Digital Transformation and Green Innovation in State-Owned Enterprises: Evidence Based on Mixed Ownership Reform
1
School of Management, Xi’an Jiaotong University, Xi’an 710049, China
2
Department of Economics and Management, Xi’an Jiaotong University City College, Xi’an 710018, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(22), 9967; https://doi.org/10.3390/su17229967
Submission received: 18 September 2025
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Revised: 3 November 2025
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Accepted: 5 November 2025
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Published: 7 November 2025
(This article belongs to the Section Economic and Business Aspects of Sustainability)
Abstract
This paper examines the impact and mechanism of digital transformation on green innovation in state-owned enterprises. The study uses Chinese A-share listed state-owned enterprises from 2012 to 2023 as the sample, with the perspective of mixed ownership reform. The research sample is 4468 panel data in total. The study uses fixed effect regression analysis, Heckman two-stage test, and instrumental variable test methods, etc. The findings indicate that digital transformation positively influences the degree of green innovation within state-owned enterprises. Furthermore, the mixed ownership reform positively moderates this influence impact. Mechanism test results indicate that digital transformation fosters green innovation by enhancing the level of information disclosure and reducing management shortsightedness. The heterogeneity analysis reveals a more pronounced effect on the relationship among enterprises in the eastern region and those operating within the high-tech industry. The study not only contributes to theoretical implication of digital transformation but also provides empirical guidance for policy formulation.
1. Introduction
Green innovation is an inevitable choice to promote economic transformation and achieve sustainable development. The report of the 20th National Congress of the Communist Party of China points out that “promoting green development and promoting harmony“ is required and explicitly proposes demands about “acceleration of research and application of advanced energy-saving and carbon reduction technologies”. Green innovation aims to reduce environmental pollution and improve ecological quality, as a key factor of reconciling economic growth with environmental protection. Green innovation not only helps companies reduce environmental costs and enhance environmental performance [1,2], but also enables companies to establish enduring competitive advantages [3]. Therefore, the level of green innovation affects the realization of sustainable development [4,5].
At present, the development of digital economy in China is very rapid, causing a huge transformation in economic and social operations. Digitization is gradually becoming the leading force in enterprise innovation and industrial transformation. Digital technology stimulates and aggregates various innovative elements, continuously enhancing the innovation capability of enterprises. With the development of digital technology, green innovation has encountered new opportunities and challenges. The integration of “green + digital” has become an essential part in fostering corporate competitiveness [6]. Integration is a key path to achieving sustainable development. Digital technology is integrated into the production operations of advanced manufacturing, modern agriculture, and modern service industries, dominating the implement of green technology. Artificial intelligence, big data, cloud computing, and the Internet of Things technologies are applied to the design of products and service lifecycle, achieving optimization and integration of systems [7]. The presence of big data and emerging digital technologies has far-reaching effects on the business of enterprise [8]. Comprehensively speaking, integrating digital technology into the green innovation process and realizing the efficient utilization of resources can effectively promote sustainable green development.
To meet the requirements of Chinese-style modernization, it is important to ardently and consistently advocate for the mixed ownership reform of state-owned enterprises. The report from the 20th National Congress of the Communist Party of China underscores the importance of the reform of state-owned enterprises. As the main force and pioneer of innovation, the green innovation of state-owned enterprises is crucial for promoting the coordinated development of social economy. State-owned enterprises are an important foundation of socialism, bearing the mission of promoting high-quality development. The mixed ownership reform can not only enhance corporate governance but also integrate complementary advantages of various capitals together. As a result, the mixed ownership reform of state-owned enterprises promotes green innovation. Sustainable development is beneficial to foster a green lifestyle and realize high-quality development of economy and society.
The marginal contribution of this paper lies in the following:
- (1)
- The mechanism effect of digital transformation and the role of mixed ownership reform have been clarified. Existing literature discusses the relationship between digital transformation and innovation of state-owned enterprises, while the innovation referred to is at the general innovation level. However, green innovation has different characteristics such as higher risks and higher investments. Existing studies suggest that non-state-owned shareholders in the governance of state-owned enterprises can enhance the level of innovation by reducing innovation risks, increasing risk-bearing levels, and alleviating agency conflicts. Only a small portion of the literature has focused on green innovation. Therefore, the impact of digital transformation and its associated economic implications within state-owned enterprises still require further research.
- (2)
- The paper expands the boundary conditions of digital transformation and green innovation in state-owned enterprises. Context literature research on the factors influencing green innovation predominantly concentrates on external environmental regulation and internal corporate governance factors. This study investigates the economic consequences of digital transformation within state-owned enterprises, emphasizing the role of green innovation. Consequently, the findings offer theoretical insights to expedite the digital transformation of these enterprises and lay a foundational framework for subsequent research on mixed ownership reform in state-owned enterprises.
- (3)
- Theoretical implication about the effects of digital transformation has been further deepened from the perspective of mixed ownership reform. This paper examines the effect of the mixed ownership reform on green innovation in state-owned enterprises. The research deepens the theoretical research on the influence factor of green innovation from perspective of corporate governance. In addition, the paper has significant implications for stimulating the vitality of enterprise green innovation. This further research enriches the economic consequence of digital transformation, by conducting fixed effect regression analysis. Problems such as selective bias and bidirectional causal relationship should be considered in the research. Therefore, this paper uses Heckman two-stage test and instrumental variable test methods to alleviate the endogeneity problems.
This paper proceeds as follows. Section 2 reviews the existing literature and proposes research hypotheses. Section 3 introduces the methods and describes the data. Section 4 conducts baseline regression analysis and examines the moderating role of mixed ownership reform. Section 5 conducts robustness tests and further mechanism tests. Section 6 discusses the research results of hypotheses. The final section, Section 7, presents the conclusion.
2. Literature Review and Hypotheses Development
The existing literature conducts research examining the effect of digital transformation from the perspective of general innovation. The digital transformation of enterprises refers to the comprehensive integration and application of digital technology across all facets of an enterprise’s production, operations, and management processes [9,10,11]. This process has instigated significant changes in traditional production processes and management methodologies. However, studies examining from the perspective of mixed-ownership reform are relatively scarce. This study aims to examine whether digital transformation has an impact on the sustainable development of green innovation. How does the digital transformation of state-owned enterprises specifically affect the degree of green innovation? This paper uses Chinese A-listed state-owned enterprises from 2012 to 2023 as the research sample to examine the impact and mechanism of digital transformation in state-owned enterprises.
2.1. Digital Transformation and Green Innovation
The essence of digital transformation is the integration of digital technologies into enterprise production and management processes. This involves a complete overhaul of research and development and production procedures, altering organizational management models. The process of digital technologies application also includes capitalizing on the value of data elements.
Drawing on existing literature, the research inductively builds a framework of digital transformation including eight blocks and proposes ethical issues for future digital information system research [12]. Scholars identify three stages of digital transformation: digitization, digitalization, and digital transformation [13]. Scholars still have disputes about the relationship between digitization and green innovation. For instance, extant literature research believes that digital transformation significantly promotes the quality and quantity of green innovation in listed companies. Further research results reveal that digital transformation can significantly promote green innovation in manufacturing by improving internal control quality and fostering industry–university cooperation [14]. The result shows that digital transformation mainly achieves the improvement of resource utilization efficiency through green upgrades in the field of information technology. Existing studies also show that digital transformation helps optimize the efficiency of corporate labor allocation, alleviate the problem of information asymmetry, motivate corporate innovation [15,16,17]. Additionally, it can optimize internal corporate governance channels, thereby improving resource allocation efficiency and providing strong support for the high-quality development of enterprises [18]. Therefore, the implementation of digital transformation can promote sustainable development of state-owned enterprises.
Thus, the following hypothesis is formulated:
Hypothesis 1.
Digital transformation has a promoting effect on green innovation of state-owned enterprises.
2.2. Digital Transformation, Reform of Mixed Ownership, and Green Innovation
The stagnation of green innovation in China’s state-owned enterprises has emerged as a significant barrier to achieve high-quality development. The mixed ownership reforms of state-owned enterprises can amalgamate the distinct resource of both state and private shareholders. State shareholders typically possess a wealth of information resources, policy support, and financial robustness. Non-state-owned shareholders have flexibility and advanced management expertise. Green innovation necessitates substantial investments in technology, capital, and forward-thinking management approaches. Thus, the integration of different shareholders builds a basic condition conducive to green innovation. When non-state-owned shareholders contribute to the strategic decision-making processes of state-owned enterprises, they can give full play to skills about green technologies and sustainable operational practices. At the same time, the policy implementation and financial strength in state-owned enterprises can also provide solid support for green innovation projects, promoting the research and application of green technology. The resource combination of different ownership shareholders effectively enables enterprises to address environmental challenges. The implementation of mixed ownership reform not only facilitates the advancement of green innovation projects but also helps enhance competitiveness and social responsibility awareness, pushing them to achieve the green and sustainable development goal. Therefore, the combination of resource elements provides a stronger support for the green innovation development of mixed ownership enterprises [19,20].
The State-owned Assets Supervision and Administration Commission of the State Council has provided a policy basis for the implementation of digital transformation in state-owned enterprises. On the one hand, the mixed ownership reform has motivated non-state-owned shareholders to participate in corporate governance, promoting the digital strategic transformation of state-owned enterprises. The senior managers of state-owned enterprises are the initiators and strategic planners of digital transformation, and their willingness and decisions directly affect the implementation of digital strategy. However, state-owned enterprises have policy burdens and high agency costs. Under the conditions of incomplete fault tolerance mechanisms, large accountability pressures may make senior executives of state-owned enterprises more inclined to choose a stable environment and avoid the risks that digital transformation may bring. The participation of non-state-owned shareholders can especially supervise the risk avoidance behavior of controlling shareholders, which is favorable for the development and consistent progress of the digital transformation strategy in state-owned enterprises [21].
On the other hand, non-state-owned shareholders bring non-financial resources such as relevant technology, information, social capital, and management concepts to state-owned enterprises, which can promote the success of digital transformation. Moreover, state-owned shareholders inherently maintain a political link with local governments and command substantial resource advantages. The fusion of their advantages enables state-owned enterprises to have sufficient resources and advanced management concepts for digital transformation, which can enhance the implementation of digital transformation in state-owned enterprises.
Thus, the following hypothesis is formulated:
Hypothesis 2.
The extent of mixed-ownership reform positively moderates the relationship between digital transformation and green innovation within state-owned enterprises.
2.3. Analysis of Impact Mechanisms
Digitization can optimize business operations such as production and sales. Enterprises can control and reduce production and sales costs through digital production models [20]. The digitization of enterprise operations effectively optimizes the management efficiency in business operations, improving resource allocation and utilization efficiency. This indirectly affects the manager’s decision preference. Existing literature research shows that the mixed ownership reform promotes green technology innovation by improving financing constraints and reducing agency costs [22]. The research also believes that the mixed ownership reform can significantly stimulate green innovation by alleviating financing constraints and reducing agency problems [23].
Based on the upper echelon theory, the context literature shows that managers make decisions and strategic choices by their experience [24,25]. These managers’ characteristics of education level, title, age, and tenure have a significant effect on decision-making, thereby affecting the corporate performance. In addition, there are other relevant factors that also have an important effect; for example, team diversity, identity, and sociodemographic characteristics [26,27,28,29]. Digital transformation may provide comprehensive data resources, reducing the risk of fault decision-making. Therefore, digital transformation can help managers to make correct decisions based on transparent and reliable information. The following hypothesis is put forward:
Hypothesis 3.
Digital transformation can reduce management shortsightedness to improve green innovation in state-owned enterprises.
Furthermore, digital transformation can enhance the efficiency of information exchange in the capital market. The utilization of advanced digital technologies, including big data, artificial intelligence, and cloud computing, allows businesses to effectively structure and standardize vast quantities of information, thereby markedly enhancing information utilization rates [30,31]. In addition, the execution of digital transformation strategies can significantly augment the focus of media coverage, analysts, and auditors, thereby further enhancing the transparency of corporate information. Therefore, the process of digital transformation within enterprises can significantly enhance information disclosure, which in turn can foster an increased level of green innovation.
Based on the information asymmetry theory, information asymmetry may have negative effects on green innovation. During the process of green innovation, typical challenges include escalating financing costs and a lack of investment, both of which are the usual problems occurred in corporate management [32]. Moreover, the implementation of digital transformation can effectively enhance the external supervision level, which can help promote the level of green innovation. Therefore, the enhancement of information disclosure can attract more investment in green innovation projects. The following hypothesis is proposed:
Hypothesis 4.
Digital transformation can increase information disclosure to improve green innovation of state-owned enterprises.
3. Materials and Methods
3.1. Sample and Data
This paper uses Chinese A-share state-owned listed companies from 2012 to 2023 as research samples to conduct the tests. The construction of informatization and digitization in China began in 2012. It seems that the development of digital informatization has rapidly progressed since 2012; thus, this paper takes 2012 as the beginning year of sample. Referring to the relevant research, this paper processes the research samples as follows: (1) excludes financial industry companies; (2) selects companies with the largest shareholder being the state or state-owned legal persons; (3) excludes companies that cannot determine the nature of equity; (4) excludes ST and *ST companies. In addition, in order to reduce the impact of extreme values on the result, this paper trims the top and bottom 1% of all continuous variables in the model. Finally, the total sample includes 4468 observations. The company financial data adopted in this paper comes from CSMAR database. The enterprise patent data comes from the Chinese Research Data Service Platform (CNRDS).
3.2. Measures of Variables
3.2.1. Dependent Variable
The dependent variable in the research model is green innovation. Ln_GreenPatent measures the level of green innovation in state-owned enterprises. Drawing on existing research, this paper aggregates the number of green utility and invention patent applications from companies, thereby determining the total count of corporate green innovation applications [33]. In the study, the quantity of green innovations is quantified by the count of green utility model patents, which are characterized by lower technical complexity and difficulty. Conversely, the quality of green innovation is assessed by the number of green invention patent applications with the highest technical level and greatest difficulty.
3.2.2. Independent Variable
The independent variable in the model is digital transformation. Following context literature practices of scholars [34], text analysis is employed to measure the independent variable. By constructing a dictionary of related keywords for digital transformation, such as “artificial intelligence technology, big data technology, blockchain technology, cloud computing technology,” the total word frequency of digital transformation is ultimately obtained.
3.2.3. Mechanism Variable
This paper selects information disclosure and managerial myopia as mechanism variables. Referring to the existing literature, the number of analysts tracking is used to measure the degree of information disclosure. Managerial myopia is measured by using the context analysis method based on corporate annual reports.
3.2.4. Moderating Variable
The moderating variable is the degree of mixed-ownership reform. Based on the research of scholars such as Cai Guilong [35], the variable is measured by using ownership structure HHI Index, which refers to the different degree of mixed ownership reform. Compared with alternative measurements, this method is more comprehensive.
3.2.5. Control Variables
Drawing on existing research, this paper also controls a series of potential factors that may influence corporate green innovation. At the enterprise characteristic level, the research controls for indicators such as enterprise size, return on assets, company age, debt-to-asset ratio, R&D intensity, and environmental protection investment. At the corporate governance level, the research controls for indicators such as board size, board independence, and equity concentration. In addition, the model controls industry effects and year effects. The indicators adopted in this paper are shown in Table 1.
3.3. Empirical Models
In order to examine the impact of digital transformation in state-owned enterprises and the moderating effect, this paper constructs the following models:
Ln_GreenPatenti,t = α0 + α1DCGi,t−1 + Controls + ∑Industry + ∑Year + εi,t−1
Ln_GreenPatenti,t = α0 + α1DCGi,t−1 + α2Mixsi,t−1 + α3Mixsi,t−1 × DCGi,t−1 + Controls + ∑Industry + ∑Year + εi,t−1
In the above equation, Ln_GreenPatent represents the level of green innovation, DCG represents the degree of digital transformation; Controls represent the control variables. Equation (1) is the baseline regression model. Equation (2) is the moderating regression model. Industry and Year variables, respectively, represent the country fixed effect and year fixed effect. The paper controls year and industry fixed effects for time trends and industry factors. In addition, in order to resolve endogenous problems, the paper conducts Heckman two stage test and instrumental variable approach. ε in the model is the random error term.
4. Empirical Results
4.1. Descriptive Atatistics
The analysis result of descriptive statistics is shown in Table 2.
From the above Table 2, the result shows that the maximum value of green innovation in the selected samples is 7.3569, the minimum value is 0.00, and the average value is 1.4219. The value of the standard deviation is 1.3213, indicating that the difference in the sample data varies greatly.
The mean level of digital transformation is 1.4830, with a standard deviation of 1.3469, indicating a significant variation in the level of digital transformation. Within the control variables, the standard deviation for firm age exhibits the greatest magnitude, followed by Ln_Enpro and Boardsize.
4.2. Regression Analysis
The regression model is used to test the impact effect of digital transformation on green innovation. The test result is shown in Table 3 below.
As can be seen from Table 3, the result reveals that the regression coefficient of digital transformation is significantly positive. Additionally, column (2) shows that the coefficient of DCG is significantly positive (t = 3.6856) at the level of 1%. Drawing on context literature, this paper also further uses utility patent applications and invents patent applications as dependent variables. In Column (4), the coefficient for DCG is significantly positive with a t-value of 2.1741, significant at the 5% level. Similarly, in Column (6), the coefficient for DCG is notably positive with a t-value of 4.1481, significant at the 1% level. Therefore, the regression test result supports Hypothesis 1. The research result reveals that digital transformation can effectively promote green innovation of state-owned enterprises. Digital transformation can reduce the costs and provide transparent information to promote green innovation. The findings indicate that digital transformation can serve as a catalyst for fostering green innovation within state-owned enterprises. Based on prior theoretical analysis, digital transformation can effectively facilitate the development of environmentally sustainable practices and technologies by reducing operational costs and offering transparent information.
4.3. Moderating Effect Analysis
The moderating model is used to test the impact of mixed-ownership reform. The test result is shown in Table 4.
It can be seen that the regression coefficient of the interaction term between mixed-ownership reformation and digital transformation is significantly positive at the level of 1% in Table 4. The study result indicates that the mixed ownership reform can effectively promote digital transformation in state-owned enterprises, which facilitates the execution of green innovation activities. Therefore, the result shows that the degree of mixed ownership reform exerts a positively moderating influence on the relationship between digital transformation and green innovation of stated-owned enterprises. Hypothesis 2 of this paper is verified.
5. Robustness Checks and Additional Analysis
5.1. Robustness Checks
- Endogenous problems. The aforementioned research results may be affected by endogeneity issues. State-owned enterprises with a higher degree of digital transformation are more likely to carry out green innovation activities. Moreover, the degree of digital transformation is influenced by some other unobservable factors, which may be related to the dependent variable, thus leading to the existence of endogeneity problems. Problems such as selective bias and bidirectional causal relationship should be considered in the research. Therefore, this paper uses the following methods to alleviate the endogeneity problems.
- Heckman two-stage test method. In order to solve the possible endogenous problems such as sample selection bias and make the results of the study more robust, this research conducts Heckman two-stage estimation method. The first stage test adds the average level of other enterprise digital transformation in the same year and industries in regression test and then calculates the value of IMR. The second stage test controls the IMR variable and relevant variables. The test result is shown in Table 5 below.
As can be seen from Table 5, the finding presented in column (1) demonstrates that the coefficient for mean_DT_ind is significantly positive at a 10% level (t = 15.15). This reveals the presence of an endogeneity issue in the initial regression analysis.
The result in column (1) shows that the coefficient of mean_DT_ind is significantly positive at the level of 10% (t = 15.15), indicating that there is an endogeneity problem in the original regression analysis. Column (2) indicates that the coefficient of IMR variable is notably positive, achieving a significance level of 1% (t = 2.3977).
In addition, the coefficient of the DCG variable is notably positive at a 10% significance level, aligning with the baseline regression results. Thus, the findings indicate that, after accounting for endogeneity issues, digital transformation exerts a beneficial impact on green innovation. Hypothesis 1 is verified robustly.
- Instrumental variable test method. The research may also have a reverse causality problem. Companies with abundant green innovation resources and high-quality green innovation platforms are more likely to have available funds to carry out corporate digital transformation. Drawing on existing literature, the paper selects the number of mobile phones in the same city as the instrumental variable [36]. The test result is shown in Table 6 below.
Table 6 shows that the coefficient of instrumental variable is significantly positive at the level of 1%.
Table 6 demonstrates that the coefficient of the instrumental variable is significantly positive at a 1% level. Both the Kleibergen-Paap rk LM and Kleibergen-Paap rk Wald F values pass the tests for unidentifiable and weak instrumental variables, indicating that the instrumental variable employed in the model is valid. The results from the second stage of regression, after adding the instrumental variable, show that the coefficient of DCG is significantly positive at a 1% level. Therefore, the research result is still robust and verifies Hypothesis 1.
- Replace the independent variable. Drawing on existing literature, this study employs Digital_A as proxy variables to measure the degree of digital transformation. Digital_A is formed by the following four steps: selected seed words, acquiring and cleaning the corpus, using the Word2vec model to extract similar words of each seed word, calculating the indicator [37]. The test result is shown in Table 7.
From the empirical test result in Table 7, it is evident that the coefficient of the independent variable remains significantly positive when using alternative methodologies. The research result has been consistent with baseline regression result. The above test result shows that the result is robust and digital transformation effectively promotes the degree of green innovation in state-owned enterprises. Therefore, the robustness test result supports Hypothesis 1.
5.2. Further Mechanism Tests
In order to further examine the mechanism of digital transformation on green innovation of state-owned enterprises, this study conducts further mechanistic examination tests. These tests use the method proposed by Baron and Kenny [38]. The test model is established as follows:
MVi,t = α0 + α1DCGi,t−1 + αi Controls + ∑Industry + ∑Year + εi,t−1
In Equation (3), MV is the mechanism variable. The model controls year and industry fixed effect.
5.2.1. Managerial Myopia Mechanism Test
Based on prior theoretical analysis, digital transformation can reduce management shortsightedness, thereby improving the degree of green innovation and ultimately achieving sustainable development of state-owned enterprises. Drawing on context literature, the paper uses the text analysis method to measure the variable of managerial myopia [39]. The mechanism test results are shown in Table 8 below.
The results presented in Table 8 provide significant insights into the impact of digital transformation. In column (1), it is observable that the estimation coefficient of digital transformation is significantly negative at a 5% significance level, suggesting that digital transformation has mitigated management myopia. Furthermore, column (2) reveals a consistently significant positive correlation with the estimation coefficient of digital transformation at a 1% level.
Therefore, the findings of this study indicate that digital transformation can mitigate managers’ shortsightedness, thereby promoting the degree of green innovation within state-owned enterprises. The test result supports Hypothesis 3.
5.2.2. Information Disclosure Mechanism Test
According to prior theoretical analysis, digital transformation can increase the level of information disclosure and attract more analyst attention, thereby alleviating information asymmetry in state-owned enterprise. Analysts can provide more information for stakeholders by analyzing financial statements and forecasting related surpluses. The higher the level of information disclosure, the more information will investors receive. This can attract more attention and obtain more investment in green innovation. The model test result is shown in column 3 and 4 of Table 8.
The result presented in column (3) of Table 8 demonstrates that the DCG estimation coefficient is significantly positive at a 1% level, suggesting that digital transformation exerts a beneficial influence on the Analysts variable. Similarly, column (4) reveals that the DCG estimation coefficient has a consistently significant positive at a 1% level. The result of this test shows that digital transformation enhances the degree of green innovation by amplifying information disclosure. Therefore, the further mechanism test result supports Hypothesis 4.
In addition, compared with Sobel test method, Bootstrap test method has a wide application and the result is relatively better. In order to examine the results robustly, the paper further conducts Bootstrap test method. The result is shown in Table 9. Table 9 shows that mechanism variables are significant at the 95% confidence interval. As a result, Bootstrap test result verified mechanism test.
5.3. Heterogeneity Analysis
According to previous studies, digital transformation can significantly enhance the degree of green innovation, thereby achieving the sustainable development of state-owned companies. Is there a difference in the empowerment effect of regional environment and industry characteristics on digital transformation? Moreover, various regions exhibit diverse levels of economic development, resulting in different levels of digital transformation. For example, the eastern region may have abundant investment and technology conditions to carry out digital transformation. This paper mainly tests the heterogeneity analysis from aspects of regional environment and industry characteristics. The study categorizes the research samples into distinct sub-groups as follows: enterprises from the eastern region versus those from the central and western regions, and high-tech enterprises versus non-high-tech ones. Consequently, the findings of this test can offer valuable insights for the government when formulating policies and making decisions. The test result is shown in Table 10 below.
Drawing on existing literature, compared with the eastern region, central, and western regions face a relatively similar environment for green innovation in enterprises. Therefore, this paper includes enterprises from the central and western regions in the same subsample. As can be seen from Table 10 above, digital transformation can effectively facilitate the enhancement of green innovation in the eastern region and high-tech enterprises. However, the coefficients of dependent variable in the central and western regions and non-high-tech enterprises are not significantly effective. One possible reason is that enterprises in the eastern region and high-tech enterprises have better financial conditions. Digital transformation effectively alleviates management shortsightedness these enterprises face, thereby encouraging these state-owned enterprises to have the capability and motivation to participate in green innovation activities. Moreover, due to the poor financial and technology situation of enterprises in the central and western regions and non-high-tech enterprises, these enterprises lack the motivation of engaging in green innovation activities. Therefore, the impact of digital transformation differs from various regions and industries.
6. Discussion
Drawing on existing related literature, the paper conducts the effect and mechanism of digital transformation on green innovation and puts forward research hypotheses. The result supports these hypotheses. Based on the empirical tests, the following results are obtained. Compared with prior research, this paper conducts the effect of digital transformation from the perspective of mixed-ownership reform. The results indicate that the promotion of green innovation can be facilitated by digital transformation, a finding that aligns with the conclusions drawn in other academic studies [33].
This study offers a theoretical foundation to verify the impact of digital transformation from the perspective of mixed-ownership reform. The article conducts mechanism tests based on managerial myopia and information disclosure, expanding the depth of previous research. In addition, the research results provide suggestions for policy formulations and enterprises management. Therefore, the study has empirical implications and theoretical implications. The following suggestions are put forward.
On the one hand, from the governmental level, the implementation of green transformation requires policy support. The government should support, guide, and promote digital transformation of enterprises through effective regulations and policies. With sustainable development becoming an important goal for enterprise development, more and more companies will actively implement digital transformation and promote sustainable development. For enterprises that actively respond to digital transformation, the government should provide certain tax deductions and financial subsidies as rewards. In addition, more attention and more investment should be paid on the western and central regions, providing fundamental conditions and more tax deductions for these enterprises. The government should pay more attention to the key nodes in the implementation of digital transformation and mixed-ownership reform, formulating policies for government and providing guidance services for enterprises.
On the other hand, from the enterprise level, companies should strengthen the construction of digital management system. This requires companies to organically integrate digital transformation with green innovation activities and enhance corporate information management capabilities. The digital management system can provide more information and increase information transparency, leading to achieve green and sustainable development. Therefore, the construction of information system is conductive to the development of green innovation.
There are two aspect limitations in the research. Firstly, in terms of sample selection, this paper uses the state-owned enterprises of Chinese A-share listed companies as the sample data and ignores the unlisted enterprises. The paper also excludes samples with incomplete data, which may have a certain impact on the results. Secondly, under the background of sustainable development, China’s green sustainable development research is still in the development stage. In future research, the study can further expand its depth and breadth. For example, the disclosure and formulation of guidelines for sustainable development accounting in the future remain to be further researched. The implementation of digital transformation and mixed-ownership reform in the future can provide empirical reference for promoting green innovation.
7. Conclusions
The mixed-ownership reform, as an important component of state-owned enterprise reform, plays a significant role in promoting economic development.
The result indicates that the implementation of digital transformation enhances the level of green innovation within state-owned enterprises. Furthermore, the mediation effect is further employed to examine the path mechanism analysis. The result shows that digital transformation can foster green innovation by mitigating managerial myopia and increasing information disclosure.
The research has a new perspective of mixed-ownership reform in state-owned enterprises. The research can provide theoretical evidence for mixed-ownership reform and digital transformation. It can also provide references for management in state-owned enterprises. Therefore, the research has important theoretical and empirical implications.
This study conducts an empirical test to assess the effects of digital transformation on the green innovation practices of state-owned enterprises from the perspective of mixed-ownership reform, providing a theoretical foundation for the mixed-ownership reform and sustainable development. Therefore, under the background of digital economic development, the research not only offers suggestions for state-owned enterprises to foster green and sustainable development but also provides policy implications for government to formulate policies.
Author Contributions
Conceptualization, J.Z. and J.J.; methodology, J.J.; software, J.J.; validation, J.Z. and J.J.; formal analysis, J.Z.; investigation, J.J.; resources, J.J.; data curation, J.J.; writing—original draft preparation, J.J.; writing—review and editing, J.Z.; visualization, J.J.; supervision, J.Z.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.
Funding
The APC was funded by Major Project of the National Social Science Fund number 23&ZD092 and the 11th Project of Xi’an Accounting Association number 22.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The datasets presented in this article are not readily available because they form part of an ongoing research project. Access may be granted upon reasonable request.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Abdullah, M.; Zailani, S.; Iranmanesh, M.; Jayaraman, K. Barriers to green innovation initiatives among manufacturers: The Malaysian case. Rev. Manag. Sci. 2016, 10, 683–709. [Google Scholar] [CrossRef]
- Aguilera-Caracuel, J.; Ortiz-de-Mandojana, N. Green innovation and financial performance: An institutional approach. Organ. Environ. 2013, 26, 365–385. [Google Scholar] [CrossRef]
- Chen, Y.S. The driver of green innovation and green image–green core competence. J. Bus. Ethics 2008, 81, 531–543. [Google Scholar] [CrossRef]
- Chen, Y.S.; Chang, C.H.; Wu, F.S. Origins of green innovations: The differences between proactive and reactive green innovations. Manag. Decis. 2012, 50, 368–398. [Google Scholar] [CrossRef]
- Yang, L.-R.; Chen, J.-H.; Li, H.-H. Validating a model for assessing the association among green innovation, project success and firm benefit. Qual. Quant. 2015, 50, 885–899. [Google Scholar] [CrossRef]
- Verhoef, P.C.; Broekhuizen, T.; Bart, Y.; Bhattacharya, A.; Dong, J.Q.; Fabian, N.; Haenlein, M. Digital transformation: A multidisciplinary reflection and research agenda. J. Bus. Res. 2021, 122, 889–901. [Google Scholar] [CrossRef]
- Dong, J.Q.; Wu, W. Business value of social media technologies: Evidence from online user innovation communities. J. Strateg. Inf. Syst. 2015, 24, 113–127. [Google Scholar] [CrossRef]
- Wedel, M.; Kannan, P.K. Marketing analytics for data-rich environments. J. Mark. 2016, 80, 97–121. [Google Scholar] [CrossRef]
- Chen, H.; Chiang, R.H.L.; Storey, V.C. Business intelligence and analytics: From big data to big impact. MIS Q. 2012, 36, 1165–1188. [Google Scholar] [CrossRef]
- Iansiti, M.; Lakhani, K.R. Digital ubiquity: How connections, sensors, and data are revolutionizing business. Harv. Bus. Rev. 2014, 92, 90–99. [Google Scholar]
- Ng, I.C.L.; Wakenshaw, S.Y.L. The internet-of-things: Review and research directions. Int. J. Res. Mark. 2017, 34, 3–21. [Google Scholar] [CrossRef]
- Vial, G. Understanding digital transformation: A review and a research agenda. J. Strateg. Inf. Syst. 2019, 28, 118–144. [Google Scholar] [CrossRef]
- Ravichandran, T. Exploring the relationships between IT competence, innovation capacity and organizational agility. J. Strateg. Inf. Syst. 2018, 1, 22–42. [Google Scholar] [CrossRef]
- Kohli, R.; Johnson, S. Digital transformation in latecomer industries: CIO and CEO leadership lessons from Encana Inc. MIS Q. Exec. 2011, 10, 141. [Google Scholar]
- Kane, G.C.; Palmer, D.; Philips, A.N.; Kiron, D.; Buckley, N. Strategy, not technology, drives digital transformation. MIT Sloan Manag. Rev. Deloitte Univ. Press 2015, 14, 1–25. [Google Scholar]
- Kolloch, M.; Dellerman, D. Digital innovation in the energy industry: The impact of controversies on the evolution of innovation ecosystems. Technol. Forecast. Soc. Change 2018, 136, 254–264. [Google Scholar] [CrossRef]
- Schallmo, D.; Williams, C.A.; Boardman, L. Digital transformation of business models—Best practice, enablers, and roadmap. Int. J. Innov. Manag. 2017, 21, 1740014. [Google Scholar] [CrossRef]
- Dabbous, A.; Barakat, K.A.; Tarhini, A. Digitalization, crowdfunding, eco-Innovation and financial development for sustainability transitions and sustainable competitiveness: Insights from complexity theory. J. Innov. Knowl. 2024, 9, 100460. [Google Scholar] [CrossRef]
- Deng, Q.; Zhang, L.; Wang, S.; Liao, Y.; Zeng, J. State-owned equity participation and corporate green innovation: Evidence from Chinese private enterprises. Int. Stud. Econ. 2024, 39, 152–171. [Google Scholar] [CrossRef]
- Zhang, S.; Zhang, W.; Chen, F.; Guo, B. Does the mixed-ownership reform of Chinese state-owned enterprises improve their total factor productivity? Pac.-Basin Financ. J. 2023, 82, 102182. [Google Scholar] [CrossRef]
- Jin, X.; Yu, J.; Yuan, G.; Zang, R. Impact of state-owned equity participation on the risk-taking capacity of private enterprises in China: Insights from a quasinatural experiment. Corp. Gov. 2024, 33, 629–662. [Google Scholar] [CrossRef]
- Zhang, X.; Yu, M.; Chen, G. Does mixed-ownership reform improve SOEs’ innovation? Evidence from state ownership. China Econ. Rev. 2020, 61, 101450. [Google Scholar] [CrossRef]
- Zhang, F.; Wang, F.; Wang, Q. Does the mixed-ownership reform improve the productivity of state-owned enterprises? Evidence from companies listed in chinese stock. Ann. Public Coop. Econ. 2022, 94, 1299–1321. [Google Scholar] [CrossRef]
- Hambrick, D.; Mason, P. Upper echelons: The organization as a reflection of its top managers. Acad. Manag. Rev. 1984, 9, 193–206. [Google Scholar] [CrossRef]
- Hambrick, D. Upper echelons theory: An update. Acad. Manag. Rev. 2007, 32, 334–343. [Google Scholar] [CrossRef]
- Cheng, L.T.; Chan, R.Y.; Leung, T. Management demography and corporate performance: Evidence from China. Int. Bus. Rev. 2010, 19, 261–275. [Google Scholar] [CrossRef]
- Agnihotri, A.; Bhattacharys, S. TMT socio-demographic traits and employee satisfaction. Int. J. Hum. Resour. Manag. 2022, 33, 1719–1753. [Google Scholar] [CrossRef]
- Ren, S.; Wang, Y.; Hu, Y.; Yan, J. CEO hometown identity and firm green innovation. Bus. Strategy Environ. 2020, 30, 756–774. [Google Scholar] [CrossRef]
- Zhang, L.; Xu, Y.; Chen, H. Do returnee executives value corporate philanthropy? Evidence from China. J. Bus. Ethics 2022, 179, 411–430. [Google Scholar] [CrossRef] [PubMed]
- Saarikko, T.; Westergren, U.H.; Blomquist, T. The internet of things: Are you ready for what’s coming? Bus. Horiz. 2017, 60, 667–676. [Google Scholar] [CrossRef]
- Dougherty, D.; Dunne, D. Digital science and knowledge boundaries in complex innovation. Organ. Sci. 2012, 23, 1467–1484. [Google Scholar] [CrossRef]
- Takalo, S.K.; Tooranloo, H.S.; Parizi, Z.S. Green innovation: A systematic literature review. J. Clean. Prod. 2021, 279, 122474. [Google Scholar] [CrossRef]
- Tao, A.; Wang, C.; Zhang, S.; Kuai, P. Does enterprise digital transformation contribute to green innovation? Micro-level evidence from China. J. Environ. Manag. 2024, 370, 122609. [Google Scholar] [CrossRef]
- Zhu, Z.; Song, T.; Huang, J.; Zhong, X. Executive cognitive structure, digital policy, and firms’ digital transformation. IEEE Trans. Eng. Manag. 2022, 71, 2579–2592. [Google Scholar] [CrossRef]
- Shiyun, Z.; Guilong, C.; Minying, C. Do non-state shareholders improve accounting information quality? Evidence from competitive state-owned listed companies. Account. Econ. Res. 2017, 31, 28–44. [Google Scholar]
- Arellano, M.; Bond, S. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev. Econ. Stud. 1991, 58, 277–297. [Google Scholar] [CrossRef]
- Wei, T.; Juan, H. Can digital transformation facilitate firms’ M&A: Empirical discovery based on machine learning. Emerg. Mark. Financ. Trade 2022, 59, 113–128. [Google Scholar]
- Baron, R.M.; Kenny, D.A. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J. Personal. Soc. Psychol. 1986, 51, 1173–1182. [Google Scholar] [CrossRef]
- Brochet, F.; Loumioti, M.; Serafeim, G. Speaking of the short-term: Disclosure horizon and managerial myopia. Rev. Account. Stud. 2015, 20, 1122–1163. [Google Scholar] [CrossRef]
Table 1.
Variables.
| Variable | Measurement | |
|---|---|---|
| Dependent Variable | Ln_GreenPatent | the degree of green innovation |
| Independent Variable | DCG | the degree of digital transformation |
| Mechanism Variables | Myopia | managerial myopia |
| Analysts | the information disclosure level | |
| Moderating Variable | Mixs | the degree of mixed ownership reform |
| Control Variables | Firmage | company age |
| Firmsize | company size | |
| R&D intensity | research and development intensity | |
| Ln_Enpro | environmental investment | |
| Leverage | asset–liability ratio | |
| ROA | total asset return | |
| Independent | independence of the Board | |
| Boardsize | board size | |
| Industry | industry | |
| Year | year |
Table 2.
Descriptive statistics.
| Variables | N | Mean | P50 | SD | Min | Max |
|---|---|---|---|---|---|---|
| Ln_GreenPatent | 4468 | 1.4219 | 1.0986 | 1.3213 | 0.0000 | 7.3569 |
| DCG | 4468 | 1.4830 | 1.0986 | 1.3469 | 0.0000 | 5.0370 |
| RD_intensity | 4468 | 0.0340 | 0.0276 | 0.0380 | 0.0003 | 0.3169 |
| Ln_Enpro | 4468 | 1.6780 | 0.0000 | 5.0348 | 0.0000 | 22.8088 |
| ROA | 4468 | 0.0313 | 0.0287 | 0.0574 | −0.3358 | 0.2406 |
| Firmage | 4468 | 18.8263 | 19.0000 | 5.6696 | 2.0000 | 32.0000 |
| Firmsize | 4468 | 8.5038 | 8.4441 | 1.2128 | 3.7136 | 11.0958 |
| Leverage | 4468 | 0.5148 | 0.5261 | 0.1956 | 0.0565 | 0.9360 |
| Boardsize | 4468 | 9.2442 | 9.0000 | 1.8373 | 5.0000 | 15.0000 |
| Independent | 4468 | 0.3727 | 0.3333 | 0.0572 | 0.2308 | 0.5714 |
| Ownership | 4468 | 0.5888 | 0.5862 | 0.1612 | 0.2222 | 0.9546 |
Note: The data used in the table is from CSMAR database and CNRDS platform.
Table 3.
Regression analysis result.
| (1) | (2) | (3) | (4) | (5) | (6) | |
|---|---|---|---|---|---|---|
| VARIABLES | Ln_GreenPatent | Ln_GreenPatent | Ln_GreenUitility | Ln_GreenUitility | Ln_GreenInvent | Ln_GreenInvent |
| DCG | 0.0528 *** (3.6856) | 0.0240 ** (2.1741) | 0.0507 *** (4.1481) | |||
| RD_intensity | 3.4265 *** | 3.3030 *** | 0.5358 | 0.4797 | 3.6342 *** | 3.5157 *** |
| (6.8580) | (6.6447) | (1.4658) | (1.3103) | (7.7365) | (7.5386) | |
| Ln_Enpro | 0.0179 *** | 0.0174 *** | 0.0192 *** | 0.0190 *** | 0.0086 *** | 0.0081 *** |
| (5.0750) | (4.9551) | (6.7774) | (6.7185) | (3.0515) | (2.8952) | |
| ROA | 1.6500 *** | 1.5685 *** | 0.5854 ** | 0.5484 ** | 1.4370 *** | 1.3588 *** |
| (5.3750) | (5.0826) | (2.4073) | (2.2406) | (5.3966) | (5.0898) | |
| Firmage | −0.0148 *** | −0.0164 *** | −0.0082 *** | −0.0089 *** | −0.0063 ** | −0.0078 ** |
| (−3.9850) | (−4.4007) | (−2.9456) | (−3.1631) | (−2.0094) | (−2.4928) | |
| Firmsize | 0.4031 *** | 0.3959 *** | 0.2552 *** | 0.2519 *** | 0.3090 *** | 0.3021 *** |
| (21.4876) | (21.0153) | (18.3940) | (18.0691) | (20.0346) | (19.4704) | |
| Leverage | 0.3799 *** | 0.3754 *** | 0.3325 *** | 0.3304 *** | 0.1878 ** | 0.1834 * |
| (3.5371) | (3.5027) | (4.0099) | (3.9864) | (1.9946) | (1.9541) | |
| Boardsize | 0.0105 | 0.0118 | 0.0171 ** | 0.0177 ** | 0.0080 | 0.0092 |
| (1.0116) | (1.1311) | (2.1666) | (2.2343) | (0.9020) | (1.0391) | |
| Independent | 1.6105 *** | 1.6306 *** | 1.1240 *** | 1.1331 *** | 1.2816 *** | 1.3009 *** |
| (4.9668) | (5.0201) | (4.5755) | (4.6142) | (4.6815) | (4.7414) | |
| Ownership | 0.3014 ** | 0.3086 *** | 0.1091 | 0.1124 | 0.3810 *** | 0.3879 *** |
| (2.5646) | (2.6326) | (1.1826) | (1.2201) | (3.6880) | (3.7638) | |
| Constant | −4.7193 *** | −4.7309 *** | −3.1206 *** | −3.1259 *** | −3.8787 *** | −3.8898 *** |
| (−16.8492) | (−16.8087) | (−14.4126) | (−14.4176) | (−16.8846) | (−16.7998) | |
| Industry | Yes | Yes | Yes | Yes | Yes | Yes |
| Year | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 4468 | 4468 | 4468 | 4468 | 4468 | 4468 |
| R-squared | 0.3995 | 0.4015 | 0.3574 | 0.3581 | 0.3348 | 0.3376 |
Note: * p < 0.1, ** p < 0.05, *** p < 0.01, t statistics in parentheses. The data used in the table are from CSMAR database and CNRDS platform.
Table 4.
Moderating effect test result.
| (1) | (2) | (3) | ||
|---|---|---|---|---|
| VARIABLES | Ln_GreenPatent | VARIABLES | Ln_GreenPatent | Ln_GreenInvent |
| DCG | 0.0528 *** | DCG | −0.0638 | −0.0691 * |
| (3.6856) | (−1.4704) | (−1.8044) | ||
| RD_intensity | 3.3030 *** | Mixs | −0.1126 | −0.0565 |
| (6.6447) | (−0.8677) | (−0.5070) | ||
| Ln_Enpro | 0.0174 *** | Mixs*DCG | 0.1881 *** | 0.1940 *** |
| (4.9551) | (2.8399) | (3.3126) | ||
| ROA | 1.5685 *** | RD_intensity | 3.5317 *** | 3.7826 *** |
| (5.0826) | (7.1333) | (8.2203) | ||
| Firmage | −0.0164 *** | Ln_Enpro | 0.0174 *** | 0.0082 *** |
| (−4.4007) | (4.9606) | (2.9142) | ||
| Firmsize | 0.3959 *** | ROA | 1.5690 *** | 1.3665 *** |
| (21.0153) | (5.0713) | (5.0920) | ||
| Leverage | 0.3754 *** | Firmage | −0.0160 *** | −0.0074 ** |
| (3.5027) | (−4.3091) | (−2.3797) | ||
| Boardsize | 0.0118 | Firmsize | 0.3967 *** | 0.3031 *** |
| (1.1311) | (21.0926) | (19.6313) | ||
| Independent | 1.6306 *** | Leverage | 0.3933 *** | 0.2002 ** |
| (5.0201) | (3.6681) | (2.1341) | ||
| Ownership | 0.3086 *** | Boardsize | 0.0125 | 0.0103 |
| (2.6326) | (1.1943) | (1.1624) | ||
| Constant | −4.7309 *** | Independent | 1.6240 *** | 1.2833 *** |
| (−16.8087) | (4.9745) | (4.6689) | ||
| Ownership | 0.2838 ** | 0.3581 *** | ||
| (2.4268) | (3.4890) | |||
| Constant | −4.7074 *** | −3.9095 *** | ||
| (−16.0299) | (−16.0592) | |||
| Industry | Yes | Yes | Yes | Yes |
| Year | Yes | Yes | Yes | Yes |
| Observations | 4468 | Observations | 4468 | 4468 |
| R-squared | 0.4015 | R-squared | 0.4029 | 0.3404 |
Note: * p < 0.1, ** p < 0.05, *** p < 0.01, t statistics in parentheses. Mixs variable is measured by HHI index; the higher HHI index, the mixed-ownership reform level is lower. The data used in the table are from CSMAR database and CNRDS platform.
Table 5.
Endogenous test result.
| Heckman Two-Stage Estimation | ||||
|---|---|---|---|---|
| (1) | (2) | (3) | ||
| VARIABLES | (DCG_Dummy = 1) | VARIABLES | Ln_GreenPatent | Ln_GreenInvent |
| mean_DT_ind | 0.5767 *** | DCG | 0.0784 *** | 0.0746 *** |
| (15.15) | (5.12) | (5.7) | ||
| IMR | 0.4591 *** | 0.4280 *** | ||
| (5.04) | (5.57) | |||
| Constant | −1.5285 | Constant | −5.3051 *** | −4.4107 *** |
| (−3.60) | (−17.4) | (−17.72) | ||
| Industry | Yes | Industry | Yes | Yes |
| Year | Yes | Year | Yes | Yes |
| Observations | 4303 | Observations | 4303 | 4303 |
| R-squared | 0.1793 | R-squared | 0.4039 | 0.3435 |
Note: *** p < 0.01, t statistics in parentheses. The data used in the table are from CSMAR database and CNRDS platform.
Table 6.
Instrumental variable test result.
| (1) | (2) | (3) | |
|---|---|---|---|
| VARIABLES | DCG | Ln_GreenPatent | Ln_GreenInvent |
| IV | 22.1180 *** | ||
| (8.98) | |||
| DCG | 0.1949 *** | 0.1555 *** | |
| (2.48) | (2.40) | ||
| Constant | Yes | Yes | Yes |
| Industry | Yes | Yes | Yes |
| Year | Yes | Yes | Yes |
| Observations | 4468 | 4468 | 4468 |
| Kleibergen-Paap rk LM Kleibergen-Paap Wald rk F | 70.102 *** 79.105 *** | 70.102 *** 79.105 *** |
Note: *** p < 0.01, t statistics in parentheses. The data used in the table are from CSMAR database and CNRDS platform.
Table 7.
Robustness test result.
| (1) | (2) | (3) | |
|---|---|---|---|
| VARIABLES | Ln_GreenInvent | Ln_GreenPatent | Ln_GreenUitility |
| Digital_A | 0.0022 *** | 0.0016 ** | −0.0002 |
| (3.25) | (2.18) | (−0.30) | |
| Constant | −3.8951 *** | −4.7312 *** | −3.1192 *** |
| (−16.93) | (−16.88) | (−14.41) | |
| Yes | Yes | Yes | Yes |
| Yes | Yes | Yes | Yes |
| Observations | 4468 | 4468 | 4468 |
| R-squared | 0.3370 | 0.4003 | 0.3574 |
Note: ** p < 0.05, *** p < 0.01, t statistics in parentheses. The data used in the table are from CSMAR database and CNRDS platform.
Table 8.
Mechanism test result.
| (1) | (2) | (3) | (4) | ||
|---|---|---|---|---|---|
| VARIABLES | Myopia | Ln_GreenPatent | VARIABLES | Analysts | Ln_GreenPatent |
| DCG | −0.0017 * | 0.0519 *** | DCG | 0.3228 *** | 0.0496 *** |
| (−1.7685) | (3.6189) | (2.81) | (3.47) | ||
| Myopia | −0.5700 *** | Analysts | 0.0099 *** | ||
| (−2.6961) | (4.75) | ||||
| Constant | 0.1150 *** | −4.6654 *** | Constant | −23.4469 *** | −4.4977 *** |
| (4.7684) | (−16.4079) | (−9.331) | (−15.95) | ||
| Industry | Yes | Yes | Industry | Yes | Yes |
| Year N | Yes 4468 | Yes 4468 | Year N | Yes 4468 | Yes 4468 |
| R-squared | 0.1270 | 0.4040 | R-squared | 0.3796 | 0.4054 |
Note: * p < 0.1, *** p < 0.01, t statistics in parentheses. The data used in the table are from CSMAR database and CNRDS platform.
Table 9.
Bootstrap test result.
| Variables | ind_eff | Bootstrap Std. Err | p > |z| | Normal-Based [95% Conf. Interval] | |
|---|---|---|---|---|---|
| Analysts | 0.0023 | 0.0011 | 2.1 ** | 0.0002 | 0.0044 |
| Myopia | −0.0639 | 0.2193 | −2.91 *** | −0.1068 | −0.0209 |
Note: ** p < 0.05, *** p < 0.01, t statistics in parentheses. The data used in the table are from CSMAR database and CNRDS platform.
Table 10.
Heterogeneity analysis test result.
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| VARIABLES | Ln_GreenPatent Eastern Region | Ln_GreenPatent Midwest Regions | Ln_GreenPatent High-Tech Enterprises | Ln_GreenPatent Non-High-Tech Enterprise |
| DCG | 0.0711 *** | 0.0135 | 0.0788 *** | 0.0236 |
| (3.53) | (0.73) | (3.69) | (1.24) | |
| Constant | −5.2509 *** | −4.0983 *** | −3.9214 *** | −4.5941 *** |
| (−10.08) | (−10.45) | (−6.29) | (−11.78) | |
| Industry | Yes | Yes | Yes | Yes |
| Year | Yes | Yes | Yes | Yes |
| Observations | 2466 | 2002 | 1584 | 2884 |
| R-squared | 0.4824 | 0.3353 | 0.4443 | 0.4175 |
Note: *** p < 0.01, t statistics in parentheses. The data used in the table are from CSMAR database and CNRDS platform.
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Ji, J.; Zhang, J. Digital Transformation and Green Innovation in State-Owned Enterprises: Evidence Based on Mixed Ownership Reform. Sustainability 2025, 17, 9967. https://doi.org/10.3390/su17229967
AMA Style
Ji J, Zhang J. Digital Transformation and Green Innovation in State-Owned Enterprises: Evidence Based on Mixed Ownership Reform. Sustainability. 2025; 17(22):9967. https://doi.org/10.3390/su17229967
Chicago/Turabian StyleJi, Jiunan, and Junrui Zhang. 2025. "Digital Transformation and Green Innovation in State-Owned Enterprises: Evidence Based on Mixed Ownership Reform" Sustainability 17, no. 22: 9967. https://doi.org/10.3390/su17229967
APA StyleJi, J., & Zhang, J. (2025). Digital Transformation and Green Innovation in State-Owned Enterprises: Evidence Based on Mixed Ownership Reform. Sustainability, 17(22), 9967. https://doi.org/10.3390/su17229967
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