Improving the policy system is a necessary prerequisite for strengthening the institutional guarantee of scientific and technological ethics governance. Taking 45 policies on technology ethics governance issued by national departments in China from 1999 to 2022 as research samples, an analysis of the external attributes of policies reveals that the temporal evolution of policy intensity showed three peaks in 2001, 2007, and 2020. The institutional types of policy issuing departments cover a diverse range. The number of publications is led by the health management department, accounting for 43%. Constructing a three-dimensional policy content analysis framework for the governance process of science and technology ethics (X) - policy tools (Y) - policy objects (Z). Using the quantitative analysis method of policy texts, it was found that the number of policy texts focused on science and technology ethics supervision in the dimension of science and technology ethics governance process was 48.2%, with system change tools accounting for 35.2%, authoritative tools accounting for 34.2%, and policy object dimensions mainly focusing on science and technology institutions accounting for 52.3%; The X-Y two-dimensional analysis found that the supervision and management, feedback and improvement processes comprehensively utilized five types of policy tools, while reporting and disclosure only used two types of policy tools: incentive tools, symbolic tools, and persuasive tools; X-Z two-dimensional analysis found that the stages of education consultation, supervision and management, and investigation and processing all covered various policy objects, while the policy objects in the reporting and disclosure stage lacked precise requirements for relevant government management departments and scientific and technological institutions; The overall distribution of policy texts in three-dimensional interactive analysis is uneven. Propose suggestions such as improving the policy system of scientific and technological ethics governance to provide institutional guidance for regulating the order of scientific and technological innovation, constructing a linkage and implementation mechanism of scientific and technological ethics governance centered on the characteristics of national strategic scientific and technological forces, and establishing a full process supervision chain support policy implementation efficiency based on scientific data digitization platform for scientific and technological activities.

Generative artificial intelligence (AIGC) is being integrated into social life and is participating in knowledge production. It also creates new digital inequalities, and scholars are concerned about deepening the digital divide. This article explores the theoretical context of artificial intelligence and its impact on society and tries to answer how AIGC shapes the "AI divide" in the context of the development of Digital China. At the technical level, the "AI divide" involves three dimensions: data, algorithms, and computing power. Data sets serve as the foundation of AIGC, similar to soil. The scale parameters, source channels, content types, and even languages of the data sets invisibly shape the "AI divide". The algorithm model consists of self-supervised learning and human feedback reinforcement learning. It is similar to the roots and stems of a plant, which determines the vitality of AIGC to bloom and bear fruit. Computing power is AIGC's information infrastructure, connecting the real world (chips) and the virtual world (data and algorithms), determining the processing speed of computing operations and the depth of integration in the digital industry. As modern technology becomes increasingly dominant in shaping our understanding of the world and history, social stratification theory can help us understand the impact of the "AI divide". The first level involves the divide among individuals, specifically between ordinary users, in accessing and utilizing AI technology. This includes language barriers, paywalls, practical skills, willingness to use, perception of benefits, and the desire to learn. The second aspect pertains to human-machine differentiation, which, on the surface, refers to the distinction between humans and autonomous AI agents. However, at its core, it represents the divide between the majority who lack technological initiative and the minority who wield it due to their control over digital resources. It refers to using digital resources by countries, regions, or individuals to participate in the development, share knowledge, and overcome differences in capabilities for autonomous growth. To bridge the "AI divide", it is important to establish an independent and self-sufficient technological innovation system. We should also explore and optimize application scenarios to make the most of this system. China needs to take the lead in AI development in the future and promote the construction, transformation, and application of AIGC resources by different enterprises, scientific research institutions, and industry organizations. It must also establish a fair and standardized platform governance ecosystem and create a good technology atmosphere. Equality should be embedded into AIGC's technology process, including algorithm design, training data selection, model generation, and optimization. This will give people of different countries, ethnic groups, and classes the right to participate in scientific development. Finally, China should plan more diverse ecological alliance forms and participate in cooperation platforms under multilateral frameworks such as the United Nations, the World Trade Organization, and Asia-Pacific Economic Cooperation. China should transform games and competitions into consultation and cooperation, connect wider upstream and downstream resources, and try to build a global AI governance community. The article proposes the concept of the "AI divide", which deepens the third-generation digital divide theory in artificial intelligence and has significant practical implications for understanding the development of digital China and the economic and social transformation.

“Algorithmic Capitalism” refers to a contemporary form of capitalism with intelligent algorithms at its central control, representing the iterative upgrade of digital capitalism under the new technological revolution. Unlike previous forms of capitalism, algorithmic capitalism follows the logic of intelligence, featuring new characteristics of trans-regional, full-process, automation, and implicit dominance. From the perspective of core structure, algorithmic capitalism follows a four-element structure of “algorithm-platform-data-capital”, where algorithms serve as the “efficient” element, platforms as the “formal” element, data as the “material” element, and capital as the “final” element. Algorithmic capitalism has the dual significance of progress and limitation, bringing more mobility and social change while also triggering serious negative ethical effects. Research on algorithmic capitalism holds significant theoretical and practical value in the current context.

The AI-driven new paradigm of scientific research is becoming a crucial driver for China to achieve technological competitive advantage and emerge as a leading science and technology powerhouse. With the development of the new generation of AI technology, the growing importance of AI in driving technological advancements and fostering interdisciplinary collaboration is gaining increasing attention. Many countries around the world have emphasized the importance of AI in promoting scientific exploration and have strengthened policy deployment and guidance across various domains, including establishing specialized research institutions, providing financial support, fostering talent development, leveraging data resources, innovating in technology, and collaborating internationally. The new paradigm of scientific research driven by artificial general intelligence has catalyzed transformations in research organizational patterns through the empowerment of large-scale AI models. The AI-driven new paradigm of scientific research represents a transformation of traditional research methods and processes, creating a knowledge-centered system with diversified stakeholder participation and a symbiotic collaboration between humans and machines. While the significance of adopting AI-driven research paradigms is widely acknowledged, existing studies have not thoroughly explored their theoretical foundations from epistemological and methodological perspectives. This paper investigates the AI-driven new research paradigm from a dual perspective of ‘Knowledge Evolution Theory’ and ‘Paradigm Theory’, systematically elucidating the main characteristics of the new research paradigm, and building a research framework for the AI-driven scientific paradigm. Knowledge evolution theory focuses on the specific mechanisms, evolutionary laws and influencing factors of knowledge growth and evolution. Paradigm Theory research on a collection of rule systems universally adopted by the scientific community to ensure efficient and orderly operation of research activities. Building on this framework, the paper conducts a multi-case analysis of the exploratory application of the AI-driven research paradigm. The study reveals that the AI-driven research paradigm encompasses elements such as research tools, research organization models, diverse application scenarios, and governance of research applications. Specifically, AI research tools optimize traditional research processes through substitution effects, enhancement effects, and autonomous effects. The AI-empowered research platform organization model is transforming the infrastructure, organizational structure, collaboration mechanisms, and talent composition of existing research paradigms. Multidisciplinary research bottlenecks provide training and iterative application scenarios for large models. Simultaneously, governance of AI research applications under the new paradigm needs to be refined based on the technological, content, and social attributes of AI. The study outlines the policy implications for developing a new AI-driven paradigm in scientific research based on its findings. First, it is essential to establish a policy framework for the "AI-driven new paradigm of scientific research". Second, there is a need to enhance database infrastructure, advance computing capabilities, and foster the development of basic models for artificial general intelligence. Concurrently, efforts should focus on enhancing decentralized large-scale model platforms and other novel scientific research models that align with the integration of artificial general intelligence applications. Finally, attention must be focused on the governance of AI applications in scientific research. This study offers theoretical support and empirical insights for the coordinated formulation and implementation of the ‘AI-driven new paradigm of scientific research’ policy.

This paper explores the impact of USA technology blockade on China's technical cross-border mergers and acquisitions (M&A) from third-party economies by using the difference-in-difference (DID) model based on the SDC M&A database. We find that USA technology blockade against China has increased China's efforts to implement technological cross-border M&A in third-party economies. The mechanism analysis shows that this is the result of the combination of demonstration effect decreasing China's technological M&A and substitution effect which encouraged that in third-party economies highly alternative or closely linked to China's high-tech sector. In addition, the political and economic relationship between the host country and USA is not a key factor of the effect we interested. Heterogeneity analysis finds that the medical and telecommunications and software service fields are positively stimulated by the USA technology blockade, but the energy fields are the opposite.

Based on the background of "talent competition" and the perspective of regional talent policies, this paper uses natural language parsing (NLP), text mining to collate the quantitative data of 3308 local talent policies in China from 2002 to 2021, designs the quantitative standard, process and evaluation system of talent policy under the dimension of "multi region-multi policy", on this basis, combined with individual micro data, investigates the influence of urban talent policy on talent migration decision. The findings are as follows: 1. The improvement of the comprehensive score of urban talent policy can significantly increase the probability of the city being selected by talents. From the perspective of cities, the effect of talent policy is more obvious in smaller and non-capital cities. In terms of individual characteristics, the group of highly-educated, high-income and the young and middle-aged talents aged 25-54 are more sensitive to the change of the comprehensive score of talent policy. 2. Specific to the four types of policy links, There are significant differences in the actual effect of talent policies issued by cities of different geographical regions. The policies of introducing, retaining and employing talents in eastern coastal cities and southern cities can significantly affect the inflow of talents, while in inland areas, the effect of the policy of cultivating and employing talents is not obvious, in northern areas, only the policy of retaining talents can make a difference. 3. From the perspective of the three types of policy tools, different age groups and different types of talent groups also have different preferences. From the perspective of age, the talent group under 35 years old is more sensitive to short-term and subsidized content changes such as subsistence allowance and salary benefits, while the talent group over 35 years old is relatively more concerned about children admission and personal career development and other safeguard and development policies. In terms of different types of talents, the innovative and entrepreneurial talents are very concerned about changes in development policies such as financial support and innovation carrier construction, the enterprise management talents are more concerned about incentive policies such as subsistence allowance and personal income tax incentives. Rural practical talents pay more attention to safeguard policys, such as household policies.

Sino-US relations have always been one of the important factors affecting the operation and development of Chinese enterprises. Since 2017, the United States has included a number of Chinese enterprises in the "Entity List" by promulgating the Export Control Regulations. Enterprises included in the Entity List will not be able to obtain key technologies and resources from the United States, which has a great impact on the innovation strategy and development strategy of Chinese enterprises. To explore the strategies and measures for Chinese enterprises to break the foreign technology blockade, it is necessary to explore the specific impact and mechanisms of technology blockade on Chinese enterprises. According to the resource dependence theory, the need for enterprises to obtain resources from the external environment will lead to dependence on the external environment, that is, environmental constraints. Mergers and acquisitions(M&As) are often regarded as an effective way for enterprises to absorb environmental constraints. However, technology blockade caused by macro-policies is different from conventional environmental constraints, which may affect the effectiveness of M&A as a means of absorbing constraints. Therefore, this paper intends to explore whether and how technology blockade affects M&A of Chinese enterprises. This paper selects the M&A events of listed companies in China from 2016 to 2022 as the initial research sample, and obtains 21,540 observations of 4,333 enterprises after processing. Based on the text analysis of the annual report of listed companies, this paper constructs the technology blockade index, and describes the degree of enterprises affected by technology blockade through the total word frequency ratio of technology blockade keywords in the annual report text. The annual report documents are from Juchao Information Network(cninfo), and other data used in this paper is from the China Stock Market Accounting Research(CSMAR) database. Then this paper constructs an OLS model to quantitatively evaluate the impact of technology blockade on M&A of Chinese enterprises and the moderating effect of resource redundancy. This paper finds that technology blockade reduces the number of M&A of Chinese enterprises, non-precipitating redundancy enhances the negative impact of technology blockade on the number of M&A, while precipitating redundancy has no significant impact on the relationship between technology blockade and the number of M&A. The conclusions mentioned above are still valid after a series of robustness tests, including changing estimation model, adjusting research samples and changing the measurement methods of dependent variables. In addition, the results of endogenous analysis show that the regression results of this paper are still robust under the condition of alleviating endogenous problems. Mechanism analysis shows that technology blockade increases the financing cost and difficulty of enterprises, increases the financing constraints faced by enterprises, and thus reduces the number of M&A. Furthermore, the heterogeneity analysis shows that technology blockade has a significant negative impact on the number of M&A when executives have overseas background or the industry has a high degree of competition, but it is not significant on the contrary. The main contributions of this paper are as follows: (1) Most of the existing literature focuses on the impact of technology blockade on innovation. This paper explores the influence of technology blockade on M&A and its boundary conditions, and further reveals its mechanism, which supplements the related research on technology blockade and M&A. (2) Based on the resource dependence theory, this paper analyzes the environmental constraints brought by the technology blockade initiated by the government, and the influence of the special nature of this environmental constraint on M&A, which expands the research on external environmental constraints. (3) Existing studies use proxy indicators such as trade freedom index and license approval number, which makes it difficult to directly describe the impact of technology blockade. This paper constructs technical blockade index through text analysis, which provides a useful reference for the measurement of technology blockade.

The digital economy, as a new engine for China's high-quality economic development, is micro-founded on the digital transformation of enterprises. Promoting the digital transformation of enterprises based on seizing their own unique endowment advantages and environmental conditions is a critical inherent requirement for fully unleashing the development potential of the digital economy. First of all, the paper explores several influencing factors that may have a potential to play an important role in promoting enterprises’ digital transformation in the framework of Technology - Organization - Environment (TOE) attempting to clarifying the reasons for digital reform in detail. Secondly, the paper conducts several empirical tests by making use of the data from the Chinese A-share listed companies on the Shenzhen and Shanghai stock exchanges from the year of 2010 to the year of 2020. And it tries to assess the relative importance of some main influencing factors that significantly promote the action of enterprises’ digital transformation by taking advantage of the method of variance decomposition and Shapley's value. Last but not least, some heterogeneity analyses are presented from the perspectives of enterprises, regions, etc. After all the steps above, the paper formally gives some research conclusions that the proportion of technical employees, research and development investments, and education background of executives are the key internal factors driving the digital transformation of enterprises analyzing from the viewpoint of internal cause, and the “bandwagon effect” arising from chasing the digitalization pace of peer companies is the key external factor analyzing from the viewpoint of external cause. According to the relative importance results measured by variance decomposition and Shapley's value, the external pressure brought by the "bandwagon effect" plays the most significant role in driving the digital transformation of enterprises, while the role of internal motivation is relatively insufficient, manifested as relatively lower values of variance decomposition and Shapley than the results of "bandwagon effect". What’s more, the heterogeneity tests find that the "bandwagon effect" presented in the process of enterprises’ digital transformation is relatively weaker in the economically developed eastern regions of China and the areas with strong intellectual property protection. And the heterogeneity tests also find that growing enterprises rely more heavily on some internal factors to enable themselves to reach the suitable level of digital transormation. It is not an appropriate way for enterprises to blindly imitate other enterprises to carry out digital transformation out of the motivation of keeping up with the trend of digital transformation, but ignoring their own actual conditions and transformation needs. A company's decision on digital transformation should be based on the maturity of internal conditions, which can truly play the role of digital empowerment. In consequence, the government should take some measures to strengthen the cultivation of digital and skilled talents, encourage enterprises’ technological innovation, improve the financial system inside the enterprises, and optimize the digital business environment outside the enterprise. It is vital and urgent for government to play a guiding and supervising role and to shift driving factors from "external pressure" to "internal motivation" as far as possible in the process of promoting Chinese enterprises' digitalization.

As the core production factor of the digital economy, how can digital asset value (DAV) promote regional economic development has become an urgent academic issue to be explored. On the basis of reviewing relevant theories, this paper constructed a statistical regression model with regional economic growth as the explained variable, DAV as the explanatory variable,digital technology level as mediating variable, and data mobility as moderator?variable.We employed panel data from 28 provinces in mainland China from 2011 to 2020, excluding Xinjiang, Tibet, and Inner Mongolia since the data availability. Our results show: (1) DAV have a significant promoting effect on regional economic growth; (2) Digital technology level plays a mediating role between DAV and economic growth; (3) Data mobility can strengthen the positive impact of DAV on economic growth. (4) Further heterogeneity analysis illustrate that DAV have no significant promoting effect on regional economic growth within the central and western regions, and the key reason is the overall low level of digital technology in these regions.

The evolution of science and technology is one of the most important and complex issues in academia. With the accelerating integration of scientific, technological, economic, and social development, industrial technological innovation has become a focal point of theoretical research. However, existing studies have primarily focused on the influencing factors of industrial innovation performance from the perspectives of policy support, technological development, and market economy, often neglecting the crucial role of scientific research at the forefront of industry. This study, from a science-driven perspective, takes the 5G communications industry—a typical science-driven industry—as an example. It creatively combines the fsQCA method with simulation modeling analysis to better depict the evolutionary process from scientific research to technological innovation. This research has yielded several valuable conclusions. Firstly, the complex and dynamic evolutionary process between science and technology is a process where quantitative changes in science lead to qualitative changes in technological innovation. This process is influenced by multiple factors such as scientific research investment and output, the intensity of scientific linkage, scientific collaboration networks, and firm scientific capabilities. The interaction between these influencing factors can accelerate the evolutionary process and shorten the evolutionary cycle. Science-based innovation is the core condition for transforming scientific research into technological achievements and is the decisive factor for industrial technological breakthroughs. The diffusion and application of science-based innovation results spur a large number of technological innovations, thereby pushing the industry into a rapid development cycle. Moreover, the heterogeneous effects of multi-actor participation in scientific research on the evolutionary process of technological innovation performance are evident. In the early stages of industrial development, scientific exploration mainly relies on universities and research institutions. However, firm participation in scientific research can effectively promote the transformation of science into technology and subsequent commercialization. On the basis of scientific breakthroughs, firm participation—particularly the scale of participation—plays a more critical role in promoting technological innovation performance. Additionally, this study proposes three configuration paths for enhancing technological innovation performance: all-element science innovation-driven type, dual-element scientific ecosystem-driven type, and single-element scientific linkage absorption type. Different development paths can be selected based on the varying conditions of science-driven factors in a country or industry. Based on the research conclusions, this paper offers several policy recommendations for developing science-driven industries in China. These include constructing a science-driven national innovation system, creating new types of industry-university-research collaborative innovation organizations, emphasizing the cultivation of science-based innovation, and optimizing the layout of scientific research investment to leverage its guiding value. These recommendations have strong practical implications. In summary, this study provides a comprehensive analysis of the evolutionary process from scientific research to technological innovation in the 5G communications industry, highlighting the multifaceted influences and interactions that drive this evolution. The findings underscore the importance of scientific research as a foundational element in industrial technological innovation and offer actionable insights for policymakers and industry stakeholders aiming to foster science-driven industrial development.

New quality productivity is a powerful driving force and supporting force to promote the high-quality development of enterprises. This view has been widely recognized by the community. Accelerating the development of new quality productivity will help enterprises enhance their competitive advantage and realize sustainable development. However, the existing literature focuses on exploring the connotation characteristics and value significance of new quality productivity, and there are few empirical research models to explore its driving forces. Some studies believe that the digital intelligence transformation is the core driving force to lead the innovation and development of enterprises, and whether the digital intelligence transformation can improve the new quality productivity level of enterprises is an urgent topic to be discussed. Based on the dynamic resource-based view, using the data of China A-share listed companies from 2015 to 2022, this paper empirically analyzes the influence and heterogeneity of digital intelligence transformation on the new quality productivity of enterprises, and tests the mediating role of absorptive capacity and the moderating role of competitive intensity. It is found that: (1) the digital intelligence transformation has a significant impact on the improvement of the new quality productivity level of enterprises, and this conclusion still holds after various robustness tests. (2) the digital intelligence transformation can improve the absorptive capacity and new quality productivity of enterprises, and the absorptive capacity plays a mediating role in the relationship between the digital intelligence transformation and new quality productivity of enterprises. (3) the competitive intensity plays a positive role in moderating the relationship between the digital intelligence transformation and the new quality productivity of enterprises. (4) heterogeneity analysis shows that, compared with state-owned enterprises, enterprises in the central and western regions and in recession, non-state-owned, eastern regions, growing and mature regions can promote the improvement of new quality productivity by implementing digital intelligence transformation. The theoretical contributions of this study are as follows: first, the existing literature focuses on the connotation characteristics and value significance of new quality productivity, while there are few empirical research models to explore its driving factors. This study examines the influence of digital intelligence transformation on the new quality productivity level of enterprises through empirical research, which not only provides theoretical reference for promoting the transformation of digital intelligence and improving the new quality productivity level of enterprises, but also enriches the empirical research on the relationship between digital intelligence transformation and new quality productivity of enterprises. Second, based on the dynamic resource-based view, the theoretical model of "digital intelligence transformation-absorptive capacity-new quality productivity of enterprises" is constructed, and the intermediate transmission mechanism of absorptive capacity is deeply analyzed, which opens the "black box" in the process of digital intelligence transformation and empowerment of new quality productivity of enterprises. Thirdly, by including the industry competitive intensity at the macro level as a moderating variable, the boundary conditions of the impact of digital intelligence transformation on the new quality productivity of enterprises are further clarified, which is helpful to clarify the complex and diverse relationship between them and enrich the relevant research on the dynamic resource-based view.

The "Small Yard, High Fence" strategy is the Biden administration's main policy for technological competition with China. It attempts to rigorously protect technologies that are crucial for national security, where the U.S. holds an advantage and China lacks, while maintaining exchanges with China in other technological areas. Unlike the Trump administration's "Total Decoupling" which is widely criticized strategy by American politicians, academics, and the business community, the Biden administration places special emphasis on a comprehensive, balanced, and rational policy towards China. It has proposed an overall policy framework of "competition—cooperation—confrontation" with China, highlighting the role of allies in the competition. The formation of the "Small Yard, High Fence" strategy is influenced by multiple factors. Economically, under the guidance of the "American School," the U.S. emphasizes productivity, values the domestic market, encourages trade protection, and focuses on internal improvements. This leads to decisions to maintain a productivity advantage at the expense of absolute economic benefits, to decouple from China, its significant trade partner, to enhance American innovation infrastructure and reform the U.S. innovation system; and to intervene in industrial development through protectionist trade policies. Politically, U.S. foreign policy has evolved from new isolationism to internationalism, eventually becoming interventionist. In terms of decision-making logic, the strategy of technological competition with the Soviet Union and Japan, under the unique bilateral relationship between China and the U.S., shows both continuity and change. The U.S. aims to suppress and contain China in high politics related to national security while considering the impact of economic decoupling on its own industrial development. Finally, The Biden administration has thoroughly reevaluated the Trump administration's "Total Decoupling" strategy, recognizing the necessity of cooperation with China. However, the "Small Yard, High Fence" strategy has multiple inherent limitations. If implemented comprehensively, it will face significant challenges and is unlikely to achieve long-term effectiveness, potentially even resulting in a "backlash" effect. First, the strategy's focus on point-specific confrontations can easily escalate to broader confrontations, creating a "chilling effect." Second, the global trend towards openness and cooperation has become a major premise for driving technological innovation, making it difficult for the U.S. to provide the sustained innovation and leadership necessary for effectively implementing the "Small Yard, High Fence" strategy. Third, the U.S. bureaucratic system hinders policy implementation, and political pluralism maintains policy balance. Fourth, the policy considerations of U.S. allies are realistic, and the "Democratic Technology Alliance" constructed by the U.S. is not monolithic. Fifth, severing technological network connections would lead to a "hegemony erosion effect." In response to the U.S.'s "Small Yard, High Fence" strategy, first, China should adhere to the principle of globalization and implement a general strategy of "Sticking to Opening-up and Promoting Cooperation". Second, China should implement a strategy of "deliberative dialogue" with the United States, using various communication and persuasion mechanisms in the international public space to maintain and expand communication, consultation, and trust between China and the U.S. Third, China should continue to promote multi-level technology diplomacy with other countries and regions, leveraging existing institutions, institutional transitions, and multilateral cooperation for multi-layered policy responses. Fourth, China should further strengthen its technological strategic autonomy, achieve key foundational breakthroughs, establish an alternative technology application system based on independent intellectual property, pay attention to intellectual property in innovation activities, optimize the business environment, advocate for research freedom, establish a fault-tolerant mechanism, and guard against the U.S.'s technological competition path misguidance.

Technology security has become a key variable that affects China's position in the new international landscape, and is the key to determining the country's future and competitive advantage. How to resolve crises and seize opportunities at the intersection of historic changes in China urgently needs to take the first step in technology security. There is relatively little research in the field of technology security in China, and there is a lack of foreign experience for reference. Research on how to maintain and ensure China technology security has become a sparsely populated "wilderness", and crossing the wilderness requires a directional compass. Therefore, based on a comprehensive study of literature related to technology security, this article combines bibliometric analysis methods to analyze the disciplinary characteristics, trends, and thematic context of technology security research, aiming to present the research landscape , and proposes a technology security research framework that follows the tracing of risk issues, goal and bottom line analysis, governance system and path construction.

Can the IT imprints experienced by executives during sensitive periods have a lasting impact on corporate behaviour and drive digital innovation? Based on the imprinting theory, using the data of the NEEQ-Listed Companies from 2013 to 2021, this paper empirically examine the effects and mechanisms of executives with information technology background influencing corporate digital innovation by identifying digital patents to measure corporate digital innovation through text analysis. The study shows that the larger the proportion of executives with IT education or experience, the more digital patents the enterprise obtains and the higher the quality, indicating that executives with IT background promote the "quantity and quality" of enterprise digital innovation; it is further found that executives with IT background promote enterprise digital innovation through the cognitive imprint of identifying digital innovation opportunities, the skill imprint of overcoming digital technology It is further found that executives with IT background promote digital innovation through the cognitive imprint of identifying digital innovation opportunities, the skill imprint of overcoming digital technology short-sightedness, and the relational network imprint of gathering innovation factors; and when market development is insufficient and property rights protection is absent, it is more obvious that executives with IT background promote digital innovation of enterprises. This paper extends the research on the antecedents of digital innovation to provide insights on promoting digital innovation from the perspective of talent allocation and talent development.

Technological innovation and employment stability are of great significance to the high-quality development of China's economy, but technological progress inevitably raises concerns about potential employment problems.Using a quasi-natural experiment produced by the SRDI ‘little giants’ enterprises identification policy, this paper takes China's A-share listed companies from 2015 to 2022 as samples, and explores the effects and mechanisms between the policy and enterprise labor employment by difference-in-difference approach. The paper finds that the identification of SRDI ‘little giants’ enterprises significantly promotes the increase of labor employment, especially in private enterprises and in private enterprises, key industries with higher density of ‘little giants’ enterprises, more competitive industries, and areas with low regional innovation level. Further tests show that tax incentives, bank loans and financial constraint are the main mechanisms．Finally，the increase of labor employment also significantly promotes enterprise human capital structure upgrading and boosts enterprise high-quality development. The results of this paper show that the identification and cultivation of SRDI ‘little giants’ enterprises is an effective policy to promote technological progress and achieve employment growth, which is instructive for optimizing the cultivation policy of small and medium-sized enterprises, stabilizing employment and safeguarding people's wellbeing.