Moving toward sustainable development: the influence of digital transformation on corporate ESG performance

Peng Yang (School of Information, Central University of Finance and Economics, Beijing, China)
Xiuzi Hao (School of Economics and Management, Haerbin Engineering University, Harbin, China)
Liang Wang (College of Business, Shanghai University of Finance and Economics, Shanghai, China)
Shizhao Zhang (School of Economics, Sungkyunkwan University, Seoul, South Korea)
Li Yang (Business School, The University of Sydney, Sydney, Australia)

Kybernetes

ISSN: 0368-492X

Article publication date: 21 August 2023

Issue publication date: 29 January 2024

1439

Abstract

Purpose

Amidst the rapid development of the global digital economy, digital transformation has become a strategic choice that firms must use to respond to the changing times. This study analyzes the impact of digital transformation on corporate environmental, social and governance (ESG) performance.

Design/methodology/approach

This study analyzes the impact of digital transformation on corporate ESG performance.

Findings

Using panel data from Chinese A-share-listed companies from 2010 to 2019, the authors found that digital transformation has a positive impact on corporate ESG performance, especially for high-tech firms and state-owned firms. In particular, the authors find that the digital production and digital marketing exert a positive effect on corporate ESG performance. Mechanism tests showed that digital transformation helps promote corporate green innovation, improve information transparency and improve corporate governance, thus enhancing ESG performance. A moderating effect analysis revealed that the positive impact of digital transformation on ESG performance is more significant in firms with government subsidies and chief executive officers (CEOs) with rich career experience.

Originality/value

Most existing research has confirmed the positive effect of digital transformation on firms' financial performance, whereas fewer studies have focused on the impact of digital transformation on the non-financial performance of firms.

Keywords

Citation

Yang, P., Hao, X., Wang, L., Zhang, S. and Yang, L. (2024), "Moving toward sustainable development: the influence of digital transformation on corporate ESG performance", Kybernetes, Vol. 53 No. 2, pp. 669-687. https://doi.org/10.1108/K-03-2023-0521

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited


1. Introduction

With the continuous development and wide application of digital technologies, the digital economy is becoming a driving force behind global economic growth. As a micro-component of macro-economy, firms bear the important function of macro-digital economy development and digital transformation has become the strategic choice that firms must take to adapt to the trend of the times. Digital transformation refers to the process by which companies deeply integrate digital technologies into business processes as a way of driving fundamental changes in production processes, organization structure, research and development activities and business models (Vial, 2021). The many potential uses and benefits of digital technologies, such as the internet of Things, big data, artificial intelligence, or blockchain, have spurred great interest from practitioners and researchers to investigate digital technology applications in sustainable product management, supply chains, logistics systems, pharmaceutical manufacturing and education (Norman et al., 2017; Scherer et al., 2019; Baziyad et al., 2022; Rusch et al., 2023). In particular, for businesses, prior research has provided extensive analyses of the value creation effect of digitalization, mainly finding that digital transformation promotes corporate innovation (Wen et al., 2022), improves financial performance and governance level (Manita et al., 2020; Ji et al., 2022) and increases productivity (Gaglio et al., 2022). However, there is relatively little research on how digital transformation affects the non-financial performance of companies.

In recent years, as environmental problems such as air pollution have become more serious, public awareness of environmental protection has been on the rise (Ding et al., 2022; Wang et al., 2023a). This has led companies to focus on improving their non-financial performance in addition to achieving financial performance goals. Environmental, social and governance (ESG) is a new concept about how firms can achieve sustainable development while balancing ESG considerations. Within this concept, environmental management primarily encompasses dimensions related to climate change and carbon emissions, the management and utilization of natural resources, environmental pollution, waste treatment and disposal and the promotion of green innovation (Wang et al., 2023b; Liu et al., 2023). Social responsibility refers to a company's management of its stakeholders, involving activities such as employee training, consumer protection, ensuring product quality and engaging in philanthropic endeavors. Corporate governance entails safeguarding shareholder rights, establishing effective board structures and managing executive compensation. The economic consequences of ESG have emerged as a prominent and widely-discussed issue within the academic community. Some studies have highlighted the potential benefits of ESG ratings provided by third-party rating agencies in mitigating information asymmetry between firms and stakeholders, thereby garnering increased support from stakeholders and fostering a conducive environment for green innovation (Zheng et al., 2023). Moreover, these studies have demonstrated a positive association between ESG performance and financial outcomes, as evidenced by improved financial performance and market value (Arvidsson and Dumay, 2022; Zhou et al., 2022).

Analysis of existing literature reveals that current research on the digital transformation of enterprises and ESG primarily focuses on their economic effects, such as fostering innovation investment, improving financial performance and enhancing market value. However, few studies have linked corporate digital transformation with ESG performance and the influence direction and internal mechanism between the two concepts remain to be studied. The investigation of this problem will help provide empirical evidence for corporations to realize sustainable development through digital transformation.

Based on this, this paper explores the impact of digital transformation on corporate non-financial performance by using the annual report data of 3,131 listed companies in China from 2009 to 2019; constructing firm-level digital transformation indicators using text analysis; and using ESG ratings from third-party rating agencies as a measure of corporate sustainability levels. The findings are as follows: first, digital transformation has a positive promoting effect on corporate ESG performance. This conclusion is still valid after an endogeneity test and a series of robustness tests. Second, mechanism tests show that digital transformation helps promote corporate green innovation, improve information transparency and corporate governance and thus improve ESG performance. Third, government subsidies and chief executive officers (CEOs) with composite career experiences have positive moderating effects, which help to better leverage the contribution of digital transformation to corporate ESG performance.

This research contributes to the existing literature in three ways. First, this paper enriches the literature related to the economic consequences of the digital transformation of firms. While existing studies mainly focused on the impact of digital transformation on the financial performance of firms, few have analyzed the direction and underlying mechanisms of the impact of digital transformation on the non-financial performance of firms, especially in terms of sustainable development. In this paper, we actively explore this issue by linking digital transformation to corporate ESG performance. Second, this paper examines the internal mechanism of the influence of corporate digital transformation on ESG performance from three perspectives: green innovation, information transparency and internal governance, and it opens the black box of the causal relationship between them. Third, this paper finds that government subsidies and compound CEOs have a positive moderating effect on the improvement of corporate ESG performance through digital transformation, which provides empirical evidence of the positive role played by government and human capital in promoting corporate sustainable development.

The content of this paper is as follows: In Section 2, the research hypotheses is presented. Section 3 introduces the data, variables and empirical models. Section 4 presents the baseline results, endogeneity test and robustness tests. Section 5 presents the mechanisms verification, moderating effects test and heterogeneity analysis. Finally, Section 6 provides conclusions, recommendations and limitations.

2. Hypothesis development

2.1 Mechanism analysis

To figure out the mechanisms between digital transformation and corporate ESG performance, we discuss them from three dimensions.

First, digital transformation enhances a company's green innovation ability, thereby improving its environmental performance. With air pollution and other environmental issues attracting more attention from the public around the world, consumers are becoming increasingly aware of environmental protection and have a higher demand for green products (Zameer et al., 2020). Digital transformation can help companies collect demand data from external consumers and internal products and improve the efficiency of their green innovation by analyzing such data (Nambisan et al., 2019). For example, Waqas et al. (2021) found that big data technologies can help companies analyze market demand, make targeted green product innovation decisions and improve green innovation capabilities. Further, green innovation can not only help firms reduce pollution emissions and improve environmental performance (Roy and Khastagir, 2016), but also have a positive impact on firm reputation (Hsu et al., 2011). Therefore, the application of digital technology can improve the ESG performance of firms by promoting green innovation.

Second, digital transformation has improved the transparency of corporate information and facilitated the effective fulfillment of corporate social responsibility. When there is a high degree of information asymmetry between the company and external stakeholders, company management tends to selectively disclose social responsibility information, such as exaggerating its environmental protection achievements, in order to obtain the support of stakeholders and yield maximum reporting benefits. In the digital economy, the “openness” of digital technology has narrowed the distance between enterprises and external stakeholders (Nambisan et al., 2019). Stakeholders can participate in the strategic decision-making process of enterprises through online means and convey user-oriented value propositions to enterprises. This strengthens the strategic orientation of social responsibility of such enterprises. Digitalization also makes the decision-making process more transparent and increases the cost of information falsification, forcing companies to effectively fulfill their social responsibility. Further, the fulfillment of social responsibility helps companies create a good social image, which in turn leads to a higher ESG rating.

Third, digital transformation enhances corporate governance quality. The application of digital technology makes the organizational structure of enterprises more networked and flat. Furthermore, embedding digital technology in the production, operation and management processes of enterprises leads to the interconnection of employees, equipment, resources, products and decision-making systems, which is conducive to enhancing the efficiency of internal information transfer and communication and improving the accuracy of enterprise operation and management decisions, thus improving governance quality.

Based on the above analysis, our first proposed hypothesis is as follows:

H1.

Digital transformation contributes to corporate ESG performance through three channels: promoting corporate green innovation, improving information transparency and improving governance quality.

2.2 Moderating effects analysis

Digital transformation is not simply information technology or the use of data resources, but a long-term systematic reform project for firms in R&D, production, management and sales, etc. From software development and purchase to system operation and maintenance, from equipment replacement to human resources training, all require continuous capital investment, which makes firms with fewer financing channels have cost concerns and are reluctant to carry out digital transformation activities. Compared with other developed countries and regions, in the context of China's incomplete industrialization and uneven informatization, the digital transformation cannot be effectively carried out by firms alone. Therefore, the necessary government subsidies can help alleviate the financial constraints of companies and help them realize smooth digital transformations, which in turn helps improve their ESG performance. For example, in order to promote the digital transformation of firms, the Henan provincial government has introduced several subsidies, giving appropriate subsidies to firms' investment in office space and broadband leasing, encouraging firms with conditions and capabilities to establish big data centers and cloud platforms and giving subsidies for service costs, as well as providing subsidies for enterprises to introduce talents in strengthening the construction of talent teams, or providing listing subsidies and loan subsidies for firms to support their digital transformation.

The executive ladder theory suggests that management's values and cognitive abilities have a significant impact on decision-making and execution and that the different traits of executives significantly influence decision-making and execution. Studies have shown that a combination of career experiences makes CEOs more knowledgeable, more able to identify significant opportunities and challenges in the business, have stronger skills overall and have a more integrated cognitive structure (Hu and Liu, 2015; Custódio et al., 2019). Therefore, compared to single function CEOs, CEOs with a combination of career experiences have better cognitive skills and are more aware of the importance of ESG investments to sustainable business growth. Thus, they are more likely to seize the opportunity of digital transformation and make ESG investments to enhance corporate value.

Based on the above analysis, we propose our second group of hypotheses:

H2a.

The greater the government subsidy, the stronger the contribution of digital transformation to corporate ESG performance.

H2b.

The richer the career experience of corporate CEOs, the stronger the promotion effect of digital transformation on corporate ESG performance.

3. Methodology

3.1 Samples and data sources

Our sample consists of panel data of China's A-share 3,131 listed firms drawn from the China Stock Market and Accounting Research (CSMAR) database from 2009 to 2019. The following samples were removed successively: (1) ST, ST* and PT companies; (2) the observed values of asset-liability ratios greater than 1 and less than 0; (3) firms with major variables missing. Corporate ESG rating data from the Wind database was also utilized. To remove the influence of extreme values, the upper and lower 1% of values for the continuous variables were winsorized. Ultimately, we collected 23,486 observations at the firm-year level.

3.2 Variables

3.2.1 Independent variable: digital transformation

Referring to the method of Saunders and Tambe (2013) and Wang et al. (2023c), we collected the annual reports of listed companies and used text analysis to construct firm-level digital transformation indicators. The basic idea of this measurement method is: the annual report is an objective statement based on the actual operation of the firm and the number of occurrences of digital technology-related keywords in the annual report can better reflect the level of digital transformation.

For this paper, the main core problem is how to extract words from the annual report that can reflect the level of digital transformation in firms. Verhoef et al. (2021) points out that the digital transformation by firms often goes through a process from relatively simple digitization to all round digitalization. The former requires firms to introduce and use digital technology, so that firms have the ability to store, process and transmit data. The latter emphasizes the integration of digital technology and the real economy, which is really applied to the production, management and operation mode of firms. Based on this logic, we construct the firms' digital transformation dictionary from two dimensions of “digital technology application breadth” and “digital technology application depth”.

The detailed steps are as follows: First, collect the annual reports of listed firms from 2007 to 2019 and convert them into text format and then extract all the text through Python.

Second, word segmentation and word frequency statistics are carried out on the annual reports of enterprises and high frequency words related to digital technology are screened out. In terms of the choice of high frequency words, we refer to the relevant national digital economic policy documents and combine the existing authoritative research reports [1] and use Python to screen out 158 words related to the application of digital technology. Then, the frequency of 158 words in policy documents and research reports was calculated by using jieba lexdictionary. In the end, we retained 68 keywords of digital technology application for 10 times or more, thus constructed the firm digital technology application dictionary of this paper. The specific keyword map is shown in Figure 1.

Third, according to the constructed firms' digital transformation dictionary, we continue to search, match and count the keywords of the full text of the annual report and obtain the final total word frequency. It should be noted that the word frequency counting method to construct the index of digital technology application will lead to the feature of “right bias”. Therefore, we add 1 to the index and take the natural logarithm.

3.2.2 Dependent variable: ESG ratings

In accordance with Lin et al. (2021), we used the Sino-Securities ESG Index to measure the ESG ratings of Chinese listed firms and utilized data from the financial terminal of Wind Information. In this paper, the core explanatory variable ESG was obtained by assigning a value of 1–9 to each of the nine ratings from C to AAA of the Sino-Securities ESG Index and transforming them into annual averages.

In the design process, Sino-Securities ESG Index refer to the structure of mainstream ESG systems overseas and build a three-tier indicator assessment system of environmental responsibility, social responsibility and governance capability with Chinese characteristics, based on public data of Chinese listed firms, combined with textual data (e.g. announcements of national regulators, news media data and social responsibility reports).

In the specific indicator construction, the environmental dimension indicators include key variables such as corporate environmental management system, products obtaining environmental certification and environmental violations; the social dimension indicators cover important events (e.g. poverty alleviation, quality of social responsibility reports and negative business events); the corporate governance dimension comprises key variables (e.g. connected transactions, independence of the board of directors, overall financial credibility and quality of information disclosure).

For time, the Sino-Securities ESG Index covers all A-share listed firms since 2009 and is updated quarterly, which is highly representative. In this study, the core explanatory variable ESG is obtained by assigning a value of 1–9 to each of the nine ratings from C to AAA of the Sino-Securities ESG Index and transforming them into annual averages.

3.2.3 Mechanism variables

  1. Green innovation (GreenInv). Following Gaglio et al. (2022), we used the natural logarithm of one plus the number of the firm's green patent applications to measure green innovation.

  2. Information transparency (Transparency). Analysts play the role of “external supervisors” in financial market capitalization. Therefore, the natural logarithm of the total number of analysts followed by firm i in year t plus one was used to measure the information transparency of firms. If the number of analysts is higher, it indicates that the company's information environment is more transparent.

  3. Corporate governance quality (Governance). Following Deng et al. (2023), we used the internal control index obtained from the DIB database to represent internal governance quality. A high value of internal control index indicates better corporate governance quality.

3.2.4 Moderating variables

  1. Governmental subsidy (Subsidy). The natural logarithm of one plus the government subsidies received by enterprises.

  2. CEOs' multi-career experience index (CEO). Referring to the research of Custódio et al. (2019), we measured the richness of a CEO's career experience using the following indicators: the number of functional departments, companies, industries and organizations the CEO had worked in and whether the CEO had overseas working experience. Next, the composite score was calculated by principal component analysis and aggregated to the firm level by weighted average.

3.2.5 Control variables

A range of control variables that may have an impact on firm ESG performance are considered, including firm age (Age), firm size (Size), debt levels (Lev), growth rate of operating income (Growth), percentage of independent directors (Indenp), the proportion of shares held by management (Share), shareholdings of institutional investors (Inst), the nature of ownership (Govcon). The definitions of the variables are shown in Table 1.

3.3 Empirical models

First, based on the related literature (Pu et al., 2023; Wang, 2023), we examined the influence of digital transformation on firms' ESG ratings by the following baseline Ordinary Least Square (OLS) regression analyses:

(1)ESGi,t=β0+β1DIGi,t-1+β2Controlsi,t-1+Firm+Year+ε
where ESG i,t is the core dependent variable, representing the ESG ratings of firm i in year t. The independent variable DIGi,t-1 represents the digital transformation degree of firm i in year t-1. Considering the possible interference of reverse causality on the regression results, the explanatory variable and control variables are lagged by one year. Controls represent a set of control variables that may affect firms' ESG rating. Firm is the firm fixed effect, Year is the year fixed effect and ε is the error term. Standard errors are clustered at the firm level.

Second, we constructed two regressions to examine the mechanisms. Our empirical strategy had two steps. In step 1, we estimated model (2). Mechanism denotes mechanism variables, including GreenInv, Transparency and Governance. The purpose of step 1 was to examine whether the digital transformation has an impact on the mechanism variables.

(2)Mechanismi,t=β0+β1DIGi,t-1+β2Controlsi,t-1+Firm+Year+ε

Theoretically, the green innovation ability, information transparency and governance level of firms are positively related to ESG performance. Therefore, in step 2, we ensured that these factors can be mechanism variables between digital transformation and firms' ESG ratings by estimating the model (3). If the coefficient β1 in models (2) and (3) is significant, it indicates that hypothesis H1 is valid.

(3)ESGi,t=β0+β1Mechanismi,t+β2Controlsi,t-1+Firm+Year+ε

Finally, we constructed model (4). In model (4), Mod represents the moderating variables, including Subsidy and CEO. DIG × Subsidy and DIG × CEO are the core explanatory variables we focused on, which significantly suggested that government subsidies and CEOs with rich career experience had a positive moderating effect.

(4)ESGi,t=β0+β1DIGi,t-1+β2Modi,t-1+β3DIG×Modi,t-1+β4Controlsi,t-1+Firm+Year+ε

3.4 Summary statistics

Table 2 presents the descriptive statistics of the main variables. Over the sample period, the mean and standard deviation of DIG are 1.07 and 1.29, respectively, indicating that there are great differences in the degree of digital transformation among firms. The mean value of ESG is 4.09, indicating that the ESG performance of companies is generally in the mid-range.

4. Empirical results and discussion

4.1 Baseline results

Table 3 presents the regression results of the relationship between “corporate digital transformation-ESG performance”. In the benchmark regression, a progressive regression strategy is used in this paper. Column (1) controls only for firm and time fixed effects and columns (2) and (3) include control variables related to firm financial characteristics and governance characteristics. The regression coefficient of DIG shrinks from 0.0357 to 0.0274, which may be due to the fact that some of the factors affecting firm ESG performance are absorbed after the inclusion of control variables, but the significance remains the same (the t-value is 2.91). Therefore, the baseline regression results indicate that the level of digital transformation is positively associated with corporate ESG performance. Most of the existing literature has predominantly focused on the financial performance of firms, found that digital transformation enhanced firms' innovation capacity (Wen et al., 2022), improved production efficiency and increased market value (Ji et al., 2022; Gaglio et al., 2022). However, few studies have examined the impact of digital transformation on corporate sustainability capacity. Therefore, our study contributes significantly by addressing this research gap.

Among the control variables based on the economic characteristics of firms, the coefficients of total assets (Size), the proportion of independent directors (Indenp), management's shareholding ratio (Share) and shareholdings of institutional investors (Inst) are significantly positive, indicating that the aforementioned variables have a positive effect on firms' ESG performance. Firms with higher debt levels (Lev) have lower ESG ratings; this may be because firms with higher debt levels are under greater financial pressure, which is not conducive to ESG investments. It has been identified that state-owned firms (Govcon) are more likely to make ESG investments, which may be due to their greater social responsibility.

Furthermore, we divided the digital transformation index into two levels: the “application breadth of digital technology” (Diguse) and the “application depth of digital technology” (Digapply). The former requires corporates to introduce and use digital technology, so that corporates have the ability to store, process and transmit data. And the latter emphasizes that corporates really apply digital technology to the production, management and business model. The results in column (1) of Table 4 show that Diguse has no significant impact on corporates ESG performance compared with Digapply, which indicate that corporates simply using digital technology cannot promote sustainable development.

Then, the “application depth of digital technology” (Digapply) is further divided into “digital production” (Digpro), “digital management” (Digmg), “digital marketing” (Digmar) and “digital products” (Digprod). The results presented in columns (2) to (5) indicate that digital production and digital marketing make a stronger contribution to the ESG performance of corporations. Specifically, the findings in column (6), which regresses all sub-indicators simultaneously, reveal that the use of digital technologies in marketing processes enhances a company's ESG performance to a greater extent. This effect may be explained by the fact that the online marketing business model enables easier access to consumer opinions and feedback, which can strengthen the connection between companies and their stakeholders, ultimately leading to better social responsibility and improved ESG performance.

4.2 Endogeneity problem

We dealt with endogeneity by instrumenting for digital transformation. Specifically, the interaction term of information and communication technology (ICT) capital input (from the 2007 input-output table) of 31 provinces and the national Internet penetration rate in 2007 (from the Statistical Report of Internet Development in China) were used as the instrumental variable (IV). The effectiveness of this IV is reflected in two aspects: On the one hand, the high ICT capital investment in the province where the enterprise is located indicates that the level of local digital economy is high, which affects the popularization and development of digital technology and makes it easy for enterprises to access and apply the same, so as to satisfy the relevance condition. On the other hand, the ICT capital investment in 2007 is a historical IV. With the development of digital technology, ICT capital investment at that time can hardly affect the ESG rating of firms at present, even if the influence can only be achieved through the indirect channel of promoting digital transformation, thus satisfying the exogenous condition.

Table 5 presents the results of the IV estimation. Column (1) shows that there is a positive correlation between IV and a corporation's digital transformation (DIG). As expected, firms located in regions with more ICT investment historically are more likely to engage in digital transformation. Meanwhile, the instrument variable passed the weak tool variable test. In the second stage regressions reported in column (2), the positive effect of digital transformation on ESG ratings remains significant at the 5% level. Therefore, it can be argued that the conclusions of this paper still hold after considering the endogeneity problem.

4.3 Robustness test

To guarantee the explanatory power of the baseline model, we conducted a series of robustness checks, including:

4.3.1 Eliminate firms' strategic environmental disclosure behavior

The ESG ratings by third-party rating agencies may be affected by the firms' strategic environmental disclosure behavior. For instance, a firm may exaggerate its environmental responsibilities to achieve a higher ESG score, so as to gain good reputation. As a result, the firm may have biased measurement results. Thus, the sample of listed firms that are only qualified and unqualified by the Stock Exchange's information disclosure quality ratings is removed since these firms may be more inclined to make strategic environmental disclosures. The conclusion that digital transformation leads to higher ESG ratings of firms still holds significantly after excluding the measurement bias issue, as indicated by the regression results in column (1) of Table 6.

4.3.2 Eliminate the problem of missing variables

Although abundant firm-level control variables and firm fixed effect are controlled, there may still be unobgable variables that change over time at the region and industry levels that affect firms' ESG performance. Therefore, we added fixed effects of province × year (Prov × Year) and industry × year (Ind × Year) into the benchmark model. The results in column (2) of Table 6 are consistent with the main conclusion, it can be considered that the possibility of missing variables in this paper is relatively weak.

4.3.3 Alternative proxies for digital transformation

Considering the differences in the application level of corporate digital technology in different industries, we replaced the independent variable with the industry mean-adjusted digital transformation indicator (DIGAdj) to reflect the relative level of corporate digital transformation in different industries. The results for proxies presented in column (3) of Table 6. It can be seen that the promotion effect of digital transformation on corporate ESG ratings has always been established.

4.3.4 Alternative proxies for ESG ratings

To further avoid the measurement bias caused by the accuracy of Sino-Securities ESG Index, we measure the ESG ratings on firms using the ESG index developed by Bloomberg. It should be noted that the Bloomberg ESG Index currently only publishes the ESG ratings of more than 1, 200 Chinese listed firms, so there are a lot of missing values in the sample of firms in the present section. As shown in column (4) of Table 6, the coefficient of DIG is still significantly positive, which is consistent with the main conclusion.

5. Additional analysis and tests

5.1 Mechanism verification

The above regression results reveal the promoting effect of digital transformation on corporate ESG performance, but the question of through which channels digital transformation affects corporate ESG performance has not been answered. Here, we empirically test the three mechanisms proposed in the second part of this paper.

5.1.1 Promoting corporate green innovation

The mechanism test results of promoting corporate green innovation are shown in Table 7. The statistics in columns (1) prove the correlation between the corporate digital transformation and green innovation, because the coefficient of DIG is consistent with our expectations and are significantly positive. In columns (2), the coefficient of GreenInv is significantly positive at the 1% level. The above regression results suggest that there is a transmission mechanism of “corporate digital transformation - promoting green innovation - improving ESG rating”.

5.1.2 Improving information transparency

Table 8 reports the mechanism test results of improving information transparency of firms. The statistics in columns (1) show that digital transformation plays an important role in improving the information transparency of companies. The regression results in column (2) further suggest that increasing the information transparency of firms is conducive to improving ESG performance. The above results suggest that the impact mechanism of digital transformation on corporate ESG performance is valid by increasing the information transparency of firms and motivating the firm to be socially responsible in a real way.

5.1.3 Improving governance quality

The mechanism test results of improving governance quality are shown in Table 9. In columns (1), the coefficient of DIG is significantly positive at the 1% level, which indicates that digital transformation can improve the governance quality of firms. Furthermore, columns (2) in Table 8 show a significantly positive correlation between Governance and ESG. The above regression results suggest that there is a transmission mechanism of “corporate digital transformation - improving governance quality - improving ESG rating”.

In general, these results jointly support hypothesis 1, e.g. digital transformation contributes to corporate ESG performance through three channels: promoting corporate green innovation, improving information transparency and improving governance quality.

5.2 Moderating effects test

Table 10 reports the results of our regression on Model (4). The coefficients on DIG × Subsidy and DIG × CEO are positive and significant at the 1% level, suggesting that the impact of digital transformation on corporate ESG performance is more pronounced in firms with government subsidies and CEOs with rich career experience. The reasons behind this may be that government subsidies ease the financial pressure on companies in the digital transformation process and CEOs with extensive professional experience are more aware of the importance of ESG investments to sustainable development. In summary, hypothesis 2a and 2b are confirmed.

5.3 Heterogeneity analysis

The contribution of digital transformation to corporate ESG performance will vary for firms with different levels of technology. Following Pang et al. (2022), we used the China High-Tech Enterprise (HNTE) program as a classification standard to classify firms into high-tech and non-high-tech firms. The results of the subgroup regressions are shown in columns (1) and (2) of Table 11, we found that the corporate digital transformation has a stronger contribution to ESG performance of high-tech firms. Possible reason for this is: compared with non-high-tech firms with backward production and operation mode, high-tech firms are characterized by high-technology intensity and strong absorption ability. As a result, high-tech firms have the ability to better integrate digital technology into their production, management, R&D and other business processes, thereby leveraging the contribution of digital technology to their businesses in terms of environmental and social aspects.

According to the different nature of firm ownership, we regressed the grouping of SOEs and non-SOEs. The regression results are shown in columns (3) and (4) of Table 10, which show that the digital transformation has a significant contribution to corporate ESG performance of both SOEs and non-SOEs and the contribution to the ESG performance of SOEs is more significant. This is mainly due to the fact that SOEs, as the main force in implementing national key strategic guidelines, usually take the initiative to respond to the government's call for environmental protection and actively assume social responsibility. Therefore, in the era of digital economy, SOEs should more actively promote the digital transformation and take more social responsibility.

6. Conclusions, recommendations and limitations

6.1 Main conclusions

Amidst the rapid development of the global digital economy, digital transformation has become a strategic choice that firms must use to respond to the changing times. Prior research has discussed the firm value creation effect of digital transformation, including promoting corporate innovation (Wen et al., 2022), improving productivity (Gaglio et al., 2022) and financial performance (Ji et al., 2022). However, there is relatively little research on how digital transformation affects the non-financial performance of firms.

Based on this, employing the panel data of Chinese A-share listed companies from 2010 to 2019, this paper analyzes the impact of digital transformation on corporate ESG performance. The findings are as follows: (1) Digital transformation has a positive promoting effect on corporate ESG performance. This conclusion is still valid after an endogeneity test and a series of robustness tests. (2) Mechanism tests show that digital transformation helps promote corporate green innovation, improve information transparency and corporate governance and thus improve ESG performance. (3) Government subsidies and chief executive officers (CEOs) with composite career experiences have positive moderating effects, which help to better leverage the contribution of digital transformation to corporate ESG performance. (4) The positive impact of digital transformation on corporate ESG performance is more significant for high-tech firms and state-owned firms. Therefore, this paper's conclusion sheds new light on the bright side of digital transformation from the aspect of ESG performance and provides insights into how to improve corporate ESG performance.

6.2 Policy recommendations and management insights

The findings of this paper provide valuable suggestions for decision makers in government as well as in firms.

  1. For governments, on the one hand, the government should not only accelerate the construction of digital infrastructure, improve the coverage level of digital technology in various regions and industries and promote the deep integration of digital technology and firms' production, R&D, management and marketing, but also provide subsidies to help firms achieve smooth digital transformation, especially for non-high-tech firms and private firms. On the other hand, the government should guide firms to apply digital technologies to enhance sustainability and actively engage in ESG practices.

  2. For firms, on the one hand, firms should and pay more attention to cultivating compound management talents, which is conducive to apply digital technologies in all aspects of their production and operation activities to promote green technological innovation, improve internal information transparency and the quality of corporate governance, so as to improve ESG performance. On the other hand, private firms and non-high-tech firms should actively carry out the digital transformation, which is conducive to the sustainable development of firms.

6.3 Limitations

There are two limitations in this study.

  1. Due to the availability of data, we do not separately investigate the effect of digital transformation on firms' environmental, social and corporate governance, based on this study, future research can to explore more comprehensively the effect of digital transformation on corporate sustainability from the three dimensions of ESG.

  2. In terms of methodology, this paper mainly used textual analysis to construct indicators of corporate digital transformation. As information such as data assets are accounted for in the financial statements of firms, future research can use more precise indicators to reexamine the relationship and mechanisms between digital transformation and corporate sustainability.

Figures

Keywords map of firms' digital transformation

Figure 1

Keywords map of firms' digital transformation

Definitions of the main variables

Variable typesVariablesDefinitions
Dependent variableESGSino-Securities ESG Index
Independent variableDIGThe index of corporate digital transformation
Mechanism variablesGreenInvLn (green patents application +1)
TransparencyLn (total number of analysts +1)
GovernanceLn (internal control index)
Moderating variablesSubsidyLn (governmental subsidy +1)
CEOCEOs' multi career experience index
Control variablesAgeLn (age of establishment +1)
SizeLn (total assets)
LevTotal liabilities divided by total assets
GrowthGrowth rate of operating income
IndenpThe proportion of independent directors
ShareManagement's shareholding ratio
InstShareholdings of institutional investors
GovconDummy variable, = 1 if a firm is state owned; = 0 otherwise

Source(s): Authors' work

Summary statistics

VariablesObservationsMeanSDMinMedianMax
ESG23,4864.090.981.004.008.00
DIG23,4861.071.290.000.696.76
GreenInv23,4860.450.900.000.007.37
Transparency23,4861.521.170.001.614.33
Governance23,4866.321.060.006.516.90
Subsidy23,48615.513.470.0016.0922.74
CEO23,4867.480.625.457.3811.25
Age23,4862.760.390.002.833.95
Size23,48622.021.3115.7221.8428.52
Lev23,4860.420.210.010.421.00
Growth23,4860.292.41−1.310.11140.24
Indenp23,4860.370.060.090.330.80
Share23,4860.130.210.000.000.90
Inst23,4860.370.240.000.373.27
Govcon23,4860.400.490.000.001.00

Source(s): Authors' work

The effect of digital transformation on firms' ESG ratings

(1)(2)(3)
ESGESGESG
DIG0.0357***0.0258***0.0274***
(0.0097)(0.0095)(0.0094)
Age −0.3233***−0.2207*
(0.1108)(0.1127)
Size 0.2121***0.2110***
(0.0203)(0.0202)
Lev −0.9667***−0.9499***
(0.0737)(0.0734)
Growth 0.00960.0088
(0.0110)(0.0110)
Indenp 0.9986***
(0.1865)
Share 0.5258***
(0.1140)
Inst 0.0923**
(0.0372)
Govcon 0.1372**
(0.0616)
Constant4.0410***0.7153−0.0307
(0.0195)(0.4917)(0.5009)
Firm F.E.YesYesYes
Year F.E.YesYesYes
Observations23,48623,48623,486
Within R20.01850.05100.0573

Note(s): Robust standard errors clustered at the firm level are in parentheses. ***, ** and * represent different significance levels, indicating p < 0.01, p < 0.05 and p < 0.1, respectively

Source(s): Authors' work

Subindex regression results of corporate digital transformation

(1)(2)(3)(4)(5)(6)
ESGESGESGESGESGESG
Diguse0.0070 −0.0001
(0.0111) (0.0115)
Digapply0.0208**
(0.0099)
Digpro 0.0423*** 0.0223**
(0.0118) (0.0111)
Digmg 0.0204 0.0067
(0.0178) (0.0183)
Digmar 0.0514*** 0.0381***
(0.0131) (0.0121)
Digprod 0.01620.0018
(0.0197)(0.0203)
ControlsYesYesYesYesYesYes
Firm F.E.YesYesYesYesYesYes
Year F.E.YesYesYesYesYesYes
Observations23,48623,48623,48623,48623,48623,486
Within R20.05700.05770.05660.05810.05650.0584

Note(s): Control variables include firm age, size, debt levels, growth rate of operating income, percentage of independent directors, management shareholding, shareholdings of institutional investors and the nature of ownership. Robust standard errors clustered at the firm level are in parentheses. ***, ** and * represent different significance levels, indicating p < 0.01, p < 0.05 and p < 0.1, respectively

Source(s): Authors' work

IV estimation

(1)(2)
DIGESG
IV0.0010***
(0.0003)
DIG 0.7057**
(0.2962)
ControlsYesYes
Firm F.E.YesYes
Year F.E.YesYes
Observations23,48623,486
Cragg-Donald Wald F statistic33.27***
(0.0000)

Note(s): Control variables include firm age, size, debt levels, growth rate of operating income, percentage of independent directors, management shareholding, shareholdings of institutional investors and the nature of ownership. Robust standard errors clustered at the firm level are in parentheses. ***, ** and * represent different significance levels, indicating p < 0.01, p < 0.05 and p < 0.1, respectively

Source(s): Authors' work

Robustness test

(1)(2)(3)(4)
ESGESGESGBloomberg ESG
DIG0.0288***0.0375*** 0.1647*
(0.0093)(0.0095) (0.0863)
DIGAdj 0.0381***
(0.0095)
ControlsYesYesYesYes
Prov × Year F.E.NoYesNoNo
Ind × Year F.E.NoYesNoNo
Firm F.E.YesYesYesYes
Year F.E.YesYesYesYes
Observations21,60323,48623,48623,486
Within R20.04930.10070.05800.2532

Note(s): Control variables include firm age, size, debt levels, growth rate of operating income, percentage of independent directors, management shareholding, shareholdings of institutional investors and the nature of ownership. Robust standard errors clustered at the firm level are in parentheses. ***, ** and * represent different significance levels, indicating p < 0.01, p < 0.05 and p < 0.1, respectively

Source(s): Authors' work

Mechanism verification: promoting corporate green innovation

(1)(2)
GreenInvESG
DIG0.0265***
(0.0067)
GreenInv 0.0746***
(0.0116)
ControlsYesYes
Firm F.E.YesYes
Year F.E.YesYes
Observations23,48623,486
Within R20.03980.0596

Note(s): Control variables include firm age, size, debt levels, growth rate of operating income, percentage of independent directors, management shareholding, shareholdings of institutional investors and the nature of ownership. Robust standard errors clustered at the firm level are in parentheses. ***, ** and * represent different significance levels, indicating p < 0.01, p < 0.05 and p < 0.1, respectively

Source(s): Authors' work

Mechanism verification: improving information transparency

(1)(2)
TransparencyESG
DIG0.0418***
(0.0106)
Transparency 0.0736***
(0.0088)
ControlsYesYes
Firm F.E.YesYes
Year F.E.YesYes
Observations23,48623,486
Within R20.16810.0627

Note(s): Control variables include firm age, size, debt levels, growth rate of operating income, percentage of independent directors, management shareholding, shareholdings of institutional investors and the nature of ownership. Robust standard errors clustered at the firm level are in parentheses. ***, ** and * represent different significance levels, indicating p < 0.01, p < 0.05 and p < 0.1, respectively

Mechanism verification: improving governance quality

(1)(2)
GovernanceESG
DIG0.0284**
(0.0126)
Governance 0.0833***
(0.0065)
ControlsYesYes
Firm F.E.YesYes
Year F.E.YesYes
Observations23,48623,486
Within R20.02070.0698

Note(s): Control variables include firm age, size, debt levels, growth rate of operating income, percentage of independent directors, management shareholding, shareholdings of institutional investors and the nature of ownership. Robust standard errors clustered at the firm level are in parentheses. ***, ** and * represent different significance levels, indicating p < 0.01, p < 0.05 and p < 0.1, respectively

Source(s): Authors' work

Moderating effects test

(1)(2)
ESGESG
DIG0.1421***0.4658***
(0.0374)(0.0868)
Subsidy0.0001
(0.0028)
DIG × Subsidy0.0105***
(0.0023)
CEO 0.0467**
(0.0219)
DIG × CEO 0.0650***
(0.0114)
ControlsYesYes
Firm F.E.YesYes
Year F.E.YesYes
Observations23,48623,486
Within R20.05980.0640

Note(s): Control variables include firm age, size, debt levels, growth rate of operating income, percentage of independent directors, management shareholding, shareholdings of institutional investors and the nature of ownership. Robust standard errors clustered at the firm level are in parentheses. ***, ** and * represent different significance levels, indicating p < 0.01, p < 0.05 and p < 0.1, respectively

Source(s): Authors' work

Heterogeneity analysis

High-techNon-high-techSOEsNon-SOEs
(1)(2)(3)(4)
ESGESGESGESG
DIG0.0370***0.01840.0638***0.0225*
(0.0130)(0.0138)(0.0151)(0.0117)
ControlsYesYesYesYes
Firm F.E.YesYesYesYes
Year F.E.YesYesYesYes
Observations12,22911,257937214,114
Within R20.05850.05670.06340.0716

Note(s): Control variables include firm age, size, debt levels, growth rate of operating income, percentage of independent directors, management shareholding, shareholdings of institutional investors and the nature of ownership. Robust standard errors clustered at the firm level are in parentheses. ***, ** and * represent different significance levels, indicating p < 0.01, p < 0.05 and p < 0.1, respectively

Source(s): Authors' work

Note

1.

Representative documents include Outline of Action for Promoting Big Data Development, Development Plan for the Next Generation of Artificial Intelligence, Implementation Guide for Promoting Cloud on Enterprise, Special Action Plan for Digital Empowerment of Small and Medium-sized Enterprises and Statistical Classification of Digital Economy and Core Industries. Research reports for reference include China Digital Economy Development White Paper, Cloud Computing White Paper, Trusted Artificial Intelligence White Paper, Artificial Intelligence Core Technology Industry White Paper, Virtual Reality White Paper, Blockchain White Paper, Big Data White Paper, IoT White Paper and Industrial Internet Industry Economic Development Report. The above research reports are from the China National Academy of Information and Communication (CAICT).

Funding statement: The authors declare that no funds, grants or other support were received during the preparation of this manuscript.

Competing interests statement: The authors have no relevant financial or non-financial interests to disclose.

References

Arvidsson, S. and Dumay, J. (2022), “Corporate ESG reporting quantity, quality and performance: where to now for environmental policy and practice?”, Business Strategy and the Environment, Vol. 31 No. 3, pp. 1091-1110.

Baziyad, H., Kayvanfar, V. and Kinra, A. (2022), “The Internet of Things—an emerging paradigm to support the digitalization of future supply chains”, in The Digital Supply Chain, Elsevier, pp. 61-76.

Custódio, C., Ferreira, M.A. and Matos, P. (2019), “Do general managerial skills spur innovation?”, Management Science, Vol. 65 No. 2, pp. 459-476.

Deng, X., Li, W. and Ren, X. (2023), “More sustainable, more productive: evidence from ESG ratings and total factor productivity among listed Chinese firms”, Finance Research Letters, Vol. 51, 103439.

Ding, J., Liu, B. and Shao, X. (2022), “Spatial effects of industrial synergistic agglomeration and regional green development efficiency: evidence from China”, Energy Economics, Vol. 112, 106156, ISSN 0140-9883, doi: 10.1016/j.eneco.2022.106156.

Gaglio, C., Kraemer-Mbula, E. and Lorenz, E. (2022), “The effects of digital transformation on innovation and productivity: firm-level evidence of South African manufacturing micro and small enterprises”, Technological Forecasting and Social Change, Vol. 182, 121785.

Hsu, C.W., Hu, A.H., Chiou, C.Y. and Chen, T.C. (2011), “Using the FDM and ANP to construct a sustainability balanced scorecard for the semiconductor industry”, Expert Systems with Applications, Vol. 38 No. 10, pp. 12891-12899.

Hu, C. and Liu, Y.J. (2015), “Valuing diversity: CEOs' career experiences and corporate investment”, Journal of Corporate Finance, Vol. 30, pp. 11-31.

Ji, H., Miao, Z., Wan, J. and Lin, L. (2022), “Digital transformation and financial performance: the moderating role of entrepreneurs’ social capital”, Technology Analysis and Strategic Management, Vol. 30, pp. 1-18.

Lin, Y., Fu, X. and Fu, X. (2021), “Varieties in state capitalism and corporate innovation: evidence from an emerging economy”, Journal of Corporate Finance, Vol. 67, 101919.

Liu, Y., Xie, Y. and Zhong, K. (2023), “Impact of digital economy on urban sustainable development: evidence from Chinese cities”, Sustainable Development, pp. 1-18, doi: 10.1002/sd.2656.

Manita, R., Elommal, N., Baudier, P. and Hikkerova, L. (2020), “The digital transformation of external audit and its impact on corporate governance”, Technological Forecasting and Social Change, Vol. 150, 119751.

Nambisan, S., Wright, M. and Feldman, M. (2019), “The digital transformation of innovation and entrepreneurship: progress, challenges and key themes”, Research Policy, Vol. 48 No. 8, 103773.

Norman, J., Madurawe, R.D., Moore, C.M., Khan, M.A. and Khairuzzaman, A. (2017), “A new chapter in pharmaceutical manufacturing: 3D-printed drug products”, Advanced Drug Delivery Reviews, Vol. 108, pp. 39-50.

Pang, Y., Zhang, F., Wang, Q., Wang, L. and Wang, C. (2022), “High-and new-technology enterprise certification, enterprise innovation ability and export product quality”, Technology Analysis and Strategic Management, Vol. 30, pp. 1-16.

Pu, G., Xie, Y. and Wang, K. (2023), “Board faultlines and risk-taking”, Finance Research Letters, Vol. 51, 103404, ISSN 1544-6123, doi: 10.1016/j.frl.2022.103404.

Roy, M. and Khastagir, D. (2016), “Exploring role of green management in enhancing organizational efficiency in petro-chemical industry in India”, Journal of Cleaner Production, Vol. 121, pp. 109-115.

Rusch, M., Schöggl, J.P. and Baumgartner, R.J. (2023), “Application of digital technologies for sustainable product management in a circular economy: a review”, Business Strategy and the Environment, Vol. 32 No. 3, pp. 1159-1174.

Saunders, A. and Tambe, P. (2013), “A measure of firms' information practices based on textual analysis of 10-K filings”, Working Paper.

Scherer, R., Siddiq, F. and Tondeur, J. (2019), “The technology acceptance model (TAM): a meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education”, Computers and Education, Vol. 128, pp. 13-35.

Verhoef, P.C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J.Q., Fabian, N. and Haenlein, M. (2021), “Digital transformation: a multidisciplinary reflection and research agenda”, Journal of Business Research, Vol. 122, pp. 889-901.

Vial, G. (2021), “Understanding digital transformation: a review and a research agenda”, Managing Digital Transformation, Vol. 28, pp. 13-66.

Wang, L. (2023), “Mitigating firm-level political risk in China: the role of multiple large shareholders”, Economics Letters, Vol. 222, 110960, ISSN 0165-1765, doi: 10.1016/j.econlet.2022.110960.

Wang, L., Qi, J. and Zhuang, H. (2023a), “Monitoring or collusion? Multiple large shareholders and corporate ESG performance: evidence from China”, Finance Research Letters, Vol. 53, 103673, ISSN 1544-6123, doi: 10.1016/j.frl.2023.103673.

Wang, L., Zhang, Y. and Qi, C. (2023b), “Does the ceos' hometown identity matter for firms' environmental, social, and governance (esg) performance?”, Environmental Science and Pollution Research, Vol. 30 No. 26, pp. 69054-69063.

Wang, L., Wang, Q. and Jiang, F. (2023c), “Booster or stabilizer? Economic policy uncertainty: New firm-specific measurement and impacts on stock price crash risk”, Finance Research Letters, Vol. 51, 103462, ISSN 1544-6123, doi: 10.1016/j.frl.2022.103462.

Waqas, M., Honggang, X., Ahmad, N., Khan, S.A.R. and Iqbal, M. (2021), “Big data analytics as a roadmap towards green innovation, competitive advantage and environmental performance”, Journal of Cleaner Production, Vol. 323, 128998.

Wen, H., Zhong, Q. and Lee, C.C. (2022), “Digitalization, competition strategy and corporate innovation: evidence from Chinese manufacturing listed companies”, International Review of Financial Analysis, Vol. 82, 102166.

Zameer, H., Wang, Y., Yasmeen, H. and Mubarak, S. (2020), “Green innovation as a mediator in the impact of business analytics and environmental orientation on green competitive advantage”, Management Decision, Vol. 60 No. 2, pp. 488-507.

Zheng, Q., Lin, D., Li, H. and Wang, Y. (2023), “Study on the path of corporate ESG performance to drive green innovation--based on data of listed companies in sichuan province”, Proceedings of the 4th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2022, Chongqing, China, December 9-11, 2022.

Zhou, G., Liu, L. and Luo, S. (2022), “Sustainable development, ESG performance and company market value: mediating effect of financial performance”, Business Strategy and the Environment, Vol. 31 No. 7, pp. 3371-3387.

Corresponding author

Liang Wang can be contacted at: luminous_wang@163.sufe.edu.cn

Related articles