January 10, 2024 | Posted in News
Table 2 shows the mixed OLS regression, random effects regression, and fixed effects regression with ESG performance as the explanatory variable. The regression results of all three econometric models indicate that the degree of DT can significantly reduce ESG performance, supporting hypothesis 1. Based on the results of the F-test, and Hausman test, the fixed-effects model is the optimal model. Specifically, the impact coefficient of the degree of DT is – 0.013, which is significant at the 5% level. DT is accompanied by the integration of digital technologies with business activities. Through DT, we can improve the efficiency of the organization’s operation, refine user needs, and improve the long-term operation of the enterprise. However, DT is a long-term process. According to the “IT paradox”, DT needs to be carried out gradually in the context of the enterprise’s development, industry conditions, and planning. Digital change implies a high degree of uncertainty, which can add some hidden costs to the enterprise. The introduction of digital technology and its skilled application also require additional management costs. At the same time, DT requires a large amount of innovation investment and long-term factor investment. According to the dynamic capability theory, enterprises face a complex and changing external environment, which will have a certain impact on internal systems during DT. ESG performance incorporates environmental responsibility and social responsibility as opposed to traditional performance evaluation. At the same time, the performance evaluation of corporate governance is also based on the sustainability evaluation of stakeholders. Influenced by self-interest behavior, enterprises tend to ignore environmental and social responsibility when facing the adjustment of DT and tend to maintain the interests of owners more. This results in a lack of investment in external non-operational indicators of enterprises, leading to a significant decline in ESG performance. This phenomenon further reflects that the DT of Chinese enterprises is not yet complete and not deeply integrated with the operational aspects. In terms of control variables, both ES and SOC are found to be able to cause a significant positive impact on ESG performance. Specifically, the coefficient of the effect of ES on ESG is 0.218, which is significant at the 1% level. The increase in size facilitates the formation of economies of scale and lower marginal costs. When purchasing raw materials, enterprises can also expand the purchase volume, which is conducive to reducing production costs and alleviating business pressure. It is conducive to improving the ability of enterprises to fulfill their responsibilities and driving the fulfillment of their social and environmental responsibilities. The coefficient of the effect of SOC on ESG is 0.027, which is significant at the 1% level. The expansion of the SOC will increase shareholder control gains but does not affect ESG performance. It indicates that the effective controlling shareholders will still focus on non-financial performance and on improving the corporate image as well as the future development space. BS and ALR can reduce ESG. At the 1% level, the impact coefficient of BS is – 0.141 and the impact coefficient of ALR is – 0.797. The increase in BS leads to diversification and decentralization of decision-making. Due to different backgrounds, industries, and understandings, the investment efficiency of non-financial performance is reduced, which leads to the decline of ESG performance. The increase in ALR weakens the solvency of enterprises and reduces their productivity of enterprises. As a result, enterprises pay less attention to sustainable development and other external performance, resulting in the decline of ESG performance.
According to the theoretical analysis, DT can reduce corporate information asymmetry by influencing the adjustment of EM, which ultimately affects ESG performance. To test this mechanism, AEM, and REM are selected as suppression variables to empirically analyze the suppression transmission mechanism, respectively. According to the results in Table 3, DT can significantly reduce AEM with an impact coefficient of - 0.196, while the effect on REM is not significant. The potential reason is that DT improves the disclosure of internal business information and increases the communication of information between internal and external companies. Relying on digital technology, the internal data generated from business operations are analyzed and transmitted promptly, and the degree of external informatization increases. Compared to traditional operations, DT facilitates the timeliness and effectiveness of corporate information disclosure, while enabling increased visibility of the production chain. In addition, DT enables dynamic real-time monitoring of operations and feedback on risks and returns in a complex and volatile external environment. It reduces information asymmetry and reduces the room for management to adjust accounting information and manipulate AEM, leading to a decline in AEM. REM is more difficult to detect than AEM because it is more insidious. AEM usually uses accounting treatment to conceal management’s performance issues. Common methods include the use of accounting policy changes and choices to abuse fair value and asset impairment accrues and reversals, etc. This type of EM generally requires the attachment of relevant information to the accounting statements, and it is difficult to distinguish REM from daily operating activities. The period of digital transformation has brought about an impact of digital technology on the original production model, making it more difficult to identify REM. Meanwhile, the greater flexibility in the timing and space of activities of REM further reduces the significance of the impact on it. Column (3) in Table 3 analyzes the suppression mechanism of DT on ESG performance using AEM as a suppression variable. The results show that the coefficient of the effect of AEM on ESG is - 0.109, which is significant at the 5% level. The coefficient of the impact of DT on ESG is – 0.009, respectively, which does not pass the significance test.
Combined with the previous findings, it is found that the indirect effect result (- 0.196*- 0.109) is opposite to the result of the direct effect (- 0.009). It indicates that DT has a negative effect on enterprise ESG, while DT can constrain AEM, thus the direct negative effect of DT on ESG is suppressed by the indirect positive effect of DT in suppressing AEM. Hypothesis 3 is supported. The possible reason is that AEM, as a means of performance manipulation by the management, reduces the quality of accounting statements, which is not conducive to the corporate image and business stability. The increase in AEM reflects the decline of the management’s internal governance mechanism and moral constraints, and the decline in the fulfillment of non-performance responsibilities such as corporate social responsibility, environmental responsibility, and internal responsibility. Therefore, AEM has a significant negative effect on ESG. The introduction of digital technology enables a large amount of data to be collected and collated, which can significantly improve information transparency. The space of EM will be limited, making the financial statements more truthful and reliable, thus restraining the manipulation of performance behavior. The asymmetry of information is gradually reduced, the speculative activities of the management for personal gain are reduced, and the long-term development capability is enhanced, and thus the adverse impact of DT on the ESG performance is reduced. That is, DT restricts AEM, thus mitigating the negative impact on ESG. In the suppression model, the results of each control variable are largely consistent with the previous findings, reflecting the relatively stable effects of each control variable on ESG performance. Specifically, BS and SOC have significant pull effects on ESG, with impact coefficients of 0.233 and 0.033, respectively. BS and ALR still have a negative impact on corporate sustainability. The expansion of the number of directors has led to lower awareness of corporate responsibility, further reflecting the difference in the level of attention given to sustainable development. In the future, there is a need to strengthen the promotion and guidance of the ESG concept. The increase in the ALR reflects the increase in debt financing, which leads to higher operating pressure and reduces the ESG level. Meanwhile, the significance of ROA has increased, with an impact coefficient of 0.058, which is significant at the 1% level. The increase in investment returns drives the efficiency of internal investment operations and thus the capital reserve for ESG investment increases. It further verifies that when internal operational pressure is reduced, firms will pay more attention to the needs of other stakeholders and increase responsible investment.
The robustness test aims to assess the stability of the explanatory power of evaluation methods and indicators. It verifies that they can still provide stable evaluation results when some parameters are changed. To further analyze the model’s robustness, the replacement variable method is chosen in this paper. To explore the influencing factors of ESG performance in-depth, replacement explanatory variables are selected. A more detailed caliber breakdown of DT is performed based on technology composition and application. And the regression analysis is re-run. Referring to Zhao, the DT indicator is constructed by counting the frequencies of 99 digitization-related words in four dimensions, including digital technology applications, Internet business models, smart manufacturing, and modern information systems. The specific word frequency information is shown in the Appendix Figure S1. The regression results are shown in Table 4, and the results show that the values of these coefficients do not change much and there is no change in direction and significance, and the empirical results in this section are robust.
The endogeneity test is performed considering the possible existence of the policy and institutional factors within the residuals, producing problems such as omitted variables. Two-stage least squares analysis is performed using instrumental variable IV estimation. The instrumental variables are subject to both exogeneity and correlation assumptions. Exogeneity requires that the instrumental variables are independent of the perturbation term. Correlation requires that it is related to and affects the explanatory variables only through the endogenous variables. The instrumental variables have a strong historical nature, therefore, the perturbation terms in the sample period cannot affect the instrumental variables and satisfy the independence condition. Referring to Zhong et al., the independent variables are determined with one period lag in the past and are independent of the current period perturbation term, and thus can be used as instrumental variables. Column (1) in Table 5 shows the regression results of the instrumental variables on DT. The regression coefficient is 0.265, which is significant at the 1% level, indicating a significant positive effect of instrumental variables on DT. Column (2) reports the results of the two-stage IV regression when the explanatory variable is ESG. The regression coefficient for DT is significant at the 1% level of – 0.145. The results are generally consistent with the benchmark regression. Hausman’s test result is 38.60, which is significant at the 1% level. Therefore, in this case, it is a reasonable approach to reject the original hypothesis and apply the instrumental variables approach. In summary, the robustness of the basic findings is further verified after considering the endogeneity issue.
The previous study shows that there is a direct effect of DT on ESG performance and a suppression effect of AEM in the path. The effect of REM is not significant, thus it is more reasonable to use AEM as a suppression variable to reflect performance manipulation. Therefore, AEM is used as one of the conditional variables. The specific driving path of DT and EM to promote ESG performance improvement is analyzed.
The previous study shows that there is a direct impact of DT on ESG performance, a suppression effect of AEM in the impact path, and an insignificant effect of REM. Therefore, AEM is used as one of the conditional variables to analyze the specific driving paths of DT and EM on ESG performance. This paper uses a direct calibration method. The 95th percentile, median, and 5th percentile of the conditional and outcome samples are set as completely affiliated, intersection, and completely unaffiliated points, respectively. The advantage of using the median rather than the mean is that the median is less sensitive to outliers. At the same time, due to technical deficiencies in some numerical techniques, the median is not differentiated from the 5th percentile. Therefore, this category of variables is artificially set to be fully affiliated with 1 and fully unaffiliated with 0. The calibration results are shown in Table 6.
Before conducting a sufficiency study, it is necessary to test whether the condition variable becomes a necessary condition for the outcome variable. A necessary condition is a bottleneck that constrains the outcome. When a condition is always present when the outcome occurs, that condition becomes a necessary condition. At the technical means level, certain necessary conditions may be eliminated as logical residuals and do not appear in the parsimonious or intermediate solutions, which can be biased for generating results. Therefore, each conditional variable should be analyzed before the group analysis to see if it constitutes a necessary condition. In this paper, we test whether a single condition (including its non-set) constitutes a necessary condition for a high valuation of ESG performance in 2021. (Consistency greater than 0.9 is a necessary condition). The results of the test are presented in the following table: as shown in Table 7, there is no univariate necessary condition for a high valuation of ESG performance.
Considering asymmetric causality, this paper uses the fsqca method to analyze DT and AEM configurations that produce high ESG performance. At the same time, this paper qualitatively analyzes and names the discovered states to deepen the configuration theory. Thus, it can help enterprises achieve high ESG performance modes through DT and EM. When conducting configuration analysis (adequacy analysis), this paper sets the threshold of case frequency as 3, the original consistency as 0.75, and the PRI consistency threshold as 0.45. The truth value table is shown in Appendix Table S1. To synthesize the simple solution, intermediate solution, and complex solution, this paper chooses to sketch the path diagram to represent the feasible path. If there are the same core conditions and different edge conditions, two different policy paths in the same path are selected for naming (S1a, S1b). At the same time, different path names (S1, S2, S3) are carried out for different core conditions, and different configuration characters are named according to different path conditions.
Table 8 is the configuration path analysis of the high valuation group of ESG performance in 2021. A total of 6 configurations are obtained by the high valuation group, and these 6 configurations are sufficient conditions to promote the improvement of ESG performance. The consistency of the overall configuration is 0.7091, indicating that in the cases of the six configurations, 70.91% of listed enterprises’ ESG performance has been improved. The coverage of the total configuration is 0.5691, indicating that the six configurations together explained 56.91% of the cases. Based on six conditional configuration paths, the influence of each conditional variable on ESG performance high valuation is further analyzed. The core condition of configuration S1 is the application of digital technology and the absence of AEM. Among them, the peripheral condition of configuration S1a is the absence of BT, and the peripheral condition of configuration S1b is the existence of AIT, BDT, and CCT. The consistency is 0.3456 and 0.1668, respectively, covering 35 cases. The core condition of configuration S2 is the existence of AIT, the absence of CCT and AEM, and the peripheral condition is the absence of BT. The consistency of this configuration is 0.0585, covering 20 cases. The configuration of S3 is driven by four peripheral conditions, namely the existence of AI and BDT, BT, and the absence of AEM. The consistency of this configuration is 0.1560, covering 20 cases. The core condition of the S4 configuration is the existence of BDT, the absence of CCT, and the peripheral condition is the absence of BT. The consistency of this configuration is 0.1715, covering 18 cases. The core condition of configuration S5 is the absence of AIT and CCT and the existence of TPA. The consistency of this configuration is 0.1753, covering 20 cases. The core condition of configuration S6 is the existence of AIT and AEM, and the absence of BT and TPA. The consistency of this configuration is 0.1423, covering 20 cases.
The threshold setting of the QCA method has a certain flexibility, and the analysis results may change with the threshold, so it is necessary to conduct a robustness test on the results. This paper adopts the method of adjusting the frequency threshold and consistency threshold to test robustness. When adjusting the consistency threshold, it is found that the number of configurations in the truth table analysis is affected, so we raise the consistency threshold to 0.8 and reprocessed the sample data. The results show that even when the threshold is adjusted, the condition configuration is consistent and no contradictory results appear, so our research conclusion is robust. The specific results are shown in Table 9.
Path 1: TPA* ~ EM* ~ BT and TPA* ~ EM*AIT*BDT*CCT are the high valuations of ESG performance driven by a high level of digital technology usage with low-performance manipulation behavior. Some of these companies lack BT, while others have a high level of AIT, BDT, and CCT. This category of enterprises mainly relies on DT and deep integration with core market tasks to update the technology of specific scenarios in the economy and society, thus forming a new business model. The use of digital technology emphasizes companies’ reliance on digital technology to generate effective innovation outputs and applications in the marketplace. By integrating digital technology with complex business scenarios, the technology chain within the enterprise is gradually transferred to the external front-end market applications. At the same time, such companies do not use information asymmetry to manipulate earnings when enhancing their digital technology practices but reflect the actual operating conditions of the company truly and concretely. Improving the level of trust of external investors in the enterprise. Combined with the peripheral conditions, it is found that some enterprises have insufficient BT, reflecting the inadequacy of digital information technology. BT is difficult to tamper with and decentralized, which provides great convenience for enterprises to store data information. Xu et al. established a K-Out-of-M candidate model by using blockchain technology and introduced the APV algorithm to alleviate the problems of lack of anonymity, excessive concentration, and easy forgery of data. However, it is affected by storage costs and privacy issues, which reduces the applicability of enterprises. Xu and He adopted the Latent Dirichlet Allocation theme model and found that there are certain technical challenges in blockchain technology. Enterprises often seek to hide transaction data, resulting in a lack of trust among participants and making the implementation of blockchain technology difficult. Other enterprises focus on investment in digital intelligence, digital resources, and digital devices to continuously improve the development of underlying digital technology and jointly promote the progress of ESG performance. The enterprises that fit this model are shown in Fig. 4.
Path 2: AIT* ~ CCT* ~ EM* ~ BT are CCT-deficient enterprises relying on AIT with low-performance manipulation behaviors to drive ESG performance. This type of enterprise BT is also deficient. The lack of CCT reflects the lack of development of enterprise digital devices and the poor computing power of data. Taking the Internet of Things (IoT) technology as an example, the widespread application of LOT technology has generated a huge amount of sensor data involving both normal and abnormal data, and the abnormal situations involved may have a great impact on the sustainability of the industry. Faced with this situation, such enterprises rely on AIT to bring into play the decision-making power of digital intelligence. According to the dynamic capability theory, in a complex competitive environment, it is important to develop dynamic capabilities based on the differences of individual firms. Mikalef and Gupta argue that heterogeneous resources constructed through artificial intelligence can enhance the level of innovation dynamics and performance of firms. Kar et al. argue that artificial intelligence significantly contributes to the ESG performance of firms. The adoption of AI technologies can improve enterprises’ efficiency, reduce polluting energy use, and promote the development of the circular economy. At the same time, this category of enterprises continues to improve external trust, reduce performance manipulation, and promote ESG performance. The indirect transmission mechanism of AEM on ESG performance is further verified. The enterprises that fit this model are shown in Fig. 5.
Path 3: AIT*BDT* ~ BT* ~ EM relies on AIT and BDT to improve ESG. This group of enterprises suffers from the lack of BT and low AEM behavior. The configuration enterprise uses BDT to organize massive amounts of data and aggregate huge amounts of information. The core digital information is extracted quickly, processed, and organized into digital resources that can improve business operations. It effectively improves the efficiency of business operations and supports the fulfillment of external responsibilities such as social responsibility. Based on organizing massive resources with BDT, relying on digital intelligence, and linking upstream and downstream partners to build a green trade chain. Digital intelligence can also enhance the science of corporate decision-making. Technologies such as data analytics, neural networks, and knowledge graphs are integrated to build a beneficial information system for business operations, thus improving the sustainability of the enterprise. The lack of BT further validates the general lack of blockchain investment in listed companies, and the issues of immutability, irrevocability, and lack of privacy lead to the lack of application of this technology. In addition, low EM behavior of management also indirectly affects ESG performance as a peripheral condition. The enterprises that fit this model are shown in Fig. 6.
Path 4: BDT* ~ CCT* ~ BT is the enterprise with insufficient CCT relying on BDT to drive ESG. Such enterprises also have a lack of BT. This type of enterprise has poor development of underlying digital technology, and there is a lack of digital equipment and digital information. Faced with the lack of development of the other three digital technologies, enterprises continue to pay more attention to the digital resources of big data. To fully exploit the information in the massive data, explore the value in the data, and rely on the advantages of digital resources to drive the sustainable development capability of enterprises. The enterprises that fit this model are shown in Fig. 7.
Path 5: TPA* ~ AIT* ~ CCT are enterprises with insufficient AIT and CCT relying on the use of digital technology to drive ESG performance. This group of enterprises has insufficient digital infrastructure construction and lacks core underlying digital technology advantages. It mainly reflects the lack of digital intelligence and digital devices. As a result, the impact of digital technology on traditional production and operation methods within this group is relatively small. At the same time, such enterprises focus on the practical application of technology and fully sink the limited digital technology into the business process. To improve the efficiency of digital technology utilization, they continue to accelerate the integration of digital technology into business activities and promote the transformation of business practices. Thus, it promotes DT to empower enterprises and improve their ESG performance. The enterprises that fit this model are shown in Fig. 8.
Path 6: AIT*EM* ~ TPA* ~ BT is that enterprises with insufficient digital technology practice and low BT rely on AIT and high EM to drive ESG performance. These companies rely on digital intelligence and high EM to drive ESG. The application of AI and automation technologies has revolutionized the economic activities and decision-making behavior of firms. This category of companies relies on the decision-making advantages of AI to compensate for the lack of development of digital application scenarios. Among the internal conditions of DT, the main reliance is on digital intelligence to drive sustainable development. Digital intelligence creates a database of ESG development status to help companies analyze and manage data dynamics. It helps companies assess their sustainability progress, reduce the negative impact of their daily operations on the environment, and build a more responsible and resilient supply chain to meet their ESG development goals. At the same time, this category of companies has high EM practices. It indicates that enterprises have high-performance manipulation in the process of using digital intelligence to enable sustainable development. The rest of the digital development is generally absent. The enterprises that fit this model are shown in Fig. 9.
First, DT can significantly reduce enterprise ESG performance, with an impact coefficient of – 0.013, which is significant at the 5% level. Corporate sustainability is distinguished from traditional performance evaluation. It emphasizes the positive impact of business operations on the surrounding environment and includes the business owner as one of the affected objects. In the process of DT, dynamic capability exploration intensifies the instability of the organizational system, and numerous hidden costs further reduce the profit space of enterprises. DT is a long-term process, and there is a time lag between the enterprise’s organizational status and the digital technology architecture. It is necessary to gradually carry out DT according to the development situation of the enterprise, the industry situation, and the enterprise planning. The introduction and skilled application of digital technology also require increased management costs and substantial investment in innovation. Innovation activities require long-term factor input, which leads to a decline in ESG investment. In fact, as a business organization, “profit-seeking” is its most prominent feature. In the process of business activities, enterprises take the maximization of interests as the basic goal. When faced with the impact of DT, enterprises tend to reduce their attention to other stakeholders. The focus on environmental responsibility, social responsibility, and internal governance will be reduced, and more resources will flow to business activities, thus significantly reducing ESG performance.
Second, DT has a significant inhibitory effect on AEM, with an impact coefficient of – 0.196, which is significant at the 10% level. The effect on REM is relatively small and has no statistical significance. DT can inhibit EM from both internal and external aspects. Within enterprises, DT can accurately depict market demand and customer feedback, thus enhancing the effectiveness of production. It reduces the motivation and inclination of management to implement EM. In the aspect of enterprise external supervision, DT converts massive business information into data. It improves the information asymmetry of enterprises and reduces the cost of external supervision, which restrains the EM activities of the management. The effect on REM is not significant, which proves that REM has a certain concealment and is more difficult to detect than AEM, so the DT has a more obvious inhibitory effect on AEM. Therefore, the rationality of accrued earnings management as a suppression variable is stronger.
Third, under the suppression model, AEM has a significant inhibitory effect on ESG performance, with a coefficient of – 0.109, which is significant at the 5% level. The adverse effect of DT on ESG decreased from – 0.013 to – 0.009, with no statistical significance. The indirect effect results are contrary to the direct effect results, reflecting that the AEM has a suppression effect. Before digital technology truly enables enterprises to operate, DT will have a certain impact on the traditional mode of operation. The integration degree with business activities is insufficient, which makes it difficult to take into account ESG performance. However, with the continuous collection and collation of digital technology, the problem of information asymmetry can be improved. Thus, it can limit the change of accounting income information and restrain the management of accrued earnings. The improvement of information transparency can increase the sense of responsibility of enterprises and promote the establishment of an external image. The transparency of information also drives the transparency of operations, and the requirements for internal governance increase accordingly. Therefore, the impact of DT on ESG performance decreases.
Fourth, we find six configuration paths of DT and EM driving ESG performance. First, high technology practice — low-performance manipulation. Technology practice reduces the impact of DT on enterprise management, increases new business scenarios, and promotes the integration of digital technology and enterprise management. Most of these companies lack BT, while other underlying technologies are relatively well-developed. BT has immutability issues, information leakage risks, and regulatory issues that hinder the development of the technology’s application. Scholars have proposed a blockchain-based security digital framework: Block-DEF. The framework has a loosely coupled structure to distinguish evidence from evidence information. Only evidence information is stored in the blockchain, and evidence is stored on a trusted storage platform. It can ensure the integrity and validity of evidence while striking a good balance between privacy and traceability. In the process of future DT, blockchain technology needs to be further developed and expanded to make up for existing application deficiencies. Second, digital intelligence – low-performance manipulation. Some enterprises have relatively weak digital technology infrastructure and focus on digital intelligence to cultivate sustainable development advantages based on internal differential data. At the same time, such enterprises continue to enhance financial credibility, reduce information asymmetry, and establish a good corporate image. Third, digital intelligence — digital resources. In the later stage of DT, enterprises can organize massive data with BDT and extract core digital information in a short time. Processing has become a digital resource that can improve the sustainable development of enterprises, and scientific decision-making using digital intelligence has effectively improved the level of enterprise ESG. Then processing them into digital resources that can improve the sustainable development of enterprises, and scientific decision-making using digital intelligence effectively improves the level of enterprise ESG. Fourth, digital resources — inadequate digital infrastructure. Some enterprises generally lack infrastructure construction and mainly rely on digital resources as the core conditions, which further reflects the digital resource advantages of BDT. Fifth, high technology practice — bottom technology deficiency. When the underlying digital technologies are weak, such enterprises pay attention to the practical application of digital technologies, actively utilize limited digital technologies in their business operations, and promote the level of ESG by expanding green business scenarios. Sixth, digital intelligence — high-performance manipulation. This kind of enterprise has a single digital technology and focuses on the decision analysis ability of artificial intelligence. This category focuses on the development of digital intelligence technologies in the process of DT. High-performance manipulation is also adopted to guide external investment orientation. Investments in blockchain and digital technology use are decreasing and converging resources to improve ESG.