The rapid advancement of Artificial Intelligence (AI) is profoundly changing the operational logic and organizational models of scientific research, fundamentally reshaping the theoretical foundation and methodological system of the Science of Science (SciSci).Based on the rise of AI-driven scientific research, this paper systematically analyzes the holistic transformation of SciSci research across its objects, core problems, and methodological tools under the empowerment of AI.The study indicates that AI acts simultaneously as a revolutionary force within the scientific system, generating new SciSci research objects and questions, and as a powerful analytical tool, breaking through the technical bottlenecks of traditional SciSci methodologies. This series of transformations will enable SciSci to achieve a fundamental expansion: from external representation to intrinsic mechanisms, from phenomenological description to mechanistic explanation, and from passive observation to active design. The new research paradigm exhibits core characteristics such as prospectiveness, systematicness, synergy, and designability.At the methodological level, AI promotes the objectification and multi-modality of SciSci's observation and measurement, the predictive and networked nature of its analysis and modeling, and the systemic and explainable nature of its explanation and prediction. This significantly enhances SciSci's practical capabilities in research evaluation, knowledge discovery, and scientific ecosystem design.Nevertheless, risks and challenges in technical, ethical, and institutional aspects urgently need to be addressed. This paper proposes that, while maintaining the unique identity of the SciSci discipline, we should drive paradigm evolution through methodological innovation, and construct an open, transparent, explainable, and prospectively guiding methodological system to achieve the continuous leap forward of SciSci empowered by AI.

Artificial Intelligence(AI) technology has experienced rapid development in recent decades, driving a profound paradigm shift in management science research and reshaping the theoretical framework, research methodologies, and practical application scenarios of the discipline. To systematically sort out the path, current situation, and challenges of the integration of AI and management science, this study conducted a large-scale quantitative analysis of high-quality journal literature indexed in the Web of Science database, with a focus on publications included in the Financial Management Society (FMS) journal list, and constructed and clarified the systematic framework of its fundamental transformation. This method covers 25,719 literatures in the fields of management science and economic science from 1996 to 2025, and uses professional analytical tools such as VOSviewer and RStudio to conduct visual analysis of keyword co-occurrence relationships, national cooperation networks, and research trend evolution. The results show that the number of publications in this field presents an exponential growth trend over the past three decades, with China and the United States as the core contributing countries; there are significant differences in the research focus between management science and economic science; research hotspots are concentrated in key clusters such as the application of AI technology in management practice, the identification and response to algorithmic risks, the construction of trust mechanisms in service science under the intelligent environment, and the AI-driven digital transformation of enterprises, while notably, significant differences exist in the research focus of management science and economic science during the integration process—management science tends to prioritize the optimization of practical management scenarios and efficiency improvement, while economic science focuses more on exploring the impact of AI on economic operation rules and resource allocation mechanisms. The conclusion indicates that AI has evolved from an auxiliary tool to a core driving force for promoting the reconstruction of management science theories and the transition of research paradigms towards the "human-object-information" three-dimensional space. Future research should focus on cutting-edge issues such as the internal mechanism of human-machine collaboration, the ethical boundaries of algorithm governance, and institutional adaptation in major decision-making, so as to promote the construction of a new management science theoretical system adapting to the intelligent era.

In an era characterized by intensifying globalization, rapid technological iteration, and geopolitical volatility, traditional organizational decision-making paradigms grounded in the theory of “bounded rationality” are facing unprecedented challenges. Decision-makers are increasingly constrained by inherent cognitive limits when processing real-time, high-dimensional information, leading to satisficing behaviors that are frequently insufficient for today’s high-risk, complex, and feedback-delayed environments. While Artificial Intelligence (AI) has emerged as a transformative force capable of reshaping the epistemological foundations of strategy and risk management, the theoretical mechanisms and boundary conditions governing “human-AI symbiosis” remain underexplored. Existing literature often treats AI as a monolithic variable or categorizes collaboration solely based on automation levels, failing to elucidate the underlying power dynamics—specifically, the allocation of decision rights and the locus of agency—that define the collaborative interface. Consequently, there is a critical need for a systematic framework that deconstructs how AI reconfigures the structural and procedural dimensions of organizational decision-making beyond mere technical functionalism. To address this significant theoretical gap, this study conducts a systematic literature review of 85 core articles published in UTD24 premier management journals between 2020 and 2025. Utilizing bibliometric analysis and systematic coding, the research maps the trajectory of AI application in organizational decision-making, identifying key thematic clusters such as information processing, machine learning, and trust dynamics. The central contribution of this work is the construction of the novel “Interaction-Support-Influence” classification framework, anchored in the power relations between humans and machines. This typology transcends prior functional descriptions to reveal distinct logics of agency and collaboration. The “Interaction Mode” represents a form of shared agency characterized by a recursive loop of output, feedback, and modification; here, decision power is dynamically shared, and AI acts as a reflexive partner that challenges human assumptions and co-creates solutions through bidirectional engagement. The “Support Mode” characterizes scenarios where AI functions as an augmentative tool for information processing or option generation, yet the final fiat remains exclusively with the human agent, focusing on enhancing efficiency and accuracy while retaining human cognitive sovereignty. The “Influence Mode” delineates scenarios where AI operates as an environmental variable or architect, shaping the decision-making context through information filtering or algorithmic choice architecture without explicit participation in the final choice, thereby structurally constraining human agency through latent algorithmic antecedents. The synthesis reveals that the efficacy of these modes is contingent upon a complex interplay of internal and external factors. Internally, individual traits such as algorithm aversion or appreciation, alongside organizational capabilities like absorptive capacity and strategic inertia, dictate adoption success. Externally, macro-institutional factors, including regulatory frameworks and cultural contexts, significantly bound the scope and legitimacy of collaboration. Regarding outcomes, the study distinguishes between performance metrics and behavioral evolution, highlighting a “double-edged sword” effect: while AI integration generally enhances financial efficiency and decision quality, it simultaneously introduces risks such as cognitive atrophy, algorithmic bias, and the erosion of unique human tacit knowledge. Despite these theoretical advances, the review identifies significant lacunae in current scholarship, particularly regarding the lack of integration between AI implementation and core organizational strategy. Existing research remains fixated on a “human-in-the-loop” paradigm that underestimates AI’s potential agency and neglects the profound impact of macro-environmental variables. To advance the field, this paper proposes a comprehensive future research agenda structured around two critical dimensions: the deep integration of AI with organizational strategy and the expansion of novel AI strategic horizons. First, regarding the deep integration of AI and strategy, the study calls for moving beyond static adoption to investigate dynamic human-machine authority allocation, the impact of macro-institutional variables—such as geopolitical shifts and cultural contexts—and the profound reconfiguration of organizational structures and relational networks. Second, regarding the expansion of new AI horizons, the paper emphasizes the urgent need to examine AI’s disruptive role across four emerging frontiers: facilitating scalable innovation and application, redefining power dynamics within platform ecosystem governance, managing systemic risks and compliance, and addressing the paradoxes of AI-driven ESG sustainability. Ultimately, this research extends the theoretical boundaries of sociotechnical systems in management, offering practitioners a diagnostic logic for designing differentiated human-AI mechanisms that align with specific strategic imperatives, thereby facilitating a shift from simple tool adoption to deep structural integration and sustainable competitive advantage.

The widespread application of the "AI-human" system in management decision-making has sparked discussions on enhancing the synergy of the "AI-human" system to improve management decision-making performance. Although existing research has proposed some methods to improve the synergy of the "AI-human" system in management decision-making, there is still a lack of validation in specific business scenarios. This article chooses the initial screening of journal articles as a scenario and delve into the specific process of sequential AI-Human collaboration to exploring the path to improving the synergy of the "AI-human" system in management decision-making.

This article proposes that we can use the scoring results of AI to eliminate some low-quality submitted papers before the editor starts the initial screening work. Then, the editor screens the remaining papers with higher scores. In this process, AI’s filtering effect will reduce the number of papers reviewed by editors. Therefore, we can use the sequential AI-Human collaborative mode to improve the overall efficiency of paper review work. That is to say, the key to this type decision-making lies in designing AI screening thresholds based on an evaluation of AI's screening effectiveness.

Firstly, this article introduces Spearman correlation and innovatively designs priority comparison coefficient to evaluate the overall decision-making efficiency of AI. Secondly, by defining the negative and positive states of the screening results, sensitivity, specificity, and accuracy indicators were introduced to evaluate the AI’s decision screening effectiveness; Then, the design of evaluating benchmarks is carried out by aggregating human collective judgments, and it is proposed that the judgments of multiple human experts can be combined, enhanced, and jointly determined to form different evaluation benchmarks. These benchmarks can avoid the limitation of using a single evaluation benchmark that cannot accurately reflect AI’s decision-making ability; Finally, by designing the minimum retention rate as an indicator, the threshold setting method for AI in screening decision was clarified. This indicator not only reflects AI’s screening effectiveness, but also enables the rapid implementation of AI applications in the specific scenario. The analysis of three groups of papers in a journal demonstrates that by retaining 75% of the papers during the initial screening phase using existing large language models, high-quality papers identified in relevant evaluation benchmarks can be effectively covered. This reduces editorial workload by approximately 25%, increases overall editorial efficiency by about 15%, and improves the efficiency of the entire editorial process by roughly 8%.

The research is not limited to the level of AI (or LLM) capabilities, but focuses on how to effectively introduce AI into management decision-making on the basis of existing AI capabilities, so as to accelerate the pace of AI implementation and application. The relevant evaluation indicators of AI decision-making capability and their usage logic proposed in this article can not only be applied to decision screening in other scenarios, but also adapt to the dynamic evolution of LLM capability. After significant changes in AI capability, the threshold for AI decision screening can be reanalyzed and calculated, achieving continuous improvement in the synergy level of the "AI-human" systems.

This study constructs a theoretical model of the relationship between AI capability, innovation agility, and innovation resilience. The positive significance of AI applications has been widely recognized, but the research results of this article indicate that AI capabilities have an inverted U-shaped impact on the innovation resilience of enterprises, which first promotes and then inhibits; There is also an inverted U-shaped relationship between AI capability and innovation agility, and innovation agility plays a mediating role between AI capability and innovation resilience. In addition, environmental turbulence positively moderates the impact of innovation agility on innovation resilience. The research results confirm the "double-edged sword" effect of AI capabilities, opening up new research perspectives for enterprise innovation management in the AI era, and providing management insights for enterprises to effectively utilize AI technology to achieve sustainable innovation development and avoid falling into the "digital trap".

The findings of this study offer three major contributions: Firstly, this paper combines AI with innovation research from a research perspective, treating AI capabilities as an important organizational capability in the digital age. It constructs an analytical framework of "AI capabilities - innovation agility - innovation resilience" and confirms the role path of AI and agility as driving forces in enhancing enterprise innovation resilience. That is, a set of key organizational strategic agility processes enabled by "digital intelligence" creates strategic resilience in innovation activities with a proactive and opportunity-centered attitude. Furthermore, this paper explores the non-linear interaction between AI capabilities and innovation agility and resilience, revealing the "double-edged sword" effect and "dark side" of AI capabilities, thereby breaking through the limitation of existing research that mainly focuses on the positive significance of AI applications.

Secondly, this paper contributes to the research on the antecedents of enterprise innovation resilience. Although the concept of innovation resilience has attracted attention in recent years, the research is rather scattered and lacks in-depth exploration of the role played by AI. Therefore, the concept of AI capability is introduced in this paper, and by revealing the role of AI capability in promoting enterprise innovation resilience and its internal mechanism, a new research perspective is opened for the discussion on how to improve enterprise resilience, and at the same time, it also enriches the existing research on improving innovation resilience.

Finally, based on the context of digital development and market transformation of Chinese enterprises, this paper takes environmental turbulence as a moderating variable and effectively reveals the contingency effect of enterprises implementing agile strategies to resist the challenges of high-turbulence environments. By closely integrating the dynamic capability factors at the organizational level with the characteristics of the external environment, it emphasizes that in highly turbulent environments, enterprises should take enhancing agility as the main development strategy. From this perspective, this study not only highlights the significant role of AI capabilities and organizational agility in enhancing enterprise resilience, but also recognizes that the effectiveness of these dynamic capabilities may vary depending on the environment and context in which they are applied. This response addresses the call from scholars in the past for future research to explore how digitalization and organizational agility interact to influence organizational resilience in different organizational environments and market scenarios.

To understand the dynamics of artificial intelligence (AI) regulation in the United States and explore policy arrangements in the context of Sino-U.S. AI competition is crucial for China's participation in global AI governance and its pursuit of global technological leadership. However, existing research has predominantly focused on macro-level comparative analyses of national AI policies, with limited exploration into state-level AI policy comparisons. The U.S. regulatory policy framework, shaped by its federal political system, exhibits significant complexity and fragmentation. This unique structure offers valuable insights for policymakers in other countries navigating the complex and rapidly changing artificial intelligence regulatory policy development settings. Using Latent Dirichlet Allocation (LDA), Dynamic Topic Modeling (DTM), and content analysis, this study conducts an in-depth examination of 397 policy texts from 36 U.S. states, illustrating state-level regulatory preferences, policy impact areas, and shifts in policy focus over time. Along with the aforementioned, the study also compares the regulatory proximity of China's artificial intelligence policy to that of the United States at the state level. The results show that the U.S. has established a foundational AI regulatory policy system, characterized by seven policy topics and four core policy instruments. The seven policy topics include consumer privacy protection, public opinion and election, fintech regulation, public-private data governance, advisory committee building, automated decision-making fairness and system security and effectiveness. Among these, system security and effectiveness emerge as the most aligned topic between Chinese and U.S. AI regulatory frameworks. Beyond the seven policy topics, four key policy tools were adopted by states in the U.S., including checklist documentation, mandatory disclosure requirements, prohibition and risk classification. Artificial intelligence regulatory committees in U.S. states fall into two categories: comprehensive committees, like those in Oregon, Maryland, and Indiana, addressing broad AI issues; and specialized committees, such as New Mexico’s group on automated decision-making and New York’s panel on AI, robotics, and automation. The study also identifies that states such as California, Illinois, and New York exhibit the most diverse distribution of policy topics. In terms of regulatory proximity to China, Hawaii, Oklahoma, and New York rank highest, with technologically advanced states like California and Massachusetts also featuring prominently. Finally, drawing on the complexity of the artificial intelligence regulatory policy system in the state of the U.S. and the need for artificial intelligence development and regulation, it is recommended that China adopt policy innovation approaches that are tailored to regulatory scenarios and enrich the scenario hierarchy of the regulatory system, deconstruct the entire system into components and improve the details of the subject of regulatory policy, prioritize compatibility and symbiosis to align the value of policy tools, and emphasize collaborative efforts to enhance agile effectiveness in artificial intelligence regulatory decision-making. The study presents the overall landscape of artificial intelligence governance arrangements in the United States and measures the proximity of Sino-US regulation policies. This could serve as a vital reference for Chinese policymakers, providing evidence-based recommendations to guide the innovation and refinement of AI regulatory policies. It is critical for aligning China's regulatory strategies with global best practices, so increasing its ability to reduce risks, encourage ethical AI development, and boost its leadership in the global AI governance environment.

For a long time, problems such as information lag and cumbersome processes have been widespread under the traditional supply chain management mode. The phenomenon of information silos among enterprises has become the primary obstacle to information connection and efficiency improvement in the supply chain. With the emergence of the digital era, the application of big data, artificial intelligence, the Internet of Things, cloud computing and blockchain technology has successively empowered supply chain management, injecting new vitality into the innovation and efficiency improvement of modern supply chain management models. There are very few studies in the existing literature that examine the employment spillover effects of digital technology application in supply chains. From the perspective of supply chain network construction, This paper intends to explore the impact of supply chain digital technology application on the scale of labor employment of enterprises, and further analysis the impact of the collaborative application of supply chain digital technology of other enterprises in the industrial chain on the scale of labor employment of enterprises in the chain. Compared with the existing literature, the potential contributions of this paper are as follows: Firstly, different from traditional literature that focuses on the analysis of the substitution effect and creation effect of digital transformation on labor employment of enterprises, this paper takes the application of digital technology in supply chain network as an entry point to discuss the spillover effect of supply chain network construction on labor employment of enterprises in the chain. Specifically, this paper clarifies two paths of the employment spillover effect of supply chain digitization from the aspects of commercial credit spillover along the supply chain and smoothing enterprise production, which enriching the existing research on the factors affecting employment in the labor market. Secondly, this paper focuses on the analysis of the synergy between the upstream and downstream of the supply chain network, emphasizing the win-win cooperation of multiple subjects from the perspective of the supply chain, providing theoretical support and empirical evidence for the subsequent promotion and construction of industrial chain digitization. Based on the supply chain research data of listed companies and the perspective of supply and demand network construction, this paper discusses the employment effect of supply chain digitization. The difference-in-differences results show that the enterprise's digitization has a substitution effect on labor employment, while the supply chain digitization within industrial chain can effectively improves the enterprises’ employment scale by smoothing production and reducing the downstream enterprises’ occupation of commercial credit funds, which helps to ease the liquidity constraints of enterprises. In addition, industrial supply chain digitization can effectively cushion the impact of supply chain risks on the labor market, which helps to achieve "the employment-stabilizing". Heterogeneity analysis shows that the employment-stabilizing effect of industrial supply chain digitization is limited by the financing constraints, state-owned enterprises and the enterprises with concentrated purchasing and marketing business show greater spillover effect on employment.The research conclusions of this article are of great significance for the subsequent development of digitized supply chain construction, the maintenance of supply chain security and stability.

Addressing climate change has become a global consensus, and the energy sector, as the primary "contributor" to global carbon emissions, has become an important reform area for countries to promote carbon neutrality. In recent years, with the rapid development of clean energy technology, the proportion of renewable energy such as wind and solar energy in the energy system continues to increase, and a new generation of global energy system with electrification, high efficiency, low carbon and interconnection is taking shape. Scientific and technological innovation will once again promote the evolution of human energy system and become the first driving force to support and lead the realization of the "double carbon" goal. This paper expounds the necessity, urgency and importance of relying on scientific and technological innovation to promote the development of China's clean energy industry from three aspects: the development of clean energy science and technology, the scientific and technological competition between China and the United States, and the development of China's traditional manufacturing-green superior industry. This paper systematically expounds the development status of clean energy industry in China from four aspects: the level of clean energy technology, the utilization rate of clean energy resources, the empowerment of digital intelligence technology and the construction of talent team, and clarifies the five major challenges facing the development of clean energy industry in China. First, China's energy system is confronted with the challenges of total demand growth and suboptimal structure in the short and medium term. Secondly, there is a spatial dislocation between energy supply and demand in China. Thirdly, the rapid development of clean energy industry forces China to speed up the construction of a new power system. Fourthly, there is still a certain gap between China's clean energy technology level and that of developed countries. Fifthly, China's strategic mineral resources supply chain is facing greater risks. To this end, the practical path of scientific and technological innovation to support the development of China's clean energy industry lies in: coordinating the national energy strategic deployment, eliminating the spatial differences between energy supply and demand; focusing on the world's leading edge of energy science and technology, speeding up the tackling of key core technologies; relying on major projects to reduce costs and increase efficiency, promoting advanced technologies to try first; promoting the construction of energy integration systems, improving the efficiency of cross-border energy utilization. We should extensively expand international scientific and technological cooperation, fully learn from the successful experience of the United States and Europe, implement the strategy of guaranteeing the supply of mineral resources, and build a safe and resilient supply system. The key policy recommendations are as follows: First, vigorously promote the development of new energy storage industry and support the transformation of energy supply from "trans-regional energy transmission" to "local energy supply". The second is to build a scientific and technological innovation consortium in the field of energy and give full play to the role of leading enterprises in science and technology. Third, we should create an innovative ecosystem for technology sharing, accelerate the cross-industry and cross-domain flow of advanced technology, and stimulate innovation vitality. Fourthly, the State Key Laboratory of Energy System Integration should be established to tackle key problems in the form of major engineering projects. Fifth, we should actively build a more proactive mechanism for international scientific and technological cooperation.

This study explores the application of complex networks and computational science of science paradigms in the field of Science of Science, focusing on uncovering universal laws behind scientific activities while addressing the challenges of avoiding the “numerical game.” In the era of big data, large-scale data has provided new perspectives for science studies, where complex networks help reveal hidden patterns in scientific research by analyzing collaboration networks and knowledge dissemination pathways. Computational science of science, powered by advanced algorithms and models, has further enabled intelligent analysis in science studies. However, this data-driven approach also poses potential risks of falling into the “numerical game,” where over-reliance on quantitative analysis may overlook the complexities and contextual factors inherent in scientific activities. This study not only examines the strengths of complex networks and computational science of science in related studies but also delves into how to balance data-driven methods with domain knowledge to avoid the limitations of excessive data dependence. Ultimately, this paper aims to offer new perspectives for the future development of science studies and calls for more cautious and integrative approaches to address the challenges of modern science.

The contemporary life science research paradigm is undergoing a major transformation. Along with the continuous integration between biotechnology and information technology, the evolution of research beliefs, methods, and cultures have jointly promoted the paradigm shift of life science research: research beliefs have shifted from reductionism to holism, and the dialectical integration of the two has been realized with the help of network thinking; the rise of deep learning has led to the realization of double leaps in efficiency and precision of computational simulation and modeling methods under the strong coupling of data, algorithms, and computing power; and the organization and management modes have gradually become data-centric, platform-based, and ecological, awakening the advocacy and support of an open scientific culture. The new paradigm of data-driven life science research has accelerated the process of scientific discovery and expanded the scope of scientific discovery, and at the same time brought about many biotechnological and ethical challenges.

As the primary resource, talents, especially the strategic talent force, play an increasingly prominent supporting role in enterprise innovation activities. However, despite the remarkable progress made in the construction of talent teams across various industries, the cultivation and utilization of the strategic talent force are still in the long - term exploratory stage. A large number of enterprises still have no understanding of the overall picture of the strategic talent force and lack systematic theoretical guidance on how to cultivate relevant talents. Regrettably, the current theoretical research on the competency structure and development laws of the strategic talent force is still in its infancy. Existing studies have carried out some exploratory work on deconstructing the competency structure of the strategic talent force, but their focus has been on describing the morphological characteristics of talent competency elements. Questions such as what differences exist in the competency structures of different types of strategic talent forces and what development trajectories the elements in a specific competency structure follow still remain to be answered. Therefore, this study, on the basis of clarifying the competency configurations of two types of strategic talents, strategic scientists and outstanding engineers, maps the evolution of their competencies with individual growth, thus providing new insights for cultivating competencies required for strategic talents.

We conducted semi-structured interviews with 34 top experts from a central state-owned enterprise. These interviews required respondents to recall 1-3 behavioral events of themselves or high-level talents around them in the process of achieving significant accomplishments, solving major problems, leading major projects, as well as 1-3 typical experiences of their own growth and participation in talent cultivation. Subsequently, adopting the programmatic grounded theory, we refined the typical competencies of strategic scientists and outstanding engineers, and the processes and mechanisms of the competencies’ formation.

The study finds that: (1) Strategic scientists possess competencies such as a commitment to scientific truth, a noble scientific mission, a strategic scientist identity, advanced theoretical insights, and exceptional exploration abilities; outstanding engineers possess competencies such as practical engineering qualities, ambitious engineering goals, an outstanding engineer identity, forward-looking engineering insights, and excellent practical skills. The competency configurations of strategic scientists and outstanding engineers exhibit heterogeneity, with the former serving scientific discovery and theoretical innovation, while the latter serves engineering practice and industrial development; (2) The evolution of the competency configurations of strategic scientists and outstanding engineers includes five core stages: enlightenment, education, career initiation, career development, and career emergence. (3) The differences in the competency configurations lead to varying key factors and pathways in their corresponding evolutionary contexts. Moreover, the development of competencies demonstrates dynamic characteristics of sequential integration and presents an evolutionary form of multiple parallel pathways.

The theoretical contributions of this study can be summarized in three aspects: First, unlike previous studies that focused on depicting the profile of a single type of strategic talents, this study deconstructs and contrasts the competency configurations of scientists and engineers, by considering the context distinction of talents. Second, differing from studies on talent development, this study goes beyond merely observing the individual development of strategic talents and, by considering the dynamic characteristics of competencies, delves deeply into the underlying trajectory of competency evolution. Third, in terms of theory, this paper combines the longitudinal perspective of career theory with the lateral perspective of competency theory, fostering dialogue between these two talent-focused theories.

Provincial strategic emerging industry development planning is a major measure to implement the national innovation strategy and promote local economic development. In order to implement the national strategic requirements of developing new quality productivity in accordance with local conditions and promoting the healthy and orderly development of strategic emerging industries, in view of the current problems of convergence, greed for perfection and forcing the growth of local strategic emerging industries, a provincial strategic emerging industry evaluation methodology based on comparative advantage theory and overcoming the "comparative advantage trap" was constructed on the basis of reviewing existing research results, so as to serve the scientific decision-making of local governments and support local governments in selecting characteristic industries with comparative advantages. This evaluation methodology introduces sustainable development theory and innovation theory, establishes two dimensions of resource endowment and comprehensive output, and innovatively constructs an indicator system that highlights scientific and technological innovation, ecological friendliness, social welfare and economic efficiency, breaking through the limitation of existing research focusing on the singleness of economic indicators. The Shanxi Province, which once faced the challenge of the "comparative advantage trap", was used as an example for verification and analysis. The results show that the evaluation method is practical, feasible and scientific. Based on the experience of empirical research, practical and policy recommendations are put forward.

Accelerating the construction of independent innovation capabilities in core technologies is an imperative requirement in the new context of international scientific and technological competition. Against this backdrop, China's innovation policy planning must transcend the traditional logic of follower catch-up and shift towards a technological strategic perspective of beyond catch-up. It is essential to balance the breakthrough of bottleneck technologies and the forward-looking layout of killer technologies, focusing not only on the autonomy and control of current key industries but also on shaping future technological competitive advantages.

Focusing on the strategic layout of national innovation policy, this study defining and discussing the scientific connotations and development patterns of killer technologies from the perspective of beyond catch-up, and emphasizes the strategic significance of killer technologies in international competition, which compensates for the bias of existing research that predominantly emphasize bottleneck technologies. Killer technologies refer to "asymmetrical technologies" mastered by technological entities at the high end of the innovation chain, which are difficult for other entities to imitate and surpass. Killer technologies are primary innovation outcomes formed within an endogenous innovation chain, fundamentally different from bottleneck technologies that rely on external sources of innovation. In the process of breaking through bottleneck technologies, latecomers who improve and expand within existing technological paradigms and tracks are prone to falling into the "catch-up trap" of continuous catching up and continuous falling behind.

This study provides a comprehensive analysis of the "valley of death" phenomenon in key core technology innovation. On the basis of summarizing and sorting out the patterns of innovation transformation chains for two types of key core technology, the study analyzes the three major innovation breakpoints and constraints of innovation achievement transformation from basic research to application development, to productization, and then to scaling up, which supplements the previous research's insufficient attention to the breakpoints of upstream technology acquisition and downstream technology promotion. The main challenges faced by killer technologies at the stages of technology acquisition, transformation, and promotion are the insufficiency of continuous basic research, goal divergence among multiple entities, and the failure of business model innovation. Bottleneck technologies face the "cold start" paradox of latecomers, the lack of "know-how" in application transformation, and the difficulty of scaling up development at three stages.

Breakthroughs in key core technologies typically require multiple entities to collaborate in complex innovation networks. Focusing on the three types of "valley of death" faced by the two categories of key core technologies, this study systematically elaborates the breakthrough path mechanisms for killer technologies and bottleneck technologies based on the "capability enhancement - resource allocation - institutional environment" logical framework, aiming to provide new theoretical perspectives and practical guidance for China's innovation policy layout. Capacity building of entities is key to breakthroughs in key core technologies, the optimization of resource allocation aims to enhance the entity capabilities, and reasonable and effective institutional arrangements support and safeguard the effective allocation of resources. These three types of innovation breakthrough elements interact and collaborate effectively in the process of breaking through key core technologies.

The intensified competition amidst the new round of technological changes and industrial revolutions has led to substantial advancements in the manufacturing industry. However, there is a notable dependency on imports in key core technology areas, such as next-generation information technology and biomedical industry, posing significant challenges. In response, the report of the 20th National Congress of the Communist Party of China in 2022 made important strategic deployments, proposing to resolutely win the battle for mastering key core technologies. The unique and in-depth professional technology accumulated by enterprises in specific knowledge fields for a long time is the key driving force to promote the breakthrough of key core technologies. In this context, SRID enterprises, leveraging their professional expertise and deep cultivation in specific segments of the industrial chain, are crucial for breaking through key core technologies.

There is a consensus in literature on the importance of knowledge resources in driving breakthroughs in key core technologies. However, existing research primarily focus on exploring this through collaboration networks, lacking literature that examines the impact mechanism of a firm's internal knowledge base on key core technology breakthrough, especially empirical research on the degree of technological specialization among SRDI enterprises.

This study takes the new generation of information technology industry in the first five batches of listed small giant enterprises in China as the research object, and deeply explores the effect of technological specialization on the breakthrough of key core technologies, the mediating role played by knowledge search in this process, and the moderating role of knowledge integration capabilities. The results show that there is an inverted U-shaped relationship between technology specialization, knowledge search and key core technology breakthroughs. A firm's specialized knowledge base and externally searched heterogeneous knowledge provide a direct resource base for breakthrough innovations in key core technology fields. The moderating effect analysis shows that the inverted “U” shaped relationship between technology specialization and key core technology breakthroughs is mediated by knowledge search. Enterprises will conduct knowledge searches based on their own knowledge characteristics and, through continuous cooperation and exchanges with the outside world, provide more extensive or in-depth heterogeneous knowledge resources for breakthrough innovations in key core technology fields, thereby freeing enterprises from resource constraints. In addition, knowledge integration ability moderates the role of technology specialization and knowledge search on key core technology breakthroughs, respectively. High knowledge integration ability will alleviate the negative impact of over-specialization and over-search, and when it breaks through a certain threshold, it can completely offset the negative impact and further promote key core technology breakthroughs.

These findings hold significant importance for enterprises to facilitate knowledge search by deepening their own specialized knowledge, thereby promoting breakthroughs in key core technologies: (1) SRDI enterprises should deepen core technology research while exploring diversified technologies, forming dedicated teams and building a knowledge base to avoid the specialization trap. (2) SRDI enterprises should integrate internal and external resources by collaborating across sectors and deeply mining existing knowledge, utilizing advanced tools for systematic analysis and integration. (3) Constructing a knowledge sharing platform and nurturing a multidisciplinary team will enhance resource integration, facilitating efficient knowledge conversion and application.

This study emphasizes the complementary synergy between knowledge search and technology specialization, expands the research scope of key core technology breakthroughs at the micro level, and provides theoretical guidance and decision-making reference for enterprises to promote key core technology breakthroughs.

Chip lithography technology is a pivotal component in the semiconductor manufacturing domain and exemplifies the “root-trunk-branch” characteristics of a complex technology cluster. Its relevance extends beyond individual processes, influencing a country’s comprehensive technological capabilities and industrial competitiveness. However, existing research has offered only limited insight into how these multifaceted core technologies evolve in clusters over time. In an effort to address this knowledge gap, researchers introduced the concept of “root technology,” employing both explicit and implicit relationships derived from patent data to explore the dynamic evolution of key core technologies. By incorporating terminology extraction, clustering analysis, and related methods, they developed an analytical model that delineates the way crucial lithography innovations emerge and transform.

The findings of this study highlight four principal root technologies within the broader chip lithography system—projection exposure technology, direct-write lithography technology, photoresist materials, and photomask manufacturing. Together, they form the backbone of chip lithography’s evolution and diverge into distinct pathways of development. While projection exposure technology tends to merge features from various domains (reflecting a fusion-oriented path), direct-write lithography gradually contracts into more specialized niches, signaling a pattern of consolidation. Photoresist materials expand and diversify to accommodate evolving demands and technical frontiers, illustrating a path of sustained growth. By contrast, photomask manufacturing branches out into multiple subfields, capturing a split-type trajectory that underscores the complexity and range of potential innovations in that area.

This comparative perspective on distinct evolutionary paths accentuates the defining traits of root technologies: they provide a fundamental foundation for subsequent developments; they exhibit high levels of complexity and intricate integration with complementary domains; and they impose significant barriers to entry, owing to their specialized expertise, substantial R&D requirements, and stringent quality standards. Such insights contribute to a deeper understanding of the unique nature of root technologies, advance theoretical frameworks on complex innovation clustering, and add practical value for policymakers and industry leaders.

In particular, these outcomes offer concrete guidance for China’s chip lithography sector, which faces urgent demands for technological breakthroughs. By pinpointing the specific root technologies and clarifying how each one evolves, researchers and practitioners can formulate strategies to distribute resources more effectively, foster targeted collaborations, and stimulate ongoing innovation in lithography manufacturing. Ultimately, appreciating the interconnected yet distinct evolution paths of projection exposure, direct-write lithography, photoresist materials, and photomask manufacturing helps lay the groundwork for robust policy decisions and industrial strategies that will shape the future of semiconductor fabrication.

Regional science and technology innovation capacity is an important starting point for leading high-quality development. Building regional science and technology innovation centers is conducive to gathering innovation elements and resources and promoting the improvement of the capabilities of various innovation entities. At the same time, in order to accelerate the strategic deployment of big data and create an innovation landscape led by big data, as a data element distribution center and a big data industry gathering place, the big data comprehensive pilot zone can provide data sharing dividends and industries for regional technological innovation development. It supports the engine and contributes to continuous practice and sums up big data industry development experience that can be promoted and replicated across the country. Based on the quasi-natural experiment established in the big data comprehensive pilot zone, this study adopted a multi-period double difference (DID) model to conduct statistical analysis on panel data of 30 provinces (including municipalities and autonomous regions) from 2011 to 2021, in order to explore the impact of the big data comprehensive pilot zone on regional science and technology innovation capacity and the implementation effects of different pilot policies. Research results show that the establishment of a national big data comprehensive pilot zone has significantly improved regional science and technology innovation capacity. This conclusion still holds true after passing the placebo test, lag one-period model and robustness test excluding first-tier cities. In order to further explore the effect of different pilots in the national big data comprehensive pilot zone on the science and technology innovation capacity of surrounding areas, this article also conducted a heterogeneity analysis on the eight pilot areas. The results show that, firstly, the policy effects of the eight major pilots are different, that is, different pilots in the national big data comprehensive pilot zone have different effects on the science and technology innovation capacity of surrounding areas, with spatial and regional differences. Second, the heterogeneity analysis results of the national big data comprehensive pilot zones in Guizhou, Beijing-Tianjin-Hebei, Henan, Inner Mongolia, and Shenyang are significantly positive, indicating that the big data comprehensive pilot zone policy has a significant effect on the regional science and technology innovation capacity of these pilots. Third, the heterogeneity analysis results of the national big data comprehensive pilot zones in Shanghai, the Pearl River Delta, and Chongqing are not significant, indicating that the policy effect of the big data comprehensive pilot zones is not obvious in these areas. This article further explores the reasons for this result, which are attributed to differences in local economic development foundations, policy implementation goals, and innovation distribution structures. Based on this, corresponding policy suggestions are put forward. On the one hand, it is recommended to strengthen inter-regional policy coordination and promote the exchange, sharing and integration of innovative resources between regions. On the other hand, for the Pearl River Delta, Shanghai, and Chongqing pilot areas, policy implementation should consider based on the actual development of local science and technology innovation, we will further optimize the adaptability of big data pilot zone policies, ensure the precise implementation of policies, and promote the coordinated development of regional science and technology innovation capacity.

To smooth the channels for the commercialization of scientific and technological achievements and convert these achievements into real productive forces, the effective support of fiscal policies is required. The commercialization of scientific and technological achievements is characterized by large capital requirements, long commercialization cycles, complex property rights relations, and high commercialization risks. Market entities such as enterprises often find it difficult to independently bear the huge capital investment and risks involved in the commercialization of scientific and technological achievements, which results in certain constraints on the commercialization of these achievements under the market mechanism. This requires the government to formulate specific fiscal and scientific and technological policies and establish a service platform that connects the supply and demand of technological elements for the transformation of scientific and technological achievements. It should be noted that the effectiveness of fiscal and technological policies is not solely achieved by increasing financial investments. It also requires a systematic examination of the complex characteristics exhibited during the specific implementation of these policies, so that flexible policy formulation and implementation can effectively promote the commercialization of scientific and technological achievements.

From the perspective of policy implementation, this paper explores the implementation characteristics of policies from the aspects of implementation intensity, policy orientation, and policy stability, and further constructs a theoretical framework for analyzing the impact of fiscal science and technology policy implementation characteristics on the commercialization of scientific and technological achievements. Moreover, it constructs a panel data sample covering 174 Chinese cities from 2017 to 2021, and empirically examines the effect of implementation characteristics of fiscal science and technology policies.

The research indicates that the government's strengthening of the implementation intensity of fiscal science and technology policies is conducive to promoting the commercialization of scientific and technological achievements. When the policy is highly targeted and the commercialization of scientific and technological achievements is taken as the goal of policy implementation, it helps promote the construction of services and realize the agglomeration of innovation resources, thus effectively promoting the commercialization of scientific and technological achievements. When the policy is relatively stable and provides a continuous and stable policy environment for market entities, it helps to enhance the expectations of market entities, and then promotes the commercialization of regional scientific and technological achievements more effectively.

The analysis of the effects based on different market conditions shows that when a region has a relatively strong foundation for scientific and technological innovation, strengthening the implementation of fiscal science and technology policies can promote the commercialization of scientific and technological achievements more significantly. When the regional technology market is more active and there is a sufficient demand for technological innovation and commercialization, the promoting effect of the implementation intensity of fiscal science and technology policies on the commercialization of scientific and technological achievements will be more evident.

The research can provide theoretical support for an in-depth understanding of the implementation characteristics of fiscal science and technology policies and their effect on the commercialization of scientific and technological achievements. Meanwhile, the research conclusions can provide important insights for the government to flexibly utilize fiscal science and technology policy tools and adjust the implementation approach based on policy objectives, thereby enhancing policy effectiveness.

The disconnection between innovation chains and industrial chains has internally led to the long-term existence of the "two worlds paradox" between science and industry, and externally, it has exacerbated the economic and political risks associated with supply chain disruptions and the recurring issue of being hindered by key core technologies. Accelerating the integration of the two chains is urgent. Technology standards not only support industrial collaboration but also play a leading role in technological cooperation. Developing and promoting fundamental, universal, and advanced technical standards around key links and critical areas provides a viable and practical approach to achieving double-chain-integration. However, existing research has paid little attention to the driving role of technical standards. Based on its linkage role of technology standards in the integration, we dedicate to elucidating the theoretical logic and mechanisms through which technology standards drive the integration of innovation chains and industrial chains. We aim to answer the following questions. First, why can technology standards drive the integration between the innovation chain and the industrial chain? What are the rational logic and internal mechanisms? Second, how can technology standards drive the integration between the innovation chain and the industrial chain? What are the process and implementation pathways?

This article first takes the connotation and extension of technical standards and double-chain-integration as the premise, and extracts the process mechanism of technology-oriented standard diffusion, demand-driven standard development and two-way synergistic standard bridging. We then analyze the functional realization of dual-chain-integration driven by technology standards from the dimensions of participants, innovation elements and operation space. Finally, we construct multidimensional implementation pathways and operational mechanisms for the integration of both chains driven by technical standards through patent pool formation, strategic knowledge disclosure, and the development of derivative technologies. Based on the strategic needs of China's innovation development and local practical experience, the study reveals the internal law, operation mechanism and realization mode of technology standard-driven integration between the innovation chain and the industrial chain, thus providing a new direction for China to realize scientific and technological self-reliance at a high level.

The theoretical and practical contributions of this article are as follows. First, we construct a theoretical model of the double-chain-integration driven by technology standards, and probes into its integration process and mechanism, which helps to form a theoretical innovation on the double-chain-integration. Second, this article innovatively introduces technology standards into the analytical framework of the double-chain-integration, thus helping to expand the theoretical boundaries of technology standardization. The conclusions do not only help to deepen the rational understanding of the double-chain-integration driven by technology standards, but also offer new analytical ideas, diversified decision-making tools and alternative policy insights for the management practices of enterprises and governments.

This study employs hypergraph modeling to examine how research content novelty and team background diversity influence scientific impact and research disruptiveness in projects funded by the Management Science Department of China's National Natural Science Foundation (NSFC). We focus specifically on youth funding projects to understand how these key dimensions of scientific research affect knowledge production and paradigm shifts in different career stages. Through constructing two distinct types of hypergraphs - content hypergraphs reflecting research content novelty and team context hypergraphs capturing team background diversity - we analyze 8,100 English publications by NSFC-funded researchers from 2008 to 2023, including 2,812 Young Scientists, 44 Outstanding Young Scientists, and 55 Distinguished Young Scientists. The content hypergraphs are built based on the Web of Science categories of each paper, while team context hypergraphs are constructed using the historical publication records (203,191 papers in total) of team members, allowing us to comprehensively capture both the novelty of knowledge combinations and the diversity of team academic backgrounds. Our analysis reveals complex relationships between these dimensions and research outcomes. Research content novelty shows a significant positive correlation with citation counts but, surprisingly, demonstrates a generally negative relationship with disruptiveness indices. This finding challenges conventional assumptions about novel research and breakthrough innovations, suggesting that while novel knowledge combinations may attract scholarly attention, they don't necessarily lead to paradigm-shifting discoveries. Team background diversity exhibits non-linear relationships with both citation impact and disruptiveness. The analysis indicates that moderate levels of knowledge diversity among team members may be optimal for producing both high-impact and disruptive research outcomes. This "sweet spot" in team composition suggests that while diversity brings fresh perspectives and innovative approaches, some degree of common knowledge foundation remains essential for effective collaboration and knowledge integration. Examining these relationships across different career stages reveals distinct patterns of innovation. Young Scientists tend to produce more disruptive findings despite receiving fewer citations, suggesting a greater propensity to challenge existing paradigms. In contrast, Distinguished Young Scientists generate research with higher content novelty and team diversity, resulting in more citations. This pattern demonstrates the complementary roles that researchers at different career stages play in scientific advancement. The distribution analysis of our indicators shows that while most research concentrates in low-novelty regions, there exists a significant peak in high-novelty areas, reflecting researchers' strategic choices between incremental advances and radical innovation. Team background diversity shows a right-skewed distribution, indicating that while most research teams maintain relatively homogeneous backgrounds, there is a growing trend toward more diverse team compositions, particularly among senior researchers. These findings have important implications for science funding policies and talent development strategies. First, evaluation criteria should be redesigned to better support potentially disruptive research, particularly from early-career scientists, whose innovative contributions might be undervalued by traditional metrics. Second, differentiated support mechanisms should be developed for researchers at various career stages, recognizing their distinct approaches to innovation. Third, funding programs should actively promote interdisciplinary collaboration while maintaining domain expertise. Our results suggest that current evaluation metrics focusing primarily on citation counts might systematically undervalue young researchers' contributions to scientific disruption. This indicates a need to develop more sophisticated assessment systems that can capture different dimensions of scientific impact. These insights contribute to our understanding of how research novelty and team diversity influence scientific outcomes in China's research funding system, offering practical guidance for optimizing funding mechanisms to foster both steady knowledge advancement and breakthrough discoveries.

As the main body of China's national scientific and technological innovation institutions, universities play a vital role in the national innovation system. China’s university should be based on the forefront of scientific research and adapt to the new paradigm of scientific research characterized by data intensive computing; effectively manage scientific data from the perspective of data assets, ensuring that scientific data meets current and future research needs, and maximizing the value of institutional data assets. The United Kingdom is one of the earliest countries in the world to put forward the concept of "data as assets". As a problem-oriented scientific data management tool, the UK’s university Data Audit Framework has important reference value for China's university scientific data management.

This paper introduces the background of the Data Audit Framework (DAF) project in UK universities, and analyzes the scope, content, procedures and methods of the implementation of the framework, as well as the main practices in Warwick University, Bath University, and Emory University. On this basis, this paper summarizes the applicability characteristics of DAF. The first is overall planning, which set up a data management committee composed of university managers and senior professors, formulate a unified data audit plan, rationally define the scope of audit, and coordinate the audit schedule. The second is classification and grading, according to the value and importance of data, important, secondary and unimportant data assets are divided to provide a basis for the adoption of targeted resource allocation and management strategies. The third is problem-orientation; the purpose of DAF is to assist institutions to assess the risk areas faced by the current environment, and to raise the awareness of managers and researchers on the best practices of scientific data management. The fourth is continuous improving. DAF designs two main lines of identifying problems and solving problems. As a closed loop, its ultimate goal is to solve problems and serve the self-improvement process of scientific data management.

This paper also discusses the enlightenment of DAF to the management of data assets in China. First, establish the awareness of data assets and optimize the audit environment. Universities should strengthen top-level design, regard data asset audit as an important scientific data management tool, and promote the excellent management of data assets while carrying out excellent research. Second, make sound management systems and improve audit basis. Universities should clearly define data assets management objectives, management methods and codes of conduct to ensure that data assets audit has good grounds. Third, promote standardized management and conduct management audit. The audit of data assets in universities should be positioned as management audit, focusing on three aspects. (1) The status of establishment and maintenance of data assets catalogue, understanding the scientific research resource base of the university. (2) The status of classification management of data assets, evaluating the value of scientific research resources of the university. (3) The status of utilization of data assets, grasping the contribution of scientific research resources of the university. Fourth, strengthen professional training and consolidate audit conditions. Universities should strengthen the training of researchers and auditors, popularize and improve data literacy to adapt to the arrival of data intensive research paradigms.