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.
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.
Abstract: 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 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. management models are gradually becoming data-centric, platform-based and ecological, awakening the advocacy and support for an open science 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.
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 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. 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 and internal mechanism of technological specialization on the breakthrough of key core technologies. The results show that there is an inverted U-shaped relationship between technology specialization, knowledge search and key core technology breakthroughs. The inverted "U" shaped relationship between technology specialization and key core technology breakthroughs is mediated by knowledge search. 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. 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.
Based on the panel data of 30 provinces (municipalities directly under the central government and autonomous regions) from 2011 to 2021, this paper conducts a research on the relevant policies of national big data comprehensive pilot zones. Two batches of policy time points in 2015 and 2016 are selected, and a multi-period DID model is established to explore the impact of the establishment of national big data pilot zones on regional science & technology innovation capacity and the effect of the implementation of different pilot policies. (1) The benchmark regression shows that: the establishment of national big data comprehensive pilot zones can significantly enhance regional science & technology innovation capacity, and the conclusion still holds after the placebo test, lag one period and excluding first-tier cities of the robustness test. (2) Regional heterogeneity analysis results show that: the different pilots of the establishment of the comprehensive pilot zone of big data have different driving effects on the surrounding regional science & technology innovation capacity, while the eight pilots of the policy, only the Pearl River Delta, Shanghai, Chongqing comprehensive pilot zone of big data pilot regional science & technology innovation to drive the effect is not significant, with the help of theoretical analysis to explore the reasons and give the corresponding recommendations, in order to comprehensively implement the national big data strategy and the regional synergistic development strategy.
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.
In this study, we use hypergraph models to analyze the innovation patterns of researchers funded by the Management Science Department of the National Natural Science Foundation of China (NSFC). By constructing content hypergraphs and team context hypergraphs, we explored the complex relationship between research content novelty and team context novelty with research impact and disruptive innovation. It was found that the number of citations showed a significant positive correlation with research content novelty, while the disruptive index was approximately negatively correlated with content novelty as a whole. Team contextual novelty showed a nonlinear relationship with both citation counts and disruptiveness, reflecting the potential of a modest combination of member knowledge diversity and similarity in producing high-impact and disruptive research. In addition, we compare the research performance of Young Science Fund, Outstanding Young Science Fund, and Distinguished Young Science Fund recipients and find that young scholars produce more disruptive results, while senior scholars produce more highly cited results. These findings provide an important basis for optimizing research funding strategies, promoting innovation and balancing the development of different types of research, as well as providing new perspectives for understanding the characteristics and challenges of China's research talent training system.
As the main body of technological innovation, universities need to do a good work in scientific data management from the perspective of data assets. As an early explorer conducting data asset management, the UK’ university has relatively mature experience. This paper first introduces the project background of the Data Audit Framework (DAF) for British universities, analyzes the implementation scope, content, procedures, and methods as a scientific data management tool, and discusses its main practices in application. On this basis, this paper summarizes the applicability characteristics of the framework from four aspects: overall planning, classification and grading, problem orientation, and continuous improvement. Finally, this paper explores the reference and implication of this framework for data asset management in Chinese universities, including establishing asset awareness from the perspective of optimizing audit environment, improving management system from the perspective of perfecting audit standard, promoting normalized management from the perspective of implementing management audit, and conducting training and propaganda from the perspective of consolidating audit condition.
