020 as an external shock event to further test the value of internal control during the crisis. The data of Chinese A-share listed companies are used as the initial research sample, and the sample is further censored by combining the research themes: (1) removing the sample of companies with an asset–liability ratio greater than 1; (2) removing the financial category, ST category, and companies with serious missing data, and finally obtaining a total of 1883 observations. In terms of data sources, financial indicators are obtained from CSMAR and WIND, patent data are obtained from the China Research Data Service Platform, and internal control data are obtained from the internal control index of DIB. In order to overcome the influence of extreme values on the research results, all continuous variables in this study are tailor-made in the 1% and 99% quartiles

3.2. Model Setting

The central question of this study is to test whether firms with a high level of internal control are more resilient during a crisis, and therefore the following model was constructed.

(1)$re{c}_i={\beta }_0+{\beta }_1\times I{C}_i+{\beta }_2\times Control{s}_{i,2019}+\Sigma Industry+\Sigma Year+{\epsilon }_i$

Among them, the explanatory variable $re{c}_i$ denotes enterprise resilience, which indicates firm resistance and resilience, respectively, where resistance is measured using the magnitude of stock price decline during the crisis; resilience is measured using the duration of the post-crisis period of stock price decline. The explanatory variable $I{C}_i$ denotes internal control, and the pre-crisis level of internal control of firms is measured using the 2019 DIB Internal Control Index. $Control{s}_{i,2019}$ is the corresponding set of control variables. Because this study used cross-sectional data, each enterprise has an observation of enterprise resilience during 2019–2021. In order to avoid certain endogeneity issues, this paper used 2019 observations to control for the impact of pre-crisis firm characteristics on firm performance during the crisis. Industry is the industry dummy variable, year is the year dummy variable, and $\epsilon$ is the error term of the model, which includes other factors affecting enterprise resilience outside the model variables.

3.3. Variable Measurement

Enterprise resilience (Rec): the current research on enterprise resilience is in its infancy, and there is no unified conclusion on the measurement of enterprise resilience. Erol et al. (2010) further analyzed resilience and argued that enterprise resilience consists of two main capabilities: (1) the ability to prevent the consequences of disruptive events from becoming worse—resistance. (2) the ability to recover from disruptive events that have already occurred—recovery [43]. Since enterprise resilience essentially reflects the resistance and resilience shown by enterprises in the face of crises. Therefore, this paper draws on Hu et al. (2020) [15] and Desjardine et al. (2019) [17] to measure enterprise resilience using the magnitude of the decline and duration of the decline. Among them, the magnitude of the decline reflects the resistance, and the duration of the decline reflects the recovery.

(1) The magnitude of the decline ($decline$): Other operating conditions being equal, a larger decline in share price caused by an external shock means that the asset price may have deviated significantly from the core value of the asset, and the weaker the resistance of the enterprise; conversely, if the decline in share price is smaller, the stronger the resistance of the enterprise. The specific calculation method is as follows:

(2)$decline=\frac{P_2-P_1}{P_1}$

where decline denotes the decline of the firm’s stock price brought by the crisis, and P₁ and P₂ denote the highest point of the firm’s stock price in the year before the crisis (23 January 2019–23 January 2020) and the lowest point after the crisis (24 January 2020–23 January 2021), respectively. The decline is a non-positive value, and its larger value indicates a smaller degree of loss for the firm, implying a stronger resistance.

(2) Duration of decline ($decline\_time$). The longer the decline duration means that the enterprise is more affected by the crisis, which also means that the enterprise’s degree of loss is greater, indicating that the enterprise’s recovery ability is worse. On the contrary, the short duration of decline means that the enterprise can recover in a shorter period of time; based on the above discussion, the calculation method of enterprise resistance is constructed.

(3)$decline\_time=T_2-T_1$

where T₁ and T₂ represent the time point of the highest value of the stock price in the year before the crisis (23 January 2019–23 January 2020) and the time point of the maximum decline of the stock price after the crisis (24 January 2020–23 January 2021), respectively. $decline\_time$ is a non-negative value. The higher the number index, the worse the resilience.

Internal control (IC): The internal control index uses the internal control index of DIB-listed companies and divides it by 1000 as the measurement index. The index is based on the internal control elements and objectives of Shenzhen DIB enterprises, then further profiling the internal control reports of enterprises, and finally calculated based on the type of audit reports and internal control deficiencies of enterprises; the index is more objective to reflect the internal control system of enterprises.

Mechanism variables: (1) Resource allocation efficiency (TFP), which is measured by using the relatively mature LP method, measures the total factor productivity of enterprises. (2) Operating risk (risk) is measured by the standard deviation of the main business income over three years. (3) Innovation output (patent) is measured using the number of patents obtained by the firm at the end of the year, which is obtained from the China Research Data Service Platform.

Moderation variables: (1) Government support, non-operating expenses, or revenues in the company’s financial statements are further manually screened for government-related revenues. (2) business environment (Market) uses the “2018 China Province Doing Business Report” to measure the level of the business environment in each company’s province.

Control variables: By drawing on Desjardine et al. (2019) [17], the fixed asset ratio (Fixed), firm size (Size), gearing ratio (Lev), nature of ownership (Soe), profitability (Roa), cash flow ratio (Cf) were selected as control variables in this paper. The specific definitions are shown in Table 1.

4. Analysis of Empirical Results and Robustness Tests

4.1. Descriptive Statistical Analysis

Table 2 shows the descriptive statistics of the total sample. From the dependent variable, the mean value of decline is −0.639; that is, the mean value of the decline of listed enterprises is 63.9%, and the larger decline also indicates the poor resistance of China’s listed companies to the crisis. The mean value of decline_time is 600.5 days, which indicates the poor recovery of China’s listed companies after the crisis. The combination of the above indicators can conclude that the resilience of Chinese listed companies is poor, so it is necessary to study the factors influencing the resilience of enterprises. From the independent variables, the mean value of internal control (IC) is 0.656, and the minimum and maximum values are 0.23 and 0.883, respectively. Overall, the level of internal control of Chinese listed companies is at a medium level, indicating that the series of regulations introduced by the state have been implemented, but it must also be recognized that the level of internal control of Chinese listed companies varies significantly and develops unevenly. The individual differences of other control variables in the sample are also relatively obvious, and overall the sample is well differentiated.

4.2. Baseline Regression Analysis

Table 3 presents the results of the benchmark regression of the level of internal control on enterprise resilience; models (1)–(2) are the regression results of the level of internal control on enterprise resistance and recovery, respectively. From column (1) of Table 3, it can be seen that the higher the level of internal control, the smaller the decline in share price after the crisis. When combined with the decline, it is a non-positive value. The larger value indicates that the degree of loss of the firm is smaller; therefore, the internal control level positively affects the enterprise’s resistance (β_decline = 0.130, p < 0.05). Column (2) of Table 3 shows that the level of internal control negatively affects the recovery time after a crisis (β_{decline_time} = −134.697, p < 0.1). The higher level of internal control, the shorter the recovery time after a crisis and the stronger the resilience of the firm. Combined with the above research analysis, it can be concluded that internal control increases the ability of enterprises to cope with crises, reduces the losses caused by crises, and enhances the resilience of enterprises during crises.

4.3. Analysis of Enterprise Characteristic Variables

Firm-level characteristics are important factors affecting the level of internal control on enterprise resilience, and the heterogeneity of the impact of firm-level characteristics on the relationship between the level of internal control and enterprise resilience can be examined, as well as avoiding bias from the mean. By drawing on the study of Hu et al. (2020) [15], firm size (Size) and firm age (Age) were divided into four quartiles, and the first quartile was used as a benchmark to generate three dummy variables, which were then multiplied with the level of internal control to obtain the cross-multiplication term of the level of internal control and firm characteristics, respectively. Table 4 shows the regression results of firm characteristics and internal control cross-multiplication terms on enterprise resilience.

Columns (1)–(2) of Table 4 show the impact of the intersection of the quartiles of firm size and internal control on enterprise resilience. From column (1), it can be seen that relative to firm size in the first quartile, the internal control of firms in the second, third, and fourth quartiles further reduces the decline by 6%, 12.7%, and 17.9%, indicating that the rise in firm size strengthens the impact of internal control on the decline. Similarly, column (2) shows that the level of internal control faced by firms in the second, third, and fourth quartiles further reduces the duration of the decline by 53, 64, and 110 days, respectively. Columns (3)–(4) of Table 4 show the impact of the intersection of the quartile of cash flow from operating activities and internal control on enterprise resilience. Consistent with the above analysis, the greater the cash flow from operating activities, the greater the value of internal control exerted by the firm and the stronger the effect on enterprise resilience.

Thus, internal control still has a significant positive contribution to enterprise resilience at different levels of firm characteristics; during the crisis, larger firms with sufficient cash flow from operating activities are more protected by internal control and more resilient, further supporting research Hypothesis 1.

4.4. Robustness Tests

In order to make the findings more reliable, a series of robustness tests were conducted using replacement study models, replacement measures, replacement shock events, instrumental variables approach, and propensity score matching analysis.

4.4.1. Replacement of Study Model

To further test the role of internal control during a crisis, the following DID model was constructed using COVID-19, an exogenous event, and drawing on Ding (2020) [44].

(4)$Volatilit{y}_i={\gamma }_0+{\gamma }_1\times I{C}_i\times time+{\gamma }_2\times Control{s}_{i,t}+\Sigma Industry+\Sigma Year+{\epsilon }_i$

where volatility is the dependent variable, based on Albuquerque et al. (2020), which uses a stock price volatility measure; enterprises will exhibit lower volatility if internal control improves resilience during a crisis [45]. Since the COVID-19 outbreak occurred in January 2020, the sample interval chosen was January 2019–January 2021 in order to make the amount of data before and after the double difference method comparable. Given the small amount of data available after the COVID-19 shock, quarterly stock price volatility data are used to measure enterprise resilience. Time is a time dummy variable, and the period after 23 January 2020 is defined as the shock period of the COVID-19 outbreak, with time taking the value of 1 and 0 otherwise. $I{C}_i\times time$ is the core explanatory variable of this paper, control is a series of control variables at the firm level, industry is the industry dummy, year is the year dummy, and ε is the error term of the model, which includes other factors affecting enterprise resilience outside the model variables.

The specific regression results are shown in Table 5. The coefficient of the interaction term $I{C}_i\times time$ in column (1)(2) is significantly negative at the 1% level, indicating that internal control has a significant negative effect on the enterprise’s resilience during a crisis, as shown by the lower share price volatility of the enterprise during COVID-19, and the lower share price volatility means that the enterprise is less affected by the crisis. Therefore, it is again verified that internal control can significantly enhance enterprise resilience during crises, increase their ability to cope with crises, and reduce the negative impact caused by crises.

4.4.2. Replacement Measures

Drawing on Ortiz-de-mandojan and Bansal’s (2016) study using stock price volatility and sales growth rate to measure enterprise resilience, higher stock price volatility and lower sales growth rate imply that firms are more affected by the crisis and, therefore, less resilient [12]. The standard deviation of stock returns is one of the more widely accepted measures of stock price volatility in existing research on firm-level stock price volatility. Therefore, the standard deviation of monthly stock returns of firms in a year is used to measure the volatility of stock prices (Volatility). The specific regression results are shown in Table 6.

The effect of internal control on share price volatility is −0.01 and is significant at the 1% level, indicating that internal control can effectively mitigate share price volatility in the post-crisis period. The effect of internal control on the sales growth rate is 0.077 and is significant at the 5% level, indicating that internal control helps to enhance the operating income of the company after the crisis. Therefore, internal control plays an important value during the crisis and enhances enterprise resilience.

4.4.3. Replacement Shock Events

In order to avoid possible bias, this study again examines the impact of the level of internal control and enterprise resilience using the 2018 US–China trade friction as an exogenous shock. By using A-share listed companies from 2017 to 2020 as the research sample, this time period was chosen because on 23 March 2018, the United States Government imposed tariffs on USD 60 billion of goods imported from China and restricted Chinese companies from investing and merging in the US. This event has a certain impact on the production and operation of Chinese companies, which can be considered as a not a small crisis. The highest point of stock prices before the crisis was calculated based on 23 March 2017–23 March 2018, and the resistance and resilience of companies were calculated using 24 January 2018–23 January 2019 as the post-crisis shock time period, and the related calculation method is consistent with the previous section.

The specific regression results are shown in Table 7; internal control helps to improve the resistance (β_decline = 0.188, p < 0.01) and resilience (β_{decline_time} = −125.695, p < 0.05) of the firm, and Hypothesis 1 is again verified.

4.4.4. Instrumental Variables Method

Considering that there may be a reverse causal relationship between enterprise resilience and the level of internal control, for example, firms with high enterprise resilience pay more attention to building internal control and improving the level of internal control in order to improve risk resilience. This study refers to the study of Doyle et al. (2007) and takes whether the company is audited by the Big 4 (big4) and whether the company is treated by irregularities (illegal) in 2019 as instrumental variables to test the relationship between internal control and enterprise resilience [6], where the two dummy variables take 1 if yes and 0 if no; and both dummy variables are from the CSMAR database, and the specific regression results are shown in Table 8.

From column (1) of Table 8, it can be seen that whether the company is subject to irregularities is significantly negatively related to internal control (β_illegal = −0.068, p < 0.01), and whether it is audited by Big 4 is significantly positively related to internal control (β_big4 = 2.71, p < 0.01), indicating that the choice of instrumental variables is appropriate. The results in columns (2) (3) indicate that internal control positively affects the magnitude of stock price decline (β_decline = 0.043, p < 0.1) and negatively affects the duration of decline (β_{decline_time} = −826.632, p < 0.1) after using two-stage least squares regression, which again proves the robustness of the findings of this study. There is a positive effect of internal control on enterprise resilience.

4.4.5. Propensity Score Matching

In order to address possible sample selection bias, the propensity score matching method was used for further testing. Matching was performed with firm size, profitability, financial leverage, cash flow ratio, the share of intangible assets, the share of fixed assets, and working capital. The matched treatment and control groups were put together to reconsider the effect of internal control on enterprise resilience. The results, as shown in Table 9, still support the original hypothesis that the level of internal control positively affects the degree of stock price decline (β_decline = 0.031, p < 0.01), and the level of internal control negatively affects the recovery time after a corporate crisis (β_{decline_time} = −22.766, p < 0.1), thus testing Hypothesis 1 and Hypothesis 2 again and the study findings are robust.

5. Further Analysis

5.1. Impact Mechanism Test

The impact of internal control on enterprise resilience mainly enhances enterprise resilience by improving resource allocation efficiencies, reducing business risks, and increasing innovation output. This paper has sufficient theoretical analysis of the effects of resource allocation efficiency, operating risk, and innovation output on enterprise resilience in the literature review section. Therefore, it draws on the following model constructed to test the intrinsic impact mechanism.

(5)$TF{P}_{it}/Ris{k}_{it}/Paten{t}_{it}={\alpha }_0+{\alpha }_1\times I{C}_{it}+{\alpha }_2\times Controls+\Sigma Industry+\Sigma Year+\epsilon$

where TFP stands for resource allocation efficiency, risk stands for operating risk, patent stands for innovation output. Controls are a set of firm-level control variables consistent with the previous section, industry is an industry dummy, year is an annual dummy, and ε is the error term of the model, which contains other factors affecting enterprise resilience outside the model variables.

The specific results are shown in Table 10; internal control can help to improve the efficiency of enterprise resource allocation (α₁ = 0.326, p < 0.01); further reduce enterprise business risk (α₁ = −0.307, p < 0.01), and effectively improve enterprise innovation output (α₁ = 0.092, p < 0.05). It can further verify that internal control is the intrinsic mechanism of influencing enterprise resilience, which verifies Hypothesis 2.

5.2. Group Test for Government Support

In this study, the CSMAR database was used to filter the details of government support by “non-operating income or expense” in the notes to financial statements, and then government support was divided into two groups according to the median. The correlation regression results are shown in Table 11, as shown in column (1) (2); for firms with more government subsidies, internal control positively affects the magnitude of share price decline (β_decline = 0.160, p < 0.05), and internal control negatively affect the duration of the firm’s share price decline (β_{decline_time} = −189.442, p < 0.1). As a non-positive value, the larger the value of the magnitude of the share price decline indicates the smaller the extent of the firm’s losses, so the better the internal control, the greater the magnitude of the share price decline, which also indicates the better the firm’s resistance. A longer duration of stock price decline implies that the firm takes longer to recover and also indicates that the firm is not resilient, so internal control negatively affects the duration of stock price decline. As shown in column (3) (4), internal control is difficult to work in firms with little government support, suggesting that firms are less resistant and less resilient in times of crisis when they rely on themselves alone. This concludes that government support plays an important role in the relationship between internal control affecting enterprise resilience.

5.3. Testing the Moderating Effect of Business Environment

In this study, we used the “2018 China Province Doing Business Report” by Wang et al. (2019) to measure the level of the business environment in each company’s province [46] and form the cross-product term IC*Market after the decentering business environment and internal control, and then conduct regression analysis on firm resistance (decline) and resilience (decline_time), respectively. The results are shown in Table 12, where the business environment enhances the resistance of internal control to the firm (β_IC*Market = 0.063, p < 0.1) and the business environment enhances the resilience of internal control to the firm (β_IC*Market = −10.929, p < 0.01), thus concluding that the business environment plays a positive moderating role in the relationship between internal control and enterprise resilience.

6. Conclusions and Future Prospects

6.1. Conclusions

With the help of the shocking event of COVID-19, the data of Chinese listed companies from 2019 to 2021 were selected as a sample to study the value of internal control during crises, and the following research conclusions were mainly drawn.

First, internal control plays a positive role in promoting enterprise resilience. According to dynamic capability theory, when the uncertainty of the external environment increases, internal control can form good power checks, balances, and coordination mechanisms within the company, which improve the ability of firms to cope with crises and have stronger resistance during crises and higher recovery after crises. Our results are similar to Zhu and Song (2021) [9] and Hao and Zhang (2022) [47]; however, Zhu and Song (2021) emphasized the impact of internal controls on firms’ financial performance, while this paper examines the value of internal controls in a crisis from an enterprise resilience perspective [9]. Hao and Zhang (2022)’s study concluded that internal control could reduce corporate risk and improve enterprise resilience in normal times [47]. This paper emphasizes the mechanism by which internal control works under crisis, which is more relevant to the concept of resilience. The connotation of enterprise resilience emphasizes a firm’s ability to recover from a crisis and grow, so this study is more convincing in examining the impact of internal controls on enterprise resilience using the shock of COVID-19.

Second, company size and cash flow from operating activities reflect the effectiveness of internal control to a certain extent, and company size and cash flow from operating activities can effectively promote the role of internal control and further enhance enterprise resilience. This finding is consistent with Hu et al. (2020), who concluded that investor protection systems have a greater protective effect on firms with a higher percentage of fixed assets and more efficient operations and are more effective in improving enterprise resilience [15]. This study and previous studies have demonstrated that the better the financial position of the firm before the crisis, the more resilient the enterprise.

Third, internal control further enhances enterprise resilience by improving resource allocation efficiency, reducing operational risk, and increasing innovation output. Resource allocation efficiency enables firms to develop adaptive capacity, operational risk improves firm resistance, and firms with higher levels of innovation will have a greater advantage during a crisis [48].

Fourth, government support can enhance the resilience of enterprises during the crisis through tax and fiscal policies; the improvement of the business environment enhances the ability of enterprises to resist risks in the crisis situation, which helps enterprises gain a competitive advantage in the crisis situation. Liu et al. (2021) research also shows that government support plays an important role in the management of business operations, and to some extent, policy advantages can reduce risks in business operations, improve the sustainability of organizational operations, and thus enhance enterprise resilience [39]. In consistency with the findings of Tsiapa and Batsiolas (2019), it is considered that the institutional environment transmits signals that influence the perception and grasp of future opportunities and have an important impact on the development of enterprise resilience [18].

6.2. Management Implications

Combined with the findings of this study, the following management insights can be obtained: First, strengthen internal control to improve enterprise resilience. The promotion of internal control on enterprise resilience highlights that internal control is an important system for enterprise development, so it is necessary to continuously increase the investment of construction funds, improve the risk management system of enterprises, improve the operational effectiveness of internal control, and perfect the construction of internal control system. Second, strengthen the efficiency of enterprise operations. The efficiency of enterprise innovation output and resource allocation is an important performance of enterprise operation efficiency and an important influencing factor of enterprise resilience, so enterprise management should pay attention to innovation input and improve the efficiency of organization management. Third, the external environment of enterprises has become more complex due to COVID-19, and enterprises can hardly survive in the turbulent market only on their own strength. Government support, as a supplement to the market mechanism, can overcome the financial problems faced by enterprises to a certain extent, so government support can give full play to the value of internal control and help enhance enterprise resilience. Fourth, we will continue to optimize the business environment and create a fairer and more transparent external market environment to create a better external environment for the value of internal control. The business environment, as the external environment faced by enterprises in their daily operations, complements the internal management system—internal control—and plays an important role in ensuring the system.

6.3. Limitations and Prospects

Limited by the research content and relevant data, this paper suffers from the following three deficiencies, which constitute directions that should be expanded for future research. First, due to the availability of data, this paper is only validated with data from 2019 to 2021, which is a short time span and does not fully reflect the outcome effects of internal control. Future research can test the value of internal control under uncertainty risk and crisis over a longer period of time to further validate the impact effect of internal control. Second, although this paper analyzes the impact path of internal control on enterprise resilience in terms of resource allocation efficiency, operational risk, and innovation output, other more optimal impact paths may exist, and the mechanism of the effect of internal control on enterprise resilience can be explored in depth in future research in terms of management efficiency, operating costs, and financing constraints. Third, further analysis is conducted from government support and business environment, which are selected as external influencing factors, lacking heterogeneity analysis of internal business characteristics of enterprises, and future research can further expand the research content around the nature of enterprise property rights, industry types, and enterprise life cycle.

Author Contributions

Conceptualization, N.W.; formal analysis, D.C.; methodology, C.J.; writing—original draft, N.W.; writing—review and editing, C.J. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The ESG rating data and other financial data used in this study are available in the Wind and CSMAR databases, China.

Acknowledgments

I thank the anonymous referees for their valuable comments. All remaining errors are my own responsibility.

Conflicts of Interest

The author declares no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Tables

Table 1

Variable definition and calculation.

Variable Type	Variable Name	Code	Calculation Method
Dependentvariable	Rec	resistance	decline, See model (2) calculation formula
		recovery	decline_time, See model (3) calculation formula
Independentvariable	Internal Control	IC	DIB Internal Control Index/1000
Mechanism variables	Resource allocation efficiency	TFP	LP method measures the total factor productivity
	Operating Risk	Risk	Standard deviation of business income over three years
	Innovation output	Patent	Number of enterprise patents granted
Moderation variables	government support	government support	Non-operating expenses or revenues in the company’s financial statements
	business environment	Market	“2018 China Province Doing Business Report” to measure the level of business environment
Control variables	Fixed assets ratio	Fixed	Net fixed assets/Total Assets
	Company Size	Size	Log (1+ total assets)
	Financial leverage	Lev	Total liabilities/Total Assets
	Nature of ownership	Soe	Set the dummy variable to 0 for non-state-owned enterprises and 1 for state-owned enterprises
	Profitability	Roa	Net Profit/Total Assets
	Cash flow Ratio	Cf	Cash flow/Total Assets

Table 2

Descriptive statistics of the variables.

Variable	N	Mean	Sd	p50	Min	Max
IC	1883	0.656	0.066	0.665	0.23	0.883
decline	1883	−0.639	0.145	−0.649	−0.96	−0.025
decline_time	1883	600.5	195.3	571	12	1036
Risk	1883	0.206	0.163	0.163	0.014	0.882
Patent	1883	0.815	1.029	0.693	0	4.804
TFP	1883	7.964	1.036	7.880	4.718	12.354
Fixed	1883	0.206	0.146	0.175	0.004	0.652
Size	1883	22.20	1.179	22.07	20.12	26.00
Lev	1883	0.382	0.184	0.369	0.061	0.847
Soe	1883	0.494	0.5	0	0	1
Roa	1883	0.048	0.048	0.042	−0.12	0.202
Cf	1883	0.041	0.064	0.038	−0.142	0.221

Table 3

Baseline regression results.

	(2)	(3)
	decline	decline_time
IC	0.130 **	−134.697 *
	(2.58)	(−1.93)
Fixed	0.045 **	77.953 **
	(2.08)	(2.36)
Size	0.044 ***	−21.556 ***
	(13.89)	(−4.45)
Lev	−0.066 ***	18.486
	(−3.12)	(0.57)
Soe	0.100	−104.103
	(1.20)	(−0.90)
Roa	−0.007	188.472
	(−0.09)	(1.54)
Cf	0.339 ***	−262.663 ***
	(5.66)	(−3.06)
Industry and Year	Yes	Yes
_cons	−1.719 ***	1162.268 ***
	(−25.28)	(10.96)
N	1883	1883
r2_a	0.165	0.122
F	42.554	25.346

Note: Numbers in parentheses are t-values of two-tailed tests; ***, **, and * denote significance levels at 1%, 5%, and 10%, respectively.

Table 4

Cross-multiplication of firm characteristics quantile.

	Size		CF
	(1)	(2)	(3)	(4)
	decline	decline_time	decline	decline_time
IC	0.149 ***	−97.630 *	0.290 ***	−180.464 ***
	(2.98)	(−1.91)	(5.66)	(−2.60)
Size*IC_2	0.060 ***	−53.601 ***
	(4.91)	(−2.99)
Size*IC_3	0.127 ***	−64.864 ***
	(9.59)	(−3.33)
Size*IC_4	0.179 ***	−110.213 ***
	(13.48)	(−5.68)
CF*IC_2			0.037 ***	−28.131
			(2.81)	(−1.53)
CF*IC_3			0.062 ***	−51.907 ***
			(4.17)	(−2.64)
CF*IC_4			0.071 ***	−37.591 **
			(5.37)	(−2.00)
Controls	Yes	Yes	Yes	Yes
Industry and Year	Yes	Yes	Yes	Yes
_cons	−0.798 ***	702.301 ***	−0.854 ***	735.922 ***
	(−24.62)	(15.77)	(−25.76)	(16.50)
N	1883	1883	1883	1883
r2_a	0.116	0.021	0.039	0.007
F	62.719	10.314	18.530	4.513

Note: ***, ** and * denote significance levels at 1%, 5%, and 10%, respectively.

Table 5

DID test.

	(1)	(2)
	Volatility	Volatility
IC × time	−0.021 **	−0.017 **
	(−2.55)	(−1.99)
Fixed		0.024 **
		(2.50)
Size		−0.006 ***
		(−4.97)
Lev		0.029 ***
		(3.57)
Roa		0.078 ***
		(3.99)
Soe		−0.009 ***
		(−4.98)
Cf		0.037
		(1.58)
Ind	Yes	Yes
Year	Yes	Yes
_cons	0.001	0.024 ***
	(0.50)	(3.09)
N	63,018	63,018
r2_a	0.332	0.037

Note: *** and ** denote significance levels at 1% and 5%, respectively.

Table 6

Replacement measurement method.

	(1)	(2)
	Volatility	Growth
IC	−0.010 ***	0.077 **
	(−2.84)	(2.24)
Fixed	−0.022 ***	−0.277 ***
	(−6.58)	(−8.76)
Size	−0.010 ***	0.028 ***
	(−23.11)	(8.11)
Lev	0.031 ***	0.253 ***
	(10.97)	(10.52)
Soe	−0.007 ***	−0.090 ***
	(−8.13)	(−12.57)
Roa	−0.013	1.548 ***
	(−1.44)	(23.51)
Cf	−0.003	−0.598 ***
	(−0.47)	(−11.10)
_cons	0.342 ***	−0.523 ***
	(36.50)	(−7.79)
N	17,058	17,058
r2_a	0.369	0.093

Note: *** and ** denote significance levels at 1% and 5%, respectively.

Table 7

Shock events of US–China trade friction.

	(1)	(2)
	decline	decline_time
IC	0.188 ***	−125.695 **
	(4.82)	(−2.10)
Fixed	0.097 ***	−166.949 ***
	(4.65)	(−5.06)
Size	0.025 ***	−20.880 ***
	(8.19)	(−4.34)
Lev	−0.073 ***	26.254
	(−3.49)	(0.80)
Soe	0.108	−133.195
	(1.23)	(−1.02)
Roa	0.228 ***	97.482
	(5.11)	(1.32)
Cf	0.121 **	−234.115 ***
	(2.05)	(−2.63)
_cons	−1.129 ***	1078.137 ***
	(−18.43)	(10.62)
N	2428	2428
r2_a	0.090	0.031
F	34.282	9.970

Note: *** and ** denote significance levels at 1% and 5%, respectively.

Table 8

Regression results of instrumental variables.

	(1)	(2)	(3)
	IC	decline	decline_time
IC		0.043 *	−826.632 *
		(1.91)	(−1.75)
illegal	−0.068 ***
	(−2.89)
Big4	0.026 ***
	(2.71)
Controls	Yes	Yes	Yes
Industry and Year	Yes	Yes	Yes
_cons	0.416 ***	−1.531 ***	1452.038 ***
	(12.08)	(−10.72)	(6.28)
N	1852	1852	1852
r2_a	0.150	0.127	0.134

Note: *** and * denote significance levels at 1% and 10%, respectively.

Table 9

Propensity score matching.

	(1)	(2)
	decline	decline_time
IC	0.031 ***	−22.766 *
	(3.23)	(−1.68)
Fixed	0.037	96.864 *
	(1.03)	(1.87)
Size	0.042 ***	−25.939 ***
	(6.97)	(−3.37)
Lev	−0.066 *	−26.998
	(−1.65)	(−0.49)
Soe	0.030 *	10.171
	(1.92)	(0.40)
Roa	−0.076	−21.275
	(−0.56)	(−0.10)
Cf	0.383 ***	−485.242 ***
	(3.66)	(−3.81)
Industry and Year	Yes	Yes
_cons	−1.626 ***	1211.671 ***
	(−13.54)	(7.69)
N	979	979
r2_a	0.148	0.045
F	16.359	5.030

Note: *** and * denote significance levels at 1% and 10%, respectively.

Table 10

Analysis of impact mechanisms.

	(1)	(2)	(3)
	TFP	Risk	Patent
IC	0.326 ***	−0.307 ***	0.092 **
	(8.61)	(4.68)	(2.14)
Controls	Yes	Yes	Yes
Ind	Yes	Yes	Yes
Year	Yes	Yes	Yes
_cons	−3.721 ***	−3.909 ***	−2.813 ***
	(−17.13)	(−17.59)	(−6.96)
N	1883	1883	1883
R2_a	0.759	0.063	0.277

Note: *** and ** denotes significance levels at 1%.

Table 11

Government grants subgroup test.

	More government support		Less government support
	(1)	(2)	(3)	(4)
	decline	decline_time	decline	decline_time
IC	0.160 **	−189.442 *	0.087	−29.050
	(2.18)	(−1.85)	(1.19)	(−0.27)
_cons	−1.648 ***	957.257 ***	−1.742 ***	1017.786 ***
	(−15.47)	(6.41)	(−14.95)	(5.88)
Controls	Yes	Yes	Yes	Yes
Industry	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
N	892	892	892	892
r2_a	0.140	0.007	0.122	0.019
F	17.116	1.700	14.718	2.905

Note: ***, **, and * denote significance levels at 1%, 5%, and 10%, respectively.

Table 12

Analysis of the moderating effect of the business environment.

	decline		decline_time
	(3)	(4)	(5)	(6)
IC	0.132 ***	0.159 ***	−135.201 *	−29.416
	(2.61)	(2.82)	(−1.94)	(−0.38)
IC*Market		0.063 *
		(1.77)
IC*Market				−10.929 ***
				(−3.07)
Controls	Yes	Yes	Yes	Yes
Ind and Year	Yes	Yes	Yes	Yes
_cons	−1.726 ***	−1.722 ***	1167.742 ***	1182.744 ***
	(−25.21)	(−25.08)	(10.97)	(11.09)
N	1882	1882	1882	1882
r2_a	0.166	0.166	0.022	0.026
F	42.506	38.642	5.409	5.628

Note: *** and * denote significance levels at 1% and 10%, respectively.

References

1. Gunasekaran, A.; Rai, K.B.; Griffin, M. Resilience and competitiveness of small and medium size enterprises: An empirical research. Int. J. Prod.; 2011; 49, pp. 5489-5509. [DOI: https://dx.doi.org/10.1080/00207543.2011.563831]

2. Zhang, G.Y.; Zhang, C.; Liu, W.Q. Turning crises into safety: A review and prospect of organizational resilience research. Econ. Manag.; 2020; 42, pp. 192-208.

3. Ashbaugh-Skaife, H.; Collins, D.W., Jr.; Lafond, R. The effect of sox internal control deficiencies and their remediation on accrual quality. Account. Rev.; 2008; 83, pp. 217-250. [DOI: https://dx.doi.org/10.2308/accr.2008.83.1.217]

4. Alexander, C.R.; Bauguess, S.W.; Bernile, G.; Lee, Y.-H.A.; Marietta-Westberg, J. Economic effects of SOX Section 404 Compliance: A Corporate insider Perspective. J. Account. Econ.; 2013; 56, pp. 267-290. [DOI: https://dx.doi.org/10.1016/j.jacceco.2013.09.002]

5. Bargeron, L.L.; Lehn, K.M.; Zutter, C.J. Sarbanes-Oxley and Corporate Risk- Taking. J. Account. Econ.; 2010; 49, pp. 34-52. [DOI: https://dx.doi.org/10.1016/j.jacceco.2009.05.001]

6. Doyle, J.; Ge, W.; McVay, S. Accruals quality and internal control over financial reporting. Account. Rev.; 2007; 72, pp. 1141-1170. [DOI: https://dx.doi.org/10.2308/accr.2007.82.5.1141]

7. David, B. Organized labor, audit quality, and internal control. Adv. Account.; 2017; 36, pp. 11-26.

8. Maureen, F.; Melvin, A.; Diane, J. PCAOB inspections: An analysis of entity—Level and application–level control audit deficiencies. Int. J. Account. Inf. Syst.; 2018; 30, pp. 19-39.

9. Zhu, P.; Song, J. The Role of Internal Control in Firms’ Coping with the Impact of the COVID-19 Pandemic: Evidence from China. Sustainability; 2021; 13, 6294. [DOI: https://dx.doi.org/10.3390/su13116294]

10. Rikhardsson, P.; Rohde, C.; Christensen, L.; Batt, C.E. Management controls and crisis: Evidence from the banking sector. Account. Audit. Account. J.; 2021; 34, pp. 757-785.

11. Marsh, S.J.; Stock, G.N. Creating Dynamic Capability: The Role of Intertemporal Integration, Knowledge Retention, and Interpretation. J. P J. Prod. Innov. Manag.; 2006; 23, pp. 422-436. [DOI: https://dx.doi.org/10.1111/j.1540-5885.2006.00214.x]

12. Ortiz-De-Mandojana, N.; Bansal, P. The Long-Term Benefits of Organizational Resilience through Sustainable Business Practices. Strateg. Manag. J.; 2016; 37, pp. 1615-1637. [DOI: https://dx.doi.org/10.1002/smj.2410]

13. Eberly, J.C.; Haskel, J.; Mizen, P. “Potential Capital”, Working from Home, and Economic Resilience; NBER Working Paper, No. 29431 National Bureau of Economic Research: Cambridge, MA, USA, 2021.

14. Wang, Y.G.; Gao, J. COVID-19 shock, economic resilience and China’s high-quality development. Econ. Manag.; 2020; 42, pp. 5-17.

15. Hu, H.F.; Song, X.S.; Guo, X.F. Investor protection system and enterprise resilience: Impact and its mechanism of action. Econ. Manag.; 2020; 42, pp. 23-39.

16. Gittell, J.H.; Cameron, K.; Lim, S.; Rivas, V. Relationships, Layoffs, and Organizational Resilience: Airline Industry Responses to September 11. J. Appl. Behav. Sci.; 2006; 42, pp. 300-329. [DOI: https://dx.doi.org/10.1177/0021886306286466]

17. Desjardine, M.; Bansal, P.; Yang, Y. Bouncing Back: Building Resilience through Social and Environmental Practices in the Context of the 2008 Global Financial Crisi. J. Manag.; 2019; 45, pp. 1434-1460. [DOI: https://dx.doi.org/10.1177/0149206317708854]

18. Tsiapa, M.; Batsiolas, I. Enterprise resilience in regions of Eastern Europe during the period 2007–2011. Post-Communist Econ.; 2019; 31, pp. 19-35. [DOI: https://dx.doi.org/10.1080/14631377.2018.1443250]

19. Teece, D.J.; Pisano, G.; Shuen, A. Dynamic capabilities and strategic management. Strateg. Manag. J.; 1997; 18, pp. 509-533. [DOI: https://dx.doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z]

20. Crichton, M.T.; Ramsay, C.G.; Kelly, T. Enhancing organizational resilience through emergency planning: Learnings from cross-sectoral lessons. J. Contingencies Crisis Manag.; 2009; 17, pp. 24-37. [DOI: https://dx.doi.org/10.1111/j.1468-5973.2009.00556.x]

21. Biddle, G.; Hilary, G.; Verdi, R.S. How does Financial Reporting Quality Relate to Investment Efficiency?. J. Account. Econ.; 2009; 81, pp. 112-131. [DOI: https://dx.doi.org/10.1016/j.jacceco.2009.09.001]

22. Ambulkar, S.; Blackhurst, J.; Grawe, S. Firm’s resilience to supply chain disruptions: Scale development and empirical examination. J. Oper. Manag.; 2015; 33–34, pp. 111-122. [DOI: https://dx.doi.org/10.1016/j.jom.2014.11.002]

23. Parker, H.; Ameen, K. The role of Resilience Capabilities in Shaping How Firms Respond to Disruptions. J. Bus. Res.; 2018; 88, pp. 535-541. [DOI: https://dx.doi.org/10.1016/j.jbusres.2017.12.022]

24. Liu, H.; Jiang, J.; Xue, R.; Meng, X.; Hu, S. Corporate environmental governance scheme and investment efficiency over the course of COVID-19. Financ. Res. Lett.; 2022; 47, 102726. [DOI: https://dx.doi.org/10.1016/j.frl.2022.102726] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35185400]

25. Petruzzi, J.; Loyear, R. Improving organisational resilience through enterprise security risk management. J. Bus. Contin. Emerg. Plan.; 2016; 10, pp. 44-56.

26. Hutton, A.P.; Marcus, A.J.; Tehranian, H. Opaque Financial Reports, R2, and Crash Risk. J. Financ. Econ.; 2009; 94, pp. 67-86. [DOI: https://dx.doi.org/10.1016/j.jfineco.2008.10.003]

27. Feng, M.; Li, C.; McVay, S. Internal Control and Management Guidance. J. Account. Econ.; 2009; 48, pp. 190-209. [DOI: https://dx.doi.org/10.1016/j.jacceco.2009.09.004]

28. Fang, H.X.; Chen, Z.H. Can high quality internal control effectively cope with idiosyncratic and systemic risks?. Account. Res.; 2015; 330, pp. 70–77, 96.

29. Leisen, R.; Steffen, B.; Weber, C. Regulatory risk and the resilience of new sustainable business models in the energy sector. J. Clean. Prod.; 2019; 219, pp. 865-878. [DOI: https://dx.doi.org/10.1016/j.jclepro.2019.01.330]

30. Bertrand, M.; Mullainathan, S. Enjoying the quiet life? Corporate governance and managerial preferences. J. Political Econ.; 2003; 111, pp. 1043-1075. [DOI: https://dx.doi.org/10.1086/376950]

31. Hsu, P.H.; Tian, X.; Xu, Y. Financial development and innovation: Cross-country evidence. J. Financ. Econ.; 2014; 112, pp. 116-135. [DOI: https://dx.doi.org/10.1016/j.jfineco.2013.12.002]

32. Chang, X.; Fu, K.; Low, A.; Zhang, W. Non-executive employee stock options and corporate innovation. J. Financ. Econ.; 2015; 115, pp. 168-188. [DOI: https://dx.doi.org/10.1016/j.jfineco.2014.09.002]

33. Manso, G. Motivating innovation. J. Financ.; 2011; 66, pp. 1823-1860. [DOI: https://dx.doi.org/10.1111/j.1540-6261.2011.01688.x]

34. Hamel, G. The why, what, and how of management innovation. Harv. Bus. Rev.; 2006; 84, pp. 72–84, 163. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16485806]

35. Alghanmi, A.O. Innovations as the competitive advantage for small and medium enterprises: The case for the Kingdom of Saudi Arabia. Mark. Manag. Innov.; 2020; 2, pp. 196-205. [DOI: https://dx.doi.org/10.21272/mmi.2020.2-14]

36. Chatzoglou, P.; Chatzoudes, D. The role of innovation in building competitive advantages: An empirical investigation. Eur. J. Innov. Manag.; 2018; 21, pp. 44-69. [DOI: https://dx.doi.org/10.1108/EJIM-02-2017-0015]

37. Du, W.J.; Li, M.J. Government support and innovation for new energy firms in China. Appl. Econ.; 2019; 51, pp. 2754-2763. [DOI: https://dx.doi.org/10.1080/00036846.2018.1558356]

38. Kinsbergen, S.; Molthof, M. The rise and fall of government support for small-scale voluntary development organizations—And their remarkable resilience. Dev. Policy Rev.; 2021; 40, e12559. [DOI: https://dx.doi.org/10.1111/dpr.12559]

39. Liu, T.; Wang, J.; Zhu, Y.; Qu, Z. Linking economic performance and sustainable operations of China’s manufacturing firms: What role does the government involvement play. Sustain. Cities Soc.; 2021; 67, 102717. [DOI: https://dx.doi.org/10.1016/j.scs.2021.102717]

40. Peng, M.W.; Sun, S.L.; Pinkham, B.; Chen, H. The institution-based view as a third leg for a strategy tripod. Acad. Manag. Perspect.; 2009; 23, pp. 63-81. [DOI: https://dx.doi.org/10.5465/amp.2009.43479264]

41. Chaney, P.K.; Faccio, M.; Parsley, D. The Quality of Accounting Information in Politically Connected Firms. J. Account. Econ.; 2011; 51, pp. 58-76. [DOI: https://dx.doi.org/10.1016/j.jacceco.2010.07.003]

42. Kim, G.S. Study on the Corporate Social Responsibility—Focus on Change of Business environment and Internal control system. Kangwon Law Rev.; 2013; 40, pp. 209-236.

43. Erol, O.; Henry, D.; Sauser, B.; Mansouri, M. Perspectives on measuring enterprise resilience. Proceedings of the 2010 IEEE International Systems Conference; San Diego, CA, USA, 5–8 April 2010; pp. 587-592.

44. Ding, D.; Guan, C.; Chan, C.M.; Liu, W. Building Stock Market Resilience through Digital Transformation: Using Google Trends to Analyze the Impact of COVID-19 Pandemic. Front. Bus. Res. China; 2020; 14, 21. [DOI: https://dx.doi.org/10.1186/s11782-020-00089-z]

45. Albuquerque, R.; Koskinen, Y.; Yang, S.; Zhang, C. Resiliency of Environmental and Social Stocks: An Analysis of the Exogenous COVID-19 Market Crash. Rev. Corp. Financ. Stud.; 2020; 9, pp. 593-621. [DOI: https://dx.doi.org/10.1093/rcfs/cfaa011]

46. Wang, X.L.; Fan, G.; Yu, J.W. China Marketization Index Report by Provinces; Social Science Literature Press: Beijing, China, 2018.

47. Hao, S.; Zhang, L. Can government audit and internal control effectively improve organizational resilience?. Audit. Econ. Res.; 2022; 6, pp. 10-20.

48. Wang, N.N.; Li, D.Y.; Cui, D.F.; Ma, X.L. Environmental, social, governance disclosure and corporate sustainable growth: Evidence from China. Front. Environ. Sci.; 2022; 10, 1015764. [DOI: https://dx.doi.org/10.3389/fenvs.2022.1015764]