Nexus between Corporate Digital Transformation and Green Technological Innovation Performance: The Mediating Role of Optimizing Resource Allocation

Abstract

Corporate digital transformation, as a key and representational component of the larger digital economy, plays a vital role in furthering both green technological innovation and the transition to a more sustainable economic model. This study collects panel data relating to firms listed on China’s A-share exchanges from 2009 to 2020 and employs textual analysis to estimate the extent of digital transformation within these organizations. It methodically examines the influence of this transformation on the volume and quality of green technological innovations. The findings reveal a substantial enhancement in both the volume and quality of green technological innovations as a result of corporate digital transformation, with a more noticeable improvement in innovation quality. This transition, driven by the mediating function of optimizing resource allocation, facilitates green technological innovation by enhancing human capital composition, curtailing information asymmetry, and augmenting investment in research and development (R&D). Heterogeneity research shows that the influence of digital transformation on green technological innovation is more pronounced in state-owned corporations, low-pollution corporations, and corporations situated in low-carbon pilot cities. Furthermore, this study discovers that the promotive influence of corporate digital transformation tends to diminish with the advancement of the corporation’s lifecycle, peaking during the growth phase. Finally, this study still has some limitations, such as its exclusive focus on Chinese corporations, the need for improvements in the measurement of digital transformation, and potential sample selection biases.

1. Introduction

Amidst its rapid economic expansion, China faces mounting challenges associated with ecological resource limitations and the escalating issue of environmental pollution [1,2]. It is of paramount importance to expedite the transition in economic growth models and promote industrial restructuring and advancement. Consequently, China is entrusted with the substantial task of crafting an effective, sustainable development plan that aligns with the principles of ecological preservation and environmental protection [3]. This entails the creation of innovative pathways for economic growth while simultaneously raising environmental standards [4,5]. Furthermore, as the world’s foremost carbon emitter, China has committed to reaching its peak carbon emissions by 2030 and achieving carbon neutrality by 2060. This ambitious transformation, unfolding over an approximate 30-year span, places significant pressure on China to curtail its emissions effectively [6,7,8].

To accomplish the objectives outlined above, China is actively promoting a strategy centered on green technological innovation, positioning it as a pivotal solution to environmental challenges. Green technological innovation is a critical component of the global new industrial revolution and technological competition, serving as a fundamental strategy for driving economic transformation and ensuring environmental sustainability [9,10]. This innovative approach, which blends environmental awareness with inventive progress, not only enhances energy efficiency and ecological performance in industrial production, leading to energy savings and reduced emissions, but also encourages the development of environmentally friendly, distinctive products [11,12]. Such advancements stimulate technological progress, significantly enhancing companies’ capacity for self-driven innovation and their competitiveness in green initiatives. This results in a dual benefit of economic growth and environmental conservation [13,14].

Corporate digital transformation is now recognized as a fundamental approach to drive green technological innovation. Firstly, digital transformation, propelled by the rapid advancements in information technologies such as the internet, artificial intelligence, and cloud computing, has become a crucial catalyst for economic advancement, industrial evolution, and the mitigation of environmental issues [15,16]. The “Digital China Development Report (2022)” highlights that in 2022, China’s digital economy expanded to approximately CNY 50 trillion, constituting around 42% of its GDP, emerging as a cornerstone in the construction of a modern economic framework [17,18]. In the course of digital transformation, digital technologies facilitate industrial metamorphosis and improvement by streamlining the flow and integration of data, knowledge, and resources. They revolutionize production methods, administrative processes, and corporate structures, ultimately leading to improved organizational management, specialized labor divisions, and reduced environmental pollution [19,20]. Consequently, China regards digital transformation as a fundamental pillar for achieving its carbon-neutrality goals and economic restructuring, intensifying digital implementation in manufacturing, and propelling green technological innovation [21,22,23]. Thus, the digital transformation within corporations, emblematic of the broader industrial digitization trend, is now an efficient and practical means for fostering green technological innovation [24,25]. On one hand, corporations constitute a vital component of the national industrial system, serving as a primary driver of economic transformation and environmental protection. The active involvement of corporations is indispensable for the advancement of green technological innovation. On the other hand, corporations possess strong incentives for undergoing digital transformation. This is because the benefits of digital technology contribute to enhancing production efficiency and economic returns for businesses. This motivation encourages corporations to allocate significant resources to embark on digital transformation, creating a positive feedback loop that yields mutually beneficial outcomes. Furthermore, the implementation of digital tools for precise carbon emission data collection and analysis within corporations offers significant advantages, including expediting emission reductions in production processes, minimizing carbon footprints in consumption, and fostering energy efficiency [26,27].

Building upon the insights provided above, it becomes evident that the digital transformation of corporations holds the potential to significantly contribute to the promotion of green technological innovation. Investigating this cause-and-effect relationship is of paramount importance in driving the broader transformation toward a greener economy. Consequently, the primary aim of this paper is to thoroughly examine the impact of corporate digital transformation on the progression of green technological innovation.

Green technological innovation and digital transformation are both topics of significant interest among scholars. Firstly, scholars have primarily concentrated on understanding the factors that influence green technological innovation [28]. Given that green technology innovation is the key pathway to achieving carbon neutrality and mitigating environmental challenges in China, and considering that the current impact of green technology innovation is not particularly remarkable, the effective promotion of green technology innovation has become an urgent matter [29]. Consequently, there has been substantial scholarly interest in investigating the factors that influence green technological innovation. Research has extensively explored the roles played by internal and external drivers such as environmental regulations, fiscal decentralization, investments in research and development, intellectual property rights, and public sentiment in fostering advances in green technology. These studies have consistently produced valuable insights [30,31,32,33,34]. Secondly, the impact of transformation on various aspects, such as economic benefits, environmental performance, and innovation capabilities, has been a focal point of scholars’ attention. With the advent of Industry 4.0, digital technologies and digital transformation have profoundly influenced various facets of industrial development, encompassing areas such as production efficiency, management models, and development strategies. Therefore, studying how digital transformation affects these dimensions holds practical significance and contributes significantly to enhancing an economy’s developmental capabilities. For instance, Cheng et al. [35] revealed that digital transformation in physical economy enterprises has an impact on total factor productivity. Shang et al. [26] found that digital transformation can significantly reduce carbon emission intensity. Zhuo and Chen [36] indicated that digital transformation can overcome innovation challenges by improving innovation quality and enhancing absorption and conversion capabilities.

Building upon the aforementioned research, scholars have started to integrate digital transformation and green technological innovation, delving into how digital transformation affects green technological innovation. Luo et al. [37] discussed the direct effect, indirect effect, spatial effect, nonlinear relationship, and policy effect of digital transformation on urban green technological innovation. Yin et al. [38] conducted a theoretical analysis using the pressure-state-response (PSR) model of digital transformation to elucidate its role in promoting green innovation in the manufacturing industry. Xue et al. [39] empirically investigated the impact of digital transformation on green technological innovation at the level of listed companies. However, research in this field is still in a nascent stage and exhibits certain limitations. Firstly, the impact of digital transformation on green technological innovation remains a subject of debate. While some scholars argue that digital transformation can facilitate green technological innovation due to technological advantages, others contend that the expansion of digital scale may not necessarily promote green technological innovation [40], thus warranting further exploration. Secondly, existing studies on the interaction between digital transformation and green technological innovation predominantly focus on the theoretical and macro levels, with limited research on the influence and mechanisms of corporate digital transformation on green technological innovation at the micro level. Thirdly, investigations into the impact of corporate digital transformation on green technological innovation tend to emphasize the quantity or scale of green technological innovation, often overlooking the crucial aspect of the quality of green technological innovation [41].

Therefore, this paper empirically studies the impact mechanism of corporate digital transformation on green technological innovation from the micro level and pays attention to the quantity and quality of green technological innovation at the same time. This study utilizes information from A-share corporations listed in China between 2009 and 2020 to assess their digital transformation using textual analysis methodology. This study investigates how this transformation influences both the amount and the quality of green technological innovation, considering two distinct aspects. To prevent the unreliability of the conclusions, comprehensive robustness and endogeneity examinations are incorporated to validate the findings. Additionally, this study probes into the optimization effect of digital transformation on various resource allocations, such as human, information, and fund resource allocations, to understand how it informs green technological innovation. The final segment of this study involves a heterogeneous analysis, taking into account various factors such as the nature of ownership, sectoral differences, geographical variations, and the stages in the lifecycle of the corporations.

This study makes several distinct contributions. Firstly, while previous studies have primarily focused on quantifying green technological innovation by counting green patent filings, only a few have delved into assessing the quality of such innovation. This paper not only addresses the volume of green technological innovation but also explores its quality by analyzing the frequency of citations received by green patents. This approach is valuable for expanding the research landscape in the field of green innovation. Secondly, this paper underscores the advantages of digital technology in optimizing resource allocation. It specifically highlights how optimizing resource allocation plays an intermediary role in the mechanisms of corporate digital transformation on green technological innovation. This exploration contributes to a better understanding of how corporate digital transformation influences green technological innovation, providing empirical evidence for future studies aiming to facilitate corporate green technological innovation. Thirdly, at the level of heterogeneity analysis, this paper draws upon the enterprise lifecycle theory to observe variations in the performance of enterprises across different lifecycle phases. This observation holds significant practical implications and provides a theoretical foundation for encouraging enterprises to accelerate their digital transformation efforts. Overall, this study offers significant insights into the evaluation of green technological innovation, the intermediary role of optimizing resource allocation, and the impact of corporate digital transformation on green technological innovation. These contributions provide a solid basis for future research in the field of corporate sustainability and innovation.

The subsequent sections of this study are organized in the manner below: Section 2 offers a theoretical framework and formulates research hypotheses. Section 3 outlines the methodology employed in the research, encompassing the model setup, variable selection, and data collection. In Section 4, empirical findings are detailed, encompassing basic regression results, robustness checks, and tests for endogeneity. Section 5 and Section 6 are dedicated to examining the mechanisms and heterogeneity of the findings, respectively. The final section, Section 7, summarizes the conclusions and discusses the recommendations of this study.

2. Theoretical Framework and Hypotheses

2.1. Advantages of Digital Transformation

Compared to traditional technological innovation, green technological innovation holds greater development potential but presents more formidable challenges in its realization. In the realm of traditional technological innovation, the introduction of novel technologies plays a pivotal role in enhancing a firm’s technological competitiveness. This process is instrumental in driving economic growth, fostering the evolution and advancement of industrial structures, and elevating the standing of businesses within the global value hierarchy [42,43]. Green technological innovation incorporates environmental awareness and responsibility into routine production and operational practices [44]. It entails the development of eco-friendly technologies, products, and managerial strategies designed to minimize environmental harm while enhancing operational efficiency [45]. Distinguished from its traditional counterpart by its dual emphasis on environmental preservation and innovation, green technological innovation offers the potential to yield both ecological and economic benefits [46]. However, this type of innovation is accompanied by more significant challenges, involving a range of complex and multifaceted issues. Green technological innovation is often a long-term, intricate process marked by considerable uncertainty. Corporations embarking on green technological innovation may encounter obstacles such as information disparities, a shortage of specialized talent, and funding limitations [47]. Furthermore, from an environmental perspective, green innovation requires low energy consumption and minimal emissions, demanding substantial technological and financial investments, often entailing extended periods before tangible results become apparent. This can lead to a general lack of enthusiasm among businesses to pursue green innovative ventures [48,49].

The advantages of corporate digital transformation can offset the disadvantages associated with green technological innovation. Corporate digital transformation fundamentally revolves around the effective integration and application of information through digital technologies, with the goal of improving the allocation of production resources and promoting systematic innovations in business processes and production methods [50,51]. This transformation empowers corporations to harness digital technology to optimize the allocation of various resources, including human resources, information, and financial assets. It facilitates the development of competitive products through green innovation, thereby enhancing the economic returns and innovation capabilities of the corporation [52]. Consequently, digital transformation has the potential to mitigate the challenges stemming from resource constraints and unstable outcomes often encountered in the green technological innovation process. Meanwhile, the establishment of digital platforms creates opportunities for collaborative innovation with external research institutions and consumers, shaping a comprehensive open innovation framework that further invigorates green innovation initiatives [53].

In summary, corporate digital transformation inherently embodies the principles of green innovation, harnessing the strengths of digital technology to advance the progress of green technological innovation. Therefore, this study puts forth the hypothesis below.

Hypothesis 1.

Corporate digital transformation can advance green technological innovation.

2.2. Optimization of Resource Allocation

Based on the analysis provided above, it is evident that corporate digital transformation plays a pivotal role in fostering innovation in green technology by optimizing resource allocation. From the perspective of how digital technology enhances resource allocation, the implementation of digital transformation within corporations drives innovation in green technology by optimizing the allocation of human, information, and fund resources. This, in turn, leads to increased economic and social benefits, as illustrated in Figure 1.

First and foremost, within the realm of the human resource dimension, corporate digital transformation plays a pivotal role in nurturing innovation in green technology by refining the composition of human capital [54]. This transformation brings about a significant enhancement in the quality of human capital, a crucial factor in bolstering a business’s competitive advantage. The implementation of digital transformation leads to a phenomenon known as the labor substitution effect. It elevates the demand for personnel with higher education and advanced skills due to the widespread adoption of information and digital technologies while concurrently phasing out roles that were traditionally filled by less educated and lower-skilled workers. This ongoing process continually improves the overall human capital composition of the company [55,56]. Moreover, the allure of the digital technology sector, known for its robust economic impact and profitability, attracts top-tier technical talent with the promise of attractive compensation, setting it apart from traditional industries [57]. Consequently, the enhanced human capital composition makes a substantial contribution to green technological innovation, thereby enhancing production efficiency. High-tech professionals, armed with their innovative knowledge and practical experience, bolster the knowledge base and technological prowess within corporations. This fosters the successful integration of internal resources and sparks green patent innovations, paving the way for significant advancements in green technology. The synergy between digitalization and human capital empowers these skilled individuals to process and interpret data in alignment with the specific needs of the enterprise. This transformation of data value directly supports corporate efforts in green technological innovation, reinforcing their core competitive capabilities [58]. Hence, we put forth the subsequent research hypothesis.

Hypothesis 2.

Corporate digital transformation fosters green technological innovation by enhancing the composition of human capital.

At the information resource level, corporate digital transformation acts as a catalyst for green technological innovation by diminishing information asymmetry. The features of digital transformation significantly enhance both the volume and the accuracy of the information that corporations disclose, thereby addressing the imbalance in information between corporations and their external stakeholders [59]. This heightened transparency and improved disclosure practices subject companies to more rigorous external scrutiny. The increased costs associated with concealing environmental data compel these corporations to embrace green technological innovation as a means to mitigate or eliminate the adverse effects of disclosing such environmental information [60,61]. In an environment of heightened external scrutiny, corporations seek to enhance their information transparency and bolster their reputation in the capital market. This, in turn, attracts additional external financial support [62]. Enhanced transparency reduces the cost of capital from investors and creditors, transforming the advantage of disclosure into a financial benefit. Consequently, this provides substantial funding for green technological innovation, thereby enhancing their capacity to implement these innovations [63]. Furthermore, from an information-sharing standpoint, the homogeneity of technical personnel within the industry and the challenges in transferring green innovation knowledge across industries often hinder progress in green technological innovation. Digitalization presents an improved avenue for sharing information, thereby increasing corporate enthusiasm and ability to participate in environmental management. This participation supports efforts in saving energy, reducing emissions, and fostering the growth of green innovation [64]. Hence, the research hypothesis is uncovered below.

Hypothesis 3.

Corporate digital transformation drives green technological innovation through diminishing information asymmetry.

In the context of fund resources, the digital transformation of corporations acts as an impetus for green technological innovation by augmenting investment in R&D [65]. Digital transformation ignites heightened consumer demand for a variety of green products, effectively aligning consumer needs with corporate initiatives in green innovation and enhancing adaptability toward such innovations [66]. More importantly, in order to meet these consumer demands, corporations are compelled to engage in substantial product innovation. This necessity drives investments in R&D focused on demand-driven green technology, thereby strengthening the benefits of product differentiation and yielding greater economic returns [67,68]. Motivated by these incentives, corporations actively augment their R&D spending on green products, further propelling advancements in green technology, which is summarized in the hypothesis below.

Hypothesis 4.

Corporate digital transformation stimulates green technological innovation via augmenting investment in R&D.

3. Methodology

3.1. Model Setup

Based on the research hypotheses proposed above, there may exist a linear relationship between corporate digital transformation and green technological innovation. Drawing upon the model-building approach employed by Feng, Zhang, and Li [32] in their study on the impact of digital finance on green technological innovation, we simultaneously account for time and individual fixed effects in relation to green technological innovation. We use a two-way fixed effects model to estimate the influence of corporate digital transformation on green technological innovation. Additionally, given the significant variations in the development characteristics of companies across different industries, in order to mitigate interference arising from these differences, we also incorporate industry-specific fixed effects into our analysis. The model is presented as follows:

$$Y_{it}={\alpha }_0+{\alpha }_1{DIG}_{it}+{\alpha }_2{Control}_{it}+{\mu }_i+{\theta }_t+{\delta }_j+{\epsilon }_{it}$$

(1)

In this model, the subscript i implies the individual company, and t signifies the year. The dependent variable Y_it is indicative of green technological innovation, encompassing both the volume (GTIN_it) and quality (GTIQ_it) of these innovations. The core explanatory variable DIG_it, captures the extent of digital transformation within a company. Control_it includes various control variables. The terms ${\mu }_i$, ${\theta }_t$, and ${\delta }_j$ are the fixed effects corresponding to companies, years, and industries, respectively, while ${\epsilon }_{it}$ represents the random error term.

3.2. Variables Selection

3.2.1. Dependent Variables

Building upon the investigations conducted by Amore and Bennedsen [69] and Luo et al [37], this paper employs the frequency of green patent filings as a metric for assessing the degree of green technological innovation. The criteria for submitting green patents are stringent, necessitating not only the advancement of green technologies but also a focus on their widespread adoption and utilization. As such, these filings serve as a reliable indicator of an organization’s prowess in green innovation. Moreover, there is often a delay in patent approvals, making the count of filed patents a more consistent measure than the number of granted patents. The duration between initiating patent-related research and its subsequent filing is comparatively brief, thus offering a more precise gauge of an entity’s productive capacity in green innovation. Consequently, we utilize the natural logarithm of the total count of green patent filings to quantify green technological innovation (GTIN). In terms of the quality of this innovation, the frequency of citations received by green patents presents an apt measure. A higher citation count of a green patent signifies its broader impact in the domain, also denoting the superior quality and technical sophistication of the patent [70,71]. Hence, this study adopts the natural logarithm of the citation count of green patents as a measurement of the quality of green technological innovation (GTIQ).

3.2.2. Explanatory Variables

This study, informed by methodologies cited in the scholarly literature [72,73,74], applies textual analysis to quantify the prevalence of terms pertinent to corporate digital transformation within corporate annual reports. This quantification is achieved by logarithmically processing these term frequencies, thereby deriving an indicator of the extent of digital transformation (DIG) within the corporations. Notably, annual reports of corporations are reflective of digital transformation characteristics, encapsulating the business ideology and progression strategies of these entities. The textual analysis encompasses a search for terms linked to digital advancement areas such as artificial intelligence, blockchain, internet, cloud computing, big data, and digital information technology. These terms are recognized as indicators of digital transformation. The frequency of these terms is ascertained through the use of Python-based web scraping technologies, adding empirical depth to the study’s findings.

3.2.3. Control Variables

Informed by prior research [32,60,64], this study incorporates several control variables. The scale of the corporation (Scale) is quantified using the logarithm of its employee count. The corporation’s age (Age) is determined by the difference between the current year and the year it was founded. The growth trajectory of the corporation (Growth) is assessed by the rate of increase in operational revenue. The profitability index (Profit) is expressed as the operating profit’s proportion to the aggregate operating revenue. The indebtedness (Debt) is determined by the whole liabilities compared to the overall assets. The proportion of tangible assets (Fixed) is quantified by the quotient of net fixed assets over the sum of all assets. The efficiency of asset utilization (Roa) is gauged by the proportion of net earnings relative to the aggregate assets. Finally, the extent of management investment (Share) is quantified through the proportion of shares in the possession of the management team compared to the overall issued shares.

3.3. Data Collection

The dataset for this research comprises information from A-share corporations listed on the Chinese stock market spanning from 2009 to 2020. To ensure data quality and validity, this study excludes companies from the financial sector, those with special treatment during the year, and those with incomplete data. This process results in a panel dataset encompassing 1684 companies over the period of 2009–2020, totaling 16,865 observations. To mitigate the impact of extreme values, we conducted winsorization for continuous variables at the 1% level. Information regarding the digital transformation of these listed entities was extracted from their annual reports. The financial data of these corporations was obtained from the China Stock Market & Accounting Research (CSMAR) database. Additionally, green patent information was collected through a combination of sources, including the Chinese Research Data Services (CNRDS), the CSMAR database, and the China Intellectual Property Administration (CIPA). The key statistics for the variables under study are presented in detail in

Table 1. The statistical characteristics of various variables.

Variable	Observations	Mean	Standard Deviation	Minimum	Maximum
GTIN	16,865	0.273	0.627	0	6.324
GTIQ	16,865	0.315	0.786	0	6.625
DIG	16,865	1.127	1.248	0	4.832
Scale	16,865	7.135	2.436	4.765	11.259
Age	16,865	18.423	6.512	0	32
Growth	16,865	0.211	0.461	−0.587	3.213
Profit	16,865	0.102	0.173	−0.624	0.663
Debt	16,865	0.232	0.421	0.042	0.865
Roa	16,865	0.043	0.058	−0.263	0.205
Fixed	16,865	0.197	0.157	0.003	0.684
Share	16,865	0.112	0.206	0	0.652

.

4. Empirical Findings

4.1. Foundational Regression

The foundational regression findings, delineated in

Table 2. Foundational regression outcomes.

Variable	(1)	(2)	(3)	(4)
	GTIN	GTIQ	GTIN	GTIQ
DIG	0.065 *** (0.010)	0.094 *** (0.012)	0.051 *** (0.008)	0.082 *** (0.009)
Scale			0.155 *** (0.098)	0.142 *** (0.092)
Age			−0.143 *** (0.096)	−0.164 ** (0.105)
Growth			−0.061 ** (0.028)	−0.057 * (0.027)
Profit			0.051 ** (0.025)	0.048 ** (0.023)
Debt			0.062 *** (0.031)	0.066 *** (0.035)
Roa			0.085 ** (0.045)	0.081 ** (0.044)
Fixed			0.054 * (0.030)	0.052 ** (0.031)
Share			0.092 ** (0.053)	0.081 *** (0.065)
Constant	−1.017 *** (0.152)	−1.025 *** (0.157)	−1.033 *** (0.162)	−1.068 *** (0.169)
Year FE	Yes	Yes	Yes	Yes
Company FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Observations	16,865	16,865	16,865	16,865
R2	0.157	0.195	0.243	0.280

Notes: Significance levels are expressed below: *** for 1%, ** for 5%, and * for 10%. Standard errors are enclosed within parentheses. These notation conventions apply consistently to subsequent tables.

, examine the influence of corporate digitalization on the volume and quality of green technological innovations. The initial regression outputs, displayed in Columns (1) and (2), incorporate only company-specific, annual, and industry-wide fixed effects. These results reveal a notably positive coefficient for corporate digital transformation (DIG), suggesting an initial indication that digitalization is beneficial in augmenting both the extent and excellence of green technological innovations in corporations. When incorporating the control variables in Columns (3) and (4), the variable DIG continues to exhibit a significantly positive coefficient. This indicates that corporate digitalization distinctly fosters advancements in green technology. Notably, the coefficient value in Column (4) stands at 0.082, surpassing the 0.051 value in Column (3). This disparity implies that digital transformation more distinctly stimulates the quality of green technological innovation than its volume. This finding supports the transition of corporations from prioritizing the ”quantity” of innovations to a more balanced approach that emphasizes ”quality”, marking a shift toward a more sustainable innovation paradigm. Consequently, Hypothesis 1 is corroborated: digital transformation within corporations positively stimulates green technological innovation, with a more pronounced influence on enhancing the quality rather than the quantity of these innovations.

4.2. Robustness Assessment

4.2.1. Alteration in Variables

In the initial phase of robustness assessment, this research shifts its focus from green patent filings to green patent grants as the dependent variable. The outcomes of this modification are detailed in columns (1) and (2) of

Table 3. Outcomes of robustness examinations.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	GTIN	GTIQ	GTIN	GTIQ	GTIN	GTIQ	GTIN	GTIQ
DIG	0.053 *** (0.007)	0.067 *** (0.005)	0.039 *** (0.004)	0.075 *** (0.009)	0.065 *** (0.006)	0.094 *** (0.007)	0.079 *** (0.005)	0.117 *** (0.006)
Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Company FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observations	16,865	16,865	16,865	16,865	7564	7564	16,865	16,865
R2	0.112	0.154	0.162	0.161	0.287	0.352	0.223	0.302

Notes: Significance levels are expressed below: *** for 1%.

. Furthermore, a change is made to the explanatory variable; this study now evaluates the ratio of terms pertaining to digital transformation activities in annual reports relative to the total word count, with these findings illustrated in columns (3) and (4) of Table 3. Throughout these variations, the principal explanatory variable, DIG, maintains a consistently significant positive coefficient, aligning with the original regression findings.

4.2.2. Refinement of Sample Selection

Considering the variability in patent R&D cycles, review durations, and sector-specific factors that could lead to the absence of green patent production within the designated timeframe and potentially skew results [75], this study excludes firms that have neither applied for nor cited green patents. The outcomes, as depicted in columns (5) and (6) of Table 3, display that the coefficient for DIG remains substantially positive, underscoring the reliability of this study’s conclusions.

4.2.3. Modified Regression Model

Acknowledging that certain companies record zero green patent applications and citations, suggesting data truncation, the research methodology re-applies the analysis using the Tobit model, which is particularly effective for truncated datasets [76]. The re-evaluated outcomes, situated in columns (7) and (8) of Table 3, reinforce that corporate digital transformation effectively influences both the volume and the quality of green technological innovation, further validating the strength of the initial regression outcomes.

4.3. Evaluation of Endogeneity

4.3.1. Heckman Two-Step Approach

The unique nature of green patents, often pursued by either highly polluting entities or science and technology firms, might lead to sample self-selection bias in this analysis [77]. To address this, this study employs the Heckman two-step approach. The first step involves a Probit regression, with “the presence of green patent counts and citations” as the dependent variable. In the subsequent step, the Inverse Mills Ratio (IMR) derived from the primary step is integrated into the model. The outcomes, displayed in columns (1) and (2) of

Table 4. Outcomes of endogeneity checks.

Variable	(1)	(2)	(3)	(5)	(6)
	GTIN	GTIQ	First Stage	Second Stage
			DIG	GTIN	GTIQ
DIG	0.049 *** (0.006)	0.077 *** (0.008)		0.172 *** (0.013)	0.264 *** (0.026)
IV			0.069 *** (0.010)
Kleibergen–Paap rk LM			853.179 ***
Cragg–Donald Wald F			1076.505
Kleibergen–Paap rk Wald F			1471.364
Controls	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes
Company FE	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes
Observations	16,865	16,865	16,865	16,865	16,865
R2	0.235	0.286	0.323	0.263	0.342

Notes: Significance levels are expressed below: *** for 1%.

, disclose that the estimated coefficients of the digital transformation variable (DIG) on both the volume and quality of green technological innovation (GTIN and GTIQ) are prominently positive. This suggests that the study’s conclusions retain their validity, even when accounting for the potential endogeneity arising from self-selection bias.

4.3.2. Application of Instrumental Variable

In addressing the potential endogeneity stemming from two-way causality between corporate digital transformation and green technological innovation (notably, corporations with advanced green innovation are more inclined toward digital transformation), we utilize the instrumental variable (IV) approach for endogeneity examination [67,78]. The instrumental variable chosen to represent a corporation’s digital transformation is the mean value of digitalization among corporations operating within the same sector and city. This variable possesses independence and remains unaffected by individual corporations, yet it exerts an influencing effect on corporate digital transformation. It is essential to acknowledge that corporate digital transformation is contingent upon external digital circumstances prevailing in the city where the enterprise is situated, and it varies across industries. As a result, the digital transformation of a single corporation exhibits a positive correlation with that of other corporations within the same industry and city. Nevertheless, it is worth noting that unless a majority of corporations collectively engage in coordinated action, their digital transformation is unlikely to significantly impact the overall digital landscape at the regional or industry level. Given the inherent randomness associated with sample selection, the probability of such collective action is exceedingly low. Therefore, the instrumental variable aligns with the principles of correlation and exogeneity.

The findings from the 2SLS regression, employing this IV, are displayed in columns (3) to (5) of Table 4. The Kleibergen–Paap rk LM statistic is distinctly positive at the 1% level, thus dismissing the hypothesis of inadequate IV identification. Additionally, both the Kleibergen–Paap rk Wald F and Cragg-Donald Wald F statistics surpass the critical threshold of 16.38, refuting the hypothesis of a weak IV. In the initial step, the IV’s estimated coefficient is notably positive, suggesting a positive association between the IV and endogenous variables. As for the subsequent step, the coefficient for DIG remains notably positive, reinforcing the consistency and reliability of the initial regression outcomes.

5. Mechanism Exploration

Drawing from theoretical insights and formulated research hypotheses, this paper posits that digital transformation within corporations serves as a catalyst, potentially fostering green technological innovation through three distinct mechanisms: enhancing the composition of human capital, diminishing information asymmetry, and augmenting investment in R&D. To empirically investigate these mechanisms, this research utilizes a mediating effect model. The model’s construction is designed to ascertain the presence and influence of these three potential mechanisms.

$$M_{it}={\alpha }_0+{\alpha }_1{DIG}_{it}+{\alpha }_2{Control}_{it}+{\mu }_i+{\theta }_t+{\delta }_j+{\epsilon }_{it}$$

(2)

$$Y_{it}={\beta }_0+{\beta }_1{DIG}_{it}+{\beta }_2{M}_{it}+{\beta }_3{Control}_{it}+{\mu }_i+{\theta }_t+{\delta }_j+{\epsilon }_{it}$$

(3)

In this model, M_it symbolizes the mediator variables pertinent to the three identified mechanisms, including human capital composition (HRS), information asymmetry (INS), and R&D investment (REI). Other variables remain aligned with those in Equation (1).

When measuring mediator variables, we contemplate three key dimensions. Firstly, the surge in demand for high-skilled labor due to digital transformation often catalyzes a refinement in the composition of human capital, primarily through elevated educational benchmarks. To gauge this aspect, the proportion of employees holding a bachelor’s degree or more advanced qualifications is utilized as a metric for the human capital composition (HRS). The higher this proportion, the more enhanced the composition of human capital. Secondly, in the context of explosive growth in information volume and imbalances in information quality, analyst attention becomes a vital means of accessing information in the capital market. Analyst attention plays a supervisory role in ensuring that corporate agents fulfill their entrusted economic responsibilities, reducing instances of earnings manipulation and lowering agency costs. Simultaneously, analyst attention can guide market judgments and mitigate resource imbalances arising from information asymmetry [79]. Research has shown that analysts enhance the information disclosure transparency of the market, and companies receiving greater analyst attention tend to exhibit higher stock market liquidity due to increased trading activity [80]. As a result, analyst attention is closely linked to corporate information asymmetry. The more analyst attention a corporation garners, the lower the level of information asymmetry. In this paper, analyst attention is utilized to represent information asymmetry (INS), and it is calculated based on the number of analysts following the corporation throughout the year. Thirdly, the impetus provided by digital transformation is manifested in the corporate investment in R&D, and a stronger impetus correlates with heightened R&D expenditure. The intensity of R&D investment (REI) is calculated in terms of its percentage of total business revenue. The higher this percentage, the greater the corporate investment in R&D. This study employs stepwise regression in the model to discern the existence of a mediating effect and, if present, to determine whether it is partial or complete. If both ${\alpha }_1$ and ${\beta }_2$ are statistically significant, it indicates the existence of a mediating effect represented by the mediator variable. Furthermore, if ${\beta }_1$ is also statistically significant, the mediating effect is considered partial; however, if it is not significant, then it is deemed a complete mediating effect.

In examining the influence channel of enhancing the composition of human capital, the outcomes are delineated in

Table 5. Outcomes of mechanism examination.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	HRS	GTIN	GTIQ	INS	GTIN	GTIQ	REI	GTIN	GTIQ
DIG	0.029 *** (0.003)	0.046 *** (0.007)	0.068 *** (0.008)	0.015 *** (0.002)	0.035 *** (0.006)	0.064 *** (0.008)	0.048 *** (0.005)	0.041 *** (0.007)	0.072 *** (0.008)
HRS		0.437 *** (0.056)	0.842 *** (0.079)
INS					0.153 *** (0.014)	0.216 *** (0.036)
REI								0.416 *** (0.049)	0.656 *** (0.072)
Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Company FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observations	16,865	16,865	16,865	16,865	16,865	16,865	16,865	16,865	16,865
R2	0.145	0.265	0.310	0.112	0.276	0.328	0.169	0.287	0.317

Notes: Significance levels are expressed below: *** for 1%.

, columns (1) to (3). Column (1) elucidates the influence of corporate digital transformation (DIG) on the composition of human capital (HRS). A significant positive coefficient in DIG implies that digital transformation can enhance the proportion of highly educated employees in corporations, which represents enhancing the composition of human capital. The incorporation of both DIG, as the independent variable, and HRS, as the mediator, forms the basis of columns (2) and (3). The analysis reveals significant positive outcomes for both DIG and HRS coefficients. Notably, the diminished magnitude of the DIG’s coefficient, compared to the fundamental regression, underscores a partial mediating effect of enhancing the composition of human capital. Thus, hypothesis 2 is confirmed.

In the context of the mechanism of diminishing information asymmetry, the analysis, outlined in columns (4) to (6) of Table 5, displays pertinent insights. Column (4) concentrates on how corporate digital transformation (DIG) impacts information asymmetry (INS). A notably positive coefficient for DIG illustrates that digital transformation can augment the analyst’s attention to corporations, which represents an evident reduction in information asymmetry. Simultaneously incorporating the independent variable DIG and the mediator variable INS into the model, we obtain columns (5) and (6), where the results reveal that the coefficients for DIG and INS are both statistically significant and positive. The reduced magnitude of the DIG’s coefficient, relative to the fundamental regression, signals a partial mediating effect of diminishing information asymmetry. Hypothesis 3 is corroborated.

Lastly, the role of augmented investment in R&D as a mediating factor is explored in columns (7) to (9) of Table 5. Column (7) highlights the influence of corporate digital transformation (DIG) on investment in R&D (REI), evidenced by a substantially positive coefficient for DIG, signifying a notable boost in investment in R&D due to digital transformation. Introducing both DIG and REI variables into the model results in the outcomes detailed in columns (8) and (9). Here, the analysis identifies positive coefficients for both DIG and REI. The comparative reduction in the DIG’s coefficient against the fundamental regression suggests a partial mediating effect of augmenting investment in R&D. Hence, hypothesis 4 is verified.

6. Heterogeneity Analysis

6.1. Divergence in Ownership Types

Different types of corporate ownership profoundly influence business decisions and strategic directions. Digital transformation and green technological innovation are crucial for a corporation’s future development [81]. Therefore, corporate ownership types may be associated with digital transformation and green technological innovation. In China, corporate ownership is traditionally categorized into state-owned or non-state-owned enterprises. State-owned enterprises are characterized by the central or local government having ownership of the company’s capital. They serve both commercial and public interests, aiming not only for commercial profits but also to maintain stability and promote development in the national economy. On the other hand, non-state-owned enterprises are characterized by private ownership of the company’s capital, with a core focus on pursuing commercial profits. Therefore, the sample under study is categorized into state-owned and non-state-owned enterprises for the purpose of analyzing heterogeneity in response to digital transformation, as detailed in

Table 6. Heterogeneity analysis: by ownership types of enterprises.

Variable	State-Owned		Non-State-Owned		Full Sample
	(1)	(2)	(3)	(4)	(5)	(6)
	GTIN	GTIQ	GTIN	GTIQ	GTIN	GTIQ
DIG	0.092 *** (0.011)	0.128 *** (0.015)	0.034 *** (0.005)	0.057 *** (0.008)	0.035 *** (0.006)	0.061 *** (0.007)
DIG×State					0.023 *** (0.005)	0.047 *** (0.006)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Company FE	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
Observations	6235	6235	10,630	10,630	16,865	16,865
R2	0.289	0.332	0.232	0.295	0.257	0.304

Notes: Significance levels are expressed below: *** for 1%.

. The outcomes uncover that both categories of enterprises exhibit a notable positive correlation between digital transformation and the enhancement of green technological innovation. Notably, the values of coefficients indicate a more prominent influence on state-owned enterprises. This is further evidenced by the coefficient of the interaction term DIG×State. Such a differential influence suggests a stronger influence of digital transformation in state-owned enterprises in comparison to their non-state-owned counterparts. A plausible explanation for this phenomenon lies in the fact that state-owned entities typically benefit from greater access to capital and technical support during digital transformation processes. This advantage enables them to effectively harness the synergy of institutional strengths and resource allocation, thereby mitigating the inherent risks associated with digital transformation initiatives. Therefore, state-owned enterprises are often better positioned to establish a robust institutional groundwork that fosters the advancement of green technological innovation.

6.2. Variation in Industrial Pollution

The extent of green technological innovation within a corporation is closely tied to the environmental impact level of the industry in which it operates. Consequently, there are noticeable disparities in the level of green technology adoption among industries with varying degrees of pollution, as well as disparities in the demand for green technological innovation. This leads to variations in the significance of digital transformation across industries with different pollution degrees. To investigate the heterogeneity brought about by the industry’s pollution degrees, we classify enterprises into two categories: high-pollution enterprises and low-pollution enterprises, utilizing the frameworks provided by the Chinese government’s “Industry Classification Management Directory for Environmental Inspection of Listed Companies” and the “Guide to Environmental Information Disclosure for Listed Companies”. The regression outcomes for these categories are compiled in

Table 7. Heterogeneity analysis: by industries.

Variable	Heavily Polluting		Non-Heavily Polluting		Full Sample
	(1)	(2)	(3)	(4)	(5)	(6)
	GTIN	GTIQ	GTIN	GTIQ	GTIN	GTIQ
DIG	0.008 (0.005)	0.016 (0.006)	0.068 *** (0.007)	0.085 *** (0.010)	0.054 *** (0.006)	0.079 *** (0.008)
DIG×Polluting					−0.024 *** (0.006)	−0.056 *** (0.007)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Company FE	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
Observations	4740	4740	12,125	12,125	16,865	16,865
R2	0.159	0.235	0.245	0.316	0.237	0.298

Notes: Significance levels are expressed below: *** for 1%.

. For enterprises classified under the lower pollution bracket, the regression analysis displays a notably positive coefficient for DIG. In contrast, for enterprises within the high-pollution category, the DIG’s coefficient appears to be statistically insignificant. Additionally, the coefficient for the interaction term DIG×Polluting is prominently negative. These findings point to the conclusion that the beneficial impact of digital transformation on green technological innovation is more evident in sectors with lower degrees of pollution. One potential explanation for this disparity is the inherent environmental characteristics of highly polluting industries. Within these sectors, digital transformation activities primarily focus on areas such as information exchange, technology learning, and production processes and are unlikely to fundamentally alter the pollution-intensive nature of these industries. Consequently, the promotion of green innovation initiatives within these sectors appears to be less prominent.

6.3. Disparity in Regional Features

Differences in regional characteristics may affect green technological innovation, as different regions have varying environmental regulations and policy requirements, leading to differences in the demand for green technological innovation. This results in varying capabilities of corporations in different regions when it comes to green technological innovation. Among various regional features, the policies of low-carbon pilot cities are most closely related to green technological innovation. Consequently, this section differentiates the influence of digital transformation based on the geographical context of the corporations, focusing on their location in relation to low-carbon pilot cities. As per the guidelines of China’s low-carbon provinces and cities pilot program, the corporations in this research are bifurcated into two categories: those situated in designated low-carbon pilot cities and those in cities not part of this initiative. The findings, detailed in

Table 8. Heterogeneity analysis: by region.

Variable	Low Carbon		Non-Low Carbon		Full Sample
	(1)	(2)	(3)	(4)	(5)	(6)
	GTIN	GTIQ	GTIN	GTIQ	GTIN	GTIQ
DIG	0.066 *** (0.009)	0.102 *** (0.012)	0.007 (0.008)	0.013 (0.009)	0.037 *** (0.007)	0.065 *** (0.008)
DIG×Low					0.018 *** (0.005)	0.036 *** (0.007)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Company FE	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
Observations	5105	5105	11,760	11,760	16,865	16,865
R2	0.276	0.302	0.212	0.259	0.286	0.301

Notes: Significance levels are expressed below: *** for 1%.

, reveal a remarkable enhancement in green technological innovation among enterprises operating in low-carbon pilot cities due to digital transformation. However, this influence is not observed to a notable extent in enterprises located in non-pilot cities. The variation can be attributed to the impact of low-carbon city policies, which impose regulatory requirements on the production and operational methods of enterprises. These policies serve as catalysts, fostering ongoing technological advancements and the creation of new environmentally friendly technologies that align with the goals of low-carbon growth.

6.4. Difference in Corporate Lifecycle

According to the theory of the corporate lifecycle, corporations in different stages of development exhibit varying economic conditions, strategic directions, and developmental objectives. These differences influence their approaches to addressing emerging areas, such as digital transformation and green technological innovation [82]. Therefore, this paper examines the heterogeneity of corporations in green technological innovation based on the various stages of their corporate lifecycle. In alignment with Dickinson [83], this analysis segments the corporate lifecycle into three distinct phases: growth, maturity, and decline, using net cash flow patterns from operations, investments, and financing as the basis for categorization. The segmented regression findings, revealed in

Table 9. Heterogeneity analysis: by corporate lifecycle.

Variable	Growth Stage		Maturity Stage		Decline Stage
	(1)	(2)	(3)	(4)	(5)	(6)
	GTIN	GTIQ	GTIN	GTIQ	GTIN	GTIQ
DIG	0.065 *** (0.011)	0.113 *** (0.015)	0.050 *** (0.007)	0.081 *** (0.009)	0.039 *** (0.006)	0.057 *** (0.008)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Company FE	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
Observations	7353	7353	8749	8749	763	763
R2	0.289	0.314	0.267	0.286	0.254	0.305

Notes: Significance levels are expressed below: *** for 1%.

, demonstrate that digital transformation facilitates the advancement of green technological innovation in enterprises at all these lifecycle stages. However, the degree of this facilitative impact diminishes as enterprises advance through their lifecycle. Enterprises in the growth phase exhibit the most pronounced benefit from digitalization, followed by those in the maturity phase, with the least benefit observed in enterprises in the decline phase. This trend can be rationalized by considering that enterprises in the growth phase are generally in a phase of establishing market presence and competitive advantages. Therefore, they are more inclined to utilize digital transformation and green innovation as strategic tools to gain a competitive edge. Furthermore, these growth-stage enterprises typically possess greater agility in adapting strategies, allocating funds, and restructuring their talent pool. They also face comparatively lower barriers and costs in adopting digital technologies, which positions them favorably to integrate digital transformation as a catalyst for green innovation.

7. Conclusions and Recommendations

7.1. Conclusions

In the context of the rapid development of digitalization and the urgent need for green technological innovation, this study delves into the influence of digital transformation within corporations on the advancement of green technological innovation, with a specific focus on companies listed on A-share markets in China during the period from 2009 to 2020. Utilizing textual analytics, this research assesses the extent of digital transformation and provides a comprehensive evaluation of corporations’ capabilities in green technological innovation, taking into account both the quantity and quality of such innovations. The empirical outcomes reveal: (1) There is a significant increase in both the volume and quality of green technological innovations attributed to corporate digital transformation, with a more pronounced improvement in quality; (2) with the mediating role of optimizing resource allocation, digital transformation within corporations bolsters green technological innovation by refining composition of human capital, diminishing information asymmetry, and augmenting research and development (R&D) investment; (3) heterogeneity analysis demonstrates that digitalization has a more substantial impact on promoting green technological innovation within state-owned corporations, those with lower environmental footprints, and businesses located in low-carbon pilot cities; (4) further research illustrates that the positive influence of corporate digitalization on green technological innovation diminishes as companies progress through their lifecycle, with the most pronounced effects observed during the growth phase.

7.2. Recommendations

Based on the conclusions mentioned above, this paper further presents recommendations for advancing corporate digital transformation and green technological innovation. (1) Accelerate digital transformation and deep integration of digital and green initiatives: given the effective role of corporate digital transformation in driving green technological innovation, advancing the digitalization process across all sectors of the national economy is of paramount importance. Governments and businesses should establish common goals and jointly create an institutional and technological environment conducive to the rapid advancement of digital transformation. Simultaneously, the government should encourage companies to integrate digital transformation with green product manufacturing, technology, and efficient management practices. This integration will facilitate a smooth transition of businesses toward digitalization and sustainability, ultimately promoting high-quality corporate development. (2) Tailor policies for different enterprises: recognizing the diversity of enterprises and variations in their performance regarding digital transformation and green technological innovation, it is essential to formulate corresponding policy measures tailored to their unique needs. Implement specific incentives for companies with distinct characteristics, including targeted financial incentives, tax benefits, and subsidies, to motivate their investments in digital transformation and green innovation. (3) Augment investment in R&D and talent: as research and development (R&D) investment and human capital play a pivotal role in facilitating green innovation through digital transformation, it is imperative to employ various means to encourage businesses to increase their R&D investments in digital technologies and green innovation. These measures can include the establishment of R&D funds and tax incentives for research investments. Augmenting R&D spending can accelerate the renewal and enhancement of digital technologies while enhancing the quality of green technological innovations. Furthermore, prioritize the recruitment and training of digital and innovative talents within organizations. Collaborating with universities, academic institutions, and research organizations can help augment the pool of talent resources and drive innovation in the realms of digital and green technologies. (4) Enhance information disclosure: eliminating information asymmetry is a crucial channel through which digital transformation influences green innovation. Therefore, leveraging the technological advantages of digital transformation to improve information disclosure within the capital market is imperative. Heightened transparency can enhance market awareness, bolster investor confidence in businesses, and facilitate the allocation of more resources toward digital transformation and green technological innovation. (5) Early adoption of digital transformation: embarking on digital transformation during the early stages of a company’s lifecycle can yield substantial benefits. We encourage businesses to commence digital transformation planning and implementation as early as possible. This approach not only reduces transformation costs but also maximizes the advantages conferred by digitalization, enabling more effective adaptation to the ever-evolving dynamics of the market.

7.3. Limitations

This study still possesses some limitations, necessitating further exploration in future research: Firstly, the study’s focus was primarily on Chinese corporations, without the inclusion of corporations from other countries for comparative analysis. Given the global trends of the digital revolution and the ascent of the digital economy, it is imperative to investigate whether the influence of digital transformation on green technological innovation is universally applicable. Additionally, gaining a comprehensive understanding of the similarities and distinctions between Chinese corporations and their counterparts in other countries in this context warrants more extensive deliberation. Secondly, due to data availability constraints, this study predominantly relied on publicly listed companies as its sample, thereby excluding non-publicly listed enterprises. It remains to be investigated whether the findings derived from this study can be extrapolated to non-publicly listed companies. Thirdly, this study assessed the extent of corporate digital transformation by examining the frequency of digital keywords within corporate annual reports. There is a pressing need to develop more precise methodologies for accurately gauging the level of corporate digital transformation. Fourthly, despite employing a variety of methods to ensure sample quality, this study may still be susceptible to certain sample selection biases. For instance, the exclusion of certain companies due to incomplete information during the sample-selection process, as well as the exclusion of companies lacking patent applications and citations during robustness tests, may introduce biases. Enhancements in sample quality in future research endeavors hold the potential to effectively address this issue.