The Low-altitude Economy is an important component of the New Quality Productive Forces, and the sound and sustainable development of the Low-altitude Economy is based on strict safety guarantees. Emerging safety risks derived from Low-altitude Economy activities include aviation safety risks, public safety risks, national information security risks and personal privacy security risks, and are characterized by the plurality of generating and bearing subjects, the arbitrariness of generating time and space, and the asymmetry of risk realization costs and governance costs. As far as the relationship between safety risks and the development of the Low-altitude Economy is concerned, the complete logical chain of risk evolution suggests an interaction with the development of the Low-altitude Economy itself. First, the development of economic efficiency leads to airspace reform. Secondly, airspace reform leads to changes in the powers (rights) structure. Thirdly, the diversification of powers (rights) leads to the evolution of safety risks. Finally, the emerging safety risks hinder the development of the Low-altitude Economy. However, China’s existing legal system, governance mechanisms, and governance tools cannot effectively regulate and prevent new safety risks caused by the Low-altitude Economy. With regard to the legal system, the existing legal system lags behind practice at the macro level, and some of the rules are characterized by rigidity. In addition, some of the new legal documents are fragmented and even contradictory. In terms of governance mechanisms, there are problems such as chaotic governance institutions, unclear responsibilities and a single structure of governance bodies. In terms of governance tools, there are still problems such as difficulties in law enforcement and high enforcement costs. The concept of Agile Governance aims to change the way policy operates in the fourth industrial revolution, and it is characterized by more progressive features. First, it is responsive. Agile Governance is more time-sensitive than the traditional plan-based policy-making approach. On the one hand, Agile Governance places more emphasis on rapid response to the object of governance and real-time optimization of governance behaviors, thus achieving the dual efficacy of immediate handling and risk prevention. On the other hand, Agile Governance can also realize the continuous iteration of governance policy and governance itself. Secondly, the multi-stakeholders of governance. Agile Governance advocates the adoption of a new paradigm that goes beyond government governance and allows more stakeholders to participate in governance. As a policymaker, the government should share the “workload” of constructing, monitoring and optimizing the governance system or policy with private entities and citizens’ groups in order to realize the checks and balances among different stakeholders and to make the process of policy-making and the policies formulated more inclusive and human-centered. Agile Governance’s characteristics of rapid response, multi-stakeholders, and continuous iteration reveal its natural interrelationship with emerging safety risks. In order to promote the high-quality and sustainable development of the Low-altitude Economy on the basis of effective prevention of emerging safety risks, firstly, the construction of “hard law” and “soft law” should be coordinated to help construct a governance legal system that balances economic development and safety protection; secondly, the powers and functions of the main public governance authorities should be clarified and synergy should be strengthened, and the effectiveness of the autonomy of other multi-stakeholders should be emphasized; and thirdly, it is necessary to innovate governance tools by improving infrastructure and promoting the integration of technological tools in order to achieve mutual good governance of emerging safety risks.

The post-Moore era marks the period following the breakdown of “Moore's Law,” which predicted that microprocessor performance would double approximately every two years. In this era, integrated circuits are approaching the physical limits of material density and process technology, while energy consumption costs are rising significantly. The onset of the post-Moore era has led to three major technological shifts. First, in advanced silicon-based semiconductor manufacturing, the first-mover advantage of leading nations, regions, and companies is gradually eroding. Second, new competitive arenas are emerging in advanced packaging technologies and the development of alternative materials for silicon-based chips. Third, the rise of the RISC-V architecture is challenging the longstanding market dominance of Intel x86 and ARM architectures. The post-Moore era has also reshaped the competitive landscape, characterized by oligopolistic market structures and the increasing nationalization of key industry players. These developments offer new opportunities for China’s integrated circuit industry. In light of China’s burgeoning digital economy, which continuously creates new scenarios and markets, we propose that the Ecosystem strategy for the development of China's integrated circuit industry in the post-Moore era should encompass four types. First, mature ecosystem, which adheres to the singular technological path of More Moore, and the goal is to enhance the industry's autonomy and controllability, as well reducing the risk of being subjected to critical constraints. Second, scenario-driven ecosystem, which follows the integrated technological path of More than Moore and More Moore, and the aim is to leverage China's rich application scenarios to expand the boundaries of the integrated circuit industry ecosystem, while continuously promoting the industry's digital transformation and achieve autonomous control over the integrated circuit industry ecosystem from the end of key application scenarios. Third, future industry ecosystem, which integrates all three technological paths, with a key focus on breakthroughs in Beyond CMOS technology, and the objective is to capitalize on the advantages of a new nationwide system, to plan moderately ahead, and to seize the high ground in future international competition. Fourth, internationalized ecosystem, which emphasizes expanding China's diversified industrial ecosystem into the international market, and the purpose is to actively integrate into the global innovation ecosystem, further expand the scope of scenario-driven ecosystems, and enhance the resilience of the ecosystem. To address the challenges in the development of China's integrated circuit industry in the post-Moore era, it is essential to leverage the advantages of China's super-large scale market and its new nationwide system. First, in the domain of mature ecosystems, reconstructing the industrial chain, along with the support and guidance of industrial policies, can help build a second ecosystem that is autonomous, controllable, and compatible. Second, based on the combined technological approach of System-on-Chip (SoC) and System-in-Package (SiP), the rich application scenarios can be utilized to expand the boundaries of the industrial ecosystem. Simultaneously, by infiltrating through scenario applications, it is possible to gain dominance in these emerging industrial ecosystems. Third, proactively layout advantageous industrial ecosystems around future industries. Additionally, there should be innovation in the organizational models of industrial internet and the promotion of industry-level data element market construction, ensuring that various production relationships meet the development needs of key core technologies in future industries. Fourth, based on the characteristics of dominant countries and dominant enterprises in different segments of the industrial chain, China can adjust its strategies targetedly to actively integrate into the global innovation ecosystem and international industrial ecosystems.

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.

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.

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 construction of high-level talent highland is an important strategic support for achieving high-level scientific and technological self-reliance. High-level talent highland means that the talent center resources are successfully transformed into innovative efficiency, and the regional talent development environment is the foundation and key, and the regional talent development environment is the foundation and key. Based on the theory of talent innovation and entrepreneurship ecosystem, this study uses the data of 35 cities around the world to analyze how the complex environment composed of economic development environment, education and technology environment, policy and institutional environment, human resources environment, natural ecological environment and social and cultural environment affects the construction of high-level talent highland from the perspective of configuration. The findings are as follows: (1) There are seven paths, including "dual-driven human resources and education technology environment" and "policy system and social and cultural environment driven under the dual logic of human resources and education technology environment". (2) The human resource environment appears simultaneously in all configurations, indicating that under different environmental configurations, the human resource environment is the core environmental influencing factor, coupled with other environmental factors to drive the construction of high-water talent highland. (3) Through configuration comparison, it is found that there is an asymmetric relationship between high efficiency and low efficiency in the construction of high-level talent highland. The conclusion of this study can further clarify the complex environmental path of constructing high-level talent highland, and provide reference for accelerating the construction of high-level talent highland.

Improving the credible governance system of artificial intelligence is necessary to promote technological innovation and a legal guarantee to comprehensively respond to technological alienation and promote the development of new quality productive forces. Uncontrollable artificial intelligence has triggered a serious crisis of confidence in its application and even threatened social stability and national security. To balance development and security, the governance consensus of "credible development" should be condensed, and the flexibility and effectiveness of governance should be improved by following the theory of life cycle and technical governance. On the one hand, improves the legal compliance guidance framework before, during, and after the event, and reduces regulatory costs while improving regulatory deterrence; on the other hand, it is necessary to innovate the technology governance system, introduce regulatory technologies such as private cloud computing, supervision fine-tuning, and SAGE analysis, and improve the human-machine collaborative framework to fully implement data, algorithms, and content credible regulatory requirements. To improve the artificial intelligence safety supervision system, prevent the risk of alienation, and create a "mutually beneficial symbiosis" man-machine relationship.

Innovation performance is an important metric for evaluating the contributions of researchers. As a strategic technology propelling the new wave of technological transformation, artificial intelligence (AI) is reshaping research paradigms across disciplines. However, prior studies have primarily focused on the direct impact of AI adoption on research activities, neglecting the role of individual differences among researchers in shaping Innovation performance. This study adopts the Stimulus-Organism-Response (SOR) theoretical model to explore the relationship between AI adoption and researchers' Innovation performance. The SOR framework posits that external stimuli influence internal psychological states, leading to specific outcomes. In this context, AI adoption acts as a stimulus that influences researchers’ psychological perceptions, which in turn affect their innovation performance. Drawing on Self-Determination Theory (SDT), the study incorporates three key psychological factors—research autonomy, research competence, and research relatedness—as mediators to capture individual differences in how researchers experience AI adoption and to provide a deeper understanding of its effects on innovation. Based on empirical data from 252 researchers, this study provides three principal findings. First, AI adoption positively influences the Innovation performance of researchers. Second, the positive relationship between AI adoption and innovation performance is mediated by research autonomy, competence, and relatedness, demonstrating the critical role of these psychological factors. Third, the organizational innovation climate moderates the impact of AI adoption on research competence and relatedness, underscoring the importance of a supportive environment for maximizing the benefits of AI in research. This study makes three key theoretical contributions. First, it extends the theoretical boundaries and application scope of the SOR model, revealing the underlying mechanisms—previously a “black box”—through which AI adoption influences researchers’ Innovation performance. Second, it offers an explanation for the divergences in research on AI technology and Innovation performance at the micro level. Third, it elucidates the mediating mechanisms and boundary conditions between AI adoption and the Innovation performance of researchers. This study provides three practical insights for exploring AI-driven research management. First, it emphasizes actively embracing AI technologies in research while adhering to research ethics and integrity standards. Second, it highlights the importance of fostering an innovative organizational climate that encourages originality and exploration, with particular attention to researchers’ autonomy, competence, and relatedness. Third, it recommends promoting the development of researchers' AI-related skills to apply them in major scientific studies and technological innovations. Future work could examine the mechanisms through which different organizational structures and modes impact Innovation performance. Additionally, the relevance of AI technologies varies across research fields, it could focus on specific subfields to provide deeper insights.

Under the background of the accelerating evolution of the once-in-a-century changes, the increasingly frequent adversity events and the unpredictable business environment have posed great challenges and threats to the digital innovation practice of enterprises. In order to effectively resist the uncertainty and disruptive impact from the outside world, many enterprises have shaped the ability to flexibly cope with decline crises in the process of digital innovation and recover, develop and evolve rapidly from the risk of failure. This paper proposes the concept of digital innovation resilience to represent this phenomenon. On this basis, clarifying the specific connotation of digital innovation resilience, and clarifying the measurement method and evolution model of digital innovation resilience are the internal requirements for building a self-reliant digital innovation system and realizing the goal of Chinese-style modernization. Firstly, the paper starts with organizational resilience, and on the basis of revealing the connotation of innovation resilience, it extends the concept of digital innovation resilience and points out the basic characteristics of it. Secondly, the time series data of digital technology patent application is used to replace the immediate response of digital innovation to external shocks, and the core variable method is used to measure enterprises’ digital innovation resilience. Finally, statistical indicators were extracted and dimensionality was reduced for the time series data of enterprise digital innovation resilience by using time series feature engineering. In combination with K-means clustering algorithm, enterprises with similar evolutionary trends of digital innovation resilience during the sample period were divided into groups, so as to identify four evolution modes: random oscillating type, weak growth type, late catch-up type and core leading type. The above research can not only provide a scientific basis for correctly evaluating the level of enterprises' digital innovation resilience, but also provide useful inspiration for the academic community to explore the differentiated improvement path of enterprises' digital innovation resilience based on different types of evolution models.

Technology convergence has become the primary way enterprises achieve technological innovation and cultivate disruptive technologies. Enterprises’ R&D cooperation was recognized as one of the crucial factors in promoting technological convergence. However, in the context of accelerated technology convergence in multiple fields, how enterprise R&D collaboration affects technology convergence remains unclear. Based on knowledge management theory and resource dependence theory, we constructed the multilayered complex network of enterprise R&D cooperation—technology convergence and measured the technology convergence degree of enterprises from the breadth, depth, and difference. Then, we explored the impact of enterprise R&D cooperation on technology convergence at different technology lifecycles and the moderating effect of engagement level with universities/scientific research institutions. We conducted empirical research on smartphone-related enterprises and found that (1) the strength of enterprises’ R&D cooperation, technology heterogeneity of enterprises’ R&D cooperation partners, and enterprises’ average R&D cooperation time all have a promoting effect on technology convergence depth and difference. The effects are more significant in the emergence and post-growth stages. (2) The strength of enterprises’ R&D cooperation, technology heterogeneity of enterprises’ R&D cooperation partners, and enterprises’ average R&D cooperation time all have an inhibitory effect on technology convergence breadth. The effects are different in each technology lifecycles. (3) The engagement level with universities/scientific research institutions positively regulates the inhibitory relationships between the strength of enterprises’ R&D cooperation, technology heterogeneity of enterprises’ R&D cooperation partners, enterprises’ average R&D cooperation time, and technology convergence breadth. The engagement level with universities/scientific research institutions negatively regulates the strength of enterprises’ R&D cooperation, technology heterogeneity of enterprises’ R&D cooperation partners, enterprises’ average R&D cooperation time, and technology convergence depth and difference. This paper revealed the intrinsic mechanism of enterprises’ R&D cooperation on technological convergence, which provided crucial theoretical and practical references for guiding enterprises to promote technology convergence through R&D cooperation, thereby cultivating disruptive technologies.

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.

Digital transformation is a new engine for multinational enterprises to break through innovation. From the perspectives of resource-based theory and transaction cost theory, based on the panel data of 1588 multinational companies listed on China’s A-share market from 2012 to 2021, it uses empirical methods such as panel fixed effects regression and two-stage least squares regression to explore the direct impact of digital transformation of multinational enterprises on their innovation performance, as well as the moderating effect of digital transformation of multinational enterprises on the relationship between their R&D investment and innovation performance. The results indicate that the digital transformation of Chinese multinational enterprises has a significant positive promoting effect on their innovation performance; and digital transformation has strengthened the positive impact of R&D investment on innovation performance. Heterogeneity testing also found that the digital transformation of multinational enterprises in high-tech industries and large multinational enterprises has a stronger positive impact on innovation performance; the innovation effect of digital transformation of state-owned multinational enterprises is higher than that of non-state-owned ones.

Realizing the deep integration of the talent chain, innovation chain, and industry chain (referred to as "three-chain deep integration") is a prerequisite and inevitable path for the deep integration of the innovation chain, industry chain, capital chain, and talent chain. It is also an effective means to smooth the virtuous cycle of education, technology, and talent. At present, the degree of integration between the talent chain, innovation chain, and industry chain is insufficient, which cannot effectively play a supporting role in the "deep integration of innovation chain and industry chain". Exploring the theoretical logic, integration status, and improvement path behind the "three-chain deep integration" is of great significance for the construction of a modern industrial system and the development of new quality productivity. Firstly, this study focuses on the talent chain as a key variable and proposes the scientific connotation of the talent chain, which includes the dimensions of "classification + growth process". From the perspective of talent classification, the talent chain includes different types of talents such as basic scientific talents, skilled talents, management talents, entrepreneurial talents, compound talents, and leading talents. From the perspective of the talent growth process, the talent chain includes three chain structures: First, the education stage: the entire chain of talent cultivation that runs from basic education to higher education; Second, the social work stage: runs through the entire chain of talent identification, cultivation, introduction, utilization, guarantee, motivation, evaluation, and mobility; Third, career development stage: runs through the entire chain of basic skilled talents, advanced professional talents, and leading top talents. Secondly, this study clarifies the theoretical logic of "three-chain deep integration". To better achieve "deep integration of the three chains", special attention should be paid to: the talent chain matches specialized talents for the innovation chain and the industrial chain, the flow and optimization of talent elements in different chains, the promotion of talent growth by the innovation chain and the industrial chain, and the coordinated development of the talent chain, innovation chain, and the industrial chain with external chains. Then, this paper analyzes the existing issues in China's practical implementation of this integration, including insufficient policy coordination and collaboration, the integration of science, education, industry, and education is "not deep enough", the circulation of innovative elements in industry-university research and application being hindered, and the lack of sound talent evaluation and incentive mechanisms. Finally, this study proposes improvement paths and strategic recommendations for the "three-chain deep integration": (1) Strengthen the top-level planning and institutional innovation for promoting the integration of talent innovation and industry. (2) Establish a mechanism for condensing talent training objectives for the key needs of deep integration of the innovation chain and industrial chain. (3) Promote the cross-chain flow of scientific and technological talent resources, and form a new pattern of co-education, sharing, and sharing. (4) Build a talent evaluation system and dynamic monitoring platform that meets the requirements for the development of new quality productivity. (5) Create a healthy ecosystem conducive to the deep integration of talent chain, innovation chain, and industry chain.

The new round of technological revolution presents new characteristics such as breakthroughs in multiple fields, disciplines, and groups. Innovative entities absorb and integrate heterogeneous innovation resources across boundaries such as technology and organization, achieve the integration of technologies, and emerge innovative points at the intersection of technology fields, driving the formation of cross-border innovation in emerging technologies. By analyzing the cross-border integration of technology and the cross-border cooperation of innovative entities, an emerging technology cross-border integration network was constructed with cooperation sub networks, technology sub networks, and auxiliary sub networks. The mechanism of emerging technology cross-border integration was deeply analyzed, and the cross-border integration of 5G technology and AI technology was taken as the research object. Patent data was used to empirically analyze the emerging technology cross-border integration network. Research has found that with the cross-border integration of 5G technology and AI technology, the number of innovative entities and technology fields increases, and the cooperative relationship between innovative entities tends to stabilize; The degree of integration between technological fields has increased, and the breadth and depth of cross-border cooperation among innovative entities and technology integration have increased; Universities and technology-based enterprises are the main forces driving the cross-border integration of 5G technology and AI technology; The cross-border integration of 5G technology and AI technology is mainly concentrated in the fields of electrical communication and computing, accelerating the development of technology applications such as intelligent vehicles.

The process of enterprise digital transformation is the reconstruction of production modes, business models, and industrial forms, as well as the promotion of enterprise core values. How to better implement digital transformation and enable high-quality development in traditional manufacturing industries has always been a challenging issue in both practice and theoretical research of Chinese manufacturing enterprises' transformation. Based on theoretical review and practical induction, this research classifies feasible paths of enterprise digital transformation into three orientations: technological production, business models, and organizational structure. Using text mining methods, this research classifies Chinese manufacturing enterprises.Using the entropy weight-TOPSIS comprehensive evaluation method, this research evaluates the performance increments brought by different transformation paths, and explores the impact of different transformation paths on comprehensive enterprise performance based on the Bayesian linear regression model. The research findings show that the digital transformation path most favored by sample manufacturing enterprises is business model-oriented; however, the technological production-oriented digital transformation path has the most significant positive impact on comprehensive enterprise performance growth, being the source of long-term competitive advantage for manufacturing enterprises. This research expands the technical methods and theoretical achievements in the field of digital transformation research, providing theoretical support and path references for the digital transformation and upgrading of manufacturing enterprises in China.

First-tier cities (such as Beijing and Shanghai) are currently grappling with the increasingly pressing challenge of talent outflow. Effectively retaining talent, particularly in high-tech fields, has emerged as a critical issue that cities must urgently address. While existing research predominantly focuses on attracting talent inflow, a comprehensive analysis of long-term talent retention strategies remains insufficient. Although some studies have begun exploring the relationship between urban characteristics and individuals’ intention to stay, a systematic theoretical model encompassing various factors has yet to be fully developed. The decision of talent residence involves a complex process of comprehensively considering the multi-dimensional urban factors, including economy, society, culture, and living environment. To gain a deeper understanding of talent retention, it is essential to comprehensively examine the diverse urban attractiveness factors that contribute to talent retention and the mechanisms by which they impact talent. In order to answer the above questions, this research constructs a research model that links the perceptions of urban attractiveness factors and talents’ intention to stay. Specifically, based on the theory of environmental psychology, this paper introduces well-being as the mediating mechanism to explain how the perception of urban attractiveness factors (including talent policy, economic development, cultural atmosphere, and living environment) affect talents’ intention to stay, because the environment can shape individual psychological well-being and then their attachment to the environment. To empirically validate this model, this research adopts a mixed-methods approach, combining quantitative surveys with qualitative interviews. In the quantitative phase, we distributed questionnaires to 1200 high-tech employees, utilizing structural equation modeling for analysis. Subsequently, we conducted face-to-face semi-structured interviews with 11 high-tech employees to further validate and interpret the quantitative findings, enhancing the reliability and depth of our conclusions. This study yields several key findings. Among the four urban attractiveness factors, only the perception of living environment has a significant direct impact on talents’ intention to stay. The higher the individual’s satisfaction with the urban living environment, the stronger their intention to stay. Further investigation of the mediating effect shows that individuals’ perception of urban talent policy, economic development, and living environment indirectly affect talents’ intention to stay by affecting their well-being, where the perception of living environment has stronger explanatory power than other factors. Furthermore, while individuals’ perception of living environment and economic development has a significant positive impact on well-being, the perception of talent policy has a relatively weak impact on well-being, and the perception of cultural atmosphere has no significant impact on well-being. We further explain the differentiated effects of different attractiveness factors based on interview data analysis. This research makes the following contributions. Theoretically, it interprets urban attractiveness factors from the perspective of individual subjective perception and systematically explores the impact of four key perceived urban attractiveness factors on talent intention to stay. This enriches the theoretical framework for studying retention intentions. Furthermore, the paper introduces well-being as a mediating mechanism, revealing the direct and indirect mechanisms through which the four urban attractiveness factors influence talent intention to stay. Practically, cities can adopt diverse measures to reduce talent outflow, with a particular focus on enhancing the urban living environment. When formulating and implementing policies, policymakers can fully consider the subjective perception and interpretation of policy effects by the audience. Finally, the well-being of talent in the city has become a critical factor influencing their residency decisions, warranting careful attention from city managers.