Digital Transformation, Board Diversity, and Corporate Sustainable Development

Abstract

The rapid advancement of information technologies, such as the Internet of Things and big data, has created favorable conditions for digital transformation, which has main effects on the sustainable development of enterprises. Drawing upon stakeholder theory, this article employs text analysis to construct indicators for corporate digital transformation using data from A-share listed companies between 2015 and 2022. Meanwhile, ESG performance is utilized as a measure of corporate sustainable development. Through both theoretical inquiry and case study, this study investigates the influence of digital transformation on sustainable development in enterprises and arrives at the following conclusions: (1) Digital transformation exerts a substantial positive effect on the sustainable development of enterprises. Board diversity plays a moderating role in this relationship; age diversity and gender diversity weaken its promoting effect while experience diversity enhances its positive influence. These findings remain robust after conducting various tests to ensure validity and addressing endogeneity concerns; (2) Heterogeneity tests reveal that compared to non-state-owned enterprises and high-tech firms, digital transformation has a more pronounced promoting effect on sustainable development levels within state-owned enterprises and non-high-tech companies. This article offers novel research perspectives on how digitization drives corporate sustainability in the digital era while providing practical insights for companies aiming to achieve both digital transformation and sustainable development.

1. Introduction

With the rapid development and widespread application of foundational digital technologies such as artificial intelligence, blockchain, cloud computing, and big data, digital technology has become a crucial driving force for corporate sustainable development [1]. Currently, accelerating digital transformation has become an essential task to build a digital nation at the level of national strategy. As a key component of innovation-driven growth [2], corporate digitization can integrate digital resources to facilitate optimization and reorganization of production factors [3], enhancing corporate governance levels, and thus providing new impetus for business expansion [4]. Therefore, it is necessary to fully comprehend the trends and patterns of digital economic development while understanding the significance and far-reaching impact of digitization in order to implement the new concept of corporate sustainable development.

In September 2015, the United Nations Sustainable Development Summit adopted the “2030 Agenda for Sustainable Development”, which established ambitious objectives encompassing economic growth, social inclusivity, and environmental sustainability. Sustainable development has emerged as a pivotal strategic direction for both national macroeconomics and enterprise microeconomics. As significant market entities, enterprises not only drive rapid economic growth but shoulder the strategic responsibility of carrying forward sustainable development. Corporate enhancing corporate governance levels, and thus providing new impetus for business expansion sustainable development, refers to enterprises achieving sustained profit growth while utilizing resources rationally and thus reducing adverse environmental impacts resulting from production activities in order to obtain secure long-term support from both internal and external stakeholders [5,6]. Corporate sustainable development is regarded as the cornerstone of the objective of economic growth; it is only through attaining corporate sustainable development that a solid foundation can be laid for national and even global progress.

The digital transformation of enterprises refers to the utilization of digital components, infrastructure, and platforms to drive changes in the original business process [7]. By applying digital technologies, enterprises can enhance the efficiency of resource utilization and bolster their “green competitiveness” [8], thereby reducing environmental pollution while garnering sustained support from stakeholders. This presents novel opportunities for enterprises to achieve sustainable development goals [9]. As pivotal microeconomic entities in the context of the rapid development of digital economy, how to make better use of digital technologies to promote digital transformation and thus achieve sustainable development goals has become a mutual concern when it comes to theoretical inquiry and management practice.

Existing research on the impact of digital transformation primarily focuses on internal control, financial performance, and financing constraints within enterprises [10,11,12]. Research on driving factors for sustainable development in enterprises mainly revolves around internal control, innovation, social responsibility, and financing constraints [13,14,15,16,17]. However, studies on the impact of digital transformation on the sustainable development of enterprises adopt the method of questionnaire survey to confirm the positive effect of digital transformation on the sustainable development of enterprises [18], and there is a lack of in-depth discussion on the relationship between digital transformation and sustainable development. Firstly, it is of vital importance to empirically test the mechanism through which digital transformation affects sustainable development from a novel theoretical perspective and construct a complete conceptual framework. Secondly, existing studies heavily rely on questionnaire surveys and virtual variables to measure digital transformation [19,20,21], which have inherent limitations in accurately capturing its intensity. Moreover, most previous research measures enterprise sustainability solely based on financial performance [22,23], failing to fully reflect an enterprise’s capacity for sustainable development. Lastly, there is a dearth of diverse research scenarios exploring the relationship between digital transformation and sustainable development beyond manufacturing enterprises [24,25].

Therefore, in order to further investigate the inherent mechanism of digital transformation on enterprise sustainability, this study selects A-share listed companies from 2015 to 2022 as research samples, with digital transformation serving as the independent variable and corporate sustainable development as the dependent variable. The research framework also incorporates the moderating role of board diversity. The findings indicate that digital transformation has a positive impact on corporate sustainable development. However, age and gender diversity among board members attenuate this effect while experience diversity enhances it.

Compared to the existing literature, this article makes academic contributions in the following aspects:

Firstly, while current research primarily focuses on the impact of internal control, innovation, and financing constraints on sustainable development [13,14,15,16,17], there is a lack of exploration regarding the relationship between digital transformation and sustainable development. This article empirically examines the positive effects of digital transformation on corporate sustainable development, thereby broadening the research perspective in this field. Secondly, existing studies have explored the influence of digital transformation from perspectives such as information asymmetry and resource dependency theory [26,27]. Building upon stakeholder theory, this article emphasizes the role of digital transformation in balancing multiple interests and provides a novel theoretical perspective for comprehending its impact. Thirdly, by introducing board member diversity at the level of corporate governance as a moderating variable, this article investigates the roles of age diversity, gender diversity, and experiential diversity of board members in the relationship of digital transformation and corporate sustainable development. This further enriches the research framework pertaining to how digital transformation affects corporate sustainable development. Fourthly, existing studies have measured digital transformation using questionnaire surveys or constructed virtual variables [19,20,21]. Also, measurements for corporate sustainable development rely primarily on financial performance indicators [22,23]. In our study, we creatively measure digital transformation using text analysis method and employ ESG performance as an indicator for measuring corporate sustainable development.

The rest of this study is organized as follows: Section 2 provides a comprehensive literature review and presents well-grounded research hypotheses. Section 3 outlines the employed methodology, including sample selection, variable measurement, and empirical models. Section 4 elucidates the empirical findings encompassing descriptive statistics, correlation analysis, and multiple regression analysis. Lastly, Section 5 offers valuable insights and conclusive remarks derived from this research endeavor.

2. Literature Review and Research Hypotheses

2.1. Literature Review

With the continuous advancement of digital transformation, there has been a growing body of academic research in this domain. The existing literature primarily focuses on the factors influencing digital transformation, as well as its economic implications. The influential factors encompass three dimensions: technology, management characteristics, and resources and capabilities. In terms of technology, the seamless integration of digital technologies with available resources significantly impacts a company’s formulation of transformation strategies, which is pivotal for its digitization [28]. Concerning management characteristics, elements such as leadership style can influence a company’s selection of digital transformation strategies [29]. Regarding a company’s resources and capabilities, small and medium-sized enterprises can leverage the potential of digital platforms to cultivate dynamic managerial capabilities and organizational capacities, thereby propelling their efforts towards digital transformation [30]. In terms of the economic implications of digital transformation, from a macro perspective, the adoption of cutting-edge digital technology by the digital government reshapes governance structures and provides crucial support for establishing a novel government–society relationship as well as a government–market relationship [31]. From the micro perspective, firstly, digital transformation can significantly enhance overall productivity by improving operational cost efficiency and reducing enterprise performance [12]. Secondly, it can strengthen internal control operation quality, elevate levels of technological innovation output, and thus enhance enterprise innovation capabilities [10]. Meanwhile, digital transformation reduces information asymmetry and transaction costs among financing parties to improve enterprise performance and enhance corporate financing [11,32].

Previous studies have paid enough attention to the significance and value of corporate sustainability, as research on the driving factors is primarily conducted from two perspectives: internal and external. The internal perspective emphasizes that by optimizing internal control mechanisms, companies can mitigate agency problems, reduce agency costs, and promote sustainable development [13]. Simultaneously, increasing investment in innovation and strengthening core competitiveness are crucial for fostering sustained growth in companies [14]. The external perspective highlights that improving information disclosure quality and transparency can foster investor trust, alleviate financing difficulties, and facilitates corporate sustainability [16]. Additionally, actively fulfilling social responsibilities and establishing a robust stakeholder network also serve as crucial strategies to ensure companies’ sustainable development [15]. According to a questionnaire survey conducted by Lu et al. [18], digital capability has emerged as the most influential factor driving sustainable development in businesses. However, the questionnaire survey method mainly relies on the subjective answers of respondents, which may lead to inaccurate conclusions. In contrast, empirical testing is more rigorous in terms of sample selection and control of experimental conditions, which helps reduce bias and improve the representativeness and reliability of research. As the research progresses, Luo [26] suggests that companies can foster sustainable development by leveraging digital intelligent technology to actively engage in operational management and business processes, facilitating end-to-end digitization and thereby reducing internal and external information asymmetry levels. However, this theoretical perspective is relatively simplistic, neglecting the multifaceted stakeholders and their complex relationships involved in the operation and development of enterprises. In contrast, this paper adopts a more comprehensive stakeholder theory perspective, considering various stakeholders both inside and outside the enterprise, and emphasizing their importance to the corporate sustainable development. Building upon studies conducted within manufacturing contexts, Wen et al. [24] and Ma et al. [25] have observed significant improvements in environmental performance as businesses are undergoing industrial digitization transformations. However, manufacturing enterprises often involve significant resource consumption and environmental pollution in their production processes. Given the specificity of the industry, focusing solely on manufacturing enterprises may compromise the universality of research conclusions. It is necessary to integrate analyses from different industries and perspectives to gain a more comprehensive understanding and explanation of the impact of digital transformation on corporate sustainable development. This research provides early empirical evidence for investigating the relationship between digital transformation and corporate sustainable development, which lays a solid foundation for future investigations.

The research on the digital transformation and sustainable development of enterprises is still in its early stages, yet it has garnered attention from an increasing number of scholars. As a whole, these studies offer valuable experiences and insights for analyzing the impact mechanism of digital transformation on enterprise sustainability. However, certain limitations need to be addressed. Few studies have thoroughly explored the impact pathway of digital transformation on enterprise sustainability. Therefore, it is imperative to adopt a fresh theoretical perspective and conduct an in-depth analysis of the role mechanism of digital transformation in promoting sustainable development within enterprises while constructing a comprehensive conceptual framework. Additionally, existing research heavily relies on questionnaire surveys and virtual variables to measure digital transformation [19,20,21]. These measurement methods fail to accurately capture the intensity of digital transformation. Utilizing text analysis techniques to construct measurement indicators for assessing digital transformation can provide more precise empirical evidence for future investigations related to this domain. The previous studies have predominantly relied on financial performance indicators to evaluate the sustainable development of enterprises [22,23], which possess certain limitations and fail to fully capture the sustainable development capabilities of companies. By utilizing ESG performance as a metric, this study not only takes economic performance into consideration but also lays more emphasis on the holistic performance of enterprises in terms of environment, society, and governance. Furthermore, existing research primarily focuses on the relationship between digital transformation and sustainable development in manufacturing firms [24,25]. Considering industry specificity and limited sample representativeness, it is crucial to enhance research comprehensiveness and universality through comparative analysis across multiple industries. Therefore, this study employs data from A-share listed companies on the Shanghai and Shenzhen stock exchanges spanning from 2015 to 2022 to investigate the impact and mechanism of digital transformation on corporate sustainable development at a micro level, thereby providing novel empirical evidence for understanding the nexus between digital transformation and sustainable development among China’s listed companies.

2.2. Research Hypotheses

The stakeholder theory necessitates businesses to assume responsibility not only towards shareholders but also towards creditors, employees, suppliers and customers, government, community, and the environment [33]. It emphasizes a stronger focus on external governance of the company in order to maximize overall stakeholder interests. The integration of new-generation digital information technology with traditional business production models constitutes the core strategy for companies to achieve sustainable development in the era of digital economy. The value of digital transformation is not solely reflected in enhancing economic performance but also extends to non-economic aspects such as environmental responsibility, social impact, and governance. Digital transformation establishes a foundation for green technological innovation that facilitates iterative upgrading of green technologies within companies [34]. This strengthens environmental responsibility practices and cultivates a positive corporate image which contributes to sustainable development. Furthermore, digital technology reduces information asymmetry and optimizes corporate governance while deepening fulfillment of social responsibilities [15], thereby elevating the level of sustainable development for companies. Lastly, digital transformation empowers efficient resource management and enhances ecological benefits while mitigating negative impacts on stakeholders [24]. This improves corporate reputation and social image, thus promoting sustainable development for companies. Based on this analysis, this study proposes Hypothesis 1:

H1: 

Digital transformation can foster corporate sustainable development.

The board of directors plays a pivotal role in corporate governance, serving as a strategic think tank and a regulatory stronghold for effective risk management. According to the upper echelon theory [35], the diverse characteristics of board members have a profound impact on companies’ strategic planning and operational strategies. Furthermore, a diversified board strengthens relationships with customers, suppliers, and other stakeholders [33]. This interaction not only expands information channels for timely access to valuable resources such as market dynamics, capital flows, and innovation trends but also significantly enhances public image and reputation. It attracts more investment opportunities and social recognition while expediting companies’ digital transformation process, thereby promoting sustainable development. Based on this premise, Hypothesis 2 is proposed:

H2: 

Board diversity moderates the relationship between digital transformation and corporate sustainable development.

Age diversity among board members influences their thinking, values, and risk preferences, thereby impacting decision-making processes. Older members demonstrate decreased cognitive agility and information acquisition efficiency, diminished interest in emerging trends, and a preference for short-term performance to uphold reputation [36]. Meanwhile, these members tend to prioritize stability over innovation, resulting in limited acceptance of innovative projects. As digital transformation necessitates systematic innovation of business models and reshaping of corporate management mechanisms as a disruptive strategy, it also requires continuous investment in funds and equipment maintenance and upgrades. The presence of higher age diversity among board members increases the likelihood that older members hold negative attitudes towards digital transformation [37], leading to conflicting values within the boardroom, reduced decision-making efficiency, hindrance to the transformation process, ultimately impeding sustainable development of the company. Based on this analysis, Hypothesis H2a is proposed:

H2a: 

Age diversity on the board mitigates the positive impact of digital transformation on corporate sustainable development.

Directors of different genders often exhibit distinct decision-making styles [38,39]. In general, female directors tend to adopt a cautious approach in the decision-making process, carefully considering risks and returns comprehensively. On the other hand, male directors may lean towards making quick decisions and pursuing high-risk, high-return opportunities. However, the cautious attitude of female directors can sometimes slow down the decision-making process, causing companies to miss out on certain opportunities for digital transformation. The gender composition of board members can also influence resource allocation preferences under limited resources [40]. Female directors typically prioritize stability and may incline towards low-risk investments, whereas digital transformation requires substantial investment and carries inherent risk. Consequently, the risk preference exhibited by female directors might restrict access to resources for digital transformation initiatives, thereby impeding their role in promoting sustainable development within companies. Based on these observations, this study proposes Hypothesis H2b:

H2b: 

Gender diversity on the board mitigates the positive impact of digital transformation on corporate sustainable development.

When faced with the complex challenge of digital transformation, a higher level of expertise among board members is more likely to converge a diverse range of innovative ideas and solutions, thereby increasing the probability of identifying and implementing effective digital transformation strategies that can drive sustainable development for the company. Furthermore, experienced board members possess stronger capabilities in identifying risks, assisting companies in evading potential risks during the process of digital transformation and ensuring a smooth transition. Lastly, throughout the process of digital transformation, companies often require external support such as funding, technology, and talents. Diverse board members can leverage their resources and influence to bring in essential assets for the company and facilitate optimal resource allocation, expediting the transformation process, and thus enhancing the company’s capacity for sustainable development. Building upon this premise, Hypothesis H2c is proposed:

H2c: 

Experience diversity on the board enhances the positive impact of digital transformation on corporate sustainable development.

Figure 1 illustrates the conceptual framework encompassing digital transformation, corporate sustainable development, and board diversity. Hypothesis 1 posits that corporate digital transformation can promote corporate sustainable development, whereas Hypothesis 2 proposes that board diversity moderates the facilitating effect of digital transformation on corporate sustainable development.

3. Research Design

3.1. Sample Selection and Data Sources

The data for digital transformation was obtained through text analysis and word frequency statistics on annual reports of listed companies, which were acquired from the official websites of the Shenzhen Stock Exchange and Shanghai Stock Exchange. The ESG performance data of companies was sourced from the Huazheng ESG rating system, while other data were obtained from the CSMAR database.

In this study, we initially selected Shanghai and Shenzhen A-share listed enterprises in China from 2015 to 2022 as our sample and followed established research practices to process the original data as follows: (1) Excluding financial industry firms due to their unique characteristics and differences in accounting methods; (2) Removing observations of companies in abnormal trading states such as ST or *ST, as these firms may exhibit abnormal financial conditions or other exceptional circumstances; (3) Eliminating samples with significant missing key panel data; (4) Trimming the upper and lower 1% tails of all continuous variables to mitigate the influence of extreme values. Following these procedures, a total of 17,751 firm-year observations were obtained.

3.2. Measurement of Variables

3.2.1. Corporate Sustainable Development

In the new stage of economic development, enterprises should not only enhance the quality of their own development but also prioritize environmental protection and emphasize long-term and sustainable growth. ESG, as a leading-edge methodology for comprehensively evaluating corporate sustainability from three dimensions—environment, social responsibility, and governance—has currently been a significant criterion used by the international community to measure the level of corporate sustainable development [41]. In terms of environmental protection, a company’s ESG performance reflects its rational utilization of natural resources along with its commitment to ecological preservation, which relates to the company’s ability to maintain competitiveness in an increasingly challenging environment directly. Regarding social responsibility, a company’s ESG performance embodies its dedication to guaranteeing employee rights, respecting consumer rights, and contributing to the development of the community. These factors constitute a crucial foundation for shaping the company’s social image, which has a huge impact on its brand reputation and market position. Strong ESG performance in corporate governance signifies that the company has robust management systems and effective internal controls, which are vital for ensuring sustainable development. Therefore, as an indicator measuring corporate sustainability, a company’s ESG performance not only takes its economic achievements into account but also emphasizes comprehensive performance in terms of environment stewardship, societal impact, and sound governance practices. It serves as a crucial guideline for steering companies towards the path of sustainable development.

The Huazheng ESG rating system integrates data from corporate annual reports, sustainability reports, government notices, third-party sources, and industry reports, comprehensively assessing companies’ ESG (Environmental, Social, Governance) performance. The ESG framework encompasses three key areas, each with detailed indicators, weighted based on their importance. Scores are assigned quantitatively, reflecting companies’ actual performance. These scores, multiplied by their weights and summed, determine a comprehensive ESG score, categorizing companies into nine rating levels: AAA, AA, A, BBB, BB, B, CCC, CC, C, DDD, DD, and D. Annually, the Huazheng ESG rating is conducted four times, and for this study, we simplify ratings into a 1–9 scale, using the average as the company’s annual ESG performance indicator. To ensure robustness, we also validate our findings using Bloomberg Consulting’s ESG scores as alternative variables, further strengthening the credibility of our conclusions.

3.2.2. Digital Transformation

The measurement of corporate digital transformation poses a challenge in current relevant research. Existing literature primarily encompasses the following measurement methods: Firstly, Ferreira et al. [19] employed virtual variables to gauge the extent of corporate digital transformation. However, virtual variables may not adequately capture the extent of digital transformation. Secondly, Nwankpa et al. [20] collected data on corporate digital transformation through questionnaire surveys. Thirdly, an increasing number of studies have utilized text analysis and word frequency statistics on annual reports of listed companies to construct indicators for assessing corporate digital transformation [42]. The vocabulary in annual reports can reflect strategic characteristics and future prospects of enterprises effectively, thereby reflecting their business philosophy and development trajectory to a large extent. Consequently, characterizing the degree of digital transformation from the perspective of word frequency statistics associated with “corporate digital transformation” in annual reports of listed companies holds both significance and feasibility.

Drawing upon the research methodologies employed by Wu et al. [42] and Li et al. [43], this study adopts the frequency of occurrence of keywords pertaining to digital transformation in annual reports of publicly listed companies as a measurement indicator for this variable. Utilizing Python web crawling, we gathered and organized annual reports of all A-share listed firms on the Shanghai and Shenzhen Stock Exchanges. The Java PDFbox library extracted text content, forming the data pool for feature word selection. To identify characteristic words for corporate digital transformation, we engaged in discussions from both academic and industrial angles, expanding the feature word bank and classifying it into “underlying technology utilization” and “technological practice application”. This resulted in the feature word map shown in Figure 2. We filtered out expressions with negative prefixes like “not”, “none”, or “without” and excluded “digital transformation” mentions unrelated to the company, such as those involving shareholders, customers, suppliers, or executive profiles. Finally, based on the data pool extracted from the annual reports of listed companies using Python 3, we searched, matched, and counted the word frequencies of the feature words according to Figure 2. These were then classified to aggregate the word frequencies of key technology directions and form a final total word frequency, thereby constructing an indicator system for corporate digital transformation. Given the data’s right-skewed nature, we log-transformed it to derive an overall indicator, DT, capturing the essence of corporate digital transformation.

3.2.3. Board Diversity

The diversity of board members refers to their heterogeneity in gender, age, experience, educational background, and profession. This article selects three indicators to quantify the diversity of board members: age diversity, gender diversity, and experience diversity. Specifically, they are measured as follows: (1) Age Diversity: quantifying the variation in ages among board members using the coefficient of variation; a higher coefficient indicates greater age diversity. (2) Gender Diversity: assessing the richness of gender representation among board members by assigning a value of 1 to female directors and 2 to male directors. The Blau index is then employed to measure this indicator; the higher the index, the more dispersed board members are in this dimension, indicating higher gender diversity. (3) Experience Diversity: The richness of experience of board members. Whether a director member holds office in a shareholder unit is used as the category variable, with additional post as category 1 and others as category 2. Then the Blau index is utilized for measuring this indicator; a lower index indicates concentration of the company’s board members within one category, whereas a higher index suggests greater experience diversity among its members.

3.2.4. Control Variables

Building upon the research conducted by Hartley et al. [44] and Ferreira et al. [19], this study selects firm size, leverage ratio, return on assets, cash flow, firm growth, ownership concentration, property rights nature, year fixed effects, and industry as control variables. Firm size (Size) is measured by taking the natural logarithm of total assets; leverage ratio (Lev) is calculated as the proportion of total liabilities to total assets; return on assets (Roa) is determined by dividing net profit by total assets; cash flow (Cashflow) is assessed by dividing operating cash flow by total assets; firm growth (Growth) is evaluated using the rate of growth in main business income; ownership concentration (Top1) is quantified based on the shareholding percentage of the largest shareholder in a company; property rights nature (SOE) represents a binary variable where state-owned enterprises are assigned 1 and non-state-owned enterprises are assigned 0. Additionally, year fixed effects and industry fixed effects are included. The specific definitions for these variables can be found in

Table 1. Variable definitions.

Variables	Abbreviations	Definitions
Corporate sustainable development	ESG	The ESG rating of the China Securities Index is assigned a score ranging from 1 to 9, based on the annual average value.
Digital transformation	DT	The frequency of keywords related to digital transformation in corporate annual reports is incremented by 1 and subsequently subjected to natural logarithm transformation.
Age diversity	Age	Coefficient of variation in the age of board members
Gender diversity	Gender	The value for female board members is assigned as 1, otherwise it is 2, measured by the Blau index.
Experience diversity	Experience	The valuation of board members who also serve as shareholders is assigned a value of 1, otherwise it is assigned a value of 2, using the Blau index for measurement.
Firm size	SIZE	The natural logarithm of total assets
Leverage ratio	LEV	Total liabilities/total assets
Return on assets	ROA	Net profit/total assets
Cash flow	CASH	Cash flow generated from business operations/total assets
Firm growth	GROWTH	Main business revenue growth rate
Ownership concentration	TOP1	Number of shares held by largest shareholder/ outstanding shares
Property rights	SOE	A binary variable with a value of 1 indicating state-owned enterprises and 0 otherwise
Year fixed effects	YEAR	Year dummy variables
Industry fixed effects	INDUSTRY	Industry dummy variables

.

3.3. Empirical Models

In this study, a mixed regression model was employed to analyze the research data in order to test our research hypothesis.

Firstly, Model (1) was constructed to examine the impact of digital transformation on the sustainable development of enterprises. Here, t represents time, ESG represents corporate sustainability, DT represents digital transformation, Control denotes a series of control variables, and ε is the random disturbance term. If the coefficient α₁ is significantly positive, it indicates that Hypothesis 1 holds true.

$${ESG}_t={\alpha }_0+{\alpha }_1{DT}_t+\mathsf{\Sigma }{\alpha }_k{Control}_t+\epsilon$$

(1)

In addition, to examine the moderating role of board diversity, we construct Model (2):

$${ESG}_t={\beta }_0+{\beta }_1{DT}_t+{\beta }_2{X}_t+{\beta }_3{DT}_t\times X_t+{\beta }_k{Control}_t+\epsilon$$

(2)

Among them, t represents time, ESG represents corporate sustainable development, DT represents digital transformation, and X represents the diversity variables of board members (Age for age diversity of board members, Gender for gender diversity of board members, Experience for experience diversity of board members). Control is a control variable, and ε denotes a random disturbance term. If the interaction term β₃ is statistically significant and exhibits the same direction as β₁, it indicates that enhancing board member diversity strengthens the impact of digital transformation on corporate sustainable development; conversely, if it shows an opposite direction to β₁, it suggests that weakening board member diversity weakens the impact of digital transformation on corporate sustainable development.

4. Empirical Results

4.1. Descriptive Statistics

The descriptive statistics of the variables are presented in

Table 2. Descriptive statistics.

Variable	N	Mean	P50	SD	Min	Max
DT	17,751	1.6684	1.3863	1.4112	0.0000	5.2095
ESG	17,751	3.9157	4.0000	1.2798	0.0000	6.2500
Age	17,751	0.1501	0.1467	0.0515	0.0000	0.4195
Gender	17,751	0.2352	0.2449	0.1640	0.0000	0.5000
Experience	17,751	0.2654	0.3200	0.1929	0.0000	0.5000
SIZE	17,751	22.2640	22.0754	1.2888	20.0067	26.3685
LEV	17,751	0.4100	0.3988	0.2023	0.0589	0.9077
ROA	17,751	0.0382	0.0408	0.0715	−0.3346	0.2093
CASH	17,751	0.0506	0.0498	0.0672	−0.1510	0.2464
GROWTH	17,751	0.3693	0.1447	0.9035	−0.6806	6.2630
TOP1	17,751	0.3393	0.3154	0.1439	0.0943	0.7356
SOE	17,751	0.3069	0.0000	0.4612	0.0000	1.0000

. The variable DT exhibits a mean value of 1.6684, with a maximum value of 5.2095, indicating relatively low levels of digitization across different companies and significant disparities in their digital transformation levels. The standard deviation for ESG performance is calculated as 1.2798, suggesting substantial variations in ESG performance among the sampled companies. Furthermore, the average rating for ESG performance stands at 3.9157, implying a relatively low overall rating and poor ESG performance with considerable room for improvement. Regarding the moderating variable Age, its mean value is determined to be 0.1501, reaching a maximum value of 0.4195; this indicates generally limited board age diversity within the sample companies’ boards. Notably, Gender demonstrates a range from minimum (0) to maximum (0.5000), highlighting that certain corporate boards lack gender differences among their members entirely or partially exhibit such differences only. The moderating variable Experience yields an average value of 0.2654 alongside a standard deviation of 0.

4.2. Correlation Analysis

The results of the correlation analysis for variables are presented in

Table 3. Results of the correlation analysis.

	DT	ESG	Age	Gender	Experience	SIZE	LEV	ROA	CASH	GROWTH	TOP1	SOE
DT	1	0.059 ***	−0.012 *	0.004	−0.067 ***	0.051 ***	0.004	−0.004	−0.064 ***	0.107 ***	−0.091 ***	−0.090 ***
ESG	0.054 ***	1	−0.086 ***	−0.044 ***	0.089 ***	0.250 ***	0.028 ***	0.120 ***	0.089 ***	0.016 **	0.092 ***	0.132 ***
Age	−0.016 **	−0.097 ***	1	0.111 ***	−0.127 ***	−0.183 ***	−0.093 ***	0.020 ***	−0.025 ***	−0.029 ***	−0.109 ***	−0.298 ***
Gender	0.002	−0.045 ***	0.105 ***	1	−0.090 ***	−0.148 ***	−0.093 ***	0.046 ***	0.017 **	0.008	−0.009	−0.170 ***
Experience	−0.080 ***	0.102 ***	−0.125 ***	−0.072 ***	1	0.272 ***	0.176 ***	−0.052 ***	0.047 ***	−0.018 **	0.221 ***	0.386 ***
SIZE	0.034 ***	0.282 ***	−0.198 ***	−0.150 ***	0.274 ***	1	0.503 ***	−0.110 ***	0.057 ***	−0.022 ***	0.127 ***	0.362 ***
LEV	−0.013 *	0.036 ***	−0.094 ***	−0.086 ***	0.178 ***	0.508 ***	1	−0.440 ***	−0.170 ***	0.034 ***	0.013 *	0.261 ***
ROA	−0.045 ***	0.126 ***	−0.023 ***	0.023 ***	0.003	−0.006	−0.360 ***	1	0.451 ***	−0.048 ***	0.139 ***	−0.178 ***
CASH	−0.061 ***	0.081 ***	−0.025 ***	0.011	0.041 ***	0.061 ***	−0.173 ***	0.395 ***	1	−0.128 ***	0.126 ***	−0.017 **
GROWTH	0.060 ***	−0.007	−0.034 ***	0.003	0.009	0.008	0.073 ***	−0.016 **	−0.104 ***	1	−0.009	0.027 ***
TOP1	−0.101 ***	0.081 ***	−0.112 ***	−0.018 **	0.217 ***	0.179 ***	0.022 ***	0.151 ***	0.117 ***	0.001	1	0.205 ***
SOE	−0.099 ***	0.139 ***	−0.291 ***	−0.157 ***	0.382 ***	0.378 ***	0.266 ***	−0.080 ***	−0.022 ***	0.070 ***	0.213 ***	1

Note: *, **, and *** refer to levels of significance of 10%, 5%, and 1%, respectively. The upper triangle represents Spearman correlation coefficients, while the lower triangle represents Pearson correlation coefficients.

. The Pearson correlation coefficient between digital transformation and sustainable development of enterprises is 0.054, while the Spearman correlation coefficient is 0.059. Both coefficients demonstrate statistical significance at a level of 1%, aligning with our research expectations and providing preliminary evidence supporting a positive relationship between digital transformation and sustainable development of enterprises. To ensure more robust research conclusions and explore the moderating effect of board diversity on the relationship between digital transformation and sustainable development, further regression analysis is warranted.

4.3. Main Analysis

To validate the relationship between digital transformation (DT) and corporate sustainable development, a regression analysis was performed, with results summarized in

Table 4. Results of main regression analysis.

	(1)	(2)	(3)	(4)
	ESG	ESG	ESG	ESG
DT	0.0356 ***	0.0356 ***	0.0362 ***	0.0370 ***
	(0.0083)	(0.0082)	(0.0082)	(0.0082)
Age		−0.7046 ***
		(0.1816)
DT × Age		−0.2789 **
		(0.1237)
Gender			−0.0048
			(0.0556)
DT × Gender			−0.1427 ***
			(0.0380)
Experience				0.1503 ***
				(0.0511)
DT × Experience				0.0762 **
				(0.0326)
SIZE	0.3307 ***	0.3271 ***	0.3301 ***	0.3282 ***
	(0.0091)	(0.0094)	(0.0094)	(0.0094)
LEV	−0.8279 ***	−0.8247 ***	−0.8257 ***	−0.8340 ***
	(0.0600)	(0.0593)	(0.0593)	(0.0593)
ROA	1.5023 ***	1.4857 ***	1.4977 ***	1.4869 ***
	(0.1489)	(0.1480)	(0.1479)	(0.1480)
CASH	0.5014 ***	0.4993 ***	0.5028 ***	0.4901 ***
	(0.1531)	(0.1495)	(0.1496)	(0.1496)
GROWTH	−0.0398 ***	−0.0396 ***	−0.0393 ***	−0.0397 ***
	(0.0106)	(0.0105)	(0.0105)	(0.0105)
TOP1	0.0007	0.0006	0.0007	0.0004
	(0.0007)	(0.0007)	(0.0007)	(0.0007)
SOE	0.2304 ***	0.2158 ***	0.2311 ***	0.2121 ***
	(0.0214)	(0.0231)	(0.0227)	(0.0235)
constant	−3.8432 ***	−3.7096 ***	−3.7746 ***	−3.7089 ***
	(0.2326)	(0.2418)	(0.2419)	(0.2425)
N	17751	17751	17751	17751
Adj-R2	0.1640	0.1649	0.1646	0.1646
F	45.8207 ***	37.8998 ***	37.8253 ***	37.8257 ***
YEAR	YES	YES	YES	YES
INDUSTRY	YES	YES	YES	YES

Note: p-values in parentheses. ** and *** refer to levels of significance of 5% and 1%, respectively.

. Column (1) highlights a significantly positive DT regression coefficient at 1%, confirming its substantial role in enhancing sustainable development and verifying Hypothesis 1. Extending Model (1), we explored the moderating effect of board diversity on this relationship by incorporating an interaction term. Age diversity among board members was found to diminish the positive impact of DT on sustainable development. Additionally, Column (3) reveals a significantly negative coefficient for the DT × Gender interaction, indicating that gender diversity weakens DT’s positive influence. Conversely, Column (4) shows a significantly positive coefficient at 5%, confirming that board experience diversity enhances the promotional effect of DT on sustainable enterprise development.

4.4. Endogeneity Checks

4.4.1. Endogeneity Check Using Instrumental Variable

The empirical test process of this paper may have endogeneity problems caused by reverse causality, that is, the degree of digital transformation will affect the sustainable development level of enterprises, and enterprises with higher sustainable development level will be more inclined to carry out digital transformation. There are many ways to solve the endogeneity problem; referring to the research of Brahm [45] and Godart et al. [46], we use the instrumental variable method to alleviate the above endogeneity problem. Considering the comprehensive impact of digital transformation on corporate sustainable development, when enterprises carry out the strategy formulation of digital transformation, it takes some time for its driving role to play. Therefore, this study employs lagged one-period digital transformation (L.DT) and lagged two-period digital transformation (L2.DT) as instrumental variables in estimation using two-stage least squares (2SLS), as presented in

Table 5. Results of endogeneity test including an instrumental variable.

	(1)	(2)
	First Stage	Second Stage
VARIABLES	DT	ESG
L.DT	0.7226 ***
	(0.010)
L2.DT	0.1849 ***
	(0.010)
DT		0.0320 ***
		(0.009)
Constant	0.2475 ***	4.0385 ***
	(0.010)	(0.020)
Observations	10,005	10,005
Adj-R2	0.806	0.002

Note: p-values in parentheses. *** refers to level of significance of 1%.

. The results in column (1) demonstrate that the regression coefficient of L.DT is 0.7226 and the regression coefficient of L2.DT is 0.1849, both passing significance tests at the 1% level, indicating a significant driving effect of digital transformation on corporate sustainable development and establishing a stable relationship between them. In column (2), the regression coefficient of DT is significantly positive at the 1% level, confirming a positive impact relationship between digital transformation and corporate sustainable development. These findings suggest that even after accounting for endogeneity issues, there remains an evident positive association between digital transformation and corporate sustainable development.

4.4.2. Endogeneity Check Using Propensity Score Matching

In the process of empirical verification in this study, besides the endogeneity problem caused by mutual causality, there may also be endogeneity problems arising from omitted variables. To address this issue, propensity score matching (PSM) is employed to handle the omitted variable problem. Firstly, listed companies are divided into a treatment group and a control group based on whether they undergo digital transformation. The control variables from previous studies are used as matching variables for PSM. Secondly, one-to-one with replacement nearest neighbor matching is utilized to eliminate unmatched samples, resulting in 12,619 matched samples. Finally, regression analysis is conducted on the remaining samples, and the results are presented in columns (1)–(4) of

Table 6. Results of endogeneity test using propensity score matching.

	(1)	(2)	(3)	(4)
	ESG	ESG	ESG	ESG
DT	0.0362 ***	0.0362 ***	0.0370 ***	0.0376 ***
	(0.0099)	(0.0099)	(0.0099)	(0.0099)
Age		−0.4842 **
		(0.2265)
DT × Age		−0.3851 ***
		(0.1482)
Gender			0.0107
			(0.0699)
DT × Gender			−0.1590 ***
			(0.0457)
Experience				0.1607 **
				(0.0639)
DT × Experience				0.0802 **
				(0.0391)
SIZE	0.3356 ***	0.3321 ***	0.3343 ***	0.3328 ***
	(0.0107)	(0.0107)	(0.0107)	(0.0107)
LEV	−0.8129 ***	−0.8083 ***	−0.8109 ***	−0.8197 ***
	(0.0632)	(0.0632)	(0.0632)	(0.0632)
ROA	0.2464 ***	0.2440 ***	0.2473 ***	0.2411 ***
	(0.0726)	(0.0726)	(0.0726)	(0.0726)
CASH	0.8784 ***	0.8739 ***	0.8766 ***	0.8614 ***
	(0.1516)	(0.1515)	(0.1515)	(0.1516)
GROWTH	−0.0019 **	−0.0018 **	−0.0019 **	−0.0018 **
	(0.0007)	(0.0007)	(0.0007)	(0.0007)
TOP1	0.0017 **	0.0016 **	0.0017 **	0.0014 *
	(0.0008)	(0.0008)	(0.0008)	(0.0008)
SOE	0.2207 ***	0.2107 ***	0.2198 ***	0.1961 ***
	(0.0273)	(0.0279)	(0.0274)	(0.0283)
constant	−4.2031 ***	−4.0580 ***	−4.1798 ***	−4.1612 ***
	(0.2926)	(0.2975)	(0.2948)	(0.2928)
N	12619.0000	12619.0000	12619.0000	12619.0000
Adj-R2	0.1650	0.1659	0.1657	0.1658
F	27.8027 ***	27.4092 ***	27.3768 ***	27.3953 ***
YEAR	YES	YES	YES	YES
INDUSTRY	YES	YES	YES	YES

Note: p-values in parentheses. *, **, and *** refer to levels of significance of 10%, 5%, and 1%, respectively.

. The signs of regression coefficients for major variables after matching have not changed significantly and all pass significance level tests. This indicates that even after mitigating the aforementioned endogeneity problems, the research conclusions still hold.

4.5. Robustness Checks

4.5.1. Robustness Test Using Alternative Measure of Dependent Variable

In order to evade the potential impact of varying measurement methods on research findings, this study replaces the ESG data provided by Huazheng Enterprise (ESG) in the benchmark model with ESG performance data from Bloomberg (ESG_PB) to assess the level of corporate sustainable development and enhance the robustness of our conclusions. Bloomberg’s ESG data are scored on a scale from 0 to 100 and are subdivided into three dimensions: environment (ENV), social responsibility (SOC), and corporate governance (GOV). It should be noted that Bloomberg’s ESG ratings do not cover all A-share listed companies in Shanghai and Shenzhen, resulting in a potential loss of significant samples due to its relatively short time window. By plugging Bloomberg’s ESG data into our regression model, as presented in

Table 7. Results of robustness tests using alternative measure of dependent variable.

	(1)	(2)	(3)	(4)
	ESG_PB	ENV	SOC	GOV
DT	0.3321 ***	0.4130 ***	0.2971 ***	0.2774 ***
	(0.0829)	(0.1585)	(0.0868)	(0.0709)
SIZE	2.7671 ***	4.6255 ***	2.3991 ***	1.2719 ***
	(0.0927)	(0.1772)	(0.0971)	(0.0793)
LEV	−1.3567 **	−1.6037	−1.6609 **	−0.8573
	(0.6343)	(1.2123)	(0.6641)	(0.5423)
ROA	4.5522 ***	5.9219 *	6.1054 ***	1.6008
	(1.6923)	(3.2343)	(1.7717)	(1.4467)
CASH	4.1865 ***	6.1291 **	4.8861 ***	1.7477
	(1.5147)	(2.8949)	(1.5858)	(1.2949)
GROWTH	−0.1215	−0.1595	−0.1489	−0.0571
	(0.1083)	(0.2070)	(0.1134)	(0.0926)
TOP1	0.0137 **	0.0300 **	0.0033	0.0076
	(0.0062)	(0.0118)	(0.0065)	(0.0053)
SOE	0.9116 ***	0.4276	1.6663 ***	0.6378 ***
	(0.2042)	(0.3903)	(0.2138)	(0.1746)
constant	−38.0951 ***	−107.5029 ***	−46.0032 ***	39.2093 ***
	(2.2713)	(4.3409)	(2.3779)	(1.9417)
N	5632	5632	5632	5632
Adj-R2	0.3952	0.3382	0.2755	0.2708
F	43.2970 ***	34.0775 ***	25.6173 ***	25.0344 ***
YEAR	YES	YES	YES	YES
INDUSTRY	YES	YES	YES	YES

Note: p-values in parentheses. *, **, and *** refer to levels of significance of 10%, 5%, and 1%, respectively.

, we can come to the conclusion that digital transformation enhances corporate sustainable development significantly even after substituting the dependent variable and conducting regressions based on different dimensions.

4.5.2. Robustness Test Using Alternative Measure of Independent Variable

In order to enhance the validity of the research findings, this study refined the measurement approach for assessing the intensity of digital transformation in companies. Drawing on methodologies employed by Wu et al. [42] and Li et al. [43] to calculate the degree of digital transformation, we aggregated and logarithmized word frequencies associated with digitization. Subsequently, a binary variable D_DT was generated to represent the level of digital transformation, where a value of 1 indicated a high degree of digitization surpassing the median level among sample companies; otherwise, it was assigned 0. The regression results incorporating D_DT into the model are presented in

Table 8. Results of robustness tests using alternative measure of independent variable.

	(1)	(2)	(3)	(4)
	ESG	ESG	ESG	ESG
D_DT	0.0465 **	0.0461 **	0.0470 **	0.0452 **
	(0.0204)	(0.0206)	(0.0206)	(0.0206)
Age		−0.7178 ***
		(0.1817)
D_DT × Age		−0.6973 **
		(0.3456)
Gender			−0.0056
			(0.0556)
D_DT × Gender			−0.4140 ***
			(0.1082)
Experience				0.1505 ***
				(0.0511)
D_DT × Experience				0.2689 ***
				(0.0929)
SIZE	0.3352 ***	0.3320 ***	0.3349 ***	0.3327 ***
	(0.0091)	(0.0093)	(0.0093)	(0.0093)
LEV	−0.8316 ***	−0.8291 ***	−0.8294 ***	−0.8370 ***
	(0.0600)	(0.0593)	(0.0593)	(0.0593)
ROA	1.4946 ***	1.4777 ***	1.4868 ***	1.4770 ***
	(0.1489)	(0.1480)	(0.1480)	(0.1480)
CASH	0.4850 ***	0.4838 ***	0.4864 ***	0.4764 ***
	(0.1530)	(0.1495)	(0.1495)	(0.1496)
GROWTH	−0.0390 ***	−0.0389 ***	−0.0389 ***	−0.0389 ***
	(0.0106)	(0.0105)	(0.0105)	(0.0105)
TOP1	0.0006	0.0005	0.0006	0.0004
	(0.0007)	(0.0007)	(0.0007)	(0.0007)
SOE	0.2256 ***	0.2092 ***	0.2261 ***	0.2082 ***
	(0.0213)	(0.0231)	(0.0227)	(0.0235)
constant	−3.9201 ***	−3.7424 ***	−3.9142 ***	−3.8902 ***
	(0.2323)	(0.2425)	(0.2405)	(0.2387)
N	17751	17751	17751	17751
Adj-R2	0.1634	0.1642	0.1640	0.1641
F	45.6531 ***	37.7147 ***	37.6555 ***	37.6841 ***
YEAR	YES	YES	YES	YES
INDUSTRY	YES	YES	YES	YES

Note: Z-values in parentheses. ** and *** refer to levels of significance of 5% and 1%, respectively.

. As shown in columns (1)–(4), all regression coefficients pertaining to digital transformation exhibit significant positive effects at a significance level of 5%. Furthermore, no substantial changes were observed in interaction terms for each moderating variable and they also passed significance tests successfully. These findings indicate that replacing the measure for digital transformation did not significantly alter relationships between variables, thereby validating robustness in our conclusions.

4.5.3. Robustness Test Deleting Digital Industry Samples

The business scope of digital industry companies encompasses businesses related to digital technology, including their revenue sources and subsidiary names, all of which are included in the digital transformation terminology database. However, it is important to note that relying solely on textual information from a company’s annual report may lead to inaccurate indicators for corporate digitization as it may only describe its involvement in internet-related businesses rather than fully representing its decisions regarding digital transformation. To address this potential deviation in empirical results, this study excludes sample companies from major categories such as computer, communication and other electronic equipment manufacturing (C39), telecommunications and satellite transmission services (I63), as well as internet and related services (I64) based on guidelines provided by the China Securities Regulatory Commission for Industry Classification Guidelines for Listed Companies. Consequently, a total of 15,851 samples were obtained. The regression analysis was conducted by incorporating the unremoved samples into the model, and the corresponding results are presented in

Table 9. Results of robustness tests deleting digital industry samples.

	(1)	(2)	(3)	(4)
	ESG	ESG	ESG	ESG
DT	0.0338 ***	0.0339 ***	0.0347 ***	0.0343 ***
	(0.0091)	(0.0091)	(0.0091)	(0.0091)
Age		−0.8350 ***
		(0.2021)
DT × Age		−0.3264 **
		(0.1375)
Gender			−0.0358
			(0.0602)
DT × Gender			−0.1630 ***
			(0.0413)
Experience				0.0636
				(0.0571)
DT × Experience				0.0447
				(0.0357)
SIZE	0.2771 ***	0.2731 ***	0.2766 ***	0.2761 ***
	(0.0088)	(0.0089)	(0.0089)	(0.0089)
LEV	−0.0069	−0.0071	−0.0066	−0.0070
	(0.0178)	(0.0177)	(0.0178)	(0.0178)
ROA	0.0810	0.0797	0.0804	0.0807
	(0.0769)	(0.0761)	(0.0775)	(0.0768)
CASH	0.9898 ***	0.9816 ***	0.9877 ***	0.9852 ***
	(0.1806)	(0.1800)	(0.1812)	(0.1804)
GROWTH	−0.0000 ***	−0.0000 ***	−0.0000 ***	−0.0000 ***
	(0.0000)	(0.0000)	(0.0000)	(0.0000)
TOP1	0.0026 ***	0.0024 ***	0.0026 ***	0.0025 ***
	(0.0007)	(0.0007)	(0.0007)	(0.0007)
SOE	0.1606 ***	0.1434 ***	0.1608 ***	0.1540 ***
	(0.0232)	(0.0239)	(0.0232)	(0.0243)
constant	−3.0779 ***	−2.9367 ***	−3.0119 ***	−2.9933 ***
	(0.2355)	(0.2391)	(0.2391)	(0.2398)
N	15851	15851	15851	15851
Adj-R2	0.1442	0.1453	0.1449	0.1442
F	34.7536 ***	34.4464 ***	34.2394 ***	34.1048 ***
YEAR	YES	YES	YES	YES
INDUSTRY	YES	YES	YES	YES

Note: p-values in parentheses. ** and *** refer to levels of significance of 5%, and 1%, respectively.

. All regression coefficients associated with digital transformation exhibit positive values and successfully pass the significance test at a 1% level. Specifically, the interaction term DT × Age demonstrates a significantly negative coefficient of −0.3264 at a 5% level, while the interaction term DT × Gender exhibits a significantly negative coefficient at a 1% level, which aligns with previous research findings. Notably, based on the outcomes depicted in column (4), it can be observed that there is no significant moderating effect of board members’ experiential diversity on the direct relationship between digital transformation and sustainable development of enterprises after removing samples from digital industries. In general, relationships among variables remain unchanged to a statistically significant extent, thereby confirming robustness of this study’s conclusions.

4.6. Heterogeneity Analysis

Enterprises with varying property rights exhibit variations in terms of resource foundation and operational objectives. To investigate the impact of digital transformation on sustainable development across different types of enterprises, regression analyses were conducted for state-owned enterprises and non-state-owned enterprises, respectively, as presented in

Table 10. Results of heterogeneity analysis regression.

	(1)	(2)	(3)	(4)
	State Owned	Non-State Owned	High-Tech	Non-High-Tech
DT	0.0613 ***	0.0239 **	0.0289 ***	0.0368 ***
	(0.0135)	(0.0095)	(0.0106)	(0.0110)
SIZE	0.3619 ***	0.2745 ***	0.2886 ***	0.3672 ***
	(0.0133)	(0.0117)	(0.0140)	(0.0112)
LEV	−1.2517 ***	−0.9392 ***	−0.7013 ***	−1.2792 ***
	(0.0908)	(0.0706)	(0.0827)	(0.0739)
ROA	1.6164 ***	1.9782 ***	2.0578 ***	1.4310 ***
	(0.2789)	(0.1590)	(0.1896)	(0.1904)
CASH	0.3473	0.1547	0.2239	0.1336
	(0.2317)	(0.1748)	(0.2069)	(0.1857)
GROWTH	−0.0317 **	−0.0371 ***	−0.0159	−0.0451 ***
	(0.0130)	(0.0141)	(0.0166)	(0.0118)
TOP1	0.0023 **	0.0017 **	−0.0001	0.0047 ***
	(0.0010)	(0.0008)	(0.0009)	(0.0008)
constant	−4.1897 ***	−2.7346 ***	−3.0206 ***	−4.6072 ***
	(0.3134)	(0.3209)	(0.4611)	(0.2632)
N	5350	11339	8681	8008
Adj-R2	0.2846	0.1558	0.1449	0.3020
F	25.4568 ***	25.3269 ***	19.6221 ***	38.6577 ***
YEAR	YES	YES	YES	YES
INDUSTRY	YES	YES	YES	YES

Note: p-values in parentheses. ** and *** refer to levels of significance of 5% and 1%, respectively.

. The results from columns (1) to (2) reveal that the regression coefficient for digital transformation in state-owned enterprises is 0.0613, which passes the significance test at a level of 1%. In contrast, the influence factor for digital transformation in non-state-owned enterprises is significantly positive at a level of 5%. These findings suggest that compared to non-state-owned enterprises, digital transformation has a higher impact on the sustainable development of state-owned enterprises. This disparity may be attributed to state-owned enterprises possessing ample continuous internal and external resources, as well as multi-level core capabilities throughout their entire processes. As a result, they have easier access to political and institutional favoritism within the realm of digital economy while having advantages in terms of capital investment and talent acquisition, thereby providing stronger assurances for successful implementation of digital transformation.

In order to assess the heterogeneity performance of enterprises at different technical levels, we categorized the sample companies into high-tech and non-high-tech groups based on their industry types for regression analysis. According to the findings presented in columns (4) to (5) of Table 10. Digital transformation plays a significant role in promoting the sustainable development of enterprises, regardless of their category. Notably, this driving effect is more pronounced in non-high-tech enterprises. This can be attributed to the fact that most non-high-tech enterprises are rooted in traditional industries, whose production and operation models are relatively outdated. Meanwhile, the adoption of digital technology has not reached the breadth and depth observed in high-tech industries. Consequently, these enterprises have greater potential for improvement regarding digital transformation. The in-depth integration of digital technology into traditional industries can significantly enhance their efficiency in production and operational management, thereby yielding more favorable outcomes concerning environmental and social performance enhancement. Under the combined effects of these factors, it becomes evident that digital transformation plays a significantly influential role in promoting sustainable development for non-high-tech enterprises.

5. Discussion

Based on the aforementioned empirical analysis, this study reveals that digital transformation can significantly enhance a company’s level of sustainable development, while board diversity can regulate the impact of digital transformation on sustainable development. Specifically, age and gender diversity among board members impede the promotional effect of digital transformation on sustainable development. On the contrary, experiential diversity among board members plays a constructive moderating role between digital transformation and sustainable development by superposing its advantages to improve a company’s level of sustainability. These empirical findings are consistent with our expectations and validate Hypothesis 1 and Hypothesis 2 proposed in this study. Overall, the regression results for control variables also align with our expectations. Firm size, return on total assets, and cash flow exhibit significant positive effects on sustainable development, whereas ownership concentration does not have a substantial impact. Additionally, leverage ratio and firm growth rate demonstrate significant negative effects on sustainable development. The signs of control variables generally correspond to existing research [47,48,49,50].

Enterprises utilize digital technologies such as big data and cloud computing to alleviate the problem of information asymmetry, thereby enhancing market share and improving economic performance. Additionally, leveraging and allocating digital technology resources improves product performance, leading to higher market acceptance and increased product competitiveness. This creates greater product value and subsequently enhances economic performance [51,52], playing a positive role in elevating the level of sustainable development for enterprises. Wen et al. [24] and Ma et al. [25] propose that digital transformation stimulates green technological innovation, improving environmental performance for enhanced sustainable development capabilities of businesses. The aforementioned studies affirm the positive impact of digital transformation on corporate sustainable development. However, they merely examine its mechanisms through single indicators such as economic performance and environmental performance. This study empirically examines the promoting effect of digital transformation on corporate sustainable development from three dimensions: environment, society, and governance, enriching the understanding of its influence mechanism and theoretical framework.

Drawing from Chinese A-share listed company data, our detailed discussions highlight that conclusions based on China’s unique context may have limited general applicability due to economic, political, and cultural disparities. To broaden perspectives and enhance universal validity, future research should explore diverse national contexts. Cross-border comparative analysis will validate and deepen understanding of digital transformation’s impact on corporate sustainability, enriching academic theory and informing international investors’ decisions. Additionally, the technical demands of word frequency statistics pose challenges for non-experts. Future studies should integrate multiple analytical approaches, optimize algorithms and models, to enhance efficiency, accuracy, and the widespread application of text analysis methodologies.

6. Conclusions and Implications

Based on the data of A-share listed companies from 2015 to 2022, this article investigates the direction and impact mechanism of digital transformation on corporate sustainable development capability from a multi-interest perspective. The study reveals that: (1) Digital transformation facilitates the enhancement of corporate sustainable development, encompassing economic, social, and environmental dimensions. It amplifies the influence of digital transformation on enterprise growth and confirms its positive role in promoting sustainable development. (2) Board diversity can moderate the positive impact of digital transformation on enterprises’ sustainable development. Age and gender diversity among board members weaken the expansionary effect of digital transformation, while experience diversity strengthens its positive influence on sustainable development. (3) Digital transformation has a more pronounced effect on improving the level of sustainable development for state-owned enterprises and those with lower technological levels.

The article creatively introduces the stakeholder theory to substantiate the positive impact of digital transformation on corporate sustainable development, thereby enhancing the existing research literature on factors influencing sustainable development. Moreover, this study expands the measurement methods of digital transformation and sustainable development for future research reference. Lastly, from a corporate governance perspective, this paper introduces board diversity as a moderating variable and constructs a research framework elucidating how digital transformation influences the corporate sustainable development.

Drawing upon the aforementioned conclusions, we present the following targeted management strategies and policy recommendations. From a governmental standpoint, firstly, the administration ought to devise dedicated policies for enterprises’ digital transformation, clearly outlining transformation objectives, pivotal tasks, and safeguard mechanisms. This will furnish enterprises with the necessary policy backing and guidance for their digital transformation endeavors. Furthermore, the government must fortify the legislative framework pertaining to data protection and cybersecurity, thereby offering legal safeguards for enterprises embarking on their digital transformation journey. Concurrently, it is imperative to enhance penalties for illicit activities to uphold market integrity and foster fair competition. Secondly, the government should expedite the development of high-speed network infrastructure, encompassing 5G and fiber-optic broadband, to diminish the financial burden of enterprise network utilization and elevate network coverage and quality. Additionally, it should foster the establishment of data centers and cloud computing platforms, empowering enterprises with efficient, secure, and dependable data storage and computing solutions. Thirdly, the establishment of a specialized digital transformation fund by the government can significantly bolster enterprises’ digital transformation projects, spanning technology research and development, equipment acquisition, and talent nurturing. Eligible enterprises undergoing digital transformation could be entitled to tax incentives or preferential policies, thereby mitigating their transformation costs and igniting their enthusiasm for the process. Lastly, the government must establish and refine a digital transformation regulatory framework, intensifying oversight and guidance throughout the enterprises’ digital transformation process to ensure seamless progress. It should also cultivate a streamlined and efficient government service environment for enterprises, simplifying approval procedures and enhancing their efficiency. Moreover, the government should bolster policy interpretation and consulting services to empower enterprises with a deeper understanding and effective application of policies.

From an enterprise standpoint, firstly, it is imperative for companies to articulate a distinct long-term vision for digital transformation, detailing the aspirations for business growth, market dominance, or unparalleled customer experiences that they envision achieving through digitalization. This vision must be dissected into precise, quantifiable, achievable, pertinent, and time-constrained short- and medium-term objectives, ensuring that the team remains steadfastly aligned and motivated. Secondly, enterprises must embrace cutting-edge technologies like cloud computing, big data analytics, and artificial intelligence to revolutionize their existing IT infrastructure, bolstering data processing prowess and operational fluidity. Furthermore, they should establish or refine pivotal digital platforms, encompassing Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Supply Chain Management (SCM), to automate and infuse intelligence into every aspect of their business processes. Thirdly, through a blend of internal training programs and strategic external recruitment, companies must foster a workforce that is adept in digital competencies. Overcoming departmental silos and fostering cross-departmental, cross-functional collaboration mechanisms will expedite the digital transformation journey. Fourthly, enterprises ought to adopt eco-friendly and energy-efficient production methodologies in their manufacturing processes, optimizing supply chain management to minimize carbon footprints and resource depletion. Concurrently, they should actively engage in social welfare initiatives, prioritize employee well-being, strengthen brand reputation and social accountability, integrate sustainable development principles into their long-term strategic roadmap, and devise and execute green product and service strategies, thereby fostering a harmonious equilibrium between economic growth, societal welfare, and environmental preservation. Lastly, enterprises must establish a robust data security management framework to safeguard customer privacy and corporate data assets. They should meticulously evaluate emerging technologies and applications to ensure that technology adoption aligns seamlessly with business requirements while effectively mitigating risks. Staying agile and attuned to market shifts, and swiftly adapting strategies and plans, is paramount to navigating the uncertainties inherent in the digital transformation process.