A Study on the Mechanism of the Impact of Digital Transformation in Manufacturing Enterprises on Sustainable Development Performance

Characterized by groundbreaking technological advancements in harnessing resources and managing the environment, the Industrial Revolution has significantly propelled the progress of modern human civilization. However, the surging demand has triggered a rapid and previously unseen expansion in production capabilities, leading to increasingly pronounced conflicts between the demand for resources, their availability, and environmental sustainability [1]. Today, the concept of development transcends the mere expansion of economic metrics, embracing a more comprehensive vision of sustainable growth that equally weighs social, economic, and environmental considerations [2]. The World Commission on Environment and Development of the United Nations defines the term sustainable development as the individuals’ capabilities to meet their needs without compromising future generations’. Corporate sustainable development, however, requires continuous profit growth and rational utilization of resources as well as reduction in adverse effects on environment (e.g., from production activities) to gain support from a company’s internal and external stakeholders [3].

To better respond to global climate change and demonstrate responsibilities as a major country, in September 2020, China (i.e., the Chinese government) set two goals to: (a) achieve carbon neutrality by the year 2060 after (b) the estimated carbon peak by the year 2030. This policy has required Chinese enterprises to enhance green total factor productivity through energy conservation and emission reduction in their production processes to offset their carbon emissions. Setting these dual carbon goals represented a widespread and profound systemic transformation across Chinese industries. Characterized as a pillar of the national economy with a long value chain, broad industry coverage, and high externalities, the Chinese manufacturing sector has played an important role in developing the national economy and thus bears a substantial social responsibility for environmental protection and energy conservation. For instance, the application of Environmental, Social, and Governance (ESG) metrics integrates performances across these three domains into the evaluation system of high-quality corporate development, which has prompted enterprises to be more aware of environmental protection and social governance responsibilities in addition to their own interest growth.

Recent research has indicated that digital technologies may be reflected in all these definitions of disruptive innovation. They may represent innovations that are new to the world, dislocate existing processes, and open up entirely new business models. [4] The rapid development of digital technologies has provided traditional industries with momentum for innovative development. Digitalization (e.g., unique factors of production, technological system, institutional framework, development space, value philosophy) has largely affected traditional industries. Because of the substantial economic volume of the real economy, it has become a pillar industry of society, and the compatibility of the digital industry makes the integration process of China’s digital economy and real economy important aspects of economic life. To enhance sustainable development, Chinese enterprises should adapt to modern business environment and move away from traditional development models. Addressing the difficulties in upgrading digital technologies for sustainable development is an issue that has been widely discussed and researched by scholars, especially in China. Consequently, the current study aimed to answer the following research questions (hereafter: RQs):

RQ1: How does digital transformation in Chinese manufacturing enterprises affect their sustainable development performance?

RQ2: What may be the underlying mechanisms of this effect?

The current study provides value in the following ways. To begin, the study contributes to the discussion by empirically proving how digital technologies improve sustainable development performance as evaluated by ROA and ESG scores. By highlighting green innovation and top management’s environmental awareness as essential mediators, it emphasizes the internal routes that allow digital transformation to support a more sustainable business model, thereby expanding current theories.

Second, the study provides a detailed knowledge of how the institutional environment influences the success of digital transformation for promoting sustainability. It merges the institution-based view with digital transformation literature, providing a fresh perspective by demonstrating how a supportive institutional environment enhances the influence on green innovation and managerial environmental consciousness.

Furthermore, the use of an unbalanced panel dataset spanning over a decade provides a solid empirical foundation for investigating these correlations in the context of China’s manufacturing economy, given its "dual carbon" aims and growing digital world. The work adds to our theoretical understanding of the role of digital transformation in sustainability, while also providing timely insights for policymakers, practitioners, and scholars interested in digital innovation, environmental strategy, and institutional dynamics.

3.1 Data collection and sample preprocessing

The digital transformation of the Chinese manufacturing industry was identified through textual analysis of annual reports from publicly listed companies, with the original annual report data sourced from Juchao Information. Similarly, the data on top management’s environmental awareness was derived using the textual analysis method (Li et al. [26]) on publicly listed companies’ annual reports. Data on the institutional environment were drawn from the research of Luo and Huang [27], wherein the government-market relationship index from the China Provincial Marketization Index Report (2021) was used for measurement. Additional data from databases such as Guotai’an and Wind, wherein information about Chinese A-share listed manufacturing companies between 2010 and 2022, were used. All of the data were processed as follows: (a) Samples with missing data were excluded; (b) extreme outliers were removed; (c) special treatment (ST) and *ST listed companies were excluded; and (d) all continuous variables were winsorized at the 1% and 99% levels to mitigate the influence of extreme values.

3.2 Model design and variable characteristics

The research model of this study, as shown in Figure 1:

pi_zhu_2024-04-02_111803.png

Figure 1. Research model

To assess the effect of digital transformation on corporate sustainable development performance, I constructed the primary regression model as follows:

$S{{D}_{it}}={{\alpha }_{0}}+{{\alpha }_{1}}Di{{g}_{it}}+{{\alpha }_{i}}{{X}_{it}}+{{\varphi }_{i}}+{{\gamma }_{j}}+{{\mu }_{t}}+{{\varepsilon }_{it}}$                (1)

$S{{D}_{it}}~$denotes the sustainable development performance of the enterprise, $Di{{g}_{it}}~$represents digital transformation, ${{X}_{it}}$ refers to various control variables, ${{\varphi }_{i~}}$indicates individual fixed effects, ${{\gamma }_{j}}$ denotes industry fixed effects, ${{\mu }_{t}}$ represents time fixed effects, and ${{\varepsilon }_{it}}$ is the random error term. ${{\alpha }_{0}}$ is the constant term, ${{\alpha }_{1}}$ represents the regression coefficient for the core explanatory variable digital transformation (Dig), and ${{\alpha }_{i}}~$represents the regression coefficients for other control variables.

To further investigate the effect of pathway of green innovation on enterprise sustainable development performance, I constructed the following mediation effect regression models:

$\text{GPaten}{{\text{t}}_{it}}={{\alpha }_{0}}+{{\alpha }_{1}}Di{{g}_{it}}+{{\alpha }_{i}}{{X}_{it}}+{{\varphi }_{i}}+{{\gamma }_{j}}+{{\mu }_{t}}+{{\varepsilon }_{it}}$                     (2)

$S{{D}_{it}}={{\alpha }_{0}}+{{\alpha }_{1}}Di{{g}_{it}}+{{\alpha }_{2}}\text{GPaten}{{\text{t}}_{it}}+{{\alpha }_{i}}{{X}_{it}}+{{\varphi }_{i}}+{{\gamma }_{j}}+{{\mu }_{t}}+{{\varepsilon }_{it}}$                (3)

$\text{GPaten}{{\text{t}}_{it}}~$refers to the level of green innovation, representing the number of green patents obtained by a listed company in the subsequent period (GPatent1) as well as the number of green patents obtained in collaboration with other entities in the subsequent period (GPatent2). The rest of the content is consistent with Eq. (1).

To further examine the effect of pathway of top management’s environmental awareness on enterprise sustainable development performance, I constructed additional mediation effect regression models as follows:

$\text{Envi}{{\text{r}}_{it}}={{\alpha }_{0}}+{{\alpha }_{1}}Di{{g}_{it}}+{{\alpha }_{i}}{{X}_{it}}+{{\varphi }_{i}}+{{\gamma }_{j}}+{{\mu }_{t}}+{{\varepsilon }_{it}}$                              (4)

$S{{D}_{it}}={{\alpha }_{0}}+{{\alpha }_{1}}Di{{g}_{it}}+{{\alpha }_{2}}\text{Envi}{{\text{r}}_{it}}+{{\alpha }_{i}}{{X}_{it}}+{{\varphi }_{i}}+{{\gamma }_{j}}+{{\mu }_{t}}+{{\varepsilon }_{it}}$                (5)

To examine the moderating role of the institutional environment (Inst) within the mediation mechanism, moderated mediation effects were established as follows:

Firstly, the moderated mediation effect of green innovation:

$\text{GPaten}{{\text{t}}_{it}}={{\alpha }_{0}}+{{\alpha }_{1}}Di{{g}_{it}}+{{\alpha }_{2}}Ins{{t}_{it}}+{{\alpha }_{3}}Dig\text{*}Ins{{t}_{it}}+{{\alpha }_{i}}{{X}_{it}}+{{\varphi }_{i}}+{{\gamma }_{j}}+{{\mu }_{t}}+{{\varepsilon }_{it~}}$                                        (6)

$S{{D}_{it}}={{\alpha }_{0}}+{{\alpha }_{1}}Di{{g}_{it}}++{{\alpha }_{2}}Ins{{t}_{it}}+{{\alpha }_{3}}Dig\text{*}Ins{{t}_{it}}+{{\alpha }_{4}}\text{GPaten}{{\text{t}}_{it}}+{{\alpha }_{i}}{{X}_{it}}+{{\varphi }_{i}}+{{\gamma }_{j}}+{{\mu }_{t}}+{{\varepsilon }_{it}}$                                (7)

Secondly, the moderated mediation effect of green innovation:

$\text{Envi}{{\text{r}}_{it}}={{\alpha }_{0}}+{{\alpha }_{1}}Di{{g}_{it}}+{{\alpha }_{2}}Ins{{t}_{it}}+{{\alpha }_{3}}Dig\text{*}Ins{{t}_{it}}+{{\alpha }_{i}}{{X}_{it}}+{{\varphi }_{i}}+{{\gamma }_{j}}+{{\mu }_{t}}+{{\varepsilon }_{it}}$                               (8)

$S{{D}_{it}}={{\alpha }_{0}}+{{\alpha }_{1}}Di{{g}_{it}}++{{\alpha }_{2}}Ins{{t}_{it}}+{{\alpha }_{3}}Dig\text{*}Ins{{t}_{it}}+{{\alpha }_{4}}\text{Envi}{{\text{r}}_{it}}+{{\alpha }_{i}}{{X}_{it}}+{{\varphi }_{i}}+{{\gamma }_{j}}+{{\mu }_{t}}+{{\varepsilon }_{it}}$                                  (9)

Based on the regression models above, I conducted research on the relationship between digital transformation and corporate sustainable development performance. The four sections below explain the key variables involved in the models:

(1) Corporate Sustainable Development Performance

Sustainable development performance was divided into economic development performance and environmental social responsibility performance. The economic development performance was measured by the ROA in the year t+2, and the ESG was indicated by the corporate social responsibility rating score disclosed in the Hexun database for the year.

(2) Digital Transformation (Dig)

Following the approach of Wu et al. [28], the digital transformation index was constructed based on digital technologies (e.g., big data, artificial intelligence, cloud computing, blockchain). First, Python’s web scraping functionality was used to collect annual reports of Chinese A-share listed companies, and Java PDFbox library was employed to extract the full text content of each company’s annual report, serving as the text data pool [29]. Second, based on the digital transformation feature word map constructed by Wu et al. [28], the frequency of feature words in the annual report text data pool was counted. Ultimately, the aggregate frequency of these words formed the indicator system for corporate digital transformation (see Table 1).

(3) Mediating Variables

Green Innovation (GPatent): The numbers of green patents, including invention patents and utility model patents, that were obtained by the listed companies in the next period was used as a proxy for corporate green innovation.

Top management’s environmental awareness (Envir): In line with the measurement method of Li et al. [26] for top management’s environmental awareness, textual analysis was used to examine the annual reports of the listed companies. Key words were selected based on three dimensions: awareness of green competitive advantage, corporate social responsibility awareness, and perception of external environmental pressure (see Table 2).

Table 1. Digital transformation indicator system

Table 2. Top management’s environmental awareness indicator system

The variable of top management’s environmental awareness for the listed companies was constructed based on the frequency of these terms in the company’s annual reports and was used to measure the green focus in the decision-making of corporate managers.

(4) Moderating Variable

Institutional Environment (Inst): In line with the research by Luo and Huang [27], this was measured using the "Government-Market Relationship Index" from the "China Provincial Marketization Index Report (2021)".

Table 3. Variable characteristics and corresponding codes

Table 4. Descriptive statistics

(5) Control Variables

To control for other variables’ effects on corporate sustainable development performance, I selected company size (Size), debt-to-asset ratio (Lev), company growth (Growth), return on equity (Roe), company age (Age), company value (Tbq), cash holdings level (Cash), CEO-Chair duality (Dual), whether audited by the Big Four (Aud), and equity concentration (Top1) as covariates. Additionally, I considered the effects of industry and year (i.e., fixed effects of industry and year, respectively; See Table 3). Table 3 shows the variables and their corresponding codes; Table 4 shows the descriptive statistics among these variables.

Based on data from Chinese A-share listed companies from 2010 to 2021, I constructed corporate digitalization indicators and top management’s green awareness indicators using Python technology. I employed analyses with data obtained from the Wind and CSMAR databases to explore the relationship between digital transformation and corporate sustainable development performance, which yields the following conclusions. First, digital transformation promotes both the economic sustainable development performance and the environmental sustainable development performance among Chinese enterprises. The validity of these results was confirmed through endogeneity and robustness testing. Second, the association between digital transformation and corporate sustainable development include the mediating mechanisms via two pathways: green innovation and top management’s environmental awareness. I found that digital transformation improved corporate sustainable development performance levels through the pathways of enhancing corporate green innovation capabilities and top management’s awareness levels. Third, the institutional environment plays a positive moderating role in the mediation effect between digital transformation and corporate sustainable development. The results indicate that in regions with better and more robust institutional environments, the effects of digital transformation on green innovation and top management’s environmental awareness are greater. This suggests that improvements in the institutional environment may effectively enhance the role of digital transformation in promoting corporate green innovation and elevating top management’s environmental consciousness.

The implications of this study are as follows: First, businesses are encouraged to expedite their digital transformation, with a focus on sustainability goals. This includes making significant investments in digital technologies like as big data analytics, cloud computing, and the Internet of Things (IoT). These technologies should be used to optimize resource utilization, improve energy efficiency, and promote green production innovation. Adopting IoT for real-time monitoring of energy consumption could be a practical step toward identifying and reducing inefficiencies. Enterprises should also devise a detailed digital transformation plan that specifies benchmarks and objectives for green development, such as decarbonizing manufacturing processes through technological advancements.

Furthermore, it is critical to regard green development as a strategic priority. Enterprises should embed green development goals within their long-term strategic planning. This includes setting clear, measurable targets for reducing the environmental footprint of their manufacturing processes, developing new eco-friendly technologies, and optimizing existing processes to curtail pollution and energy use. Seeking certification for environmental standards, such as ISO 14001, can also help the company demonstrate its commitment to sustainable operations and improve its reputation.

Another critical area is strengthening collaborative innovation. Manufacturing companies should collaborate with research institutions and other businesses to create environmentally friendly new products and materials. This could include organizing or joining industrial consortiums focusing on green innovation, as well as investing in R&D projects targeted at developing sustainable product lines or improving the environmental performance of existing products.

Increasing business environmental awareness and incorporating sustainability into corporate strategy are also important ideas. This includes providing frequent training to leaders and employees to foster a culture of environmental awareness and sustainable development. Sustainable development goals should be integrated into the fabric of company strategy and decision-making, with projects and investments prioritized according to their environmental impact.

Finally, it is vital to capitalize on external opportunities and adapt to regulatory changes. Enterprises should be aware about and respond to changes in the external institutional environment, such as new legislation and policy incentives for sustainability. Participating in government sustainability initiatives and using governmental incentives for green technology investments can help an organization’s sustainability efforts.

Chinese manufacturing businesses can greatly improve their performance in terms of sustainable development by implementing these practical guidelines. This not only coincides with the Chinese government’s sustainable development goals, but also provides a competitive edge in the marketplace, establishing these businesses as leaders in green innovation and corporate social responsibility.