Research on the Evolutionary Mechanism of Disruptive Innovation in Electric Power Enterprises Driven by Artificial Intelligence - Based on the Adjustment Effect of Economic Policy Uncertainty

Research on the Evolutionary Mechanism of Disruptive Innovation in Electric Power Enterprises Driven by Artificial Intelligence - Based on the Adjustment Effect of Economic Policy Uncertainty | 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 Research on the Evolutionary Mechanism of Disruptive Innovation in Electric Power Enterprises Driven by Artificial Intelligence - Based on the Adjustment Effect of Economic Policy Uncertainty Cui Aiping, Zeng Bing, Yi Ruowen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7069434/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 The world economy is very competitive. Tech is changing fast. For companies, the ability to make big new changes is key to winning in the market.This study uses information. It looks at 83 Chinese power companies that are listed. The time is from 2001 to 2022. It starts with the research experience of the companies' top leaders. It tries to find out how big new changes grow in these power companies. It also looks at how unsure economic policies affect this.The research experience of the top leaders helps a lot to make big new changes. This is true even after many checks to make sure.There are other tests too. They look at different types of companies. They show this help is stronger in power companies not owned by the state. They also show something else. In eastern, central and western power companies, the help is greater when the area's economy is more developed.Labor intensive and capital-intensive industries have stronger promotion effects compared to labor-intensive industries. The moderating effect indicates that economic policy uncertainty positively moderates the promotion of disruptive innovation by the research experience of executives in power companies. The mediation effect test found that the research experience of executives in power companies promotes disruptive innovation through two paths: accelerating digital transformation and enhancing R&D information disclosure. Based on this, this article proposes policy recommendations from the perspectives of optimizing talent policies, promoting coordinated development of regional innovation, innovating executive selection and training mechanisms, and accelerating digital transformation and fulfilling social responsibilities, in order to provide theoretical and practical references for power enterprises to enhance their disruptive innovation capabilities. Classification number: F272 Document identification code: A Research experience of senior executives in power companies Disruptive innovation Economic policy uncertainty Research and development information disclosure Digital transformation Figures Figure 1 1. Introduction In the current era of intense global economic competition,New tech is growing fast. For companies, the ability to make new things is key. It decides if they can stay and grow in the market.The 20th National Congress of the Communist Party of China made a report. It says companies should lead more in new tech and science. This shows clearly that company innovation is very important. It helps the country’s economy grow.In the diversified innovation path of enterprises, disruptive innovation can help enterprises open new market tracks, reshape the competitive landscape, and become a strong core driving force for enterprise innovation and development. The disruptive innovation achievements of enterprises are closely related to the decision-making and leadership of the executive team. Taking the power industry as an example, among the many award-winning projects of the 2022 China Electric Power Science and Technology Award, a considerable number of first prize projects were led by executives of power companies. For example, Liu Zhenya, the former general manager of State Grid Corporation of China, played a key role in the development of ultra-high voltage power grid technology. In 1994, Wuhan High Voltage Research Institute built China's first million-volt level transmission research line segment. At that time, the prospects of this technology were unclear, but Liu Zhenya elevated the technology of ultra-high voltage power grid to the strategic level of State Grid Corporation of China. Through continuous investment and research and development, significant breakthroughs have been made in ultra-high voltage power grid technology and widely applied, changing the pattern of power transmission in China, achieving long-distance and high-capacity transmission, greatly improving the transmission capacity and efficiency of the power grid, and effectively promoting the optimization of energy resource allocation in China. Liu Zhenya graduated from the School of Electrical Engineering at Shandong University and has in-depth research and rich experience in the fields of power system operation, planning, and management. His forward-looking strategic decisions have played an irreplaceable leading role in the birth and promotion of the disruptive innovation of ultra-high voltage technology. This inevitably prompts deep reflection: Against the backdrop of the booming development of artificial intelligence and increasing economic policy uncertainty, how will the research experience of executives in power companies use artificial intelligence to achieve disruptive innovation? What are the characteristics of its impact path? This issue not only concerns the strategic choices of power enterprises in complex environments but also has important practical significance for them to seize innovation opportunities and achieve sustainable development. At present, literature on the research experience and disruptive innovation of corporate executives mainly focuses on keywords such as "dual innovation", "technological innovation", "absorptive capacity", and "innovation performance". However, the breadth and depth of research in this field is insufficient, and there is still ample room for expansion. Reviewing existing research, the impact of disruptive innovation is mainly analyzed from two dimensions: internal and external factors. Internal factors include digital transformation, knowledge management, industry university research cooperation, human resources systems, etc. (Kou Mingting et al., 2024; Li Yuhua et al., 2024), aiming to explore the internal path for enterprises to achieve disruptive innovation; External factors mainly consider environmental uncertainty, market analysis, government policies, financial environment, organizational distance, etc. (Li Zhengwei et al., 2023; Shan Wei et al., 2025), with a focus on exploring the direct or moderating effects of complex external environments on disruptive innovation in enterprises. At the level of research experience of executives in power companies, there is currently no direct literature exploring the impact of executive research experience on disruptive innovation in power companies, which provides a starting point for this study. Overall, research on the research experience of executives in power companies mainly focuses on the internal and external influences of the company. Internally, the research experience of senior executives in power companies can have an impact on corporate innovation, dual innovation, digital transformation, R&D disclosure and manipulation, debt financing, etc. (Yangzhen et al., 2022; Yuan Zemin et al., 2020); Externally, it mainly revolves around the spillover effects of factors such as corporate social responsibility, information disclosure quality, and external correlations (Dai Lu et al., 2023; Wang Zhenshan et al., 2023). After comprehensively reviewing the current research results, there are still many areas that need to be improved. Firstly, existing research lacks in-depth exploration of heterogeneity. Disruptive innovation varies significantly depending on the industry, region, and nature of the enterprise, and research conclusions may differ greatly in different contexts. Secondly, although there have been studies focusing on the impact of corporate R&D information disclosure and digital transformation on innovation, the influence of R&D information disclosure and digital transformation by executive teams in power companies, as well as their relationship with disruptive innovation, has not been fully explored. Furthermore, there is currently a lack of research on the relationship between the research experience of executives in power companies and disruptive innovation in the industry. Although there have been studies on the research experience of executives in power companies and their dual innovation, dual innovation includes disruptive innovation and incremental innovation, and the impact of executives' research experience on dual innovation cannot be simply applied to disruptive innovation. Therefore, this article will clearly define the impact of research experience of executives in power companies on both, filling this research gap. In addition, there is no consensus on the promoting or inhibiting effects of economic policy uncertainty on corporate innovation, which is also one of the key research contents of this article. The innovation of this article is as follows: First, it is the first to focus on the direct link between the research experience of power company executives and disruptive innovation. It breaks the limits of existing studies on dual innovation.Second, it builds a two-part middle path. The path is "digital transformation and R&D information sharing". It shows the inner way that executive research experience affects disruptive innovation.Third, it confirms that economic policy uncertainty has a positive moderating effect on their relationship. It fills the research gap in macroeconomic environment regulation mechanisms.At the theoretical level, this article does several things. It extends the use of upper echelon theory in the power industry. It makes clear the driving path of disruptive innovation from executive research experience. It adds to studies on the non-linear relationship of economic policy uncertainty's moderating effect. It offers new evidence for the theoretical system in this area.In practice, it provides references. These are for power companies to choose executives and make digital transformation plans. It helps them get better at disruptive innovation in complex situations. It also pushes the energy industry to make tech changes and develop sustainably. 2. Theoretical analysis and research hypotheses ( 1 ) Research Experience of Senior Executives in Electric Power Enterprises and Disruptive Innovation in Enterprises The research and development innovation of power enterprises is closely related to the decision-making of the executive team. The executive team not only determines the direction of enterprise innovation but also ensures the smooth progress of the entire innovation process. Since the 20th century, with the progress of society, the popularization of higher education, and the development needs of power enterprises, more and more senior executives with research experience have moved towards the core management of the enterprise. Electric power company executives with certain research experience have more rigorous thinking logic and broader development vision in enterprise innovation decision-making. Disruptive innovation in power enterprises has been a hot topic in recent years, and many companies have achieved significant breakthroughs in key technologies to overtake their peers in the industry. Disruptive innovation has higher requirements for the quality and effectiveness of innovation compared to incremental innovation and faces more serious financing constraints in the implementation process of innovation activities (Dai et al., 2024). At the same time, enterprise disruptive innovation has a longer R&D cycle, higher R&D investment, and relatively greater R&D risks compared to general innovation. Huang Can et al. (2019) found that all academic experiences of executives can directly promote innovation in enterprises. In environments where the enterprise has a good innovation atmosphere and a low proportion of state-owned equity, this promoting effect is more prominent. They pointed out that the academic background of executives can also improve the information environment of the enterprise, reduce information asymmetry, attract more analysts' attention, and indirectly promote innovation in the enterprise. Yuan Zemin et al. (2020) explored from another perspective the role of executives' academic backgrounds in inhibiting corporate R&D manipulation behavior. The study showed that executives with academic backgrounds can effectively curb R&D manipulation behavior in enterprises, and their role is more prominent in situations where tax collection and management are weak or internal controls are weak. However, Yang Junxiao et al. (2021) proposed that the academic background of executives has a "double-edged sword" effect on corporate innovation. They found that although academic executives can enhance the overall innovation capability of enterprises, the positive innovation effect generated by academic background will actually decrease when executives are in key positions. As the proportion of academic executives increases, the innovation effect will gradually weaken.Kang He et al. (2021) studied how executives’ academic backgrounds affect corporate green innovation. They found that executives with academic backgrounds strongly drive green innovation. Executives in key positions see their academic backgrounds play a more notable role. Their academic experience helps companies respond better and innovate more when facing green technology challenges. This role in promoting green innovation shows their critical part in disruptive innovation.In industries needing new technologies to solve environmental problems, Cai, Chunhua et al. (2024) used imprint theory. They found that executives’ academic backgrounds positively affect corporate digital transformation. Academic executives can drive enterprise digital transformation by increasing innovation investment. This is especially true in the context of fierce industry competition.Overall, the role of executives with academic backgrounds in enterprise innovation and disruptive innovation cannot be ignored. By enhancing the innovative atmosphere of enterprises, reducing R&D manipulation, promoting green innovation, and driving digital transformation, academic executives inject new impetus into the innovation capabilities of enterprises. However, having too many academic background executives or executives in key positions may have a negative impact on innovation effects. When companies use the academic background of executives to promote innovation, they need to comprehensively consider the proportion of executives' backgrounds and job arrangements to maximize their role in driving disruptive innovation. Based on relevant theories and existing literature, this article summarizes the influencing factors of the research experience of senior executives in power companies on disruptive innovation as follows: Firstly, starting from the theory of resource dependence, the social capital and human capital accumulated by executives in the scientific research process of power enterprises are closely related to the disruptive innovation of the enterprise, greatly ensuring the talent and technology for achieving disruptive innovation in power enterprises. Dual social capital and informal personal connections between schools and enterprises will promote the external learning ability of power enterprises, gather more talent resources, and positively promote the realization of disruptive innovation (Liang Minxin et al., 2023); At the same time, research experience will bring redundancy to organizational resources, which will help power companies adapt to the uncertainty of disruptive innovation processes, reduce internal strife and conflicts, alleviate resource shortages, and focus more organizational resources on the implementation of disruptive innovation (Shi Lei et al., 2025). Secondly, social network theory suggests that in terms of capability, knowledge transfer and sharing within the network are beneficial for innovation activities (Wu L et al., 2025), and diverse experiences help company leaders develop the ability to learn and exchange knowledge across boundaries. This ability will promote the organizational absorption of disruptive innovation theory knowledge in power companies and solve potential challenges that may arise during the innovation process (Xiaoshan J et al., 2023); Huang Bingyi et al., 2023). The scientific research experience of executives in power companies can effectively alleviate the short-sighted phenomenon of management, while disruptive innovation, due to its particularity, requires managers to have a certain international perspective and stay at the forefront of academia. Based on this, the scientific research experience of executives in power companies can promote the realization of disruptive innovation in the enterprise from a theoretical knowledge level, ensuring industry leadership in research and development. Thirdly, the theory of upper echelons suggests that when faced with complex and ever-changing internal and external environments that prevent decision-making, executive teams typically rely on long-term concepts and values related to innovation (Ewa F G et al., 2025). Based on transaction cost theory, academic research experience plays an important role in alleviating information asymmetry within and outside power enterprises, and helps to timely avoid risks in the research and development process (Wang Z et al., 2024); In addition, the research experience of executives in power companies will effectively reduce the company's debt financing costs, alleviate the difficulty of financing in achieving disruptive innovation, and thus allocate more funds to research and development investment (Li S et al., 2024); At the same time, the shaping and tempering of scientific research experience enables executives of power companies to have high moral standards and social responsibility awareness in the process of carrying out disruptive innovation, making them more likely to disclose R&D during the R&D process, thereby improving the efficiency of disruptive innovation production in the enterprise. Based on this, this article proposes the following research hypotheses: H1: Research experience of executives in power companies can promote disruptive innovation in the enterprise. ( 2 ) The moderating effect of economic policy uncertainty The process of disruptive innovation is often accompanied by high risks and uncertainties, and the risks arising from the dynamic nature of the environment place higher demands on executives when making decisions. In an environment of economic policy uncertainty, corporate innovation is significantly negatively affected, especially disruptive innovation with higher levels of innovation (Zhang Dongyu and Wu Peng, 2024). Disruptive innovation is more difficult to achieve compared to incremental innovation, not only because of the high risks and costs involved in the research and development process of disruptive innovation, but also because of various uncertain macro factors. Huo Yuan et al. found that there is a "U-shaped" nonlinear relationship between economic policy uncertainty and the sustainability of corporate innovation, with R&D investment playing a partial mediating role. They also proposed policy recommendations that the government should carefully grasp the scale of policy regulation when adopting macroeconomic policies to regulate the economy (Shan Wei et al., 2025). Li Enji et al. (2022) found that when the economic policy index rises, two different types of corporate investment behaviors will occur. However, contrary to the above viewpoint, some scholars have found that when faced with increased economic policy uncertainty, some speculators among executives of power companies often realize that it is both a risk and an opportunity (Li F et al., 2022; Li Y and Tu X, 2021; Zhao X, 2021), therefore willing to take on certain risks and increase investment in innovation, hoping to obtain excess profits (Chen Ziwei and Gao Jiameng, 2024; Sun Chuanwang et al., 2024). Based on this, does the increase in economic policy uncertainty positively regulate the disruptive innovation path of power company executives influenced by their research experience? The increasing uncertainty about economic policies has made market forecasting more difficult. Electric power company executives will be more cautious in making decisions about the main direction of the company and will be more inclined to maintain existing customers rather than increase research and development investment. Financial decisions of the company will also deviate, and disruptive innovation paths will be affected; Accordingly, from an institutional perspective, the increase in economic policy uncertainty has led to a mismatch between existing mechanisms and the actual environment of enterprises, as well as a mismatch between existing mechanisms and the needs for achieving disruptive innovation in enterprises, resulting in obstacles to disruptive innovation in enterprises. In response to the above questions, this article proposes the following assumptions: H2a: Economic policy uncertainty will positively regulate the role of research experience of executives in power companies in promoting disruptive innovation. H2b: Economic policy uncertainty will negatively regulate the role of research experience of executives in power companies in promoting disruptive innovation. ( 3 ) The mediating effect of R&D information disclosure and enterprise digital transformation The imprinting theory suggests that individual level imprints have long-term persistence once they exceed the sensitive period (Stankowska A and Niedziolka D, 2021; Vaisman E D et al., 2021). It can be inferred that the beliefs, behaviors, and orientations of executives in power companies during their research experience will profoundly influence the strategic decision-making process in the operation of the enterprise. During the forging process of scientific research experience, executives of power companies are influenced by various mechanisms, gradually forming a clear understanding of the importance of innovative mechanisms. Scientific research experience has laid the "roots" for executives of power companies to optimize the driving force of enterprise innovation. The research experience of executives in power companies has a subtle influence on internal and external decision-making. For internal decision-making in power companies, executives with some research experience speed up the company’s digital transformation. This helps achieve industrial transformation and changes in management ideas.Tang Chunyong et al. (2022) analyzed the internal mechanism. They studied how senior executives’ research experience in power companies affects digitalization. It shows in three ways. These are easing financing limits, strengthening the strategic focus on corporate social responsibility, and getting government resource subsidies.For external decision-making in power companies, research experience often makes executives more steady and careful in business processes. This reduces possible R&D manipulation in the company’s innovation process (Chen Y et al., 2021). It also makes them more aware of reducing risks and improving accounting conservatism. This significantly raises the quality of the company’s information disclosure.In daily operations of power companies, executives with research backgrounds often make more forward-looking decisions. They can better avoid short-sighted actions (Yuan Zemin et al., 2020). This improves the company’s overall innovation efficiency.Executives’ research experience in power companies lets them better understand the industry’s latest R&D knowledge. This builds their ability to predict market trends. They can then deal with the impact of market changes. They can correct mistakes in time based on innovation progress. They can also push new innovation breakthroughs in power companies.Based on this, this article studies the impact. It looks at how senior executives’ research experience affects disruptive innovation in power companies. It does this through two paths. These are speeding up the companies’ digital transformation and improving their R&D information disclosure. 1. Mediating variable one: R&D information disclosure Information disclosure refers to the act of a company disclosing various types of information, such as its operating status, financial situation, strategic decisions, and research and development activities, to external stakeholders. Information disclosure can improve the transparency of the company and establish trust between the company and investors, customers, and other stakeholders. In companies driven by innovation, R&D information disclosure can convey the company's innovation intentions and capabilities to the market and provide corresponding support for obtaining external resources. Related studies have shown that the research background of corporate executives can have an impact on the innovation behavior of enterprises, particularly in promoting disruptive innovation. Research and development information disclosure, by reducing information asymmetry, attracting external attention, and resource support, promotes the innovation capability of enterprises. Zhang Xiaoliang et al. (2019) conducted a study based on the theory of high-level echelons and pointed out that the academic experience of CEOs can improve the innovation level of enterprises. CEOs with academic experience are more inclined to use the combination of industry, academia, and research to enhance their innovation level. The most crucial aspect of this process is to improve the disclosure of R&D information, so that enterprises can present their innovation capabilities to the outside world, attract more resources and cooperation opportunities. Research and development information disclosure plays a key mediating role in the promotion of innovation by executives' research backgrounds. Chen Xingyu et al. (2022) found that scholar CEOs can reduce stock price synchronicity and improve the level of voluntary research and development information disclosure, which is a keyway to generate this inhibitory effect. By improving the level of research and development information disclosure, scholar CEOs can reduce external market doubts and uncertainties about their R&D activities, effectively promoting innovation in enterprises. In technology intensive enterprises, R&D information disclosure is crucial. Fu Chen et al. (2023) pointed out that the personal traits and leadership style of the CEO determine the company's response to disruptive innovation. The CEO can rely on increasing R&D information disclosure, reducing external information asymmetry, and promoting the implementation of disruptive innovation.Disclosing R&D information gives external support to corporate innovation. It can also show the company’s innovation abilities and potential to investors and the market. This helps the company get more resources. It also boosts the degree and scope of innovation.Yuanxing Wan et al. (2025) did a study. They found a close link between the recognition of high-tech enterprises and R&D information disclosure. When there is high demand for information, real high-tech enterprises will disclose more R&D information. But fake high-tech enterprises may hide their innovation level. They do this by manipulating accounting items and business activities. In short, the research experience possessed by executives in power companies plays a significant role in driving disruptive innovation. R&D information disclosure plays an intermediary role in this process. If companies increase their R&D information disclosure, it can improve external trust in their innovation capabilities and obtain more resources and cooperation opportunities to promote disruptive innovation. However, companies need to pay attention to the quality and transparency of information disclosure, which will directly affect their innovation effectiveness. How to effectively improve the level of R&D information disclosure has become a key issue in their innovation strategy. 2. Mediating variable 2: Enterprise digital transformation Enterprise digital transformation means making deep changes. It upgrades products, services, management and business models. It uses digital technologies. These include big data, cloud computing and artificial intelligence. It aims to improve operational efficiency. It also aims to boost innovation capabilities and market competitiveness.Information technology is developing fast. Digital transformation has become a key way. It promotes enterprise innovation. It also promotes disruptive innovation. The research experience possessed by corporate executives enables them to have stronger innovation awareness, systematic thinking, and the ability to solve complex problems, which can lead companies to carry out disruptive innovation more effectively. The research background of executives will have an impact on corporate strategic decision-making and promote digital transformation, indirectly promoting the implementation of disruptive innovation. Digital transformation is a new method. It helps power companies break time and space limits in R&D resource allocation. It enables them to achieve disruptive innovation.Digital transformation can greatly improve the environmental and social performance of power enterprises (Gao Yuan et al., 2024). It stimulates innovation vitality. It also improves innovation performance (Li Zhiguo et al., 2024).Digital transformation helps power companies balance economic and social benefits. It also eases financing difficulties in R&D processes.Sun Jian et al. (2024) found some things. Digital transformation can effectively raise enterprise innovation levels. It eases financing constraints. It also enhances corporate social responsibility.Ge Pengfei and Huang Xiulu (2024) analyzed something. Digital transformation mainly promotes enterprise integration and innovation. It does this by increasing the flow of innovative knowledge. It also expands the diversity of such knowledge.Liu Yexin and Wu Weiwei (2024) researched and showed something. CEOs’ academic experience drives enterprise digital transformation. It also improves their innovation capabilities. Specifically, CEOs with academic backgrounds can deeply understand the importance of digital transformation for enterprise development. They have strong logical thinking and problem-solving abilities. This helps promote changes in technology upgrades and business model innovation. CEOs’ academic experience promotes digital transformation. This helps enterprises achieve disruptive innovation.Zheng Minggui et al. (2025) put forward a view. Differences in executive teams, such as research backgrounds, can enhance a company’s risk-taking ability. They promote innovation by driving digital transformation.Wei Yanjie et al. (2023) also researched and indicated something. Enterprise digital transformation relies not only on technology investment. It also relies on increasing R&D investment and improving strategic flexibility. CEOs’ social capital and research backgrounds work together. They increase R&D investment. They strengthen the flexibility of strategic decision-making. This promotes enterprise digital transformation.In the transformation process, digital technology application directly promotes enterprise disruptive innovation. It allows enterprises to quickly respond to market changes. It helps them launch innovative products or services.Corporate executives’ research experience has a key impact on enterprise development. It can directly enhance innovation capabilities. It promotes enterprise disruptive innovation.In the process where executives’ research experience promotes enterprise disruptive innovation, enterprise digital transformation plays a key intermediary role. Digital transformation helps enterprises achieve breakthroughs in technology and models, creating a good soil for disruptive innovation. When selecting and cultivating executives, companies need to pay attention to their research background, promote digital transformation, and lead the company towards the forefront of innovation to gain competitive advantages. Dai Lu et al. (2023) also pointed out the role of executives' academic background in corporate innovation. In terms of deep integration of industry, academia, and research, executives' research background can enhance the level of cooperation between enterprises and research institutes, drive technological innovation. Similarly, digital transformation of enterprises can also rely on strengthening the connection with external technological resources to help enterprises achieve disruptive innovation. Based on the above analysis, this article constructs a research hypothesis model as shown in the following figure: 3. Research Design ( 1 ) Indicator measurement This article uses Zhou Kaitang’s method. It measures executives’ research experience by counting the number of executives in power companies’ executive teams with such experience.For disruptive innovation, this article uses Kong Dongmin et al.’s approach. It calculates the measure of disruptive innovation in power enterprises. It adds the natural logarithms of objective weights. The weights are 0.5 for invention patents, 0.3 for utility model patents, and 0.2 for design patents. ( 2 ) Research Model To verify the impact of research experience of executives in power companies on disruptive innovation, this paper refers to previous scholars' relevant research and constructs the following regression model (such as 3 − 1): where the dependent variable Innovation i, t is the disruptive innovation output of company i in year t, measured by the number of authorized invention patents of power companies; The explanatory variable Academy i, t represents the number of executives in the power company i in year t. $$\:{\text{I}\text{n}\text{n}\text{o}\text{v}\text{a}\text{t}\text{i}\text{o}\text{n}}_{\text{i},\text{t}}={\alpha\:}+{{\beta\:}\text{A}\text{c}\text{a}\text{d}\text{e}\text{m}\text{y}}_{\text{i},\text{t}}+{\gamma\:}\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}\text{s}+{{\epsilon\:}}_{\text{i},\text{t}}\:\:\:\:\:\:\:\:\:\:\:\:\:\left(3-1\right)$$ ( 3 ) Data sources and descriptive statistics The data source of this article is the CSMAR database, which includes research background data of executives from 83 listed power companies in China from 2001 to 2022, as well as the cumulative number of patent authorizations as of the end of the reporting period. At the enterprise level, control variables such as company establishment year (Firm age), company size (Size), board size (Board), dual role (Dual), company nature (Soe), number of employee shares (M share), return on assets (Roe), and asset liability ratio (Lev) were selected. For the above data, this article matched the data based on securities codes, deleted the data with many missing values for the main variables, and performed Winsorize processing on the first and last 1% of continuous variables, finally obtaining 516 continuous observations. The descriptive statistical results of the main variables are shown in Table 3 − 1. Table 3 − 1 Descriptive Statistical Results ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) Variable N mean sd min max ID 516 2,172 1,440 1 5,331 Year 516 15.36 6.004 1 24 Industry 516 38.56 18.32 1 86 Academy 516 0.04 0.12 0 0.75 Innovation 516 0.43 0.79 0 4.19 EPU 516 167.37 111.083 53.13 439.12 Firm age 516 17.42 6.695 1 62 Size 516 17.42 6.695 1 62 Board 516 8.735 1.962 1 22 Dual 516 1.250 0.433 1 3 Soe 516 1.375 0.484 1 3 M share 516 6,115 6,278 1 21,700 Top 516 12,586 7,592 1 25,487 Fix 516 27,772 16,031 1 55,503 LEV 516 27,765 16,022 1 55,505 Roe 516 27,599 15,757 1 51,434 From the table, the means of disruptive innovation in power enterprises is 0.43, the standard deviation is 0.79, and the maximum value is 4.19. The data indicates that most power enterprises have weak disruptive innovation capabilities, and there are significant differences in disruptive innovation achievements among different enterprises. The average academic research experience of executives in power companies is 0.04, with a standard deviation of 0.12, indicating that it is common for most power companies to hire people with relevant research backgrounds as executives. The distribution of other control variables can also be seen from the table, and they are all within a reasonable range. 4. Empirical analysis ( 1 ) Benchmark regression The benchmark regression results are shown in Table 4 − 1. The research experience of executives in power companies is positively correlated with disruptive innovation at a 1% significance level before and after controlling for variables, years of participation, and industry fixed effects. This indicates that the research experience of executives in power companies significantly promotes disruptive innovation. Specifically, after incorporating enterprise control variables, year, and industry fixed effects, the coefficient of research experience (Academy) for executives in power companies is 1.4017***, which preliminarily confirms the hypothesis of H1. Table 4 − 1 Results of Principal Regression Analysis Variable Model 1 Model 2 Model 3 Innovation Innovation Innovation Academy 2.2496*** 2.0932*** 1.4071*** (7.9619) (7.5910) (4.9326) Control variable NO YES YES Fixed effects of year and industry NO NO YES _cons 0.4441*** 0.4618 0.7879 (3.0293) (1.3016) (1.4869) N 516 516 516 adj. R 2 0.102 0.063 0.256 Note: ***, **, and * represent significance levels of 1%, 5%, and 10%, respectively. The values in parentheses are the standard errors clustered to the industry year level. The same applies to the following tables. ( 2 ) Heterogeneity test 1. Nature of Enterprise Property Rights Property rights determine a company’s equity concentration and investment methods.We did regression analysis on state-owned and non-state-owned power enterprises. The results are in Table 4 − 2.The results showed some numbers. The regression coefficient for state-owned power enterprises was 1.1339***. The one for non-state-owned ones was 1.5957***.This means property rights do not affect the research conclusions of this paper. But the coefficient for state-owned power enterprises is smaller than that for non-state-owned ones. This shows that senior executives’ research experience in power enterprises can better promote disruptive innovation in non-state-owned enterprises. Table 4 − 2 Regression Results of State-owned Power Enterprises and Non-state-owned Power Enterprises Variable State-owned power enterprises Non-state-owned power enterprises Innovation Innovation Academy 1.1339** 1.5957*** (2.4239) (3.8266) Control variable YES YES Fixed effects of year and industry YES YES _cons -4.6084*** 0.1007 (-3.8834) (0.0824) N 293 223 adj. R 2 0.316 0.186 2.Different regions where the enterprise is located Table 4 − 3 shows regression results for enterprises in different regions.The results tell us something. Sample power companies in eastern and central regions have positive and significant regression results. But the western region has a negative regression coefficient.In terms of coefficients, the positive promotion effect of executives’ research experience on disruptive innovation in power companies is weaker. It decreases from eastern to central to western regions. This matches the decreasing level of economic development in these three regions.This indicates that the location of power companies in different regions will affect the promotion effect of scientific research experience of executives in science and engineering on disruptive innovation. Table 4 − 3 Regression Results of Electric Power Enterprises in Different Regions Variable Eastern region Central region Western region Innovation Innovation Innovation Academy 0.8668** 0.5578** -0.0512 (2.1332) (2.2536) (-0.0923) Control variable YES YES YES Fixed effects of year and industry YES YES YES _cons 1.0911 -32.3493** 33.9706** (1.3294) (-2.7522) (2.0880) N 285 68 148 adj. R 2 0.022 0.770 0.010 3.Different types of industries Table 4 – 4 reveals the differences in research conclusions among different types of industries. In the regression model where the main industries of power enterprises are labor-intensive and capital intensive, the coefficients of Academy are 1.3800** and 0.1530**, respectively, which is consistent with the conclusion of this study. However, in technology intensive regression models, the regression coefficients are 0.2895, indicating an insignificant state. This indicates that the research experience of executives in power companies significantly promotes disruptive innovation in labor-intensive and capital-intensive industries but has a weaker positive effect on technology intensive industries. Table 4 4 Regression Results Based on Different Industry Types Variable Labor intensive Technology intensive Capital intensive Innovation Innovation Innovation Academy 1.3800** 0.2895 0.1530* (2.1251) (1.0103) (1.9263) Control variable YES YES YES Fixed effects of year and industry YES YES YES _cons 23.8886** 12.0458 8.0485*** (2.3358) (1.0455) (4.2335) N 78 144 297 adj. R 2 0.492 0.250 0.012 ( 3 ) Robustness test 1.Replace the independent and dependent variables To check if the research conclusions here are robust, we replaced the independent and dependent variables in the main regression model. We then did regression tests. The results appear as Model 1 and Model 2 in Table 4 –5.For the variable of senior executives’ research experience in power companies, we used Zhou Kaitang et al.’s research. We set a standard: whether the company has executives with research experience in the current year. We gave a value of 1 if there are such executives, 0 otherwise.We used a substitute variable for enterprise disruptive innovation. It is the natural logarithm of the number of invention patents power companies get in the current year plus one.The results show that the research conclusion of this article still holds true. 2.Add control variables Drawing on the research of Sun Jian et al., to avoid missing variables, this study added Tobin's Q value as a control variable and tested it on behalf of the main regression model. The regression results are shown in Model 3, which indicates that after adding Tobin's Q value, the conclusions of this study remain robust. Table 4 5 Regression Results of Robustness Test Variable Model 1 Model 2 Model 3 Innovation Innovation Innovation Academy 1.6244*** 1.8334*** 1.8330*** (5.2336) (5.9651) (5.9583) Control variable NO YES YES Fixed effects of year and industry YES YES YES _cons -0.4225 0.2151 -0.0267 (-0.5171) (0.1842) (-0.0210) N 516 516 516 adj. R 2 0.403 0.430 0.429 ( 4 ) Research on the moderation effect of economic policy uncertainty This article also constructs a model of the moderating effect of economic policy uncertainty (EPU) on the promotion of disruptive innovation in electric power company executives through their research experience. The interaction term is specific to formula 4 − 1 and is included in the benchmark regression model, specifically formula 4 − 2 and formula 4 − 3. The results can be obtained as shown in Table 4 –7. $$\:\text{I}\text{n}\text{t}\text{e}\text{r}\text{a}\text{c}\text{t}=\:\text{A}\text{c}\text{a}\text{d}\text{e}\text{m}\text{i}\text{c}\:\times\:\text{E}\text{P}\text{U}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:(4-1)$$ $$\:{\text{I}\text{n}\text{n}\text{o}\text{v}\text{a}\text{t}\text{i}\text{o}\text{n}}_{\text{i},\text{t}}={\alpha\:}+{{\beta\:}\text{A}\text{c}\text{a}\text{d}\text{e}\text{m}\text{i}\text{c}}_{\text{i},\text{t}}+\partial\:\text{E}\text{P}\text{U}+{\gamma\:}\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}\text{s}+{{\epsilon\:}}_{\text{i},\text{t}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(4-2\right)$$ $$\:{\text{I}\text{n}\text{n}\text{o}\text{v}\text{a}\text{t}\text{i}\text{o}\text{n}}_{\text{i},\text{t}}={\alpha\:}+{{\beta\:}\text{A}\text{c}\text{a}\text{d}\text{e}\text{m}\text{i}\text{c}}_{\text{i},\text{t}}+\partial\:\text{E}\text{P}\text{U}+{\mu\:}\text{I}\text{n}\text{t}\text{e}\text{r}\text{a}\text{c}\text{t}+{\gamma\:}\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}\text{s}+{{\epsilon\:}}_{\text{i},\text{t}}\:\:\:\:\:\:\:\left(4-3\right)$$ This article uses the "China Economic Policy Uncertainty Index" to measure the uncertainty of economic policies. As the data is monthly, this article takes the arithmetic mean of the monthly data and divides the arithmetic mean by 100 to obtain the annual data. The meaning of this data is that the higher the value, the higher the economic policy uncertainty. Models ( 1 ) to ( 3 ) demonstrate the regression results of the moderating effect of economic policy uncertainty on the research experience and disruptive innovation of executives in power companies. The coefficient of Academy in Model 1 is 1.4071***, and the interaction term in Model 3 is 0.1115**, indicating that economic policy uncertainty positively moderates the research experience of executives in power companies to promote disruptive innovation, confirming research hypothesis H2a. Table 4 6 Considering the moderation effect of economic policy uncertainty Variable Moderation effect model 1 Moderation effect model 2 Moderation effect model 3 Innovation Innovation Innovation Academy 1.4071*** 2.0133*** 2.2040*** (4.9326) (5.3347) (3.8574) EPU 0.2150 0.1088 (0.3430) (1.6208) Interact 0.1115** (2.5688) Control variable YES YES YES Fixed effects of year and industry YES YES YES _cons 0.7879 -1.6576 1.1345 (1.4869) (-0.1495) (0.4281) N 516 516 516 adj. R 2 0.256 0.284 0.054 ( 5 ) Intermediary effect test To verify the correctness of the mediating effect, this article constructs measurement indicators for R&D information disclosure and digital transformation, specifically: using the information disclosure evaluation level of the Shenzhen Stock Exchange website as a proxy variable for R&D information disclosure; Digital transformation draws on the approach of Wu Fei et al., using the frequency of five digital transformation terms in the annual report text of listed companies as a measurement indicator. Using Sobel Goodman's mechanism testing method, the indicators of the two mechanisms were included in the regression model, and the results of the mediation effect test are shown in Table 4 –7. The results indicate that the coefficients of R&D information disclosure and digital transformation are 0.9970* and 0.4742***, both showing significant mediating effects. This indicates that R&D information disclosure and digital transformation are intermediary mechanisms through which the research experience of science and engineering executives in power enterprises affects disruptive innovation. Table 4 7 Results of Mediation Effect Test Variable Model 1 Model 2 Model 3 Innovation Innovation Innovation Academy 1.4071*** 0.3354** 0.3358** (4.9326) (2.3421) (2.3704) Disclosure 0.9970* (1.8544) Transformation 0.4742*** (3.3120) Control variable YES YES YES Fixed effects of year and industry YES YES YES _cons 0.7879 16.1798*** 17.9844*** (1.4869) (3.1270) (3.6872) N 516 516 516 adj. R 2 0.256 0.112 0.128 ( 6 ) Research Conclusion This study uses data from 83 power companies. The data covers 2001 to 2022. It tests research hypotheses through empirical analysis. The main conclusions are as follows.The research experience of power company executives significantly promotes disruptive innovation (coefficient 1.4017***). This conclusion remains valid after robust tests. These tests include replacing variables and adding control variables.The promotion effect of executive research experience is stronger in non-state-owned power enterprises (coefficient 1.5957*** vs. state-owned 1.1339***).In eastern and central regions, there is a significant positive correlation (coefficient 0.8668**, 0.5578**). In western regions, it is not significant. The promotion degree is positively correlated with the level of economic development.The promotion effect is significant in labor-intensive (1.3800**) and capital-intensive (0.1530*) industries. It is not significant in technology-intensive industries.Economic policy uncertainty positively moderates the promoting effect. This is on the link between executive research experience and disruptive innovation (interaction coefficient 0.1115**). This means higher policy uncertainty leads to a stronger driving effect of executive research experience.Executive research experience promotes disruptive innovation through two paths. One is accelerating digital transformation (coefficient 0.4742**). The other is improving R&D information disclosure quality (0.9970*). This confirms the dual intermediary mechanism works. 5. Conclusion and Policy Suggestions ( 1 ) Conclusion This study focuses on the empowering effect of executive research experience on disruptive innovation in power companies. The research found that: ( 1 ) Executive research experience significantly promotes disruptive innovation in enterprises through resource dependence, social networks, and high-level echelons theory, and has a stronger effect in non-state-owned enterprises, the eastern and central regions, and labor-intensive industries. ( 2 ) The research experience of executives in power companies promotes disruptive innovation by accelerating digital transformation and enhancing R&D information disclosure. Specifically, enterprises use R&D information disclosure to alleviate financing constraints, digital transformation breaks through resource allocation boundaries, and ultimately helps enterprises achieve disruptive innovation. ( 3 ) In terms of the nature of corporate property rights, the regression results of both state-owned and non-state-owned power enterprises support the research conclusions, but the promotion effect of scientific research experience of senior executives in non-state-owned power enterprises is stronger; At the regional level, the regression results of the sample of power enterprises in the eastern, central, and western regions are all positively significant, and the degree of promotion is positively correlated with the level of regional economic development; In terms of industry types, labor-intensive and capital intensive industries are significantly promoted, while technology intensive industries are relatively weak. ( 4 ) Economic policy uncertainty plays a positive moderating role, indicating that uncertainty prompts executives with research experience to make cautious decisions and strengthens the driving force for disruptive innovation in power companies. ( 2 ) Policy recommendations 1.Government level ( 1 ) Optimize talent policies and innovation ecosystem layout. The government should formulate and implement a special plan for the introduction of high-level talents, establish an overseas academic talent introduction fund, provide funding subsidies and policy convenience for power enterprises to introduce high-end talents with international scientific research backgrounds, and encourage them to engage in enterprise innovation work. At the same time, establish a platform for talent exchange between industry, academia, and research institutions, regularly organize academic seminars and technical exchange meetings, promote talent flow and knowledge sharing between universities, research institutions, and power enterprises, and broaden the channels for power enterprises to obtain academic talents. ( 2 ) Promote coordinated development of regional innovation. In terms of network infrastructure construction, we will increase investment in the central and western regions, implement the "Central and Western Digital Infrastructure Acceleration Project", improve network coverage and quality, and lower the hardware threshold for digital transformation of power enterprises. Establish an industrial innovation guidance fund for the central and western regions, focusing on supporting the development of local high-precision and cutting-edge industries, and guiding power enterprises to transform towards technology intensive directions. Encourage universities and research institutions in the eastern region to engage in industry university research cooperation with power enterprises in the central and western regions and assist them in achieving disruptive innovation through technology transfer, joint research and development, and other means. ( 3 ) Improve the policy support system. Develop differentiated fiscal preferential policies for the central and western regions, non-state-owned, manufacturing power enterprises, and small and medium-sized enterprises. Implementing tax reduction and exemption plans to lower the corporate income tax rate; Establish special innovation subsidies and provide financial rewards based on enterprise innovation investment and achievements. In the process of formulating economic policies, fully consider the innovation needs of enterprises, enhance the stability and predictability of policies, and help power enterprise executives, especially those with research experience, understand policies through policy interpretation meetings, online Q&A sessions, etc., make accurate decisions in uncertain environments, and promote the innovative development of power enterprises. 2.At the enterprise level ( 1 ) Reform the selection and training mechanism for senior executives in power enterprises. In the executive selection process, a scientific talent evaluation system should be established. In addition to professional skills and management experience, research background, innovative achievements, research potential, etc. should be included in the key assessment scope. Special research background evaluation indicators should be set, such as the quantity and quality of academic papers published, the level and contribution of participation in research projects, etc., to ensure the selection of executive talents with strong innovation driven abilities. In terms of executive training, we collaborate with well-known universities and research institutions to customize personalized academic enhancement courses, covering cutting-edge technologies, innovation management, and other fields. Establish an internal research award fund to encourage executives to conduct academic research related to power enterprise business and enhance their academic and research capabilities. Regularly organize academic exchange activities for executives, invite academic authorities in the industry to share the latest research results and innovative ideas, and promote knowledge collision and experience exchange among executives in power enterprises. ( 2 ) Building an academic oriented culture in the power industry. Advocate the values of academic research and innovative exploration within the power enterprise, promote successful cases of academic achievements being transformed into innovative driving forces through internal publications, bulletin boards, online platforms, and other channels, and create a cultural atmosphere that respects academia and encourages innovation. Establish academic honor awards for power enterprises, publicly recognize outstanding executives and teams in academic research and innovative practice and motivate all employees to actively engage in innovative activities. Carry out cross departmental academic cooperation projects, break down departmental barriers, promote the integration of knowledge from different fields, build a broader platform for senior executives of power enterprises to leverage their academic expertise, and stimulate the overall innovation vitality of the enterprise. ( 3 ) Accelerate the digital transformation and social responsibility fulfillment of power enterprises. Develop a detailed digital transformation strategic plan, establish a special budget for digital transformation, increase investment in technology fields such as artificial intelligence, big data, and cloud computing, promote the full process digital upgrading of power enterprise production, management, marketing, etc., and break through the time and space limitations of resource allocation. Establish a sound system for disclosing R&D information, standardize the disclosure process and content, regularly disclose R&D progress, patent achievements, and other information to investors and the public through channels such as the company's official website and annual reports, enhance corporate information transparency, and alleviate financing constraints. Actively fulfilling social responsibilities, participating in public welfare innovation projects such as environmental protection technology research and development, education technology popularization, etc., enhancing the corporate social image, attracting more high-quality investors, creating a favorable environment for corporate innovation, and fully leveraging the role of senior executives' research experience in promoting disruptive innovation in power enterprises. In addition, strengthen cooperation with digital transformation service providers, introduce professional consulting and technical support, and ensure the smooth progress of digital transformation; Establish an investor relations management team, proactively communicate with investors about the company's innovation strategy and research and development achievements, and enhance investor confidence. Declarations Author Contribution I first sorted out the background and core issues of the research. Considering the fierce global economic competition and rapid technological updates, coupled with the increasingly prosperous development of artificial intelligence and the increasing uncertainty of economic policies, I am thinking of exploring how the research experience of executives in power companies utilizes artificial intelligence to achieve disruptive innovation, as well as the characteristics of the impact path in this process. These issues are actually quite related to how power companies choose strategies and whether they can sustain development in complex environments.Then, I consulted a lot of relevant literature. After reviewing the current research on executive research experience and disruptive innovation in enterprises, it is found that there are still shortcomings in some aspects, such as insufficient exploration of heterogeneity, the impact of R&D information disclosure and digital transformation on executive teams, and the relationship between executive research experience and industry disruptive innovation. The research is not thorough enough. This also helped me find the starting point for my research and clarify where I could create something new.Then, based on theories such as resource dependence, social networks, and upper echelons, I constructed a theoretical framework and proposed some research hypotheses. I believe that the research experience of executives may promote disruptive innovation in enterprises, so I proposed hypothesis H1. Regarding the moderating effect of economic policy uncertainty, I considered both the possibility of positive regulation and the possibility of negative regulation, resulting in H2a and H2b; In addition, I have also considered the mediating role that R&D information disclosure and enterprise digital transformation may play, and have constructed corresponding theoretical models.In terms of research design, I selected 83 listed power companies in China from 2001 to 2022 as samples. I referred to some existing methods to measure variables such as executive research experience, disruptive innovation, and economic policy uncertainty, and also constructed regression models. Afterwards, the data was processed, such as matching data, deleting data with many missing values, and performing Winsorize processing, resulting in 516 consecutive observations.I did a lot of work during the empirical analysis. Firstly, benchmark regression was used to verify that executive research experience can significantly promote disruptive innovation; Then a heterogeneity test was conducted to see if there were differences in the promoting effect among enterprises with different property rights, regions, and industry types; In order to make the research conclusions more reliable, I also conducted robustness tests, such as replacing variables and adding control variables; In addition, I have also studied the moderating effects of economic policy uncertainty, as well as the mediating effects of research and development information disclosure and digital transformation, and have obtained some significant results.Finally, I summarized the research findings and provided some policy recommendations from both the government and enterprise perspectives. 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Zhou Kaitang, Ma Zhiming, Wu Liansheng. Academic experience of executives and the cost of corporate debt financing [J]. Economic Research, 2017, 52 (07): 169-183. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7069434","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505360978,"identity":"f6a751bf-0d80-4e7e-88b3-4d3d16d67ac1","order_by":0,"name":"Cui Aiping","email":"","orcid":"","institution":"Jiangxi University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Cui","middleName":"","lastName":"Aiping","suffix":""},{"id":505360979,"identity":"5df80546-024a-43e2-86c3-dece02832549","order_by":1,"name":"Zeng Bing","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIie3RLQsCMRjA8YmwK8M8OJmfQHhkoAiH+lH2ICxdMBpPDryi3XAfQhDEODjQMvvVKyaLzWDQqOlmE9y//+B5IcTn+8FokBnARyTGp9S4kRazalYlWhJ7VG5E8AncqqTApIzBcTBOcIsHLRsreyuvZCS6SR0JFwWgjUQzWO+GOZnKvqkjbaNBUS0pO+9DRgzuawlXg7uiBa54fHEmALgscMNj6kheRwZltQR2lMMcHHbpZJnp3eeRgCCtyut8JGrJR5w5vuadfCt8Pp/vL3oC78VF9m7pp98AAAAASUVORK5CYII=","orcid":"","institution":"Jiangxi University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Zeng","middleName":"","lastName":"Bing","suffix":""},{"id":505360980,"identity":"a86d6f60-ac93-4c9a-8919-f3f3c8e5e819","order_by":2,"name":"Yi Ruowen","email":"","orcid":"","institution":"East China Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Ruowen","suffix":""}],"badges":[],"createdAt":"2025-07-08 01:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7069434/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7069434/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89890592,"identity":"71ccc326-942b-4ab2-ba90-d8a96e12065c","added_by":"auto","created_at":"2025-08-26 07:28:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60963,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2-1 Research Hypothesis Model\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7069434/v1/777bcb4ac99fd2c7aefe19f9.png"},{"id":100994981,"identity":"83cb9a93-b3ec-45de-b7a6-bf71e756bf06","added_by":"auto","created_at":"2026-01-23 15:12:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1368370,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7069434/v1/b54b4850-5609-455f-a088-d3f5193955d2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on the Evolutionary Mechanism of Disruptive Innovation in Electric Power Enterprises Driven by Artificial Intelligence - Based on the Adjustment Effect of Economic Policy Uncertainty","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the current era of intense global economic competition,New tech is growing fast. For companies, the ability to make new things is key. It decides if they can stay and grow in the market.The 20th National Congress of the Communist Party of China made a report. It says companies should lead more in new tech and science. This shows clearly that company innovation is very important. It helps the country\u0026rsquo;s economy grow.In the diversified innovation path of enterprises, disruptive innovation can help enterprises open new market tracks, reshape the competitive landscape, and become a strong core driving force for enterprise innovation and development.\u003c/p\u003e\u003cp\u003eThe disruptive innovation achievements of enterprises are closely related to the decision-making and leadership of the executive team. Taking the power industry as an example, among the many award-winning projects of the 2022 China Electric Power Science and Technology Award, a considerable number of first prize projects were led by executives of power companies. For example, Liu Zhenya, the former general manager of State Grid Corporation of China, played a key role in the development of ultra-high voltage power grid technology. In 1994, Wuhan High Voltage Research Institute built China's first million-volt level transmission research line segment. At that time, the prospects of this technology were unclear, but Liu Zhenya elevated the technology of ultra-high voltage power grid to the strategic level of State Grid Corporation of China. Through continuous investment and research and development, significant breakthroughs have been made in ultra-high voltage power grid technology and widely applied, changing the pattern of power transmission in China, achieving long-distance and high-capacity transmission, greatly improving the transmission capacity and efficiency of the power grid, and effectively promoting the optimization of energy resource allocation in China. Liu Zhenya graduated from the School of Electrical Engineering at Shandong University and has in-depth research and rich experience in the fields of power system operation, planning, and management. His forward-looking strategic decisions have played an irreplaceable leading role in the birth and promotion of the disruptive innovation of ultra-high voltage technology. This inevitably prompts deep reflection: Against the backdrop of the booming development of artificial intelligence and increasing economic policy uncertainty, how will the research experience of executives in power companies use artificial intelligence to achieve disruptive innovation? What are the characteristics of its impact path? This issue not only concerns the strategic choices of power enterprises in complex environments but also has important practical significance for them to seize innovation opportunities and achieve sustainable development.\u003c/p\u003e\u003cp\u003eAt present, literature on the research experience and disruptive innovation of corporate executives mainly focuses on keywords such as \"dual innovation\", \"technological innovation\", \"absorptive capacity\", and \"innovation performance\". However, the breadth and depth of research in this field is insufficient, and there is still ample room for expansion. Reviewing existing research, the impact of disruptive innovation is mainly analyzed from two dimensions: internal and external factors. Internal factors include digital transformation, knowledge management, industry university research cooperation, human resources systems, etc. (Kou Mingting et al., 2024; Li Yuhua et al., 2024), aiming to explore the internal path for enterprises to achieve disruptive innovation; External factors mainly consider environmental uncertainty, market analysis, government policies, financial environment, organizational distance, etc. (Li Zhengwei et al., 2023; Shan Wei et al., 2025), with a focus on exploring the direct or moderating effects of complex external environments on disruptive innovation in enterprises. At the level of research experience of executives in power companies, there is currently no direct literature exploring the impact of executive research experience on disruptive innovation in power companies, which provides a starting point for this study. Overall, research on the research experience of executives in power companies mainly focuses on the internal and external influences of the company. Internally, the research experience of senior executives in power companies can have an impact on corporate innovation, dual innovation, digital transformation, R\u0026amp;D disclosure and manipulation, debt financing, etc. (Yangzhen et al., 2022; Yuan Zemin et al., 2020); Externally, it mainly revolves around the spillover effects of factors such as corporate social responsibility, information disclosure quality, and external correlations (Dai Lu et al., 2023; Wang Zhenshan et al., 2023).\u003c/p\u003e\u003cp\u003eAfter comprehensively reviewing the current research results, there are still many areas that need to be improved. Firstly, existing research lacks in-depth exploration of heterogeneity. Disruptive innovation varies significantly depending on the industry, region, and nature of the enterprise, and research conclusions may differ greatly in different contexts. Secondly, although there have been studies focusing on the impact of corporate R\u0026amp;D information disclosure and digital transformation on innovation, the influence of R\u0026amp;D information disclosure and digital transformation by executive teams in power companies, as well as their relationship with disruptive innovation, has not been fully explored. Furthermore, there is currently a lack of research on the relationship between the research experience of executives in power companies and disruptive innovation in the industry. Although there have been studies on the research experience of executives in power companies and their dual innovation, dual innovation includes disruptive innovation and incremental innovation, and the impact of executives' research experience on dual innovation cannot be simply applied to disruptive innovation.\u003c/p\u003e\u003cp\u003eTherefore, this article will clearly define the impact of research experience of executives in power companies on both, filling this research gap. In addition, there is no consensus on the promoting or inhibiting effects of economic policy uncertainty on corporate innovation, which is also one of the key research contents of this article. The innovation of this article is as follows: First, it is the first to focus on the direct link between the research experience of power company executives and disruptive innovation. It breaks the limits of existing studies on dual innovation.Second, it builds a two-part middle path. The path is \"digital transformation and R\u0026amp;D information sharing\". It shows the inner way that executive research experience affects disruptive innovation.Third, it confirms that economic policy uncertainty has a positive moderating effect on their relationship. It fills the research gap in macroeconomic environment regulation mechanisms.At the theoretical level, this article does several things. It extends the use of upper echelon theory in the power industry. It makes clear the driving path of disruptive innovation from executive research experience. It adds to studies on the non-linear relationship of economic policy uncertainty's moderating effect. It offers new evidence for the theoretical system in this area.In practice, it provides references. These are for power companies to choose executives and make digital transformation plans. It helps them get better at disruptive innovation in complex situations. It also pushes the energy industry to make tech changes and develop sustainably.\u003c/p\u003e"},{"header":"2. Theoretical analysis and research hypotheses","content":"\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003cb\u003eResearch Experience of Senior Executives in Electric Power Enterprises and Disruptive Innovation in Enterprises\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe research and development innovation of power enterprises is closely related to the decision-making of the executive team. The executive team not only determines the direction of enterprise innovation but also ensures the smooth progress of the entire innovation process. Since the 20th century, with the progress of society, the popularization of higher education, and the development needs of power enterprises, more and more senior executives with research experience have moved towards the core management of the enterprise. Electric power company executives with certain research experience have more rigorous thinking logic and broader development vision in enterprise innovation decision-making. Disruptive innovation in power enterprises has been a hot topic in recent years, and many companies have achieved significant breakthroughs in key technologies to overtake their peers in the industry. Disruptive innovation has higher requirements for the quality and effectiveness of innovation compared to incremental innovation and faces more serious financing constraints in the implementation process of innovation activities (Dai et al., 2024). At the same time, enterprise disruptive innovation has a longer R\u0026amp;D cycle, higher R\u0026amp;D investment, and relatively greater R\u0026amp;D risks compared to general innovation. Huang Can et al. (2019) found that all academic experiences of executives can directly promote innovation in enterprises. In environments where the enterprise has a good innovation atmosphere and a low proportion of state-owned equity, this promoting effect is more prominent. They pointed out that the academic background of executives can also improve the information environment of the enterprise, reduce information asymmetry, attract more analysts' attention, and indirectly promote innovation in the enterprise. Yuan Zemin et al. (2020) explored from another perspective the role of executives' academic backgrounds in inhibiting corporate R\u0026amp;D manipulation behavior. The study showed that executives with academic backgrounds can effectively curb R\u0026amp;D manipulation behavior in enterprises, and their role is more prominent in situations where tax collection and management are weak or internal controls are weak. However, Yang Junxiao et al. (2021) proposed that the academic background of executives has a \"double-edged sword\" effect on corporate innovation. They found that although academic executives can enhance the overall innovation capability of enterprises, the positive innovation effect generated by academic background will actually decrease when executives are in key positions. As the proportion of academic executives increases, the innovation effect will gradually weaken.Kang He et al. (2021) studied how executives\u0026rsquo; academic backgrounds affect corporate green innovation. They found that executives with academic backgrounds strongly drive green innovation. Executives in key positions see their academic backgrounds play a more notable role. Their academic experience helps companies respond better and innovate more when facing green technology challenges. This role in promoting green innovation shows their critical part in disruptive innovation.In industries needing new technologies to solve environmental problems, Cai, Chunhua et al. (2024) used imprint theory. They found that executives\u0026rsquo; academic backgrounds positively affect corporate digital transformation. Academic executives can drive enterprise digital transformation by increasing innovation investment. This is especially true in the context of fierce industry competition.Overall, the role of executives with academic backgrounds in enterprise innovation and disruptive innovation cannot be ignored.\u003c/p\u003e\u003cp\u003eBy enhancing the innovative atmosphere of enterprises, reducing R\u0026amp;D manipulation, promoting green innovation, and driving digital transformation, academic executives inject new impetus into the innovation capabilities of enterprises. However, having too many academic background executives or executives in key positions may have a negative impact on innovation effects. When companies use the academic background of executives to promote innovation, they need to comprehensively consider the proportion of executives' backgrounds and job arrangements to maximize their role in driving disruptive innovation.\u003c/p\u003e\u003cp\u003eBased on relevant theories and existing literature, this article summarizes the influencing factors of the research experience of senior executives in power companies on disruptive innovation as follows: Firstly, starting from the theory of resource dependence, the social capital and human capital accumulated by executives in the scientific research process of power enterprises are closely related to the disruptive innovation of the enterprise, greatly ensuring the talent and technology for achieving disruptive innovation in power enterprises. Dual social capital and informal personal connections between schools and enterprises will promote the external learning ability of power enterprises, gather more talent resources, and positively promote the realization of disruptive innovation (Liang Minxin et al., 2023); At the same time, research experience will bring redundancy to organizational resources, which will help power companies adapt to the uncertainty of disruptive innovation processes, reduce internal strife and conflicts, alleviate resource shortages, and focus more organizational resources on the implementation of disruptive innovation (Shi Lei et al., 2025).\u003c/p\u003e\u003cp\u003eSecondly, social network theory suggests that in terms of capability, knowledge transfer and sharing within the network are beneficial for innovation activities (Wu L et al., 2025), and diverse experiences help company leaders develop the ability to learn and exchange knowledge across boundaries. This ability will promote the organizational absorption of disruptive innovation theory knowledge in power companies and solve potential challenges that may arise during the innovation process (Xiaoshan J et al., 2023); Huang Bingyi et al., 2023). The scientific research experience of executives in power companies can effectively alleviate the short-sighted phenomenon of management, while disruptive innovation, due to its particularity, requires managers to have a certain international perspective and stay at the forefront of academia. Based on this, the scientific research experience of executives in power companies can promote the realization of disruptive innovation in the enterprise from a theoretical knowledge level, ensuring industry leadership in research and development.\u003c/p\u003e\u003cp\u003eThirdly, the theory of upper echelons suggests that when faced with complex and ever-changing internal and external environments that prevent decision-making, executive teams typically rely on long-term concepts and values related to innovation (Ewa F G et al., 2025). Based on transaction cost theory, academic research experience plays an important role in alleviating information asymmetry within and outside power enterprises, and helps to timely avoid risks in the research and development process (Wang Z et al., 2024); In addition, the research experience of executives in power companies will effectively reduce the company's debt financing costs, alleviate the difficulty of financing in achieving disruptive innovation, and thus allocate more funds to research and development investment (Li S et al., 2024); At the same time, the shaping and tempering of scientific research experience enables executives of power companies to have high moral standards and social responsibility awareness in the process of carrying out disruptive innovation, making them more likely to disclose R\u0026amp;D during the R\u0026amp;D process, thereby improving the efficiency of disruptive innovation production in the enterprise.\u003c/p\u003e\u003cp\u003eBased on this, this article proposes the following research hypotheses:\u003c/p\u003e\u003cp\u003eH1: Research experience of executives in power companies can promote disruptive innovation in the enterprise.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003cb\u003eThe moderating effect of economic policy uncertainty\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe process of disruptive innovation is often accompanied by high risks and uncertainties, and the risks arising from the dynamic nature of the environment place higher demands on executives when making decisions. In an environment of economic policy uncertainty, corporate innovation is significantly negatively affected, especially disruptive innovation with higher levels of innovation (Zhang Dongyu and Wu Peng, 2024). Disruptive innovation is more difficult to achieve compared to incremental innovation, not only because of the high risks and costs involved in the research and development process of disruptive innovation, but also because of various uncertain macro factors. Huo Yuan et al. found that there is a \"U-shaped\" nonlinear relationship between economic policy uncertainty and the sustainability of corporate innovation, with R\u0026amp;D investment playing a partial mediating role. They also proposed policy recommendations that the government should carefully grasp the scale of policy regulation when adopting macroeconomic policies to regulate the economy (Shan Wei et al., 2025). Li Enji et al. (2022) found that when the economic policy index rises, two different types of corporate investment behaviors will occur. However, contrary to the above viewpoint, some scholars have found that when faced with increased economic policy uncertainty, some speculators among executives of power companies often realize that it is both a risk and an opportunity (Li F et al., 2022; Li Y and Tu X, 2021; Zhao X, 2021), therefore willing to take on certain risks and increase investment in innovation, hoping to obtain excess profits (Chen Ziwei and Gao Jiameng, 2024; Sun Chuanwang et al., 2024).\u003c/p\u003e\u003cp\u003eBased on this, does the increase in economic policy uncertainty positively regulate the disruptive innovation path of power company executives influenced by their research experience? The increasing uncertainty about economic policies has made market forecasting more difficult. Electric power company executives will be more cautious in making decisions about the main direction of the company and will be more inclined to maintain existing customers rather than increase research and development investment. Financial decisions of the company will also deviate, and disruptive innovation paths will be affected; Accordingly, from an institutional perspective, the increase in economic policy uncertainty has led to a mismatch between existing mechanisms and the actual environment of enterprises, as well as a mismatch between existing mechanisms and the needs for achieving disruptive innovation in enterprises, resulting in obstacles to disruptive innovation in enterprises.\u003c/p\u003e\u003cp\u003eIn response to the above questions, this article proposes the following assumptions:\u003c/p\u003e\u003cp\u003eH2a: Economic policy uncertainty will positively regulate the role of research experience of executives in power companies in promoting disruptive innovation.\u003c/p\u003e\u003cp\u003eH2b: Economic policy uncertainty will negatively regulate the role of research experience of executives in power companies in promoting disruptive innovation.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003cb\u003eThe mediating effect of R\u0026amp;D information disclosure and enterprise digital transformation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe imprinting theory suggests that individual level imprints have long-term persistence once they exceed the sensitive period (Stankowska A and Niedziolka D, 2021; Vaisman E D et al., 2021). It can be inferred that the beliefs, behaviors, and orientations of executives in power companies during their research experience will profoundly influence the strategic decision-making process in the operation of the enterprise. During the forging process of scientific research experience, executives of power companies are influenced by various mechanisms, gradually forming a clear understanding of the importance of innovative mechanisms. Scientific research experience has laid the \"roots\" for executives of power companies to optimize the driving force of enterprise innovation.\u003c/p\u003e\u003cp\u003eThe research experience of executives in power companies has a subtle influence on internal and external decision-making. For internal decision-making in power companies, executives with some research experience speed up the company\u0026rsquo;s digital transformation. This helps achieve industrial transformation and changes in management ideas.Tang Chunyong et al. (2022) analyzed the internal mechanism. They studied how senior executives\u0026rsquo; research experience in power companies affects digitalization. It shows in three ways. These are easing financing limits, strengthening the strategic focus on corporate social responsibility, and getting government resource subsidies.For external decision-making in power companies, research experience often makes executives more steady and careful in business processes. This reduces possible R\u0026amp;D manipulation in the company\u0026rsquo;s innovation process (Chen Y et al., 2021). It also makes them more aware of reducing risks and improving accounting conservatism. This significantly raises the quality of the company\u0026rsquo;s information disclosure.In daily operations of power companies, executives with research backgrounds often make more forward-looking decisions. They can better avoid short-sighted actions (Yuan Zemin et al., 2020). This improves the company\u0026rsquo;s overall innovation efficiency.Executives\u0026rsquo; research experience in power companies lets them better understand the industry\u0026rsquo;s latest R\u0026amp;D knowledge. This builds their ability to predict market trends. They can then deal with the impact of market changes. They can correct mistakes in time based on innovation progress. They can also push new innovation breakthroughs in power companies.Based on this, this article studies the impact. It looks at how senior executives\u0026rsquo; research experience affects disruptive innovation in power companies. It does this through two paths. These are speeding up the companies\u0026rsquo; digital transformation and improving their R\u0026amp;D information disclosure.\u003c/p\u003e\n\u003ch3\u003e1. Mediating variable one: R\u0026D information disclosure\u003c/h3\u003e\n\u003cp\u003eInformation disclosure refers to the act of a company disclosing various types of information, such as its operating status, financial situation, strategic decisions, and research and development activities, to external stakeholders. Information disclosure can improve the transparency of the company and establish trust between the company and investors, customers, and other stakeholders. In companies driven by innovation, R\u0026amp;D information disclosure can convey the company's innovation intentions and capabilities to the market and provide corresponding support for obtaining external resources. Related studies have shown that the research background of corporate executives can have an impact on the innovation behavior of enterprises, particularly in promoting disruptive innovation. Research and development information disclosure, by reducing information asymmetry, attracting external attention, and resource support, promotes the innovation capability of enterprises.\u003c/p\u003e\u003cp\u003eZhang Xiaoliang et al. (2019) conducted a study based on the theory of high-level echelons and pointed out that the academic experience of CEOs can improve the innovation level of enterprises. CEOs with academic experience are more inclined to use the combination of industry, academia, and research to enhance their innovation level. The most crucial aspect of this process is to improve the disclosure of R\u0026amp;D information, so that enterprises can present their innovation capabilities to the outside world, attract more resources and cooperation opportunities. Research and development information disclosure plays a key mediating role in the promotion of innovation by executives' research backgrounds. Chen Xingyu et al. (2022) found that scholar CEOs can reduce stock price synchronicity and improve the level of voluntary research and development information disclosure, which is a keyway to generate this inhibitory effect. By improving the level of research and development information disclosure, scholar CEOs can reduce external market doubts and uncertainties about their R\u0026amp;D activities, effectively promoting innovation in enterprises. In technology intensive enterprises, R\u0026amp;D information disclosure is crucial. Fu Chen et al. (2023) pointed out that the personal traits and leadership style of the CEO determine the company's response to disruptive innovation. The CEO can rely on increasing R\u0026amp;D information disclosure, reducing external information asymmetry, and promoting the implementation of disruptive innovation.Disclosing R\u0026amp;D information gives external support to corporate innovation. It can also show the company\u0026rsquo;s innovation abilities and potential to investors and the market. This helps the company get more resources. It also boosts the degree and scope of innovation.Yuanxing Wan et al. (2025) did a study. They found a close link between the recognition of high-tech enterprises and R\u0026amp;D information disclosure. When there is high demand for information, real high-tech enterprises will disclose more R\u0026amp;D information. But fake high-tech enterprises may hide their innovation level. They do this by manipulating accounting items and business activities.\u003c/p\u003e\u003cp\u003eIn short, the research experience possessed by executives in power companies plays a significant role in driving disruptive innovation. R\u0026amp;D information disclosure plays an intermediary role in this process. If companies increase their R\u0026amp;D information disclosure, it can improve external trust in their innovation capabilities and obtain more resources and cooperation opportunities to promote disruptive innovation. However, companies need to pay attention to the quality and transparency of information disclosure, which will directly affect their innovation effectiveness. How to effectively improve the level of R\u0026amp;D information disclosure has become a key issue in their innovation strategy.\u003c/p\u003e\n\u003ch3\u003e2. Mediating variable 2: Enterprise digital transformation\u003c/h3\u003e\n\u003cp\u003eEnterprise digital transformation means making deep changes. It upgrades products, services, management and business models. It uses digital technologies. These include big data, cloud computing and artificial intelligence. It aims to improve operational efficiency. It also aims to boost innovation capabilities and market competitiveness.Information technology is developing fast. Digital transformation has become a key way. It promotes enterprise innovation. It also promotes disruptive innovation.\u003c/p\u003e\u003cp\u003eThe research experience possessed by corporate executives enables them to have stronger innovation awareness, systematic thinking, and the ability to solve complex problems, which can lead companies to carry out disruptive innovation more effectively. The research background of executives will have an impact on corporate strategic decision-making and promote digital transformation, indirectly promoting the implementation of disruptive innovation.\u003c/p\u003e\u003cp\u003eDigital transformation is a new method. It helps power companies break time and space limits in R\u0026amp;D resource allocation. It enables them to achieve disruptive innovation.Digital transformation can greatly improve the environmental and social performance of power enterprises (Gao Yuan et al., 2024). It stimulates innovation vitality. It also improves innovation performance (Li Zhiguo et al., 2024).Digital transformation helps power companies balance economic and social benefits. It also eases financing difficulties in R\u0026amp;D processes.Sun Jian et al. (2024) found some things. Digital transformation can effectively raise enterprise innovation levels. It eases financing constraints. It also enhances corporate social responsibility.Ge Pengfei and Huang Xiulu (2024) analyzed something. Digital transformation mainly promotes enterprise integration and innovation. It does this by increasing the flow of innovative knowledge. It also expands the diversity of such knowledge.Liu Yexin and Wu Weiwei (2024) researched and showed something. CEOs\u0026rsquo; academic experience drives enterprise digital transformation. It also improves their innovation capabilities. Specifically, CEOs with academic backgrounds can deeply understand the importance of digital transformation for enterprise development. They have strong logical thinking and problem-solving abilities. This helps promote changes in technology upgrades and business model innovation. CEOs\u0026rsquo; academic experience promotes digital transformation. This helps enterprises achieve disruptive innovation.Zheng Minggui et al. (2025) put forward a view. Differences in executive teams, such as research backgrounds, can enhance a company\u0026rsquo;s risk-taking ability. They promote innovation by driving digital transformation.Wei Yanjie et al. (2023) also researched and indicated something. Enterprise digital transformation relies not only on technology investment. It also relies on increasing R\u0026amp;D investment and improving strategic flexibility. CEOs\u0026rsquo; social capital and research backgrounds work together. They increase R\u0026amp;D investment. They strengthen the flexibility of strategic decision-making. This promotes enterprise digital transformation.In the transformation process, digital technology application directly promotes enterprise disruptive innovation. It allows enterprises to quickly respond to market changes. It helps them launch innovative products or services.Corporate executives\u0026rsquo; research experience has a key impact on enterprise development. It can directly enhance innovation capabilities. It promotes enterprise disruptive innovation.In the process where executives\u0026rsquo; research experience promotes enterprise disruptive innovation, enterprise digital transformation plays a key intermediary role.\u003c/p\u003e\u003cp\u003eDigital transformation helps enterprises achieve breakthroughs in technology and models, creating a good soil for disruptive innovation. When selecting and cultivating executives, companies need to pay attention to their research background, promote digital transformation, and lead the company towards the forefront of innovation to gain competitive advantages. Dai Lu et al. (2023) also pointed out the role of executives' academic background in corporate innovation. In terms of deep integration of industry, academia, and research, executives' research background can enhance the level of cooperation between enterprises and research institutes, drive technological innovation. Similarly, digital transformation of enterprises can also rely on strengthening the connection with external technological resources to help enterprises achieve disruptive innovation.\u003c/p\u003e\u003cp\u003eBased on the above analysis, this article constructs a research hypothesis model as shown in the following figure:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"3. Research Design","content":"\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003cb\u003eIndicator measurement\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis article uses Zhou Kaitang\u0026rsquo;s method. It measures executives\u0026rsquo; research experience by counting the number of executives in power companies\u0026rsquo; executive teams with such experience.For disruptive innovation, this article uses Kong Dongmin et al.\u0026rsquo;s approach. It calculates the measure of disruptive innovation in power enterprises. It adds the natural logarithms of objective weights. The weights are 0.5 for invention patents, 0.3 for utility model patents, and 0.2 for design patents.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003cb\u003eResearch Model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo verify the impact of research experience of executives in power companies on disruptive innovation, this paper refers to previous scholars' relevant research and constructs the following regression model (such as 3\u0026thinsp;\u0026minus;\u0026thinsp;1): where the dependent variable Innovation\u003csub\u003ei, t\u003c/sub\u003e is the disruptive innovation output of company i in year t, measured by the number of authorized invention patents of power companies; The explanatory variable Academy\u003csub\u003ei, t\u003c/sub\u003e represents the number of executives in the power company i in year t.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\text{I}\\text{n}\\text{n}\\text{o}\\text{v}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}}_{\\text{i},\\text{t}}={\\alpha\\:}+{{\\beta\\:}\\text{A}\\text{c}\\text{a}\\text{d}\\text{e}\\text{m}\\text{y}}_{\\text{i},\\text{t}}+{\\gamma\\:}\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}\\text{s}+{{\\epsilon\\:}}_{\\text{i},\\text{t}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(3-1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003cb\u003eData sources and descriptive statistics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe data source of this article is the CSMAR database, which includes research background data of executives from 83 listed power companies in China from 2001 to 2022, as well as the cumulative number of patent authorizations as of the end of the reporting period. At the enterprise level, control variables such as company establishment year (Firm age), company size (Size), board size (Board), dual role (Dual), company nature (Soe), number of employee shares (M share), return on assets (Roe), and asset liability ratio (Lev) were selected. For the above data, this article matched the data based on securities codes, deleted the data with many missing values for the main variables, and performed Winsorize processing on the first and last 1% of continuous variables, finally obtaining 516 continuous observations. The descriptive statistical results of the main variables are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1.\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1 Descriptive Statistical Results\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(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003esd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003emin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emax\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,440\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\u003cp\u003e5,331\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\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.004\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\u003cp\u003e24\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\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.32\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\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEPU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e167.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e111.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e439.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.695\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\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.695\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\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.962\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\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.433\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\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.484\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\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM share\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6,115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,278\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\u003cp\u003e21,700\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12,586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7,592\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\u003cp\u003e25,487\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFix\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27,772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16,031\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\u003cp\u003e55,503\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLEV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27,765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16,022\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\u003cp\u003e55,505\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRoe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27,599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15,757\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\u003cp\u003e51,434\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\u003eFrom the table, the means of disruptive innovation in power enterprises is 0.43, the standard deviation is 0.79, and the maximum value is 4.19. The data indicates that most power enterprises have weak disruptive innovation capabilities, and there are significant differences in disruptive innovation achievements among different enterprises. The average academic research experience of executives in power companies is 0.04, with a standard deviation of 0.12, indicating that it is common for most power companies to hire people with relevant research backgrounds as executives. The distribution of other control variables can also be seen from the table, and they are all within a reasonable range.\u003c/p\u003e"},{"header":"4. Empirical analysis","content":"\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003cb\u003eBenchmark regression\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe benchmark regression results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1. The research experience of executives in power companies is positively correlated with disruptive innovation at a 1% significance level before and after controlling for variables, years of participation, and industry fixed effects. This indicates that the research experience of executives in power companies significantly promotes disruptive innovation. Specifically, after incorporating enterprise control variables, year, and industry fixed effects, the coefficient of research experience (Academy) for executives in power companies is 1.4017***, which preliminarily confirms the hypothesis of H1.\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1 Results of Principal Regression Analysis\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\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.2496***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0932***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4071***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(7.9619)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(7.5910)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(4.9326)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variable\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\u003eFixed effects of year and industry\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\u003eNO\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\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4441***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7879\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3.0293)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.3016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.4869)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eadj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: ***, **, and * represent significance levels of 1%, 5%, and 10%, respectively. The values in parentheses are the standard errors clustered to the industry year level. The same applies to the following tables.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003cb\u003eHeterogeneity test\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003e1. Nature of Enterprise Property Rights\u003c/h3\u003e\n\u003cp\u003eProperty rights determine a company\u0026rsquo;s equity concentration and investment methods.We did regression analysis on state-owned and non-state-owned power enterprises. The results are in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;2.The results showed some numbers. The regression coefficient for state-owned power enterprises was 1.1339***. The one for non-state-owned ones was 1.5957***.This means property rights do not affect the research conclusions of this paper. But the coefficient for state-owned power enterprises is smaller than that for non-state-owned ones. This shows that senior executives\u0026rsquo; research experience in power enterprises can better promote disruptive innovation in non-state-owned enterprises.\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026thinsp;\u0026minus;\u0026thinsp;2 Regression Results of State-owned Power Enterprises and Non-state-owned Power Enterprises\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\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eState-owned power enterprises\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-state-owned power enterprises\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1339**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.5957***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.4239)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.8266)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variable\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\u003eFixed effects of year and industry\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\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-4.6084***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-3.8834)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0824)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e223\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eadj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.186\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e2.Different regions where the enterprise is located\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;3 shows regression results for enterprises in different regions.The results tell us something. Sample power companies in eastern and central regions have positive and significant regression results. But the western region has a negative regression coefficient.In terms of coefficients, the positive promotion effect of executives\u0026rsquo; research experience on disruptive innovation in power companies is weaker. It decreases from eastern to central to western regions. This matches the decreasing level of economic development in these three regions.This indicates that the location of power companies in different regions will affect the promotion effect of scientific research experience of executives in science and engineering on disruptive innovation.\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\u003e\u0026thinsp;\u0026minus;\u0026thinsp;3 Regression Results of Electric Power Enterprises in Different Regions\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\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEastern region\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCentral region\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWestern region\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.8668**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5578**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0512\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.1332)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.2536)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-0.0923)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variable\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\u003eFixed effects of year and industry\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\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-32.3493**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.9706**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.3294)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-2.7522)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.0880)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eadj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e3.Different types of industries\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u0026lt;link rid=\"tb8\"\u0026gt;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e4\u0026lt;/link\u0026gt;\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveals the differences in research conclusions among different types of industries. In the regression model where the main industries of power enterprises are labor-intensive and capital intensive, the coefficients of Academy are 1.3800** and 0.1530**, respectively, which is consistent with the conclusion of this study. However, in technology intensive regression models, the regression coefficients are 0.2895, indicating an insignificant state. This indicates that the research experience of executives in power companies significantly promotes disruptive innovation in labor-intensive and capital-intensive industries but has a weaker positive effect on technology intensive industries.\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e4 Regression Results Based on Different Industry Types\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\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLabor intensive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTechnology intensive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCapital intensive\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3800**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1530*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.1251)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.0103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.9263)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variable\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\u003eFixed effects of year and industry\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\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.8886**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.0458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.0485***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.3358)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.0455)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(4.2335)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e297\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eadj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.012\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\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003cb\u003eRobustness test\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003e1.Replace the independent and dependent variables\u003c/h3\u003e\n\u003cp\u003eTo check if the research conclusions here are robust, we replaced the independent and dependent variables in the main regression model. We then did regression tests. The results appear as Model 1 and Model 2 in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;5.For the variable of senior executives\u0026rsquo; research experience in power companies, we used Zhou Kaitang et al.\u0026rsquo;s research. We set a standard: whether the company has executives with research experience in the current year. We gave a value of 1 if there are such executives, 0 otherwise.We used a substitute variable for enterprise disruptive innovation. It is the natural logarithm of the number of invention patents power companies get in the current year plus one.The results show that the research conclusion of this article still holds true.\u003c/p\u003e\n\u003ch3\u003e2.Add control variables\u003c/h3\u003e\n\u003cp\u003eDrawing on the research of Sun Jian et al., to avoid missing variables, this study added Tobin's Q value as a control variable and tested it on behalf of the main regression model. The regression results are shown in Model 3, which indicates that after adding Tobin's Q value, the conclusions of this study remain robust.\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e5 Regression Results of Robustness 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\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.6244***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.8334***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.8330***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(5.2336)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(5.9651)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(5.9583)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variable\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\u003eFixed effects of year and industry\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\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.4225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0267\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-0.5171)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.1842)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-0.0210)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eadj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.429\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\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003cb\u003eResearch on the moderation effect of economic policy uncertainty\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis article also constructs a model of the moderating effect of economic policy uncertainty (EPU) on the promotion of disruptive innovation in electric power company executives through their research experience. The interaction term is specific to formula 4\u0026thinsp;\u0026minus;\u0026thinsp;1 and is included in the benchmark regression model, specifically formula 4\u0026thinsp;\u0026minus;\u0026thinsp;2 and formula 4\u0026thinsp;\u0026minus;\u0026thinsp;3. The results can be obtained as shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;7.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{I}\\text{n}\\text{t}\\text{e}\\text{r}\\text{a}\\text{c}\\text{t}=\\:\\text{A}\\text{c}\\text{a}\\text{d}\\text{e}\\text{m}\\text{i}\\text{c}\\:\\times\\:\\text{E}\\text{P}\\text{U}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:(4-1)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{\\text{I}\\text{n}\\text{n}\\text{o}\\text{v}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}}_{\\text{i},\\text{t}}={\\alpha\\:}+{{\\beta\\:}\\text{A}\\text{c}\\text{a}\\text{d}\\text{e}\\text{m}\\text{i}\\text{c}}_{\\text{i},\\text{t}}+\\partial\\:\\text{E}\\text{P}\\text{U}+{\\gamma\\:}\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}\\text{s}+{{\\epsilon\\:}}_{\\text{i},\\text{t}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(4-2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{\\text{I}\\text{n}\\text{n}\\text{o}\\text{v}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}}_{\\text{i},\\text{t}}={\\alpha\\:}+{{\\beta\\:}\\text{A}\\text{c}\\text{a}\\text{d}\\text{e}\\text{m}\\text{i}\\text{c}}_{\\text{i},\\text{t}}+\\partial\\:\\text{E}\\text{P}\\text{U}+{\\mu\\:}\\text{I}\\text{n}\\text{t}\\text{e}\\text{r}\\text{a}\\text{c}\\text{t}+{\\gamma\\:}\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}\\text{s}+{{\\epsilon\\:}}_{\\text{i},\\text{t}}\\:\\:\\:\\:\\:\\:\\:\\left(4-3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis article uses the \"China Economic Policy Uncertainty Index\" to measure the uncertainty of economic policies. As the data is monthly, this article takes the arithmetic mean of the monthly data and divides the arithmetic mean by 100 to obtain the annual data. The meaning of this data is that the higher the value, the higher the economic policy uncertainty.\u003c/p\u003e\u003cp\u003eModels (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) demonstrate the regression results of the moderating effect of economic policy uncertainty on the research experience and disruptive innovation of executives in power companies. The coefficient of Academy in Model 1 is 1.4071***, and the interaction term in Model 3 is 0.1115**, indicating that economic policy uncertainty positively moderates the research experience of executives in power companies to promote disruptive innovation, confirming research hypothesis H2a.\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e6 Considering the moderation effect of economic policy uncertainty\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\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModeration effect model 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModeration effect model 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModeration effect model 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.4071***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0133***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.2040***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(4.9326)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(5.3347)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.8574)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEPU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1088\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.3430)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.6208)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInteract\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\u003e0.1115**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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(2.5688)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variable\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\u003eFixed effects of year and industry\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\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.7879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.6576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1345\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.4869)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.1495)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.4281)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eadj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.054\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\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003cb\u003eIntermediary effect test\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo verify the correctness of the mediating effect, this article constructs measurement indicators for R\u0026amp;D information disclosure and digital transformation, specifically: using the information disclosure evaluation level of the Shenzhen Stock Exchange website as a proxy variable for R\u0026amp;D information disclosure; Digital transformation draws on the approach of Wu Fei et al., using the frequency of five digital transformation terms in the annual report text of listed companies as a measurement indicator. Using Sobel Goodman's mechanism testing method, the indicators of the two mechanisms were included in the regression model, and the results of the mediation effect test are shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;7. The results indicate that the coefficients of R\u0026amp;D information disclosure and digital transformation are 0.9970* and 0.4742***, both showing significant mediating effects. This indicates that R\u0026amp;D information disclosure and digital transformation are intermediary mechanisms through which the research experience of science and engineering executives in power enterprises affects disruptive innovation.\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e7 Results of Mediation Effect 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\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInnovation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.4071***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3354**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3358**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(4.9326)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.3421)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.3704)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisclosure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9970*\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.8544)\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\u003eTransformation\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\u003e0.4742***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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(3.3120)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variable\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\u003eFixed effects of year and industry\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\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.7879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.1798***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.9844***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.4869)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.1270)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.6872)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eadj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.128\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\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003cb\u003eResearch Conclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study uses data from 83 power companies. The data covers 2001 to 2022. It tests research hypotheses through empirical analysis. The main conclusions are as follows.The research experience of power company executives significantly promotes disruptive innovation (coefficient 1.4017***). This conclusion remains valid after robust tests. These tests include replacing variables and adding control variables.The promotion effect of executive research experience is stronger in non-state-owned power enterprises (coefficient 1.5957*** vs. state-owned 1.1339***).In eastern and central regions, there is a significant positive correlation (coefficient 0.8668**, 0.5578**). In western regions, it is not significant. The promotion degree is positively correlated with the level of economic development.The promotion effect is significant in labor-intensive (1.3800**) and capital-intensive (0.1530*) industries. It is not significant in technology-intensive industries.Economic policy uncertainty positively moderates the promoting effect. This is on the link between executive research experience and disruptive innovation (interaction coefficient 0.1115**). This means higher policy uncertainty leads to a stronger driving effect of executive research experience.Executive research experience promotes disruptive innovation through two paths. One is accelerating digital transformation (coefficient 0.4742**). The other is improving R\u0026amp;D information disclosure quality (0.9970*). This confirms the dual intermediary mechanism works.\u003c/p\u003e"},{"header":"5. Conclusion and Policy Suggestions","content":"\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study focuses on the empowering effect of executive research experience on disruptive innovation in power companies. The research found that: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Executive research experience significantly promotes disruptive innovation in enterprises through resource dependence, social networks, and high-level echelons theory, and has a stronger effect in non-state-owned enterprises, the eastern and central regions, and labor-intensive industries. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) The research experience of executives in power companies promotes disruptive innovation by accelerating digital transformation and enhancing R\u0026amp;D information disclosure. Specifically, enterprises use R\u0026amp;D information disclosure to alleviate financing constraints, digital transformation breaks through resource allocation boundaries, and ultimately helps enterprises achieve disruptive innovation. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) In terms of the nature of corporate property rights, the regression results of both state-owned and non-state-owned power enterprises support the research conclusions, but the promotion effect of scientific research experience of senior executives in non-state-owned power enterprises is stronger; At the regional level, the regression results of the sample of power enterprises in the eastern, central, and western regions are all positively significant, and the degree of promotion is positively correlated with the level of regional economic development; In terms of industry types, labor-intensive and capital intensive industries are significantly promoted, while technology intensive industries are relatively weak. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Economic policy uncertainty plays a positive moderating role, indicating that uncertainty prompts executives with research experience to make cautious decisions and strengthens the driving force for disruptive innovation in power companies.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003cb\u003ePolicy recommendations\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003e1.Government level\u003c/h3\u003e\n\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Optimize talent policies and innovation ecosystem layout. The government should formulate and implement a special plan for the introduction of high-level talents, establish an overseas academic talent introduction fund, provide funding subsidies and policy convenience for power enterprises to introduce high-end talents with international scientific research backgrounds, and encourage them to engage in enterprise innovation work. At the same time, establish a platform for talent exchange between industry, academia, and research institutions, regularly organize academic seminars and technical exchange meetings, promote talent flow and knowledge sharing between universities, research institutions, and power enterprises, and broaden the channels for power enterprises to obtain academic talents.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Promote coordinated development of regional innovation. In terms of network infrastructure construction, we will increase investment in the central and western regions, implement the \"Central and Western Digital Infrastructure Acceleration Project\", improve network coverage and quality, and lower the hardware threshold for digital transformation of power enterprises. Establish an industrial innovation guidance fund for the central and western regions, focusing on supporting the development of local high-precision and cutting-edge industries, and guiding power enterprises to transform towards technology intensive directions. Encourage universities and research institutions in the eastern region to engage in industry university research cooperation with power enterprises in the central and western regions and assist them in achieving disruptive innovation through technology transfer, joint research and development, and other means.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Improve the policy support system. Develop differentiated fiscal preferential policies for the central and western regions, non-state-owned, manufacturing power enterprises, and small and medium-sized enterprises. Implementing tax reduction and exemption plans to lower the corporate income tax rate; Establish special innovation subsidies and provide financial rewards based on enterprise innovation investment and achievements. In the process of formulating economic policies, fully consider the innovation needs of enterprises, enhance the stability and predictability of policies, and help power enterprise executives, especially those with research experience, understand policies through policy interpretation meetings, online Q\u0026amp;A sessions, etc., make accurate decisions in uncertain environments, and promote the innovative development of power enterprises.\u003c/p\u003e\n\u003ch3\u003e2.At the enterprise level\u003c/h3\u003e\n\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Reform the selection and training mechanism for senior executives in power enterprises. In the executive selection process, a scientific talent evaluation system should be established. In addition to professional skills and management experience, research background, innovative achievements, research potential, etc. should be included in the key assessment scope. Special research background evaluation indicators should be set, such as the quantity and quality of academic papers published, the level and contribution of participation in research projects, etc., to ensure the selection of executive talents with strong innovation driven abilities. In terms of executive training, we collaborate with well-known universities and research institutions to customize personalized academic enhancement courses, covering cutting-edge technologies, innovation management, and other fields. Establish an internal research award fund to encourage executives to conduct academic research related to power enterprise business and enhance their academic and research capabilities. Regularly organize academic exchange activities for executives, invite academic authorities in the industry to share the latest research results and innovative ideas, and promote knowledge collision and experience exchange among executives in power enterprises.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Building an academic oriented culture in the power industry. Advocate the values of academic research and innovative exploration within the power enterprise, promote successful cases of academic achievements being transformed into innovative driving forces through internal publications, bulletin boards, online platforms, and other channels, and create a cultural atmosphere that respects academia and encourages innovation. Establish academic honor awards for power enterprises, publicly recognize outstanding executives and teams in academic research and innovative practice and motivate all employees to actively engage in innovative activities. Carry out cross departmental academic cooperation projects, break down departmental barriers, promote the integration of knowledge from different fields, build a broader platform for senior executives of power enterprises to leverage their academic expertise, and stimulate the overall innovation vitality of the enterprise.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Accelerate the digital transformation and social responsibility fulfillment of power enterprises. Develop a detailed digital transformation strategic plan, establish a special budget for digital transformation, increase investment in technology fields such as artificial intelligence, big data, and cloud computing, promote the full process digital upgrading of power enterprise production, management, marketing, etc., and break through the time and space limitations of resource allocation. Establish a sound system for disclosing R\u0026amp;D information, standardize the disclosure process and content, regularly disclose R\u0026amp;D progress, patent achievements, and other information to investors and the public through channels such as the company's official website and annual reports, enhance corporate information transparency, and alleviate financing constraints. Actively fulfilling social responsibilities, participating in public welfare innovation projects such as environmental protection technology research and development, education technology popularization, etc., enhancing the corporate social image, attracting more high-quality investors, creating a favorable environment for corporate innovation, and fully leveraging the role of senior executives' research experience in promoting disruptive innovation in power enterprises. In addition, strengthen cooperation with digital transformation service providers, introduce professional consulting and technical support, and ensure the smooth progress of digital transformation; Establish an investor relations management team, proactively communicate with investors about the company's innovation strategy and research and development achievements, and enhance investor confidence.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eI first sorted out the background and core issues of the research. Considering the fierce global economic competition and rapid technological updates, coupled with the increasingly prosperous development of artificial intelligence and the increasing uncertainty of economic policies, I am thinking of exploring how the research experience of executives in power companies utilizes artificial intelligence to achieve disruptive innovation, as well as the characteristics of the impact path in this process. These issues are actually quite related to how power companies choose strategies and whether they can sustain development in complex environments.Then, I consulted a lot of relevant literature. After reviewing the current research on executive research experience and disruptive innovation in enterprises, it is found that there are still shortcomings in some aspects, such as insufficient exploration of heterogeneity, the impact of R\u0026amp;D information disclosure and digital transformation on executive teams, and the relationship between executive research experience and industry disruptive innovation. The research is not thorough enough. This also helped me find the starting point for my research and clarify where I could create something new.Then, based on theories such as resource dependence, social networks, and upper echelons, I constructed a theoretical framework and proposed some research hypotheses. I believe that the research experience of executives may promote disruptive innovation in enterprises, so I proposed hypothesis H1. Regarding the moderating effect of economic policy uncertainty, I considered both the possibility of positive regulation and the possibility of negative regulation, resulting in H2a and H2b; In addition, I have also considered the mediating role that R\u0026amp;D information disclosure and enterprise digital transformation may play, and have constructed corresponding theoretical models.In terms of research design, I selected 83 listed power companies in China from 2001 to 2022 as samples. I referred to some existing methods to measure variables such as executive research experience, disruptive innovation, and economic policy uncertainty, and also constructed regression models. Afterwards, the data was processed, such as matching data, deleting data with many missing values, and performing Winsorize processing, resulting in 516 consecutive observations.I did a lot of work during the empirical analysis. Firstly, benchmark regression was used to verify that executive research experience can significantly promote disruptive innovation; Then a heterogeneity test was conducted to see if there were differences in the promoting effect among enterprises with different property rights, regions, and industry types; In order to make the research conclusions more reliable, I also conducted robustness tests, such as replacing variables and adding control variables; In addition, I have also studied the moderating effects of economic policy uncertainty, as well as the mediating effects of research and development information disclosure and digital transformation, and have obtained some significant results.Finally, I summarized the research findings and provided some policy recommendations from both the government and enterprise perspectives. I hope these conclusions and suggestions can provide some reference for power companies to enhance their disruptive innovation capabilities, and also contribute to theoretical research in related fields.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCai C, Geng Y, Yang F. Academic background of senior executives and enterprise digital transformation[J]. Scientific Reports, 2024, 14(1): 21865.\u003c/li\u003e\n\u003cli\u003eChen Y, Xie W, Yu X, et al. Research and Application of Power Engineering Science and Technology Project Management Based on All Life-cycle[J]. EDP Sciences, 2021.\u003c/li\u003e\n\u003cli\u003eEwa F G, Renata K, Jerzy W. Opportunities for ESG Reporting in Railway Electric Power Supply Enterprises Based on Annual Reports[J]. 2025.\u003c/li\u003e\n\u003cli\u003eFu C, Indiran L, Kohar U H A. Disruptive Innovation (DI) and Chief Executive Officer (CEO): A synthetic literature review[J]. 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Economic Research, 2017, 52 (07): 169-183.\u003c/li\u003e\n\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":"Research experience of senior executives in power companies, Disruptive innovation, Economic policy uncertainty, Research and development information disclosure, Digital transformation","lastPublishedDoi":"10.21203/rs.3.rs-7069434/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7069434/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe world economy is very competitive. Tech is changing fast. For companies, the ability to make big new changes is key to winning in the market.This study uses information. It looks at 83 Chinese power companies that are listed. The time is from 2001 to 2022. It starts with the research experience of the companies' top leaders. It tries to find out how big new changes grow in these power companies. It also looks at how unsure economic policies affect this.The research experience of the top leaders helps a lot to make big new changes. This is true even after many checks to make sure.There are other tests too. They look at different types of companies. They show this help is stronger in power companies not owned by the state. They also show something else. In eastern, central and western power companies, the help is greater when the area's economy is more developed.Labor intensive and capital-intensive industries have stronger promotion effects compared to labor-intensive industries. The moderating effect indicates that economic policy uncertainty positively moderates the promotion of disruptive innovation by the research experience of executives in power companies. The mediation effect test found that the research experience of executives in power companies promotes disruptive innovation through two paths: accelerating digital transformation and enhancing R\u0026amp;D information disclosure. Based on this, this article proposes policy recommendations from the perspectives of optimizing talent policies, promoting coordinated development of regional innovation, innovating executive selection and training mechanisms, and accelerating digital transformation and fulfilling social responsibilities, in order to provide theoretical and practical references for power enterprises to enhance their disruptive innovation capabilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClassification number: \u003c/strong\u003eF272 \u0026nbsp;\u003cstrong\u003eDocument identification code: \u003c/strong\u003eA\u003c/p\u003e","manuscriptTitle":"Research on the Evolutionary Mechanism of Disruptive Innovation in Electric Power Enterprises Driven by Artificial Intelligence - Based on the Adjustment Effect of Economic Policy Uncertainty","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-26 07:28:37","doi":"10.21203/rs.3.rs-7069434/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":"e839bd0a-65d4-49b6-9d3a-469128c17de3","owner":[],"postedDate":"August 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-23T15:11:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-26 07:28:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7069434","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7069434","identity":"rs-7069434","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}