The effect and newly developed mechanisms of digital transformation on green innovation: evidence from listed firms in China

The effect and newly developed mechanisms of digital transformation on green innovation: evidence from listed firms in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The effect and newly developed mechanisms of digital transformation on green innovation: evidence from listed firms in China Lei Zhu, Chunyan Wang, Xiaohan Wang, Tong Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4270176/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study explores the impact of digital transformation on green innovation and reveals two newly developed mechanisms including technical imprint of senior executives and media attention. The results show that digital transformation of enterprises promote green innovation significantly. In addition, the enabling role of digital transformation on green innovation is reinforced by executives who have a high technological footprint and by firms' significant media attention. These findings not only provide novel insights to drive enterprise green innovation in the digital economy age, but also offer useful measures to policy makers and firms to implement sustainable development in developing countries. Digital transformation Green innovation Executive imprint Media attention Sustainable development 1. Introduction Faced with the challenge of sustainable development, all countries globally attach great importance to environmental protection. Due to the traditional mode of economic growth which is characterized by high investment, high consumption, high emission and low benefit, China has become the world’s largest carbon dioxide emitter (Shao et.al,2019). In order to solve the issue of environmental pollution, Chinese government has promoted carbon dioxide peaking and carbon neutrality as a new development philosophy, for example “Working actively and prudently toward the goals of reaching peak carbon emissions and carbon neutrality” and “Pursuing green development and promoting harmony between humanity and nature” ( Report to the 20th National Congress of the Communist Party of China ). Against this backdrop, it is important and necessary for firms to carry out green innovation activities (Polzin and Sanders, 2020 ). Many scholars have confirmed the role of green innovation in low-carbon development (Lin and Ma, 2022 ), it can reduce carbon dioxide emissions (Zhao et al., 2023) and coordinate the nexus between sustainable economic and environmental development (Wang et al., 2019 ). In order to achieve its carbon peak and neutrality targets, firms need to transit from a stage of resource dependence to a stage of technology dependence, to which the booming development of digital economy provide good conditions. With the emergence of so called "ABCD" technologies such as Artificial Intelligence, Blockchain, Cloud Computing, and Big Data, the digital transformation is gradually becoming an important strategic path for global corporations' technological evolution. Digital transformation can alter business model of enterprises and generate new paths for value creation (Gregory Vial, 2019 ), promote sustainable development (Yasanur Kayikci, 2018 ). This represents an important strategic opportunity for companies to promote green innovation through digital transformation. However, there are few studies on whether digital transformation can enhance green innovation, and the conclusions are not the same. Some research has looked at the impact of digital technology. Digital technologies contribute positively to the innovation (Gaglio et al.,2022). Big data and artificial intelligence can reduce financial and environmental costs, and improve the performance (Singh et al., 2021) and sustainability of industrial enterprises (Andrew Kusiak, 2017 ). Also, the dynamic capability was promoted (Mikalef et al., 2021 ) and the competitive advantage was reinforced (Shan et al., 2019 ) in enterprises with high digitalization. Then, the improvement of dynamic capability can stimulate business model innovation (Ciampi F et al., 2020 ), and therefore create value for customers (Matarazzo et al., 2020 ). Especially, the facilitating role of big data analytics in green innovation and environmental performance were also confirmed (Waqas et al., 2021 ). From a strategic point of view, some research has explored the economic impacts of digital transformation. Digital transformation has significantly improved corporate innovation (Zhai et al., 2022 ; Zhang et al., 2022 .). Many manufacturers have significantly increased their investment in innovation in the process of digital transformation (Wen et al., 2022 ). Digital transformation also make business more convenient, real-time and scenario-based, optimize the production and operation process (Thomas, 2016), help enterprises achieve energy conservation and emission reduction. Similarly, digital transformation is booming business performance (Peng and Tao, 2022 ), and reduce stock price crash risk (Wu et al., 2022 ). What’s more, the initial development of digitalization can enable European countries to transit to the circular economy (Nham Nguyen Thi Hong and Le Thanh Le Ha, 2022). And digital transformation not only improves pollution emission reduction (Xiong et al., 2022 ), but also can positively affect enterprises’ environmental protection cognition (Xie et al., 2022 ). To enrich the theoretical and empirical research in this area, this paper focuses on the role of digital transformation in green innovation. Moreover, prior studies have also shown that executives’ characteristics and competitive environment would affect green innovation (Hojnik and Ruzzier, 2016 ). Therefore, we also consider the role of internal and external factors when exploring the relationship between digital transformation and green innovation. We further consider two critical factors: executive imprint and media attention. Digital transformation is a kind of strategic decision, so as green innovation, both have technical attributes. Corporate strategic decisions are likely influenced by the characteristics of executives (Akroyd and Kober., 2020; Tian et al., 2020 ; Zhang et al., 2022 ) such as academic experience (He et al., 2021 ; Zhao et al., 2022 ) based on imprinting theory. There exists a lack of system in transition economies compared with developed market economies (Khanna and Palepu, 1997 ) although informal institutions such as media attention can play an important role in corporate governance (Stelios et al., 2012 ; Tavakolifar et al., 2021 ; An et al., 2022 ). Firstly, as a connecting between enterprises and other stakeholders, media can complete the transmission of information (Gao et al., 2018 ; Aman et al., 2022 ). Secondly, with the advent of information era, media is gradually becoming more and more important in shaping social image of firms (Cabral, 2016 ; Teng and Yang., 2021), which will have an effect on enterprise behavior. As a result, we propose that the relationship between digital transformation and green innovation will be affected by executive experience and media attention. Therefore, we try to investigate how enterprise digital transformation affects green innovation as well as how executives’ imprint and media attention moderate this relationship, by means of data analysis from 2010–2020 of A-share listed companies in China. Compared to previous literature, our paper makes three major contributions. First, existing studies mainly explore the impact of factors such as green credit, environmental governance, organization capital and economic policy uncertainty on green innovation (Chen et al., 2023; Zhong and Peng, 2022 ; Qu and Cheung, 2023 ; Yu and Chen, 2023 ). We contribute to the research in the field of green innovation based on the perspective of resource and information. We not only extend the literature on the factors that influence green innovation; but also enrich the research on the microeconomic consequences of digital transformation. Secondly, we empirically examining the moderating effects of executives’ technical imprint and media attention on the digital transformation-green innovation relationship. As such, our study indicates that the role of digital transformation in green innovation depends on the internal and external factors of enterprise. Thirdly, we further demonstrate the detailed mechanism by testing mediating effect of dynamic capability. Altogether, the conclusions from this paper may provide novel insights into how to promote green development in China. The remainder of our paper is as follows. Section 2 represents the theoretical framework and hypotheses. Section 3 shows the research design. Section 4 reports empirical results. Section 5 discusses conclusion. 2. Theoretical framework and hypotheses Green technology refers to a process or a product that reduces environmental pollution, consumption of energy and raw materials, which firstly proposed by Ernest et al. (1994). Any activities which are valuable or can be carried out to promote green technology called green technology innovation. In the light of Schumpeter's innovative theory, innovation is the combination of factors of production in new ways that can stimulate new economic growth points and generate more profits. Specially, green innovation, as a form of innovation, not only will bring benefits to both consumers and firms, but also will reduce adverse effect on environment (James P, 1997 ). There is a need for firms to use resources effectively in the development of green innovation, which makes it important to move from dependence on resources to technological dependence through digital transformation. Digital transformation tries to change the existing organizational management mode of enterprises. By emphasizing this essence, digital transformation can reshape management mode and operating mechanism, improve the efficiency of enterprise resource allocation, and promote management mode innovation (Frynas et al., 2018 ), hence empower green innovation activities from multiple dimensions. 2.1. Enterprise digital transformation and green innovation 2.1.1. Resource perspective on digital transformation Green innovation, belonging to breakthrough innovation, is the result of cooperation and interaction among different entities (Yuan et al., 2022 ), which requires systematic layout and strategic planning of firms to reform the existing technological paradigm. With the rise of platform economy, corporate innovation is changing from close to open. This toughened firms to depend on the internal accumulation of resources to invest in green innovation. Therefore, enterprises need to break through organizational boundaries to promote green innovation activities by obtaining abundant resources in various ways. Apparently, Digital transformation can be a direct or indirect source of resources for green innovation. On one hand, digital transformation induces enterprises to upgrade organizational structure and business model through information technology (AlNuaimi et al., 2022 ). And digital transformation brings up other relevant technologies such as cloud computing, which can also provide new technical resources for green innovation. On the other hand, digital transformation promotes enterprises to develop social capital and build business teams (Li et al., 2018 ). Those can strengthen ties among different departments, and therefore enterprises can obtain green innovation resources through broader channels and more convenient ways. Specifically, how can digital transformation affect the green innovation ability of enterprises? First of all, digital transformation makes it easy to master diversified technical resources in the process of production, hence the transformation and upgrading will be expedited. Also, digital resource can facilitate staff to create better product features when designing product. Under the guidance of new philosophy of green and sustainable development, products are endowed with "green" attribute from the beginning. At stage of innovation output, digital technology can realize end-governance and reduce environmental problems caused by production. Secondly, the improvement of green innovation performance not only depends on resource endowment of enterprises, but also on efficiency of resource allocation (Chen et al., 2022). Digital transformation can help enterprises get rid of the traditional extensive growth style. The efficient flow of data resources makes the use of enterprise resources more efficient, therefore enterprises can save energy and reduce emissions, and raise the level of green innovation. Finally, with the help of digital technology, information and knowledge can be generated, shared and exchanged in the innovation network through a low-cost, rapid and real-time way, which will significantly improve the efficiency of green innovation. Digital enterprises also can make better use of digital technology. For example, it is very easy for firms to collect all kinds of big data from internal and external environment (Waqas et al., 2021 ), which can increase knowledge reserve of green technology innovation and therefore promote green technology innovation. In a nutshell, digital transformation can provide heterogeneous resources for green innovation directly or indirectly and encourage companies to carry out relevant activities on green innovation. 2.1.2. Information perspective on digital transformation According to information asymmetry theory, there are different kinds of information asymmetry both inside and outside the enterprises, which hinders the process of green innovation. The stakeholders, which include investors, the public, government departments, social media and so on, may not understand the real situation of the enterprise's green innovation timely due to information asymmetry and lag of information disclosure, partly affecting the enthusiasm of enterprises about carrying out green innovation. Digital transformation of enterprises can alleviate the above information asymmetry problem. First of all, the flowing of data enables enterprises to focus on value of customers and get the users’ green demands for product timely through digital transformation, thus improving the efficiency of communication between enterprises and the public (Thomas et al., 2016), optimizing operation process based on customer requirements and market conditions and realizing energy conservation and emission reduction and therefore improve green innovation capability (Waqas et al., 2021 ). Secondly, equity market participants incorporate digitization-related information into their business valuation processes, and companies that are highly regarded for their sustainability earn higher praise by disclosing their digitization efforts (Ricci et al., 2020 ). Green innovation, belonging to the category of breakthrough innovation, should balance innovation performance and environmental performance compared with the traditional innovation mode, yet being restricted by capital constraints and development costs (Tobias Stucki, 2019 ). For one thing, the disclosure of information about digitalization can be considered as an important signal sent by companies (Salvi et al., 2021 ), which attracting investors to provide capital. For another, digital technology can promote the development of intelligent finance, improve financial management, and optimize the quality of financial information disclosure. Accordingly, investors can get a wealth of high-quality information, which is conducive to attracting amounts of capital inflow for green innovation. Taken together, we propose the following hypothesis. H1. Digital transformation of enterprises has a positive impact on green innovation. 2.2. The moderating role of executive imprint According to the upper echelon’s theory, the characteristics of executives will affect their cognitive ability and values, then further promote them to make highly personalized decisions (Hambrick and Mason, 1982). The emergence of CDO (Chief Digital Officer) also stems from the need for digital transformation to promote the integration of traditional organizational operation models and digital technologies. In addition to the internalized characteristics of senior executives such as age and gender, personal academic and employment experience usually have a more direct impact on their strategic decisions. The imprint theory holds that an individual will form an internal trait that adapts to the environment of the sensitive period during a specific short-term sensitive period, which is called imprinting (Marquis and Tilcsik, 2013 ). The formation of imprint can also be regarded as a special learning process, which may occur in a specific stage of life, such as school experience, and the imprint will have a lasting and profound impact on the individual. Executives with different types of imprints will exhibit different characteristics in decision-making. As founders of enterprises, the scientific research experience of scientists encourages enterprises to carry out open innovation, which will significantly affect the performance of new ventures (Hahn et al., 2019 ). However, financial expert CEOs does not encourage firm innovation (Yang et al., 2021 ). Entrepreneurial leadership can enhance the performance of SMEs (Nguyen et al., 2021 ). But private entrepreneurs who once worked in state-owned, collective enterprises or government agencies will be more inclined to intervene in real estate and other businesses to seek short-term benefits (Dai et al., 2016 ). And digital knowledge in the TMT(top management team) is positively associated with digital innovation (Firk et al., 2022 ). Based on the existing literature, we argue that the study and work experience of senior executives can cultivate their unique workforce skills and enable them to acquire more cutting-edge scientific knowledge and technical experience. Therefore, the lasting and profound impact of technical imprints enables the transfer of advanced concepts and knowledge, thereby promoting the enabling effect of digital transformation on green innovation. Executives with technical imprint have a more comprehensive and profound cognition of digital transformation (Firk et al., 2022 ). They can better utilize the heterogeneous resources directly or indirectly brought by digital transformation. Similarly, senior executives with technical imprint have a wealth of cutting-edge theoretical knowledge, and are better able to recognize the long-term benefits brought by green innovation. Such executives are more able to give full play to the resource and information brought by digital transformation to empower green innovation. Thus, we predict that: H2. Executives with technical imprint can positively moderate the promoting effect of digital transformation on green innovation. 2.3. The moderating role of media attention In transitional countries, informal institutions, as an important supplement to formal institutions, have a binding force that cannot be ignored (Hilary and Hui, 2009). Media attention, as an important informal institution outside the enterprise, has become an important supplement or substitute factor to be incorporated into the corporate governance framework (Joe et al., 2009 ; Dyck et al., 2008 ; Miller and Gregory, 2006). The media can influence corporate governance (Dyck et al., 2008 ) through reputation mechanisms or attracting executive involvement (Zhang and Su, 2015 ; Li et al., 2021 ). Moreover, media governance is not simply a single reinforcement, because media coverage has an asymmetric impact on stakeholders' perceptions and expectations (Wen and Zhou et al., 2017). Therefore, when the degree of media attention is higher than a certain level, as the degree of attention increases, the inconsistency of expectations will increase, and the media will play an “inverted U-shaped” moderating role (Luo et al., 2022 ). Although media attention can stimulate enterprises' green innovation behavior, and new media environment has a significantly positive effect on corporate EID quality (Fan et al., 2020 ), excessive media attention causes public opinion pressure on enterprises and inhibits their enthusiasm for green innovation (Luo et al., 2022 ). As an important external governance factor, media attention will significantly affect the enabling effect of enterprise digital transformation on green innovation. In the era of information explosion, media is not only an important platform for information release and transmission, but also an important way for stakeholders to obtain information. Media coverage spreads quickly and has a wide audience. Media reports can timely and quickly convey the relevant situation of the enterprise to the outside world. What’s more, media coverage can influence the public image of enterprises. As public opinion is an important factor in informal system (Douglass C. North, 1990 ), so media attention can stimulate corporate reputation. In the process of green innovation, an important function of digital transformation is to reduce information asymmetry and attract the inflow of innovation funds. Firstly, digital transformation is the behavior and method of sustainable development, which will deliver good news to the market. At the same time, media coverage will strengthen investors' positive expectations for the enterprises and improve the company's stock liquidity, and introduce funds for innovation. Secondly, the application of digital technology improves the level of financial management and the quality of information disclosure. Media can provide stakeholders high quality financial information through diverse channels in a more rapid and intuitive way. It is convenient for investors to make long-term and sustainable capital investment in enterprises. Finally, empowering green innovation through digital transformation is also an act of using technology to help environmental protection. The media coverage about that can form external incentive for enterprises, and further promote enterprises to implement green innovation. Thus, we develop the following hypothesis: H3. Media attention can positively moderate the promoting effect of digital transformation on green innovation. 3. Research design 3.1. Sample selection and data sources We take A-share non-financial companies listed on Shanghai and Shenzhen Stock Exchanges as the research object, to empirically test the promoting effect of enterprise digital transformation on green innovation. Due to the availability of green patent data, this study covers the period from 2010 to 2020. The digital transformation data and financial data in this paper are derived from CSMAR database, while we obtained the green patent data and media attention data of enterprises from CNRDS database. According to the demands of this study, samples were selected as follows: (i) enterprises marked with ST and *ST were removed, since the financial performance of the enterprises in financial distress may be significantly different from that of other enterprises; (ii) financial industry companies were removed;(iii) samples with missing major variables and control variables were removed. After this sorting, the final sample contained ten-year information on 3011 listed companies with a total of 12371 observed values. For all continuous non-ratio variables, 1% winsorize is used to reduce the interference of outliers on the regression model. 3.2. Variable measurements 3.2.1. Dependent variable: green innovation The IPC Green Inventory was launched by WIPO in 2010 to facilitate the retrieval of patent information related to environmentally friendly technologies (EST).The number of green patent applications is selected to measure the performance of green innovation in this paper (Li and Zheng, 2016 ; Tan Y, 2014). First of all, the number of patent applications is more stable and reliable, it can better reflect the innovation level of enterprises than the number of patents granted because patents are susceptible to uncertainty in the process of granting patents. Secondly, technological innovation reflects the results of resource investment and allocation of enterprises, and the number of patent applications can reflect the innovation momentum of enterprises. Specifically, in this paper, the total number of green patent applications of enterprises is added by 1 to take natural logarithm processing to obtain GI . In the robustness test, it is further subdivided into the number of green invention patent applications Inv and the number of green utility model patent applications Use as the comparative index, which is used as the proxy variable of green innovation of enterprises. 3.2.2. Independent variable: digital transformation Previous study shows that text analysis of annual reports of listed companies is a reasonable way to measure enterprise strategy and development orientation. The frequency of some keywords in the annual report can represent the degree of resources invested in this field. Therefore, we adopt the index of digital transformation degree of digital economy database in CSMAR database as the proxy variable of enterprise digital transformation. Based on the technical elements necessary for digital transformation, this database counts the number of keywords from five aspects: A(Artificial Intelligence), B(Blockchain), C(Cloud Computing), D(Big Data) and digital technology applications. Keywords come from the enterprises’ annual report. In this paper, the total number of word frequency freq is calculated by summing up the frequency of four kinds of A、B、C、D technology keywords. The natural logarithm of the number of A/B/C/D technical word frequency plus one to obtain the measurement variable DT and the natural logarithm of the number of the frequency of digital technology’s application plus one to obtain the measurement variable Appl . 3.2.3. Moderators According to above theoretical analysis, the promoting effect of enterprise digital transformation on green innovation is also affected by factors such as senior executives' technological imprint and media attention. Therefore, technical imprint and media attention are introduced into the estimation model as moderating variables in the empirical study. The technical imprint of senior executives is mainly formed in the process of acquiring knowledge and skills. Studying and scientific research are the period of acquiring a lot of knowledge. Therefore, in this paper, senior executives with scientific research experience are regarded as possessing technological imprint. It can be measured in the following steps:(i) calculate the number of senior executives who have research experience; (ii) calculate the proportion of senior executives with above background in the total number of senior management team ( acade ). Acade is used as the measurement of the moderating variable of senior executives' technology imprint. We examine the impact of media attention on the relationship between digital transformation and green innovation through media coverage and emotion. In view of the fact that online media coverage is more real-time and communicative, we measure the media attention by the number of financial and economic news about listed companies reported in online media directly. Since media coverage is divided into positive, neutral and negative categories due to data provided by the CNRDS database, we adopt this category to measure media sentiment directly. In this paper, the number of news reports plus one and takes the natural logarithm to obtain the media coverage variable News , and the proportion of positive, neutral and negative news reports in the total number is calculated respectively to obtain the media attention emotion variables Positive news , Neutral news and Negative news . 3.2.4. Control variables In addition, in order to eliminate bias caused by the unobservable heterogeneity of enterprises, a series of control variables were added to ensure the accuracy of the results. The first variable was firm size, used to consider the tendency of larger firms to achieve better green innovation performance. Following Lin et al. ( 2015 ), we used a firm's total assets to measure its size( Size ). Representing the firm age with enterprise establishment year( Age ); representing the growth of enterprises with growth rate of operating income( Growth ) ; representing the debt paying ability of enterprises with an asset-liability ratio ( Debt ); representing the profitability of enterprises with return on total assets ( ROA ) ; representing the corporate cash flow with cash asset ratio( Cash ) ; representing firm value with Tobin’s Q ; representing the equity concentration of enterprises with the major equity concentration ratios ( Top1 ) ; representing enterprise financial risk with financial leverage( Lev ) ; representing capital intensity with fixed asset ratio( Tan ). The definition and description of variables are listed in Table 1 . Table 1 Selection and description of related variables Variables Symbol Variable Definitions Green innovation GI The natural logarithm of the number of the green innovation applications plus one Use The natural logarithm of the number of the invention patents in the green innovation applications plus one Inv The natural logarithm of the number of the utility model patents in the green innovation applications plus one Digital transformation DT The natural logarithm of the number of A/B/C/D technical word frequency plus one Appl The natural logarithm of the number of the frequency of digital technology’s application plus one Executives' technological imprint Acade Proportion of people with scientific research experience in the senior management team Media attention News The natural logarithm of the number of financial news reporting on listed companies plus one Positive news The proportion of positive news reports Neutral news The proportion of neutral news reports Negative news The proportion of negative news reports Firm size Size The natural logarithm of total assets Age (The year of the current year - the year of establishment of the enterprise) Take the natural logarithm Enterprise growth capacity Growth (Amount of operating income in the current year - amount of operating income in the previous year) / (Amount of operating income in the previous year) Asset-liability ratio Debt Total Liabilities/Total Assets Net interest rate on total assets ROA Net profit/Average total assets Cash flow from operating activities Cash Net cash flow from operating activities/total assets Tobin’s Q Tobin Q (tradable market value + non-tradable face value)/(total assets - net intangible assets - net goodwill) Ownership concentration Top1 Shareholding ratio of the largest shareholder Financial leverage Lev Total Liabilities/Owner's Equity Tangible asset ratio Tan Net Fixed Assets/Total Assets 3.3 Research models This study uses the following OLS regression model to test the positive impact of enterprise digital transformation on green innovation: $${GI}_{i,t}=\alpha +\beta {DT}_{i,t-1}+\gamma {Controls}_{i,t}+\sum Year+\sum Industry+{\epsilon }_{i,t}$$ 1 In this model, the dependent variable is enterprise green innovation (GI), the core independent variable is enterprise digital transformation (DT), Controls is the aforementioned control variables. ε is the random error term of this model. In order to improve the reliability of the model, we carry out the following processing. Firstly, the t statistic of Cluster clustering robust standard error adjustment is adopted in the regression equation; Secondly, considering that enterprises' digital transformation has a certain time lag in its impact on green innovation, this paper processes the core independent variables in the master regression test and the regression test of moderating effect with a lag of one period, which also alleviates the interference of the endogenous problem of reverse causality to a certain extent. Thirdly, this paper controls both time and industry fixed effects in the model. Furthermore, model (2) and model (3) are constructed to test the moderating effect of executives' technical imprint and media attention. $${GI}_{i,t}=\alpha +{\beta }_{1}{DT}_{i,t-1}+{\beta }_{2}{DJG}_{i,t}+{\beta }_{3}{DT}_{i,t-1}\times {DJG}_{i,t}+\gamma {Controls}_{i,t}+\sum Year+\sum Industry+{\epsilon }_{i,t}$$ 2 $${GI}_{i,t}=\alpha +{\delta }_{1}{DT}_{i,t-1}+{\delta }_{2}{media}_{i,t}+{\delta }_{3}{DT}_{i,t-1}\times {media}_{i,t}+\gamma {Controls}_{i,t}+\sum Year+\sum Industry+{\epsilon }_{i,t}$$ 3 DJG in the model represents the proportion of senior executives with technical background in the senior management team, which is represented by acade . Media refers to the variable of media attention, which can be measured in two ways: degree and emotion. 4. Empirical test and result analysis 4.1. Summary statistics and correlation analysis Table 2 reports the descriptive statistics of the variables in this study. The standard deviation of the total number of green patent applications is 0.963, which includes all three types patents. The minimum of green patents among sample firms is 0, and the maximum value is 4.413, showing that the green innovation ability varies widely, some firms may have not carried out green innovation. The minimum value of digital transformation is 0, the maximum value is 4.605, and the standard deviation is 1.216, indicating that most firms have tried to implement digital transformation strategy, yet the degree varies continuously. Table 2 Descriptive statistical results of variables Variable N Mean p50 Std. Dev Min Max GI 12371 0.561 0 0.963 0 4.143 DT 12371 1.166 0.693 1.216 0 4.605 Acade 12371 0.218 0.200 0.177 0 0.571 News 12325 5.161 5.081 1.024 2.996 8.198 Size 12371 22.290 22.130 1.285 20.050 26.220 Age 12371 3 3.023 0.276 2.270 3.541 Growth 12371 0.206 0.125 0.426 -0.457 2.870 Debt 12371 0.406 0.399 0.195 0.056 0.855 ROA 12371 0.056 0.046 0.044 0.001 0.226 Cash 12371 0.052 0.050 0.066 -0.135 0.246 TobinQ 12371 2.969 2.268 2.194 0.883 13.090 Top1 12371 33.860 31.610 14.770 8.200 74.890 Lev 12371 1.316 1.065 0.857 0.393 6.760 Tan 12371 0.181 0.147 0.142 0.002 0.651 In Table 3 , we report the Pearson and Spearman correlations of main variables. The digital transformation positively correlates with green innovation, which preliminarily verifies our hypothesis H1. As expected, the correlation coefficients of all variables are mostly significantly at less than 0.5, which indicates that there is no serious multi-collinearity. Table 3 Correlation coefficient between variables GI DT Acade News Size Age Growth Debt ROA Cash Tobin Q Top1 Lev Tan GI 1 DT 0.149*** 1 Acade 0.107*** 0.111*** 1 News 0.174*** -0.020* 0.039*** 1 Size 0.203*** -0.064*** -0.032*** 0.432*** 1 Age -0.068*** -0.109*** -0.085*** 0.050*** 0.173*** 1 Growth -0.006 0.038*** -0.007 0.018* 0.020* -0.028** 1 Debt 0.119*** -0.101*** -0.082*** 0.181*** 0.560*** 0.196*** 0.072*** 1 ROA 0.019* 0.019* 0.036*** 0.087*** -0.109*** -0.100*** 0.167*** -0.359*** 1 Cash 0.015 -0.045*** 0.001 0.086*** 0.048*** -0.027** -0.023* -0.151*** 0.446*** 1 TobinQ -0.077*** 0.124*** 0.047*** 0.115*** -0.400*** -0.141*** 0.141*** -0.381*** 0.404*** 0.146*** 1 Top1 -0.010 -0.154*** -0.042*** 0.050*** 0.162*** -0.036*** -0.027** 0.061*** 0.079*** 0.097*** -0.061*** 1 Lev 0.001 -0.084*** -0.042*** 0.025** 0.168*** 0.068*** -0.074*** 0.398*** -0.360*** -0.111*** -0.188*** -0.057*** 1 Tan -0.029** -0.261*** -0.086*** 0.009 0.113*** 0.056*** -0.075*** 0.079*** -0.109*** 0.235*** -0.176*** 0.076*** 0.190*** 1 Note: *、**、*** represent significant at 10%, 5% and 1% levels respectively. 4.2. Effects of digital transformation on green innovation Table 4 reports the regression results for model (1), the dependent variable is the total of green patent applications. In column (1), we only control the time and industry fixed effect, the coefficient for digital transformation is 0.145 and statistically significant at the level of 1%. In column (2), we add all other control variables which are defined in Table 1 on the basis of column (1). The coefficient of digital transformation is 0.128 and significant at the level of 1%, also positive. These results indicate that there is a positive correlation between digital transformation and green innovation, that is the higher digital transformation, the higher green innovation. Thus, hypothesis H1 is proved. Table 4 Regression results of digital transformation and green innovation (1) (2) GI GI DT 0.145 ** (7.105) 0.128*** (6.746) Size 0.212*** (7.507) Age -0.186** (-2.396) Growth -0.134*** (-5.019) Debt 0.593*** (4.447) ROA 0.567 (1.139) Cash 0.329 (1.423) Tobin Q -0.011 (-1.274) Top1 -0.002 (-1.504) Lev -0.079*** (-3.864) Tan -0.566*** (-3.421) Constant 0.525*** (3.112) -3.498*** (-5.502) Observations 7,533 7,533 adj. \({R}^{2}\) 0.0993 0.188 F 27.05 15.80 Industry Yes Yes Year Yes Yes *** p < 0.01, ** p < 0.05, * p < 0.1 4.3. The moderating effect of executives’ technological imprint In the benchmark regression, we examine whether digital transformation promoted green innovation. Further, in order to verify the promoting effect, more detailed analysis about moderating effects was conducted. These results are listed in Table 5 . The main focuses are the intersections of digital transformation and the moderators. As the table shows in column (1), the coefficient of Acade*DT is 0.179 and significant at the level of 1%, meaning that increase in senior executives with technological imprint will facilitate the impact of digital transformation on green innovation, which is consistent with H2. Senior executives with technological imprint will have a deeper understanding of the effect of digital transformation on green innovation and take advantage of the resource brought by digital technology, mainly because they can accumulate rich knowledge and experience. By virtue of the opportunity of digital transformation, enterprise should improve the talent training system and the decision-making mechanism of technical talents and increase R&D investment. 4.4. The moderating effect of media attention To test hypothesis H3, we further analyze moderating effect of media attention. Column (2)-(5) in Table 5 report regression results for model (3). In column (2), the coefficient of News*DT is 0.045 and significant at the level of 1%, indicating that increased in media attention will promote the effect of enterprise digital transformation on green innovation. Thus, the hypothesis H3 is verified. Meanwhile, we find that only neutral media coverage had significant moderating effect (δ = 0.156, P < 0.05) through subdividing the types of media reports. Consequently, harmonious and stable public opinion environment is conducive to promoting digital transformation strategy, which enables green innovation. A certain degree of media attention has an incentive effect on enterprises implementing digital transformation, thus promoting their green innovation activities. Furthermore, neutral media cannot exert public pressure on businesses, allowing them to focus on implementing transformation strategies and innovation activities. The positive and negative media coverage has strong directivity. Investor sentiment can significantly affect market liquidity (Liu, 2015 ), public opinion is likely to prompt firms to reduce short-sighted behavior in order to avoid short-term risk (Du et al., 2020 ). In this situation, managers may prefer earning management to prevent abnormal stock price fluctuations, and green innovation is avoided deliberately. On this basis, government should build a harmonious and stable public opinion environment and form effective external supervision on enterprises. Table 5 The moderating effect of executive technology imprint and media attention (1) (2) (3) (4) (5) GI GI GI GI GI DT 0.120*** (5.277) 0.125*** (6.832) 0.122*** (5.347) 0.128*** (5.813) 0.124*** (5.348) Acade 0.513*** (5.917) Acade *DT 0.179** (2.579) News 0.108*** (4.751) News*DT 0.045*** (2.821) Positive news 0.614*** (6.829) Positive news*DT -0.077 (-0.700) Neutral news -0.117 (-1.237) Neutral news*DT 0.156** (2.403) Negative news -0.659*** (-8.222) Negative news*DT 0.011 (0.108) Constant -3.340*** (-3.784) -2.174*** (-3.607) -3.371*** (-3.836) -3.392*** (-3.736) -3.187*** (-3.528) Observations 7533 7,498 7,498 7,498 7,498 adj. \({R}^{2}\) 0.192 0.197 0.197 0.192 0.197 F . 16.06 15.39 15.01 15.70 Controls Yes Yes Yes Yes Yes Industry Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes *** p < 0.01, ** p < 0.05, * p < 0.1 4.5. Robustness rests 4.5.1. Alternative the dependent variable We use an alternative measure of the dependent variable, green innovation. The Chinese National Intellectual Property Administration (CNIPA, originally SIPO) grants three types of patents: invention patents, utility model patents, and design patents. Invention patents and utility patents receive the more substantive and rigorous examination in terms of utility, novelty, and non-obviousness before being granted. Columns (1)-(2) in Table 6 report the regression results. The coefficient is 0.049/0.117 and significant at the level of 1%. This indicating that no matter which measurement method is adopted, enterprises' digital transformation will have a significant promoting effect on green innovation. 4.5.2. Alternative the independent variable We use an alternative measure of the independent variable. Firstly, we define a dummy variable DTC_dummy based on the judgment standard of “whether to carry out digital transformation” (1 if the relevant digital transformation keywords appear in the annual report). Column (3) in Table 6 reports the result. The coefficient is 0.092 and significant at the level of 1%, and it is still significant after changing the measurement of green innovation (columns 4 and 5 in Table 6 ). Secondly, we use "digital technology application" keyword frequency to measure the independent variables. In column (6), the coefficient of Appl is 0.049 and significant at 1% level. Therefore, the positive correlation between enterprise digital transformation and green innovation is highly robust. 4.5.3. Narrow the research sample Considering that high-tech enterprises themselves have scientific and technological attributes, in order to eliminate the influence of sample selection on regression results, we exclude the information transmission, software and information technology service industry from the samples according to the industry classification standard of CSRC in 2012. Column (7) in Table 6 reports the regression results. The coefficient is 0.131 and significant at the level of 1%, which doesn’t change our core conclusion. 4.5.4. Change the econometric model Because the number of green patent applications is 0 as the lower limit, we use the Tobit model to rerun the regression. Column (8) in Table 6 reports the result. It shows that no matter which measurement model is used, the regression coefficient of digital transformation is significantly positive, which is consistent with the core research conclusion of this paper. Table 6 Robustness Tests (1) (2) (3) (4) (5) (6) (7) (8) Use Inv GI Use Inv GI GI GI DT 0.049*** (6.853) 0.117*** (11.991) 0.131*** (10.301) 0.333*** (8.171) DTC_dummy 0.092*** (6.618) 0.021** (2.334) 0.089*** (6.993) Appl 0.049*** (2.737) Constant 0.301*** (3.211) 0.357*** (3.305) -5.415*** (-12.181) -3.152*** (-9.441) -5.327*** (-12.935) -3.522*** (-5.451) -4.937*** (-12.713) -9.606*** (-6.699) Observations 7,533 7,533 5,210 5,210 5,210 7533 6,480 7533 adj. \({R}^{2}\) 0.172 0.172 0.200 0.190 0.182 0.173 0.208 - Wald chi2(36) - - - - - - - 647.86 Controls Yes Yes Yes Yes Yes Yes Yes Yes Industry Yes Yes Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Yes Yes *** p < 0.01, ** p < 0.05, * p < 0.1 4.5.5. Endogenous test We lag the independent variable by one period in the main hypothesis testing, which alleviates the endogeneity problem of mutual causation to a certain extent, but the empirical results may still be affected by some unobservable factors. Firstly, there may be factors that affect both digital transformation and green innovation, such as local industrial policies; secondly, companies that are actively engaged in green innovation activities pay more attention to the upgrading of production methods, and they may be more inclined to carry out digital transformation, which leads to endogenous problems such as selection bias and omitted variables. We solve the endogeneity problem using the following methods: First, controlling for firm-fixed, which can alleviate the endogeneity problem caused by missing variables to some extent. Table 7 reports the regression results. We only control for firm-fixed and year-fixed in column (1), and add the set of control variables in column (2). The coefficient of DT is all significantly positive, indicating that the results of the benchmark regression are robust. Second, considering that enterprise digital transformation is the process of gradual implementation of enterprise strategy, we employ a DID model to test our hypotheses in order to eliminate the bias caused by individual difference and time trend, as Eq. ( 4 ) shows. $${GI}_{i,t}={\theta }_{0}+{\theta }_{1}{treat}_{i,t}\times {post}_{i,t}+{\theta }_{2}{Controls}_{i,t}+\sum Year+\sum Industry+{\epsilon }_{i,t}$$ 4 In light of the DID specification, treat is an individual dummy variable. The enterprises that have undergone digital transformation are the treated group( treat = 1). Post is the dummy variable of the period. When an enterprise carries out digital transformation, post equal to 1 in this year and subsequent years. In particular, the companies that digital transformation lasted for two years or more were identified as treat is 1, and the samples that had been undergoing digital transformation during the sample period were excluded. Column (3) in Table 7 shows that the coefficient of treat*post is 0.255 and significant at 1% level. This indicates that enterprise digital transformation promotes green innovation, and the core conclusion remains highly consistent. Table 7 Endogenous test (1) (2) (3) GI GI GI DT 0.027 ** (2.21) 0.022 * (1.79) treat*post 0.255*** (6.92) Constant 0.345 *** (7.77) -1.057 * (-1.79) -3.015*** (-4.52) N 7533 7533 7924 \({R}^{2}\) 0.049 0.052 0.181 adj. \({R}^{2}\) - - 0.177 F 10.286 8.186 . Controls No Yes Yes Company Yes Yes No Industry Yes Yes Yes Year Yes Yes Yes *** p < 0.01, ** p < 0.05, * p < 0.1 4.6. Further analysis -- the mediating effect of dynamic capability Digital transformation can provide dynamic management and organizational capabilities for enterprises (Li et al., 2018 ). Dynamic capabilities can adjust and reshape the resource base of enterprises (Ambrosini et al., 2010 ), enable efficient allocation of innovative resources, help enterprises to obtain sustainable competitive advantages (Teece et al., 1998 ). Referring to the research of Wang and Ahmed ( 2007 ), we measure the dynamic capabilities of enterprises as three dimensions: absorptive capacity, adaptive capacity and innovation capacity. Firstly, enterprises accomplish the deep application of digital technology through digital transformation, so as to efficiently acquire and allocate green innovation resources, so that promote green innovation by improving the absorptive capacity of enterprises. Secondly, digitalization has already been recognized as a key ingredient of the paradigm shift towards sustainable business model (Maffei et al.,2019), and it makes the internal operation of enterprises more flexible. Through more keen insight and perception of the market environment (Helfat et al., 2018), it can quickly adapt to the dual carbon goal and bring about great changes in the development environment. Using digital technology to explore new ways to create value (Gregory Vial, 2019 ), bring sustainable competitive advantage (Mikalef et al., 2020 ), and thus promote green innovation by improving adaptability. Finally, digital transformation can not only directly or indirectly provide enterprises with innovative resources such as information and capital, but also promote the introduction of scientific and technological talents and the optimization and upgrading of enterprise human capital structure (Loebbecke et al., 2015), so that enhance the proactive innovation capability of enterprises (Helfat et al., 2018), thus promoting green innovation. We employ dynamic capabilities as the mediating variable for the impact of enterprise digital transformation on green innovation. We measure the dynamic capability as follows: (i) return on assets, which reflects the resource utilization and management level of an enterprise and highlights the ability of absorbing and utilizing resources. (ii) Proportion of employees with bachelor degree or above, which reflects the overall quality of employees. The higher the education level of employees, the more flexibility they can stimulate the internal operation of enterprises. So, it can measure the adaptability of enterprises. (iii) R&D investment intensity, which reflects how much an enterprise attaches importance to innovation, measures innovation ability. We employed the mediation effect model of Wen Zhonglin et al. (2004), as shown in model (5) -model (7). $${GI}_{i,t+1}=\alpha +\beta {DT}_{i,t-1}+\gamma {Controls}_{i,t}+\sum Year+\sum Industry+{\epsilon }_{i,t}$$ 5 $${capability }_{i,t}={\alpha }^{’}+\vartheta {DT}_{i,t-1}+\gamma {Controls}_{i,t}+\sum Year+\sum Industry+{\epsilon }_{i,t}$$ 6 $${GI}_{i,t+1}=\alpha +{\delta }_{1}{capability }_{i,t}+{{\delta }_{2}DT}_{i,t-1}+\gamma {Controls}_{i,t}+\sum Year+\sum Industry+{\epsilon }_{i,t}$$ 7 In the model, capability represents the dynamic capability, which is measured by absorptive capability , adaptive capability and innovative capability respectively. Table 8 reports the test results. In column (3), (6) and (9), absorptive capability , adaptive capability and innovative capability are significant positively correlated with green innovation. Column (2), (5) and (8) show that only adaptive capability and innovative capability were significantly positive correlated with DT . Column (6) and (9) report that the dynamic capability has a partial mediating effect on the relationship between digital transformation and green innovation. Digital transformation significantly improves the dynamic capability of enterprises supported by digital technology (Mikalef et al., 2017), thus enhancing the level of green innovation of enterprises. Specifically, the mediating mechanism of green innovation empowered by digital transformation lies in the improvement of adaptive capability and innovative ability. The deep application of digital technology affects the strategy and goals of enterprises (Ciampi et al., 2020 ). On the one hand, it enables enterprises to quickly adapt to changes in the highly active market competition, timely change their business model, and implement green innovation activities to seek sustainable competitive advantages. On the other hand, firms can optimize the human capital structure according to strategic needs, improve independent innovation capacity through scientific and technological talent, and strengthen green innovation activities. Table 8 Regression results of the mediating effect of. dynamic capability Absorptive capacity Adaptability Creativity (1) (2) (3) (4) (5) (6) (7) (8) (9) GI Absorptive capacity GI GI Adaptability GI GI Creativity GI Absorptive capacity 1.213*** (2.607) adaptive capability 0.840*** (8.815) innovative capability 0.038*** (10.798) DT 0.134*** (10.698) -0.003*** (-5.931) 0.137*** (9.585) 0.132*** (9.959) 0.036*** (13.307) 0.099*** (6.380) 0.135*** (10.071) 0.747*** (11.884) 0.107*** (7.030) Constant -4.216*** (-11.384) -0.097*** (-6.599) -4.312*** (-9.612) -4.123*** (-10.384) 0.000 (0.000) -4.476*** (-9.500) -4.351*** (-10.060) 3.866** (2.230) -4.681*** (-9.320) Observations 5,886 4,596 4,596 5,510 4,290 4,290 4,920 3,902 3,902 adj. \({R}^{2}\) 0.188 0.337 0.196 0.184 0.488 0.205 0.168 0.412 0.191 F 45.73 59.01 38.32 40.77 135.3 34.12 31.66 81.61 28.09 Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry Yes Yes Yes Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Yes Yes Yes *** p < 0.01, ** p < 0.05, * p < 0.1 5. Conclusions The traditional mode of economic growth has severely constrained sustainable development, and a growing number of studies have shown that green innovation can help enterprises enhance their competitiveness and achieve sustainable development while contributing to environmental protection. Digital transformation is gradually becoming an important strategic path for the technological evolution of global enterprises. This paper holds the view that digital transformation can provide heterogeneous resources that attract large amounts of capital to enterprises and influence green innovation. Specifically, the digital transformation of enterprises promotes green innovation. At the same time, we consider the role of internal and external factors in the firm. Executives with a technological imprint have a more comprehensive and profound knowledge of digital transformation. They can better utilize the heterogeneous resources that digital transformation brings directly or indirectly to enhance the company's R&D investment in green innovation. Digital transformation is a sustainable behavior, and media attention can stimulate corporate reputation, which can also have a positive impact on the development of green innovation activities. We also provide strong evidence that digital transformation influences green innovation by enhancing a firm's dynamic capabilities. The findings of this paper pass a series of robustness tests. This article sheds light on how digital transformation affects green innovation and clarifies the detailed mechanism, which has profound implications for enterprise managers and policy makers. In addition, we emphasize the importance of digital transformation and put forward constructive suggestions on how to promote green innovation in China. To optimize production and operation processes, enterprises should apply digital technologies actively and refine the talent training and technology system. For government, here are three practical ways: promote the construction of digital transformation infrastructure, implement the incentive mechanism of green innovation supported by digital technology, strengthen supervision over media reports. Declarations Funding This work was supported by the National Social Science Fund of China (Funding Number: 20BGL073) Data availability Data and materials can be accessed by the corresponding author Ethics Approval and Consent to Participate Not applicable. Consent for Publication Not applicable. Competing Interests The authors declare no competing interests Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Lei Zhu, Chunyan Wang and Xiaohan Wang. 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Pacific-Basin Finance Journal, 68,101604. https://doi.org/10.1016/j.pacfin.2021.101604 . Tian GN, Zhou SY, Hsu S., (2020). Executive financial literacy and firm innovation in China. Pacific-Basin Finance Journal, 62,101348. https://doi.org/10.1016/j.pacfin.2020.101348 . Vial G., (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003 . Wang C L, Ahmed P K., (2007). Dynamic capabilities: A review and research agenda. International Journal of Management Reviews, 9(1), 31–51. https://doi.org/10.1111/j.1468-2370.2007.00201.x . Wang, Q., Qu, J., Wang, B., Wang, P., Yang, T., (2019). Green technology innovation development in China in 1990–2015. Science of Total Environment, 696, 134008 https://doi.org/10.1016/j.scitotenv.2019.134008 . Waqas M, X HG, Ahmad N, et al., (2021). 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Finance Research Letters, 48. https://doi.org/10.1016/j.frl.2022.102888 . Xie Y, Chen Z, Boadu F, et al., (2022). How does digital transformation affect agricultural enterprises’ pro-land behavior: The role of environmental protection cognition and cross-border search. Technology in Society, 70, 101991. https://doi.org/10.1016/j.techsoc.2022.101991 . Xiong LX, Ning JJ, Dong YH., (2022). Pollution reduction effect of the digital transformation of heavy metal enterprises under the agglomeration effect. Journal of Cleaner Production, 330. https://doi.org/10.1016/j.jclepro.2021.129864 . Yang CL, Xia XP, Li YG, et al., (2021). CEO financial career and corporate innovation: Evidence from China. International Review of Economics & Finance, 74, 81–102. https://doi.org/10.1016/j.iref.2021.01.018 . Yuan B, Cao X., (2022). Do corporate social responsibility practices contribute to green innovation? The mediating role of green dynamic capability. Technology in Society, 68. https://doi.org/10.1016/j.techsoc.2022.101868 . Yu Chen et al. (2024). Green credit, financial regulation and corporate green innovation: Evidence from China. Finance Research Letters, 59, 104768 https://doi.org/10.1016/j.frl.2023.104768 . Yunying Zhao et al. (2024). Racing towards zero carbon: Unraveling the interplay between natural resource rents, green innovation, geopolitical risk and environmental pollution in BRICS countries. Resources Policy, 88, 104379. https://doi.org/10.1016/j.resourpol.2023.104379 . Zhai H, Yang M, Chan KC., (2022). Does digital transformation enhance a firm's performance? Evidence from China. Technology in Society, 68. https://doi.org/10.1016/j.techsoc.2021.101841 . Zhang HL, Su ZR., (2015). Does media governance restrict corporate overinvestment behavior? Evidence from Chinese listed firms. China Journal of Accounting Research 8(1), 41–5. https://doi.org/10.1016/j.cjar.2014.10.001 . Zhang XX, Gao CY, Zhang SC., (2022). The niche evolution of cross-boundary innovation for Chinese SMEs in the context of digital transformation–Case study based on dynamic capability. Technology in Society, 68. https://doi.org/10.1016/j.techsoc.2022.101870 . Zhang Z, Zhang BK, Jia M., (2022). The military imprint: The effect of executives’ military experience on firm pollution and environmental innovation. The Leadership Quarterly, 33(2). https://doi.org/10.1016/j.leaqua.2021.101562 . Zhao B, Tan J, Chan K C., (2022). Does a CEO's prior academic experience helpful to an IPO firm? The case of IPO discount. Finance Research Letters, 47, https://doi.org/10.1016/j.frl.2022.102688 . Zhong, Z., Peng, B., (2022). Can environmental regulation promote green innovation in heavily polluting enterprises? Empirical evidence from a quasi-natural experiment in China. Sustainable Production and Consumption, 30, 815–828. https://doi.org/10.1016/j.spc.2022.01.017 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4270176","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291701403,"identity":"3618feae-1d4e-4894-bec3-5d9dba29c463","order_by":0,"name":"Lei Zhu","email":"","orcid":"","institution":"Shandong University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhu","suffix":""},{"id":291701404,"identity":"0d9969a1-cba2-427a-995a-60d580e83470","order_by":1,"name":"Chunyan Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIie3RMUsDMRTA8XcEesu1t+YI6FfIcXB0KPhV7ijc5CAIcoPQHCnp2PX8FoogjjkDdgm4ZnBoEZyVriKmc0uu3RzyJ28J/HjDA/D5/mExCpr1Vz1BMQCFevcle0iy4DxtdRUmzBJ9DKF6Jchw/hJTeSwBU3I6ZJJkb+oJyxrORqYItlcOEbRls06e37PcVDdYasgSUyDSOgjCdkuqP6e5iXLyLaC8N8UARQ4ywKWwT80elzrH3S/MekkUdQJ3QiEKl5YwKGgfwWHDU6YrhE11PZavOL3TG05c5EKFm4+f3SmX6sHI28n5aDXtti6yt9VOwE4APp/P5zvUH2+TVWVaakjUAAAAAElFTkSuQmCC","orcid":"","institution":"Shandong University of Finance and Economics","correspondingAuthor":true,"prefix":"","firstName":"Chunyan","middleName":"","lastName":"Wang","suffix":""},{"id":291701405,"identity":"1072450f-c05a-4060-9efd-ed8e87db676a","order_by":2,"name":"Xiaohan Wang","email":"","orcid":"","institution":"Shandong University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Xiaohan","middleName":"","lastName":"Wang","suffix":""},{"id":291701406,"identity":"7e9dff87-b656-4334-837a-c625745600cd","order_by":3,"name":"Tong Li","email":"","orcid":"","institution":"Shandong University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-04-15 13:47:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4270176/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4270176/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55265958,"identity":"aad868f2-84c7-4240-845b-de2aa418aca6","added_by":"auto","created_at":"2024-04-25 02:18:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1283275,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4270176/v1/8451e6e3-582f-4717-b201-d39ef01f48c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The effect and newly developed mechanisms of digital transformation on green innovation: evidence from listed firms in China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFaced with the challenge of sustainable development, all countries globally attach great importance to environmental protection. Due to the traditional mode of economic growth which is characterized by high investment, high consumption, high emission and low benefit, China has become the world\u0026rsquo;s largest carbon dioxide emitter (Shao et.al,2019). In order to solve the issue of environmental pollution, Chinese government has promoted carbon dioxide peaking and carbon neutrality as a new development philosophy, for example \u0026ldquo;Working actively and prudently toward the goals of reaching peak carbon emissions and carbon neutrality\u0026rdquo; and \u0026ldquo;Pursuing green development and promoting harmony between humanity and nature\u0026rdquo; (\u003cem\u003eReport to the 20th National Congress of the Communist Party of China\u003c/em\u003e). Against this backdrop, it is important and necessary for firms to carry out green innovation activities (Polzin and Sanders, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Many scholars have confirmed the role of green innovation in low-carbon development (Lin and Ma, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), it can reduce carbon dioxide emissions (Zhao et al., 2023) and coordinate the nexus between sustainable economic and environmental development (Wang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn order to achieve its carbon peak and neutrality targets, firms need to transit from a stage of resource dependence to a stage of technology dependence, to which the booming development of digital economy provide good conditions. With the emergence of so called \"ABCD\" technologies such as Artificial Intelligence, Blockchain, Cloud Computing, and Big Data, the digital transformation is gradually becoming an important strategic path for global corporations' technological evolution. Digital transformation can alter business model of enterprises and generate new paths for value creation (Gregory Vial, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), promote sustainable development (Yasanur Kayikci, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This represents an important strategic opportunity for companies to promote green innovation through digital transformation.\u003c/p\u003e \u003cp\u003eHowever, there are few studies on whether digital transformation can enhance green innovation, and the conclusions are not the same. Some research has looked at the impact of digital technology. Digital technologies contribute positively to the innovation (Gaglio et al.,2022). Big data and artificial intelligence can reduce financial and environmental costs, and improve the performance (Singh et al., 2021) and sustainability of industrial enterprises (Andrew Kusiak, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Also, the dynamic capability was promoted (Mikalef et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and the competitive advantage was reinforced (Shan et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in enterprises with high digitalization. Then, the improvement of dynamic capability can stimulate business model innovation (Ciampi F et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and therefore create value for customers (Matarazzo et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Especially, the facilitating role of big data analytics in green innovation and environmental performance were also confirmed (Waqas et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). From a strategic point of view, some research has explored the economic impacts of digital transformation. Digital transformation has significantly improved corporate innovation (Zhai et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e.). Many manufacturers have significantly increased their investment in innovation in the process of digital transformation (Wen et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Digital transformation also make business more convenient, real-time and scenario-based, optimize the production and operation process (Thomas, 2016), help enterprises achieve energy conservation and emission reduction. Similarly, digital transformation is booming business performance (Peng and Tao, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and reduce stock price crash risk (Wu et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). What\u0026rsquo;s more, the initial development of digitalization can enable European countries to transit to the circular economy (Nham Nguyen Thi Hong and Le Thanh Le Ha, 2022). And digital transformation not only improves pollution emission reduction (Xiong et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but also can positively affect enterprises\u0026rsquo; environmental protection cognition (Xie et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To enrich the theoretical and empirical research in this area, this paper focuses on the role of digital transformation in green innovation.\u003c/p\u003e \u003cp\u003eMoreover, prior studies have also shown that executives\u0026rsquo; characteristics and competitive environment would affect green innovation (Hojnik and Ruzzier, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, we also consider the role of internal and external factors when exploring the relationship between digital transformation and green innovation. We further consider two critical factors: executive imprint and media attention. Digital transformation is a kind of strategic decision, so as green innovation, both have technical attributes. Corporate strategic decisions are likely influenced by the characteristics of executives (Akroyd and Kober., 2020; Tian et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) such as academic experience (He et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) based on imprinting theory. There exists a lack of system in transition economies compared with developed market economies (Khanna and Palepu, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) although informal institutions such as media attention can play an important role in corporate governance (Stelios et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tavakolifar et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; An et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Firstly, as a connecting between enterprises and other stakeholders, media can complete the transmission of information (Gao et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Aman et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Secondly, with the advent of information era, media is gradually becoming more and more important in shaping social image of firms (Cabral, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Teng and Yang., 2021), which will have an effect on enterprise behavior. As a result, we propose that the relationship between digital transformation and green innovation will be affected by executive experience and media attention.\u003c/p\u003e \u003cp\u003eTherefore, we try to investigate how enterprise digital transformation affects green innovation as well as how executives\u0026rsquo; imprint and media attention moderate this relationship, by means of data analysis from 2010\u0026ndash;2020 of A-share listed companies in China. Compared to previous literature, our paper makes three major contributions. First, existing studies mainly explore the impact of factors such as green credit, environmental governance, organization capital and economic policy uncertainty on green innovation (Chen et al., 2023; Zhong and Peng, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Qu and Cheung, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yu and Chen, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We contribute to the research in the field of green innovation based on the perspective of resource and information. We not only extend the literature on the factors that influence green innovation; but also enrich the research on the microeconomic consequences of digital transformation. Secondly, we empirically examining the moderating effects of executives\u0026rsquo; technical imprint and media attention on the digital transformation-green innovation relationship. As such, our study indicates that the role of digital transformation in green innovation depends on the internal and external factors of enterprise. Thirdly, we further demonstrate the detailed mechanism by testing mediating effect of dynamic capability. Altogether, the conclusions from this paper may provide novel insights into how to promote green development in China.\u003c/p\u003e \u003cp\u003eThe remainder of our paper is as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e represents the theoretical framework and hypotheses. Section 3 shows the research design. Section \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports empirical results. Section \u003cspan refid=\"Sec28\" class=\"InternalRef\"\u003e5\u003c/span\u003e discusses conclusion.\u003c/p\u003e"},{"header":"2. Theoretical framework and hypotheses","content":"\u003cp\u003eGreen technology refers to a process or a product that reduces environmental pollution, consumption of energy and raw materials, which firstly proposed by Ernest et al. (1994). Any activities which are valuable or can be carried out to promote green technology called green technology innovation. In the light of Schumpeter's innovative theory, innovation is the combination of factors of production in new ways that can stimulate new economic growth points and generate more profits. Specially, green innovation, as a form of innovation, not only will bring benefits to both consumers and firms, but also will reduce adverse effect on environment (James P, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). There is a need for firms to use resources effectively in the development of green innovation, which makes it important to move from dependence on resources to technological dependence through digital transformation. Digital transformation tries to change the existing organizational management mode of enterprises. By emphasizing this essence, digital transformation can reshape management mode and operating mechanism, improve the efficiency of enterprise resource allocation, and promote management mode innovation (Frynas et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), hence empower green innovation activities from multiple dimensions.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Enterprise digital transformation and green innovation\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1. Resource perspective on digital transformation\u003c/h2\u003e \u003cp\u003eGreen innovation, belonging to breakthrough innovation, is the result of cooperation and interaction among different entities (Yuan et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which requires systematic layout and strategic planning of firms to reform the existing technological paradigm. With the rise of platform economy, corporate innovation is changing from close to open. This toughened firms to depend on the internal accumulation of resources to invest in green innovation. Therefore, enterprises need to break through organizational boundaries to promote green innovation activities by obtaining abundant resources in various ways. Apparently, Digital transformation can be a direct or indirect source of resources for green innovation. On one hand, digital transformation induces enterprises to upgrade organizational structure and business model through information technology (AlNuaimi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). And digital transformation brings up other relevant technologies such as cloud computing, which can also provide new technical resources for green innovation. On the other hand, digital transformation promotes enterprises to develop social capital and build business teams (Li et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Those can strengthen ties among different departments, and therefore enterprises can obtain green innovation resources through broader channels and more convenient ways. Specifically, how can digital transformation affect the green innovation ability of enterprises?\u003c/p\u003e \u003cp\u003eFirst of all, digital transformation makes it easy to master diversified technical resources in the process of production, hence the transformation and upgrading will be expedited. Also, digital resource can facilitate staff to create better product features when designing product. Under the guidance of new philosophy of green and sustainable development, products are endowed with \"green\" attribute from the beginning. At stage of innovation output, digital technology can realize end-governance and reduce environmental problems caused by production.\u003c/p\u003e \u003cp\u003eSecondly, the improvement of green innovation performance not only depends on resource endowment of enterprises, but also on efficiency of resource allocation (Chen et al., 2022). Digital transformation can help enterprises get rid of the traditional extensive growth style. The efficient flow of data resources makes the use of enterprise resources more efficient, therefore enterprises can save energy and reduce emissions, and raise the level of green innovation.\u003c/p\u003e \u003cp\u003eFinally, with the help of digital technology, information and knowledge can be generated, shared and exchanged in the innovation network through a low-cost, rapid and real-time way, which will significantly improve the efficiency of green innovation. Digital enterprises also can make better use of digital technology. For example, it is very easy for firms to collect all kinds of big data from internal and external environment (Waqas et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which can increase knowledge reserve of green technology innovation and therefore promote green technology innovation. In a nutshell, digital transformation can provide heterogeneous resources for green innovation directly or indirectly and encourage companies to carry out relevant activities on green innovation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2. Information perspective on digital transformation\u003c/h2\u003e \u003cp\u003eAccording to information asymmetry theory, there are different kinds of information asymmetry both inside and outside the enterprises, which hinders the process of green innovation. The stakeholders, which include investors, the public, government departments, social media and so on, may not understand the real situation of the enterprise's green innovation timely due to information asymmetry and lag of information disclosure, partly affecting the enthusiasm of enterprises about carrying out green innovation.\u003c/p\u003e \u003cp\u003eDigital transformation of enterprises can alleviate the above information asymmetry problem. First of all, the flowing of data enables enterprises to focus on value of customers and get the users\u0026rsquo; green demands for product timely through digital transformation, thus improving the efficiency of communication between enterprises and the public (Thomas et al., 2016), optimizing operation process based on customer requirements and market conditions and realizing energy conservation and emission reduction and therefore improve green innovation capability (Waqas et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Secondly, equity market participants incorporate digitization-related information into their business valuation processes, and companies that are highly regarded for their sustainability earn higher praise by disclosing their digitization efforts (Ricci et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Green innovation, belonging to the category of breakthrough innovation, should balance innovation performance and environmental performance compared with the traditional innovation mode, yet being restricted by capital constraints and development costs (Tobias Stucki, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For one thing, the disclosure of information about digitalization can be considered as an important signal sent by companies (Salvi et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which attracting investors to provide capital. For another, digital technology can promote the development of intelligent finance, improve financial management, and optimize the quality of financial information disclosure. Accordingly, investors can get a wealth of high-quality information, which is conducive to attracting amounts of capital inflow for green innovation. Taken together, we propose the following hypothesis.\u003c/p\u003e \u003cp\u003eH1. Digital transformation of enterprises has a positive impact on green innovation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2. The moderating role of executive imprint\u003c/h2\u003e \u003cp\u003eAccording to the upper echelon\u0026rsquo;s theory, the characteristics of executives will affect their cognitive ability and values, then further promote them to make highly personalized decisions (Hambrick and Mason, 1982). The emergence of CDO (Chief Digital Officer) also stems from the need for digital transformation to promote the integration of traditional organizational operation models and digital technologies. In addition to the internalized characteristics of senior executives such as age and gender, personal academic and employment experience usually have a more direct impact on their strategic decisions. The imprint theory holds that an individual will form an internal trait that adapts to the environment of the sensitive period during a specific short-term sensitive period, which is called imprinting (Marquis and Tilcsik, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The formation of imprint can also be regarded as a special learning process, which may occur in a specific stage of life, such as school experience, and the imprint will have a lasting and profound impact on the individual.\u003c/p\u003e \u003cp\u003eExecutives with different types of imprints will exhibit different characteristics in decision-making. As founders of enterprises, the scientific research experience of scientists encourages enterprises to carry out open innovation, which will significantly affect the performance of new ventures (Hahn et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, financial expert CEOs does not encourage firm innovation (Yang et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Entrepreneurial leadership can enhance the performance of SMEs (Nguyen et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). But private entrepreneurs who once worked in state-owned, collective enterprises or government agencies will be more inclined to intervene in real estate and other businesses to seek short-term benefits (Dai et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). And digital knowledge in the TMT(top management team) is positively associated with digital innovation (Firk et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Based on the existing literature, we argue that the study and work experience of senior executives can cultivate their unique workforce skills and enable them to acquire more cutting-edge scientific knowledge and technical experience. Therefore, the lasting and profound impact of technical imprints enables the transfer of advanced concepts and knowledge, thereby promoting the enabling effect of digital transformation on green innovation. Executives with technical imprint have a more comprehensive and profound cognition of digital transformation (Firk et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). They can better utilize the heterogeneous resources directly or indirectly brought by digital transformation. Similarly, senior executives with technical imprint have a wealth of cutting-edge theoretical knowledge, and are better able to recognize the long-term benefits brought by green innovation. Such executives are more able to give full play to the resource and information brought by digital transformation to empower green innovation. Thus, we predict that:\u003c/p\u003e \u003cp\u003eH2. Executives with technical imprint can positively moderate the promoting effect of digital transformation on green innovation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3. The moderating role of media attention\u003c/h2\u003e \u003cp\u003eIn transitional countries, informal institutions, as an important supplement to formal institutions, have a binding force that cannot be ignored (Hilary and Hui, 2009). Media attention, as an important informal institution outside the enterprise, has become an important supplement or substitute factor to be incorporated into the corporate governance framework (Joe et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Dyck et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Miller and Gregory, 2006). The media can influence corporate governance (Dyck et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) through reputation mechanisms or attracting executive involvement (Zhang and Su, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, media governance is not simply a single reinforcement, because media coverage has an asymmetric impact on stakeholders' perceptions and expectations (Wen and Zhou et al., 2017). Therefore, when the degree of media attention is higher than a certain level, as the degree of attention increases, the inconsistency of expectations will increase, and the media will play an \u0026ldquo;inverted U-shaped\u0026rdquo; moderating role (Luo et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although media attention can stimulate enterprises' green innovation behavior, and new media environment has a significantly positive effect on corporate EID quality (Fan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), excessive media attention causes public opinion pressure on enterprises and inhibits their enthusiasm for green innovation (Luo et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs an important external governance factor, media attention will significantly affect the enabling effect of enterprise digital transformation on green innovation. In the era of information explosion, media is not only an important platform for information release and transmission, but also an important way for stakeholders to obtain information. Media coverage spreads quickly and has a wide audience. Media reports can timely and quickly convey the relevant situation of the enterprise to the outside world. What\u0026rsquo;s more, media coverage can influence the public image of enterprises. As public opinion is an important factor in informal system (Douglass C. North, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), so media attention can stimulate corporate reputation. In the process of green innovation, an important function of digital transformation is to reduce information asymmetry and attract the inflow of innovation funds.\u003c/p\u003e \u003cp\u003eFirstly, digital transformation is the behavior and method of sustainable development, which will deliver good news to the market. At the same time, media coverage will strengthen investors' positive expectations for the enterprises and improve the company's stock liquidity, and introduce funds for innovation. Secondly, the application of digital technology improves the level of financial management and the quality of information disclosure. Media can provide stakeholders high quality financial information through diverse channels in a more rapid and intuitive way. It is convenient for investors to make long-term and sustainable capital investment in enterprises. Finally, empowering green innovation through digital transformation is also an act of using technology to help environmental protection. The media coverage about that can form external incentive for enterprises, and further promote enterprises to implement green innovation. Thus, we develop the following hypothesis:\u003c/p\u003e \u003cp\u003eH3. Media attention can positively moderate the promoting effect of digital transformation on green innovation.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research design","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Sample selection and data sources\u003c/h2\u003e \u003cp\u003eWe take A-share non-financial companies listed on Shanghai and Shenzhen Stock Exchanges as the research object, to empirically test the promoting effect of enterprise digital transformation on green innovation. Due to the availability of green patent data, this study covers the period from 2010 to 2020. The digital transformation data and financial data in this paper are derived from CSMAR database, while we obtained the green patent data and media attention data of enterprises from CNRDS database. According to the demands of this study, samples were selected as follows: (i) enterprises marked with ST and *ST were removed, since the financial performance of the enterprises in financial distress may be significantly different from that of other enterprises; (ii) financial industry companies were removed;(iii) samples with missing major variables and control variables were removed. After this sorting, the final sample contained ten-year information on 3011 listed companies with a total of 12371 observed values. For all continuous non-ratio variables, 1% winsorize is used to reduce the interference of outliers on the regression model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Variable measurements\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Dependent variable: green innovation\u003c/h2\u003e \u003cp\u003eThe IPC Green Inventory was launched by WIPO in 2010 to facilitate the retrieval of patent information related to environmentally friendly technologies (EST).The number of green patent applications is selected to measure the performance of green innovation in this paper (Li and Zheng, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tan Y, 2014). First of all, the number of patent applications is more stable and reliable, it can better reflect the innovation level of enterprises than the number of patents granted because patents are susceptible to uncertainty in the process of granting patents. Secondly, technological innovation reflects the results of resource investment and allocation of enterprises, and the number of patent applications can reflect the innovation momentum of enterprises. Specifically, in this paper, the total number of green patent applications of enterprises is added by 1 to take natural logarithm processing to obtain \u003cem\u003eGI\u003c/em\u003e. In the robustness test, it is further subdivided into the number of green invention patent applications \u003cem\u003eInv\u003c/em\u003e and the number of green utility model patent applications \u003cem\u003eUse\u003c/em\u003e as the comparative index, which is used as the proxy variable of green innovation of enterprises.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Independent variable: digital transformation\u003c/h2\u003e \u003cp\u003ePrevious study shows that text analysis of annual reports of listed companies is a reasonable way to measure enterprise strategy and development orientation. The frequency of some keywords in the annual report can represent the degree of resources invested in this field. Therefore, we adopt the index of digital transformation degree of digital economy database in CSMAR database as the proxy variable of enterprise digital transformation. Based on the technical elements necessary for digital transformation, this database counts the number of keywords from five aspects: A(Artificial Intelligence), B(Blockchain), C(Cloud Computing), D(Big Data) and digital technology applications. Keywords come from the enterprises\u0026rsquo; annual report. In this paper, the total number of word frequency \u003cem\u003efreq\u003c/em\u003e is calculated by summing up the frequency of four kinds of A、B、C、D technology keywords. The natural logarithm of the number of A/B/C/D technical word frequency plus one to obtain the measurement variable \u003cem\u003eDT\u003c/em\u003e and the natural logarithm of the number of the frequency of digital technology\u0026rsquo;s application plus one to obtain the measurement variable \u003cem\u003eAppl\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Moderators\u003c/h2\u003e \u003cp\u003eAccording to above theoretical analysis, the promoting effect of enterprise digital transformation on green innovation is also affected by factors such as senior executives' technological imprint and media attention. Therefore, technical imprint and media attention are introduced into the estimation model as moderating variables in the empirical study.\u003c/p\u003e \u003cp\u003eThe technical imprint of senior executives is mainly formed in the process of acquiring knowledge and skills. Studying and scientific research are the period of acquiring a lot of knowledge. Therefore, in this paper, senior executives with scientific research experience are regarded as possessing technological imprint. It can be measured in the following steps:(i) calculate the number of senior executives who have research experience; (ii) calculate the proportion of senior executives with above background in the total number of senior management team (\u003cem\u003eacade\u003c/em\u003e). \u003cem\u003eAcade\u003c/em\u003e is used as the measurement of the moderating variable of senior executives' technology imprint.\u003c/p\u003e \u003cp\u003eWe examine the impact of media attention on the relationship between digital transformation and green innovation through media coverage and emotion. In view of the fact that online media coverage is more real-time and communicative, we measure the media attention by the number of financial and economic news about listed companies reported in online media directly. Since media coverage is divided into positive, neutral and negative categories due to data provided by the CNRDS database, we adopt this category to measure media sentiment directly. In this paper, the number of news reports plus one and takes the natural logarithm to obtain the media coverage variable \u003cem\u003eNews\u003c/em\u003e, and the proportion of positive, neutral and negative news reports in the total number is calculated respectively to obtain the media attention emotion variables \u003cem\u003ePositive news\u003c/em\u003e, \u003cem\u003eNeutral news\u003c/em\u003e and \u003cem\u003eNegative news\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4. Control variables\u003c/h2\u003e \u003cp\u003eIn addition, in order to eliminate bias caused by the unobservable heterogeneity of enterprises, a series of control variables were added to ensure the accuracy of the results. The first variable was firm size, used to consider the tendency of larger firms to achieve better green innovation performance. Following Lin et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), we used a firm's total assets to measure its size(\u003cem\u003eSize\u003c/em\u003e). Representing the firm age with enterprise establishment year(\u003cem\u003eAge\u003c/em\u003e); representing the growth of enterprises with growth rate of operating income(\u003cem\u003eGrowth\u003c/em\u003e) ; representing the debt paying ability of enterprises with an asset-liability ratio (\u003cem\u003eDebt\u003c/em\u003e); representing the profitability of enterprises with return on total assets (\u003cem\u003eROA\u003c/em\u003e) ; representing the corporate cash flow with cash asset ratio(\u003cem\u003eCash\u003c/em\u003e) ; representing firm value with \u003cem\u003eTobin\u0026rsquo;s Q\u003c/em\u003e ; representing the equity concentration of enterprises with the major equity concentration ratios (\u003cem\u003eTop1\u003c/em\u003e) ; representing enterprise financial risk with financial leverage(\u003cem\u003eLev\u003c/em\u003e) ; representing capital intensity with fixed asset ratio(\u003cem\u003eTan\u003c/em\u003e). The definition and description of variables are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelection and description of related variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariable Definitions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e \u003cp\u003eGreen innovation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe natural logarithm of the number of the green innovation applications plus one\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eUse\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe natural logarithm of the number of the invention patents in the green innovation applications plus one\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eInv\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe natural logarithm of the number of the utility model patents in the green innovation applications plus one\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eDigital transformation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe natural logarithm of the number of A/B/C/D technical word frequency plus one\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAppl\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe natural logarithm of the number of the frequency of digital technology\u0026rsquo;s application plus one\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eExecutives' technological imprint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAcade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProportion of people with scientific research experience in the senior management team\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c2\" namest=\"c1\" rowspan=\"4\"\u003e \u003cp\u003eMedia attention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNews\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe natural logarithm of the number of financial news reporting on listed companies plus one\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePositive news\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe proportion of positive news reports\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNeutral news\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe proportion of neutral news reports\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNegative news\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe proportion of negative news reports\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFirm size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe natural logarithm of total assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(The year of the current year - the year of establishment of the enterprise) Take the natural logarithm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEnterprise growth capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Amount of operating income in the current year - amount of operating income in the previous year) / (Amount of operating income in the previous year)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAsset-liability ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDebt\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Liabilities/Total Assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNet interest rate on total assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNet profit/Average total assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCash flow from operating activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNet cash flow from operating activities/total assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTobin\u0026rsquo;s Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTobin Q\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(tradable market value\u0026thinsp;+\u0026thinsp;non-tradable face value)/(total assets - net intangible assets - net goodwill)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOwnership concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTop1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShareholding ratio of the largest shareholder\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFinancial leverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Liabilities/Owner's Equity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTangible asset ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTan\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNet Fixed Assets/Total Assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Research models\u003c/h2\u003e \u003cp\u003eThis study uses the following OLS regression model to test the positive impact of enterprise digital transformation on green innovation:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${GI}_{i,t}=\\alpha +\\beta {DT}_{i,t-1}+\\gamma {Controls}_{i,t}+\\sum Year+\\sum Industry+{\\epsilon }_{i,t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this model, the dependent variable is enterprise green innovation (GI), the core independent variable is enterprise digital transformation (DT), \u003cem\u003eControls\u003c/em\u003e is the aforementioned control variables. ε is the random error term of this model. In order to improve the reliability of the model, we carry out the following processing. Firstly, the t statistic of Cluster clustering robust standard error adjustment is adopted in the regression equation; Secondly, considering that enterprises' digital transformation has a certain time lag in its impact on green innovation, this paper processes the core independent variables in the master regression test and the regression test of moderating effect with a lag of one period, which also alleviates the interference of the endogenous problem of reverse causality to a certain extent. Thirdly, this paper controls both time and industry fixed effects in the model.\u003c/p\u003e \u003cp\u003eFurthermore, model (2) and model (3) are constructed to test the moderating effect of executives' technical imprint and media attention.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${GI}_{i,t}=\\alpha +{\\beta }_{1}{DT}_{i,t-1}+{\\beta }_{2}{DJG}_{i,t}+{\\beta }_{3}{DT}_{i,t-1}\\times {DJG}_{i,t}+\\gamma {Controls}_{i,t}+\\sum Year+\\sum Industry+{\\epsilon }_{i,t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${GI}_{i,t}=\\alpha +{\\delta }_{1}{DT}_{i,t-1}+{\\delta }_{2}{media}_{i,t}+{\\delta }_{3}{DT}_{i,t-1}\\times {media}_{i,t}+\\gamma {Controls}_{i,t}+\\sum Year+\\sum Industry+{\\epsilon }_{i,t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eDJG in the model represents the proportion of senior executives with technical background in the senior management team, which is represented by \u003cem\u003eacade\u003c/em\u003e. Media refers to the variable of media attention, which can be measured in two ways: degree and emotion.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Empirical test and result analysis","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Summary statistics and correlation analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the descriptive statistics of the variables in this study. The standard deviation of the total number of green patent applications is 0.963, which includes all three types patents. The minimum of green patents among sample firms is 0, and the maximum value is 4.413, showing that the green innovation ability varies widely, some firms may have not carried out green innovation. The minimum value of digital transformation is 0, the maximum value is 4.605, and the standard deviation is 1.216, indicating that most firms have tried to implement digital transformation strategy, yet the degree varies continuously.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistical results of variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep50\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Dev\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAcade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNews\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.870\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDebt\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTobinQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTop1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74.890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.760\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTan\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we report the Pearson and Spearman correlations of main variables. The digital transformation positively correlates with green innovation, which preliminarily verifies our hypothesis H1. As expected, the correlation coefficients of all variables are mostly significantly at less than 0.5, which indicates that there is no serious multi-collinearity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation coefficient between variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDT\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAcade\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eNews\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eDebt\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eTobin Q\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eTop1\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eTan\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.149***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAcade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.107***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.111***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNews\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.174***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.020*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.039***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.203***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.064***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.032***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.432***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.068***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.109***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.085***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.050***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.173***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.020*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.028**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDebt\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.119***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.101***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.082***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.181***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.560***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.196***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.072***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.019*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.036***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.087***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.109***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.100***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.167***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.359***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.045***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.086***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.048***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.027**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.023*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.151***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.446***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTobinQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.077***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.124***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.047***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.115***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.400***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.141***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.141***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.381***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.404***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.146***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTop1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.154***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.042***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.050***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.162***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.036***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.027**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.061***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.079***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.097***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.061***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.084***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.042***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.168***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.068***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.074***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.398***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.360***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.111***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.188***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.057***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTan\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.029**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.261***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.086***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.113***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.056***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.075***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.079***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.109***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.235***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.176***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.076***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.190***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003eNote: *、**、*** represent significant at 10%, 5% and 1% levels respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Effects of digital transformation on green innovation\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the regression results for model (1), the dependent variable is the total of green patent applications. In column (1), we only control the time and industry fixed effect, the coefficient for digital transformation is 0.145 and statistically significant at the level of 1%. In column (2), we add all other control variables which are defined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e on the basis of column (1). The coefficient of digital transformation is 0.128 and significant at the level of 1%, also positive. These results indicate that there is a positive correlation between digital transformation and green innovation, that is the higher digital transformation, the higher green innovation. Thus, hypothesis H1 is proved.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression results of digital transformation and green innovation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.145\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(7.105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.128***\u003c/p\u003e \u003cp\u003e(6.746)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.212***\u003c/p\u003e \u003cp\u003e(7.507)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.186**\u003c/p\u003e \u003cp\u003e(-2.396)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.134***\u003c/p\u003e \u003cp\u003e(-5.019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDebt\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.593***\u003c/p\u003e \u003cp\u003e(4.447)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003cp\u003e(1.139)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCash\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003cp\u003e(1.423)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTobin Q\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003cp\u003e(-1.274)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTop1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003cp\u003e(-1.504)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.079***\u003c/p\u003e \u003cp\u003e(-3.864)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTan\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.566***\u003c/p\u003e \u003cp\u003e(-3.421)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.525***\u003c/p\u003e \u003cp\u003e(3.112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.498***\u003c/p\u003e \u003cp\u003e(-5.502)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eadj. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3. The moderating effect of executives\u0026rsquo; technological imprint\u003c/h2\u003e \u003cp\u003eIn the benchmark regression, we examine whether digital transformation promoted green innovation. Further, in order to verify the promoting effect, more detailed analysis about moderating effects was conducted. These results are listed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The main focuses are the intersections of digital transformation and the moderators. As the table shows in column (1), the coefficient of \u003cem\u003eAcade*DT\u003c/em\u003e is 0.179 and significant at the level of 1%, meaning that increase in senior executives with technological imprint will facilitate the impact of digital transformation on green innovation, which is consistent with H2. Senior executives with technological imprint will have a deeper understanding of the effect of digital transformation on green innovation and take advantage of the resource brought by digital technology, mainly because they can accumulate rich knowledge and experience. By virtue of the opportunity of digital transformation, enterprise should improve the talent training system and the decision-making mechanism of technical talents and increase R\u0026amp;D investment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.4. The moderating effect of media attention\u003c/h2\u003e \u003cp\u003eTo test hypothesis H3, we further analyze moderating effect of media attention. Column (2)-(5) in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e report regression results for model (3). In column (2), the coefficient of \u003cem\u003eNews*DT\u003c/em\u003e is 0.045 and significant at the level of 1%, indicating that increased in media attention will promote the effect of enterprise digital transformation on green innovation. Thus, the hypothesis H3 is verified. Meanwhile, we find that only neutral media coverage had significant moderating effect (δ\u0026thinsp;=\u0026thinsp;0.156, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) through subdividing the types of media reports. Consequently, harmonious and stable public opinion environment is conducive to promoting digital transformation strategy, which enables green innovation. A certain degree of media attention has an incentive effect on enterprises implementing digital transformation, thus promoting their green innovation activities. Furthermore, neutral media cannot exert public pressure on businesses, allowing them to focus on implementing transformation strategies and innovation activities. The positive and negative media coverage has strong directivity. Investor sentiment can significantly affect market liquidity (Liu, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), public opinion is likely to prompt firms to reduce short-sighted behavior in order to avoid short-term risk (Du et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this situation, managers may prefer earning management to prevent abnormal stock price fluctuations, and green innovation is avoided deliberately. On this basis, government should build a harmonious and stable public opinion environment and form effective external supervision on enterprises.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe moderating effect of executive technology imprint and media attention\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.120***\u003c/p\u003e \u003cp\u003e(5.277)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.125***\u003c/p\u003e \u003cp\u003e(6.832)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.122***\u003c/p\u003e \u003cp\u003e(5.347)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.128***\u003c/p\u003e \u003cp\u003e(5.813)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.124***\u003c/p\u003e \u003cp\u003e(5.348)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAcade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.513***\u003c/p\u003e \u003cp\u003e(5.917)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAcade *DT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.179**\u003c/p\u003e \u003cp\u003e(2.579)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNews\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.108***\u003c/p\u003e \u003cp\u003e(4.751)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNews*DT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045***\u003c/p\u003e \u003cp\u003e(2.821)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePositive news\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.614***\u003c/p\u003e \u003cp\u003e(6.829)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePositive news*DT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.077\u003c/p\u003e \u003cp\u003e(-0.700)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNeutral news\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003cp\u003e(-1.237)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNeutral news*DT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.156**\u003c/p\u003e \u003cp\u003e(2.403)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNegative news\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.659***\u003c/p\u003e \u003cp\u003e(-8.222)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNegative news*DT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003cp\u003e(0.108)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.340***\u003c/p\u003e \u003cp\u003e(-3.784)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.174***\u003c/p\u003e \u003cp\u003e(-3.607)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.371***\u003c/p\u003e \u003cp\u003e(-3.836)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.392***\u003c/p\u003e \u003cp\u003e(-3.736)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.187***\u003c/p\u003e \u003cp\u003e(-3.528)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eadj. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Robustness rests\u003c/h2\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.5.1. Alternative the dependent variable\u003c/h2\u003e \u003cp\u003eWe use an alternative measure of the dependent variable, green innovation. The Chinese National Intellectual Property Administration (CNIPA, originally SIPO) grants three types of patents: invention patents, utility model patents, and design patents. Invention patents and utility patents receive the more substantive and rigorous examination in terms of utility, novelty, and non-obviousness before being granted. Columns (1)-(2) in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e report the regression results. The coefficient is 0.049/0.117 and significant at the level of 1%. This indicating that no matter which measurement method is adopted, enterprises' digital transformation will have a significant promoting effect on green innovation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.5.2. Alternative the independent variable\u003c/h2\u003e \u003cp\u003eWe use an alternative measure of the independent variable. Firstly, we define a dummy variable \u003cem\u003eDTC_dummy\u003c/em\u003e based on the judgment standard of \u0026ldquo;whether to carry out digital transformation\u0026rdquo; (1 if the relevant digital transformation keywords appear in the annual report). Column (3) in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e reports the result. The coefficient is 0.092 and significant at the level of 1%, and it is still significant after changing the measurement of green innovation (columns 4 and 5 in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Secondly, we use \"digital technology application\" keyword frequency to measure the independent variables. In column (6), the coefficient of \u003cem\u003eAppl\u003c/em\u003e is 0.049 and significant at 1% level. Therefore, the positive correlation between enterprise digital transformation and green innovation is highly robust.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.5.3. Narrow the research sample\u003c/h2\u003e \u003cp\u003eConsidering that high-tech enterprises themselves have scientific and technological attributes, in order to eliminate the influence of sample selection on regression results, we exclude the information transmission, software and information technology service industry from the samples according to the industry classification standard of CSRC in 2012. Column (7) in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e reports the regression results. The coefficient is 0.131 and significant at the level of 1%, which doesn\u0026rsquo;t change our core conclusion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.5.4. Change the econometric model\u003c/h2\u003e \u003cp\u003eBecause the number of green patent applications is 0 as the lower limit, we use the Tobit model to rerun the regression. Column (8) in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e reports the result. It shows that no matter which measurement model is used, the regression coefficient of digital transformation is significantly positive, which is consistent with the core research conclusion of this paper.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness Tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eUse\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eInv\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eUse\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eInv\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.049***\u003c/p\u003e \u003cp\u003e(6.853)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.117***\u003c/p\u003e \u003cp\u003e(11.991)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.131***\u003c/p\u003e \u003cp\u003e(10.301)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.333***\u003c/p\u003e \u003cp\u003e(8.171)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDTC_dummy\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.092***\u003c/p\u003e \u003cp\u003e(6.618)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021**\u003c/p\u003e \u003cp\u003e(2.334)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.089***\u003c/p\u003e \u003cp\u003e(6.993)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAppl\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.049***\u003c/p\u003e \u003cp\u003e(2.737)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.301***\u003c/p\u003e \u003cp\u003e(3.211)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.357***\u003c/p\u003e \u003cp\u003e(3.305)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.415***\u003c/p\u003e \u003cp\u003e(-12.181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.152***\u003c/p\u003e \u003cp\u003e(-9.441)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.327***\u003c/p\u003e \u003cp\u003e(-12.935)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.522***\u003c/p\u003e \u003cp\u003e(-5.451)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-4.937***\u003c/p\u003e \u003cp\u003e(-12.713)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-9.606***\u003c/p\u003e \u003cp\u003e(-6.699)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6,480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eadj. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald chi2(36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e647.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e4.5.5. Endogenous test\u003c/h2\u003e \u003cp\u003eWe lag the independent variable by one period in the main hypothesis testing, which alleviates the endogeneity problem of mutual causation to a certain extent, but the empirical results may still be affected by some unobservable factors. Firstly, there may be factors that affect both digital transformation and green innovation, such as local industrial policies; secondly, companies that are actively engaged in green innovation activities pay more attention to the upgrading of production methods, and they may be more inclined to carry out digital transformation, which leads to endogenous problems such as selection bias and omitted variables. We solve the endogeneity problem using the following methods:\u003c/p\u003e \u003cp\u003eFirst, controlling for firm-fixed, which can alleviate the endogeneity problem caused by missing variables to some extent. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e reports the regression results. We only control for firm-fixed and year-fixed in column (1), and add the set of control variables in column (2). The coefficient of \u003cem\u003eDT\u003c/em\u003e is all significantly positive, indicating that the results of the benchmark regression are robust.\u003c/p\u003e \u003cp\u003eSecond, considering that enterprise digital transformation is the process of gradual implementation of enterprise strategy, we employ a DID model to test our hypotheses in order to eliminate the bias caused by individual difference and time trend, as Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) shows.\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${GI}_{i,t}={\\theta }_{0}+{\\theta }_{1}{treat}_{i,t}\\times {post}_{i,t}+{\\theta }_{2}{Controls}_{i,t}+\\sum Year+\\sum Industry+{\\epsilon }_{i,t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn light of the DID specification, \u003cem\u003etreat\u003c/em\u003e is an individual dummy variable. The enterprises that have undergone digital transformation are the treated group(\u003cem\u003etreat\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1). \u003cem\u003ePost\u003c/em\u003e is the dummy variable of the period. When an enterprise carries out digital transformation, \u003cem\u003epost\u003c/em\u003e equal to 1 in this year and subsequent years. In particular, the companies that digital transformation lasted for two years or more were identified as \u003cem\u003etreat\u003c/em\u003e is 1, and the samples that had been undergoing digital transformation during the sample period were excluded. Column (3) in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows that the coefficient of \u003cem\u003etreat*post\u003c/em\u003e is 0.255 and significant at 1% level. This indicates that enterprise digital transformation promotes green innovation, and the core conclusion remains highly consistent.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEndogenous test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.027\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etreat*post\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.255***\u003c/p\u003e \u003cp\u003e(6.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.345\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(7.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.057\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(-1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.015***\u003c/p\u003e \u003cp\u003e(-4.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eadj. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Further analysis -- the mediating effect of dynamic capability\u003c/h2\u003e \u003cp\u003eDigital transformation can provide dynamic management and organizational capabilities for enterprises (Li et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Dynamic capabilities can adjust and reshape the resource base of enterprises (Ambrosini et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), enable efficient allocation of innovative resources, help enterprises to obtain sustainable competitive advantages (Teece et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Referring to the research of Wang and Ahmed (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), we measure the dynamic capabilities of enterprises as three dimensions: absorptive capacity, adaptive capacity and innovation capacity. Firstly, enterprises accomplish the deep application of digital technology through digital transformation, so as to efficiently acquire and allocate green innovation resources, so that promote green innovation by improving the absorptive capacity of enterprises. Secondly, digitalization has already been recognized as a key ingredient of the paradigm shift towards sustainable business model (Maffei et al.,2019), and it makes the internal operation of enterprises more flexible. Through more keen insight and perception of the market environment (Helfat et al., 2018), it can quickly adapt to the dual carbon goal and bring about great changes in the development environment. Using digital technology to explore new ways to create value (Gregory Vial, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), bring sustainable competitive advantage (Mikalef et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and thus promote green innovation by improving adaptability. Finally, digital transformation can not only directly or indirectly provide enterprises with innovative resources such as information and capital, but also promote the introduction of scientific and technological talents and the optimization and upgrading of enterprise human capital structure (Loebbecke et al., 2015), so that enhance the proactive innovation capability of enterprises (Helfat et al., 2018), thus promoting green innovation.\u003c/p\u003e \u003cp\u003eWe employ dynamic capabilities as the mediating variable for the impact of enterprise digital transformation on green innovation. We measure the dynamic capability as follows: (i) return on assets, which reflects the resource utilization and management level of an enterprise and highlights the ability of absorbing and utilizing resources. (ii) Proportion of employees with bachelor degree or above, which reflects the overall quality of employees. The higher the education level of employees, the more flexibility they can stimulate the internal operation of enterprises. So, it can measure the adaptability of enterprises. (iii) R\u0026amp;D investment intensity, which reflects how much an enterprise attaches importance to innovation, measures innovation ability. We employed the mediation effect model of Wen Zhonglin et al. (2004), as shown in model (5) -model (7).\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$${GI}_{i,t+1}=\\alpha +\\beta {DT}_{i,t-1}+\\gamma {Controls}_{i,t}+\\sum Year+\\sum Industry+{\\epsilon }_{i,t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$${capability }_{i,t}={\\alpha }^{\u0026rsquo;}+\\vartheta {DT}_{i,t-1}+\\gamma {Controls}_{i,t}+\\sum Year+\\sum Industry+{\\epsilon }_{i,t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$${GI}_{i,t+1}=\\alpha +{\\delta }_{1}{capability }_{i,t}+{{\\delta }_{2}DT}_{i,t-1}+\\gamma {Controls}_{i,t}+\\sum Year+\\sum Industry+{\\epsilon }_{i,t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the model, \u003cem\u003ecapability\u003c/em\u003e represents the dynamic capability, which is measured by \u003cem\u003eabsorptive capability\u003c/em\u003e, \u003cem\u003eadaptive capability\u003c/em\u003e and \u003cem\u003einnovative capability\u003c/em\u003e respectively. Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e reports the test results. In column (3), (6) and (9), \u003cem\u003eabsorptive capability\u003c/em\u003e, \u003cem\u003eadaptive capability\u003c/em\u003e and \u003cem\u003einnovative capability\u003c/em\u003e are significant positively correlated with green innovation. Column (2), (5) and (8) show that only \u003cem\u003eadaptive capability\u003c/em\u003e and \u003cem\u003einnovative capability\u003c/em\u003e were significantly positive correlated with \u003cem\u003eDT\u003c/em\u003e. Column (6) and (9) report that the dynamic capability has a partial mediating effect on the relationship between digital transformation and green innovation. Digital transformation significantly improves the dynamic capability of enterprises supported by digital technology (Mikalef et al., 2017), thus enhancing the level of green innovation of enterprises. Specifically, the mediating mechanism of green innovation empowered by digital transformation lies in the improvement of adaptive capability and innovative ability. The deep application of digital technology affects the strategy and goals of enterprises (Ciampi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). On the one hand, it enables enterprises to quickly adapt to changes in the highly active market competition, timely change their business model, and implement green innovation activities to seek sustainable competitive advantages. On the other hand, firms can optimize the human capital structure according to strategic needs, improve independent innovation capacity through scientific and technological talent, and strengthen green innovation activities.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression results of the mediating effect of. dynamic capability\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAbsorptive capacity\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAdaptability\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003eCreativity\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAbsorptive capacity\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eAdaptability\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eCreativity\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eGI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAbsorptive capacity\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.213***\u003c/p\u003e \u003cp\u003e(2.607)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eadaptive capability\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.840***\u003c/p\u003e \u003cp\u003e(8.815)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003einnovative capability\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.038***\u003c/p\u003e \u003cp\u003e(10.798)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.134***\u003c/p\u003e \u003cp\u003e(10.698)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.003***\u003c/p\u003e \u003cp\u003e(-5.931)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.137***\u003c/p\u003e \u003cp\u003e(9.585)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.132***\u003c/p\u003e \u003cp\u003e(9.959)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.036***\u003c/p\u003e \u003cp\u003e(13.307)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.099***\u003c/p\u003e \u003cp\u003e(6.380)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.135***\u003c/p\u003e \u003cp\u003e(10.071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.747***\u003c/p\u003e \u003cp\u003e(11.884)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.107***\u003c/p\u003e \u003cp\u003e(7.030)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.216***\u003c/p\u003e \u003cp\u003e(-11.384)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.097***\u003c/p\u003e \u003cp\u003e(-6.599)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.312***\u003c/p\u003e \u003cp\u003e(-9.612)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.123***\u003c/p\u003e \u003cp\u003e(-10.384)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4.476***\u003c/p\u003e \u003cp\u003e(-9.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-4.351***\u003c/p\u003e \u003cp\u003e(-10.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.866**\u003c/p\u003e \u003cp\u003e(2.230)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-4.681***\u003c/p\u003e \u003cp\u003e(-9.320)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4,290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3,902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3,902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eadj. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e135.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e81.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe traditional mode of economic growth has severely constrained sustainable development, and a growing number of studies have shown that green innovation can help enterprises enhance their competitiveness and achieve sustainable development while contributing to environmental protection. Digital transformation is gradually becoming an important strategic path for the technological evolution of global enterprises. This paper holds the view that digital transformation can provide heterogeneous resources that attract large amounts of capital to enterprises and influence green innovation. Specifically, the digital transformation of enterprises promotes green innovation.\u003c/p\u003e \u003cp\u003eAt the same time, we consider the role of internal and external factors in the firm. Executives with a technological imprint have a more comprehensive and profound knowledge of digital transformation. They can better utilize the heterogeneous resources that digital transformation brings directly or indirectly to enhance the company's R\u0026amp;D investment in green innovation. Digital transformation is a sustainable behavior, and media attention can stimulate corporate reputation, which can also have a positive impact on the development of green innovation activities.\u003c/p\u003e \u003cp\u003eWe also provide strong evidence that digital transformation influences green innovation by enhancing a firm's dynamic capabilities. The findings of this paper pass a series of robustness tests.\u003c/p\u003e \u003cp\u003eThis article sheds light on how digital transformation affects green innovation and clarifies the detailed mechanism, which has profound implications for enterprise managers and policy makers. In addition, we emphasize the importance of digital transformation and put forward constructive suggestions on how to promote green innovation in China. To optimize production and operation processes, enterprises should apply digital technologies actively and refine the talent training and technology system. For government, here are three practical ways: promote the construction of digital transformation infrastructure, implement the incentive mechanism of green innovation supported by digital technology, strengthen supervision over media reports.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Social Science Fund of China (Funding Number: 20BGL073)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e Data and materials can be accessed by the corresponding author\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e The authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Lei Zhu, Chunyan Wang and Xiaohan Wang. The first draft of the manuscript was written by Lei Zhu, Chunyan Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkroyd C, Kober R. (2020). Imprinting founders\u0026rsquo; blueprints on management control systems. Management Accounting Research, 46,100645. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.mar.2019.07.002\u003c/span\u003e\u003cspan address=\"10.1016/j.mar.2019.07.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlnuaimi B K, Kumar Singh S and Ren S. (2022). 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Sustainable Production and Consumption, 30, 815\u0026ndash;828. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.spc.2022.01.017\u003c/span\u003e\u003cspan address=\"10.1016/j.spc.2022.01.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digital transformation, Green innovation, Executive imprint, Media attention, Sustainable development","lastPublishedDoi":"10.21203/rs.3.rs-4270176/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4270176/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explores the impact of digital transformation on green innovation and reveals two newly developed mechanisms including technical imprint of senior executives and media attention. The results show that digital transformation of enterprises promote green innovation significantly. In addition, the enabling role of digital transformation on green innovation is reinforced by executives who have a high technological footprint and by firms' significant media attention. These findings not only provide novel insights to drive enterprise green innovation in the digital economy age, but also offer useful measures to policy makers and firms to implement sustainable development in developing countries.\u003c/p\u003e","manuscriptTitle":"The effect and newly developed mechanisms of digital transformation on green innovation: evidence from listed firms in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 19:21:05","doi":"10.21203/rs.3.rs-4270176/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"50763557-73f3-410f-b084-ccf65509154f","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-24T10:18:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-19 19:21:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4270176","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4270176","identity":"rs-4270176","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}