The large language model is highland of global technological competition and engine for economic development. As new innovation paradigm emerging in the digital economy era, exploring the mechanism and path of context driven innovation in the industrial architecture of large language model has important theoretical value and practical significance for solving major application and industrialization problems and better supporting high-quality economic development. Based on the theory of context driven innovation, exploring the connotation, core elements, and process mechanisms of context driven innovation in the industrial architecture of large language model industries. It summarizes the specific practical path of context driven innovation in the industrial architecture of large language industries, and verifies and concretizes the practical path through multiple case analyses. It has been found that under the context driven innovation paradigm, the mechanism of innovation in large language model industry architecture can be summarized into four key links: major mission and strategic traction, scenario identification and classification, industrial architecture innovation process, and innovation effects; The innovation of large language model industry architecture under the context driven innovation paradigm covers the systematic integration of technological architecture, the dual transformation of organizational architecture, and the integration of industrial chain architecture. The research results provide important theoretical basis and practical guidance for promoting the higher-level application of large language model and better supporting the high-quality development of the digital economy.
Artificial Intelligence (AI) has demonstrated immense potential in the scientific domain. AI for Science (AI4S) is rapidly advancing and gaining widespread adoption, driving a comprehensive transformation in scientific research paradigms. However, the characteristics and mechanisms of AI4S in empowering scientific innovation remain unclear, and its strategic direction has yet to be systematically planned. Focusing on the 2024 Nobel Prize in Chemistry-winning projects, AlphaFold and Rosetta, this study employs a comparative case analysis to examine the distinct features and mechanisms of AI-enabled scientific research. It proposes two AI4S empowering models: "AI+" and "+AI." From the perspective of knowledge production modes, the study further analyzes the knowledge production characteristics of these two models. It identifies the emergence of "intelligence" as a fifth major actor in the "AI+" model, with knowledge production exhibiting traits of growth, independence, collaboration, and platformization. Additionally, the "AI+" and "+AI" models each have their own suitable application scenarios and frameworks. Finally, this paper offers policy recommendations, including differentiated strategies for AI+ and +AI, prioritizing small-model development, and advancing infrastructure construction. These findings contribute to both AI4S-related research and knowledge production mode theory, while also providing insights for China's development of the AI industry, exploration of AI applications, and advancement of AI models.
The development of emerging technology industry is an important way to reshape the international competition pattern, open up new fields and new paths for development, and shape new momentum and new advantages for national development. However, the development of emerging technology industry not only faces the double uncertainty of technology and market, but also faces the challenge and competition of old technology.The combination of creation and then destruction is an important policy implementation strategy of the Chinese government to coordinate the competition between old and emerging technologies and promote the development of emerging technologies. The strategy combination of creation and destruction refers to the policy implementation strategy set that promotes the growth of emerging technologies and gradually replaces old technologies. However, the creation and destruction strategies have their best background conditions, and the optimal combination of policy implementation strategies should be determined by combining the implementation scenarios and characteristics of innovation policies. How to implement the combination of creation and destruction strategies for emerging technology industries has become an important practical policy issue.
Therefore, this study takes the combination of creation and destruction policy implementation strategies as the theme, takes the adoption of innovation policies in China's new energy automobile industry as a case, analyzes the specific process of the Chinese government's promotion of creation and destruction policy implementation strategies, and focuses on the question of how to determine the combination of creation and destruction policy implementation strategies? What are the paths of creation and destruction? What is the logic of the strategic combination of creation and destruction? The research conclusions have practical significance for improving the implementation efficiency of innovation policies.
The findings are as follows: (1) the dynamic adjustment process of combination of creation and then destruction are a discretionary process. In the three stages of "achieve innovative breakthroughs", "achieve commercialize" and "enhance international competitiveness", the Chinese government successively adopted the policy implementation strategies of "creation first", " simultaneous combination from creation and destruction" and "creation from destruction"; (2) The implementation strategy of combination of creation and then destruction requires a systematic realization path. "Creation" includes the layout and cultivation of diversified technologies, the creation of market opportunities for commercialization of emerging technologies and the creation of technological opportunities for innovation of emerging technologies. "Destruction" includes reducing the relative competitive advantage of old technologies, breaking the policy protection space of emerging technologies, and destroying the mainstream technical route expectations and visions of old technologies. (3) combination of creation and then destruction are essentially the government's behavior of intervening the competition between old and emerging technologies by creating and adjusting the policy protection space, which reflects the organic combination of "promising government" and "effective market".
The contributions of this paper are as follows: Firstly, it puts forward a three-dimensional theoretical analysis framework of "goal-setting - strategy selection - implementation path" for innovation policy implementation strategies, which opens the black box of the combination of innovation policy implementation strategy in the field of new energy automobile industry in China. Thus, we enrich the research on innovation policy. Second, it interprets the theoretical logic of the combination of creation and destruction strategies, reveals the coupling relationship between "promising government" and "efficient market" in the development process of emerging technology industries. It can expand the theory of policy implementation, and is conducive to improving the efficiency of policy implementation.
Adopting a spatial perspective on talent policies, this paper constructs a quantitative standard for Chinese urban talent policy, and scores 3308 talent policies in 293 prefecture-level cities. On this basis, by using spatial analysis methods, this paper characterizes the spatiotemporal differentiation and evolutionary processes of talent policies at the prefecture-level in China from 2012 to 2021. Meanwhile, this paper designs an evaluation index system for new quality productive forces in cities, and conducts a quantitative study on the relationship between urban talent policies and new quality productive forces. The results show that: (1) The comprehensive scores of talent policies exhibit a southeast-to-northwest gradient, featuring higher scores in the southeast and lower scores in the northwest. Specifically, the eastern region has maintained a high level of scores throughout, while the central region, despite starting from a lower base, has experienced rapid growth. In contrast, the western and northeastern regions have experienced relatively slower improvements. (2) Based on the analysis of urban types from the three dimensions of "policy intensity-policy objective-policy measure", it is evident that cities with simultaneous progress in all three dimensions tend to expand from the coast towards the inland. Additionally, internal polarization is prominent in the western and northeastern regions. (3) The analysis of urban types based on the three dimensions of "policy instrument-policy link- prioritized talent" reveals that the central region exhibits shortcomings in the use of safeguard policy instruments, while the western and northeastern regions lack adequate development policy instruments. The western region faces notable shortcomings in talent retention policies, while the northeastern region exhibits deficiencies in talent cultivation policies. While regions vary in their preferences for talent types, highly educated talents have universally emerged as the primary focus. (4) In practical terms, talent policies significantly enhance the development of new quality productive forces by leveraging the talent agglomeration effect, innovation-driven effect, and industrial upgrading effect, with distinct heterogeneities observed across regions and policy types. The research conclusions can provide decision-making references for local governments to formulate more practical talent policies. The marginal contributions of this paper lie in: Firstly, it innovatively designs a quantitative standard system for prefecture-level talent policies in China, encompassing three dimensions: policy intensity, policy objectives, and policy measures. This system was applied to score 3308 talent policy texts across 293 prefecture-level cities from 2002 to 2021, thereby establishing a panel database (2012-2021) of comprehensive scores and detailed indicators for China's prefecture-level talent policies. This provides comprehensive and robust data support for subsequent research. Secondly, employing spatial analysis methods, the paper systematically characterizes the spatiotemporal evolution of talent policies at the prefecture-level in China. It presents a panoramic view of their gradient distribution pattern, internal regional polarization characteristics, and the trajectory of spatial center shifts, constituting a valuable exploration into the spatial dynamics of nationwide prefecture-level talent policies. Thirdly, it constructs an evaluation index system for urban new quality productive forces and conducts a quantitative study on the relationship between talent policies and new quality productive forces. This research precisely identifies the heterogeneous effects of policies across regions, policy instruments, and policy links, offering new perspectives and evidence for understanding the spatiotemporal evolution and practical effectiveness of talent policies in Chinese cities.
Radical technological changes reveal significant variations in the adaptability of incumbent firms. Existing studies on the factors influencing adaptability often overlook the heterogeneity of technological changes across industries and attempts to discover the marginal effect of individual antecedent variables on firms' adaptability. These approaches have been increasingly critiqued in recent years.
This study investigates the transition within the automotive industry from internal combustion engine (ICE) technology to new energy vehicle (NEV) technology. The salient features of this change are: first, different incumbent firms had different historical deposits before the change, and how they treat their historical legacy will affect their ability to successfully adapt to technological change. Second, the ICE power technology as the core knowledge has fundamentally changed, and how the incumbent firms acquire the NEV core knowledge will affect their ability to adapt to technological change. Third, production systems, distribution and service networks are still largely available as complementary assets, and how these complementary assets are exploited will affect the incumbent firm's ability to adapt to technological change.
Based on these characteristics, the study develops a Qualitative Comparative Analysis (QCA) framework that includes seven antecedent variables across three critical dimensions: firm historical factors, organizational structures for new business, and strategies for new business development. Firm historical factors includes whether it was a Sino-foreign joint venture before the transition, its R&D experience and market position in the fuel vehicle era. Organizational structures for new business includes whether it has set up an independent business unit for the new energy vehicle business, and whether it has established new energy technology alliances with other firms. And new business development strategy includes whether it has adopted a highly innovative product strategy, and whether it has adopted a diversified product strategy. The degree of technological transition is utilized as the outcome variable.
Using a sample of 42 automotive industry incumbents, the findings reveal that incumbents can achieve successful transition through two distinct pathways: the "history-constrained" pathway and the "opportunity-oriented" pathway. Firms that adopt a "history-constrained" pathway have a rich heritage (R&D experience, market position), and therefore choose a smaller degree of product innovation (e.g., hybrids) in their new business development strategy and diversify their product offerings to test the new energy vehicle market, which they are unfamiliar with. In order to free themselves from the constraints of a strong legacy business, these firms will set up a separate business unit for new business. Firms that have taken the "opportunity-oriented" pathway do not have a strong fuel-vehicle business, so the establishment of a separate new energy division is not a core requirement. For these firms, the main concern is how to seize the opportunity of the industry's technological transition to ‘overtake’. These firms are less likely to diversify into new products because their traditional business is weak and their ability to invest in new business is limited. Both "history-constrained" pathway and "opportunity-oriented" pathway require firms to be free of joint venture constraints and to acquire core knowledge through new technology alliances in order to be successful. Each pathway is closely associated with the specific characteristics of the technological change under study. While the conclusions do not encompass all possible types of technological changes, they provide feasible pathways for incumbents to achieve successful transition in specific types of technological transitions.
The evolution of scientific paradigm is the core issue of scientific development, and the inheritance of tacit knowledge is crucial to the shaping of scientific paradigm as it continues to accumulate in the academic genealogy. Focusing on the mechanism of tacit knowledge transformation in the transmission of academic genealogy on the formation of scientific paradigm, 967 valid samples were collected through questionnaires and empirically examined by using structural equation modeling. The study shows that tacit knowledge transformation significantly contributes to scientific consensus through cognitive, methodological, and normative consensus, and then promotes scientific paradigm formation. The attribute characteristics of the discipline play a moderating role in this process, the stronger the maturity, intersectionality and openness of the discipline, the higher the effectiveness of the transformation of tacit knowledge, and the more likely it is to promote the formation of innovative scientific paradigm. The study deepens the theoretical connotation of academic genealogy and expands the rationale for analyzing the influence mechanism of tacit knowledge, which is inspirational for government departments to formulate policies on talent cultivation and discipline construction, universities and research institutions to improve academic management and promote scientific research innovations, and scholars to pursue excellence in innovations and expand the paths of their academic careers.
The relationship between scientific knowledge—a critical input to technological systems—and invention value remains a central concern in science policy and innovation management. While existing studies have primarily focused on correlational analyses, yielding inconsistent findings, the integration of scientific knowledge into technological systems is not random but intimately linked to knowledge recombination patterns and invention team characteristics. The inconsistency in research findings likely stems from endogeneity issues caused by selection bias, which traditional regression analyses fail to address. Causal inference methods are thus necessary to accurately estimate the net causal effect of scientific knowledge on invention value, thereby providing more reliable evidence for science and technology policymaking.
Using a counterfactual causal framework, we employed Propensity Score Matching (PSM) and Generalized Propensity Score Matching (GPSM) to estimate the causal effects of both the presence and intensity of scientific knowledge inputs on patents' technological and commercial value. Existing measures of scientific knowledge input intensity focus solely on citation counts, overlooking variations in scientific literature impact. We address this limitation by developing the Patent Scientific Citation h-index (PSH), inspired by the h-index methodology, which integrates both quantitative and qualitative dimensions of scientific citations to more accurately assess patents' scientific knowledge absorption. We measured patents' technological value using two indicators: forward citation counts and technological generality. Commercial value was quantified through the frequency of patent transfers. Analyzing 1,685,970 utility patents granted by the USPTO (2001-2010) and their 6,473,214-citation links to 1,407,439 scientific papers, we established the following findings:
(1) Our findings demonstrate positive causal effects of scientific knowledge input on both the technological and commercial value of patents. Patents incorporating scientific knowledge receive significantly more forward citations than those without such inputs. This indicates that scientific knowledge integration enhances a patent's capacity to serve as a foundation for subsequent innovations. Moreover, scientific knowledge input significantly enhances patents' technological generality. Specifically, we observe that scientific knowledge incorporation enables the development of more influential general-purpose technologies. The theoretical mechanism underlying this relationship lies in how comprehensive scientific understanding enhances researchers' cognitive capabilities to effectively identify, access, and recombine knowledge from distant technological domains. Finally, patents incorporating scientific knowledge demonstrate significantly higher transfer frequencies compared to those without such knowledge inputs. This pattern suggests that science-based inventions exhibit enhanced potential for private value realization, primarily because established scientific frameworks provide a shared cognitive platform and common language among market participants.
(2) A nonlinear relationship exists between scientific knowledge input intensity and inventive value. Empirical analysis reveals a fluctuating "rise-decline-rise" pattern in patents' forward citations and transfer frequencies as the PSH index increases. Patents with either low or high PSH intensities demonstrate superior technological impact and market transaction potential. In low-PSH domains, moderate scientific knowledge integration maintains technological advancement while aligning with market cognition. Mid-range PSH shows diminishing returns due to potential divergence from user expectations. High-PSH patents leverage rich scientific knowledge to overcome technical barriers, yielding enhanced technological impact and market value. Regarding technological generality, our findings reveal that patents characterized by higher PSH indices embody more fundamental scientific principles. The universal applicability of these underlying principles facilitates technology extension and diffusion across diverse application contexts, resulting in enhanced levels of technological generality.
This study makes several significant contributions to the innovation policy literature. First, we employ a causal inference framework to establish the causal relationship between scientific knowledge input and inventive value. Second, we advance methodological development by introducing a novel approach that incorporates the H-index to quantify scientific knowledge input intensity. These empirically grounded findings provide robust evidence for optimizing science and technology policies, bridging the gap between theoretical frameworks and practical policy implementation.
Emerging technology risks constitute a "perpetual topic" in the process of human scientific and technological innovation. As a kind of "processual existence," it is urgent to clarify the process mechanism through which emerging technology risks are established from the perspective of social construction. Based on the social construction paradigm, this study constructs a processual explanatory framework, revealing that the social construction of emerging technology risks involves a process of "risk generation, risk rhetoric, and risk perception." It also presents a derivation logic where risk states gradually "externalize, objectify, and socialize" as they connect across the levels of "macro society, meso-subjects, and micro-individuals." Specifically, the generation of emerging technology risks has a clear "field" within the social system, giving rise to multiple types of risk patterns through systemic interactions. The rhetoric and interpretation of emerging technology risks are based on the strategies of multi-subject discourse construction and translation links, essentially representing a "discourse competition" for the power to interpret risks. The public's perception of risks associated with emerging technologies is a specific risk perception state formed by the interweaving of the "objective knowledge path" and the "simplified mechanism of trust" under information stimulation. The governance of emerging technology risks urgently requires logical transformation and paradigm transcendence. From a macro-structural perspective, it is necessary to adhere to the balanced logic of "systematic equilibrium," achieving "dimensional balance" and "intensity balance" among systems. From a meso-subject perspective, it is crucial to establish a collaborative logic for the division of labor among subjects, forming an "expanded knowledge community" to facilitate the integration of "public knowledge." From a micro-individual perspective, it is essential to adhere to the neutralizing logic of cognitive adaptation, bringing the public's perception of emerging technology risks "back" to a non-polarized rational space.
Generative Artificial Intelligence (GAI) has become deeply embedded in the socio-economic fabric of human society, and its growing agency has emerged as a focal point of both theoretical debate and practical concern. On the one hand, GAI increasingly exhibits capabilities traditionally regarded as uniquely human, including interactive intelligence, cognitive flexibility, and creative expression. These characteristics not only enhance GAI’s functionality but also open new avenues for augmenting human cognition. On the other hand, the generative processes of GAI are frequently associated with factual inaccuracies, reasoning biases, and ethical dilemmas, leading to a fragile trust relationship between humans and machines. As a result, human reliance on GAI coexists with mounting skepticism, creating a fundamental tension between its empowering potential and the risks of technological unpredictability and moral ambiguity.
This paper investigates the question of what paradoxical tensions accompany the rise of GAI’s agency and how these tensions may be addressed. We first identify that GAI’s agency is characterized by three core features—interactivity, autonomy, and creativity—each of which is enabled by distinct forms of technological affordance. Interactivity is shaped by perceptual and communicative affordances, autonomy by operational and situational affordances, and creativity by the interplay of interactive and situational affordances. While these features empower GAI with remarkable generative capabilities, they simultaneously give rise to complex system-level risks and trust challenges. Specifically, the high degree of interactivity leads to a lack of transparency in generative logic, producing a “black-box” effect that impedes user understanding. The rise in autonomy undermines users’ sense of control, as GAI’s decision-making processes and behavioral evolution become increasingly opaque and less predictable. Meanwhile, its expanding creative potential complicates the attribution of accountability, creating asymmetries in responsibility where the more novel the output, the more ambiguous its moral and legal ownership.
These contradictions manifest in three interconnected paradoxes: the interaction-transparency paradox, wherein GAI’s interactive flexibility conflicts with the opacity of its generative mechanisms; the autonomy-controllability paradox, where heightened decision-making independence erodes human oversight; and the creativity-accountability paradox, in which increasing innovation introduces diffuse and deferred responsibility. Addressing these challenges requires a shift in governance strategies: enhancing interpretability through explainable AI and knowledge mediation, reinforcing human oversight via participatory design and feedback mechanisms, and clarifying ethical boundaries through shared responsibility and robust accountability frameworks. Only by navigating these tensions thoughtfully can we ensure that the technological advancements of GAI genuinely serve collective human interests and foster a synergistic development of both technological and societal values.
With the advancement of Internet and information technology, the data generated across various disciplinary domains have witnessed exponential growth. The swift emergence, efficient organization and scientific processing of vast amounts of data have not only become potent instruments but also a novel foundation for academic research. This development has thus given rise to the so-called “data-intensive research paradigm”. The core characteristic of data-intensive research paradigm is its data-driven nature, which means computers are employed to uncover correlations within extensive datasets without the necessity of pre-established hypotheses, enabling the generation of more scientifically rigorous predictive outcomes. This paradigm has opened up new avenues for scientific research, facilitating the re-evaluation of numerous classical propositions on a more expansive empirical scale.
This research focuses on the age aspect within the scientific research life-cycle in the field of scientific sociology, delving deeply into novel conclusions regarding the traditional topic of “the statistical distribution of scientists” peak ages in scientific research. Scholars such as Zhao Hongzhou proposed that the age law of scientific discovery adheres to a Weibull distribution, and Liu Junwan, conversely, based on the ages of significant scientific discoveries of 435 Nobel laureates in natural sciences, put forward a corresponding log-normal distribution. However, in contrast to numerous previous studies, the sampling criteria of the self-constructed dataset in this paper have undergone substantial modifications.
Firstly, the scope of disciplines has been broadened to comprehensively incorporate a wide range of achievements in mathematics, mechanics, geology and computer science. Secondly, considerations have been given to the diversification of scientific elite types. Instead of being confined to scientists solely recognized by top international scientific awards, the scope has been extended to encompass all outstanding scholars who have left an indelible mark on the annals of human science and technology over the past few centuries. Upon conducting software analysis of current dataset, the authors have determined that the optimal fitting function is the “normal inverse Gaussian distribution”.
Further analysis reveals that the optimal fitting distribution of the present dataset exhibits distinct non-static characteristics. Variations in data sampling criteria can lead to disparities in the connotations and extensions of datasets. Regarding the classical topic of “the distribution of scientists’ peak ages in scientific research”, it appears that there is no fixed or static singular conclusion. Given the lack of a unified consensus among scholars regarding the definition of “outstanding scientists”, different datasets established accordingly may well correspond to diverse optimal fitting distributions. Consequently, despite the efforts to gather relevant information through web data mining and manual identification methods, the current dataset inevitably suffers from limitations in terms of sample size and type. In future research, if scholars can design datasets with larger sample sizes and greater sample diversity, it is highly likely that more profound and dynamic conclusions can be unearthed.
In addition to the above, this study also undertakes descriptive analysis and contemplation of local indicators such as the mean age, median age, minimum age, and maximum age of datasets from different epochs. Through observation of recent elections of academicians of Chinese Academy of Sciences, it has been noted that middle-aged and young academicians around the age of 50 are predominantly from the group of “National Science Fund for Distinguished Young Scholars” recipients. This finding further corroborates the distinct connection and isomorphic progression characteristics between “National Science Fund for Distinguished Young Scholars” recipients and “academicians”.
Based on the above, this study finally posits that the concept of “optimal age for scientific discovery” has exerted a profound and nuanced influence on China’s science and technology talent policies. This is evident in the explicit age limits stipulated for applications in various scientific research talent programs across different levels in China, which are set as stringent mandatory criteria. This has led to an increasingly prominent trend of youthfulness and precocity in the selection of scientific elites in China. However, considering the rich individuality and heterogeneous nature exhibited by each scientist during the innovation process, an overemphasis on age restrictions may potentially impede the full realization of individual scientists’ capabilities and runs counter to the intrinsic laws governing scientific and technological innovation. In light of that, the academic community should accord greater importance to the dialectical relationship between statistical regularities and microscopic case studies. While acknowledging the significance of general statistical patterns regarding peak ages in scientific research, it is equally crucial to delve deeply into the rich typical significance embedded within specific scientist cases.
Promoting innovation in the manufacturing industry and promoting high-quality development of the industrial economy are important policy guidelines of the country. Taking the most representative industrial enterprises above designated size in the industrial economy as the research object, innovation modes are divided into two categories: product or process innovation and organizational or marketing innovation. Panel data from 31 provinces in China (excluding Hong Kong, Macao, and Taiwan) for a total of 7 years from 2016 to 2022 are selected, and a panel binary choice model is used to empirically analyze the internal and external influencing factors of industrial enterprise innovation mode selection in each province, revealing the specific reasons why industrial enterprises in each province choose different innovation modes. It is found that: Firstly, the higher the regional economic level and local R&D manpower investment, the more inclined the industrial economy is towards organizational or marketing innovation. The more innovative achievements there are in the local area, the more inclined the industrial economy is towards product or process innovation. Secondly, the higher the proportion of large enterprises, the more inclined the industrial economy is towards organizational or marketing innovation. Thirdly, when selecting innovation models for industrial enterprises above a certain scale, the main influencing factor is not the industrial enterprise itself, but rather the external social environment factors of the location where the enterprise is located, especially the regional economic level, local R&D manpower investment, and local innovation achievements. Fourthly, industrial enterprises choose too many organizational or marketing innovation modes, and there is a phenomenon of market failure in industrial economic innovation. Based on this, suggestions are made: Firstly, in order to achieve more product or process innovation results, policy makers should introduce policies to adjust industrial organizational structure, guide and restrict corporate mergers and acquisitions, oppose large enterprise monopolies, increase free competition among enterprises, and encourage the development of small and medium-sized enterprises. If choosing organizational or marketing innovation models, policy makers should encourage corporate mergers and the formation of more large enterprise groups, committed to enhancing the international competitiveness of large industrial enterprises. Secondly, when the local economic level is high and there is a significant investment in research and development manpower, but there are few local innovation achievements, industrial enterprises should prioritize organizational or marketing innovation models. When the local economic level is not high and there is less investment in R&D manpower, but there are more local innovation achievements, industrial enterprises should prioritize choosing product or process innovation models. Thirdly, policy makers need to attach great importance to the local socio-economic environment when planning innovative models for industrial enterprises. Funds and manpower should be invested to conduct sufficient research on the local economic level, R&D manpower investment, and innovation achievements. Fourthly, in order to promote industrial enterprises to carry out more product or process innovation, policy makers should adopt various policy measures such as bank credit, fiscal subsidies, and tax incentives to help industrial enterprises reduce their initial R&D investment, lower the cost of product or process innovation, compensate for market failures, and guide industrial enterprises to adopt more scientific and reasonable innovation mode choices.
As the importance of science and technology innovation rises in global competition, taking the BRICS expansion as an opportunity, deepening the science and technology (S&T) innovation cooperation among BRICS countries is of great and far-reaching significance to reshape the world’s scientific and technological governance pattern and enhance the discourse power of the countries in the “Global South”. Based on the data of BRICS countries' thesis cooperation from 2000 to 2023, the article analyzes the topology and centrality of BRICS S&T innovation cooperation network from the perspective of dynamic evolution, adopting social network analysis and geographic detector, and examines the influence of multi-dimensional proximity and interaction on the evolution of S&T innovation cooperation network in each stage and the mechanism of the influence. The results show that: (1) during the study period, the scale of BRICS science and technology innovation cooperation network has been enlarged, the cooperation links between the subjects have been gradually increased, the network density has shown a trend of growth followed by a slow decline and remained stable, there is a trend of intensive cooperation and cluster development within the innovation network, the important centers of the innovation subjects have gradually come to the fore, and the topology of the network has gradually developed and matured, and it is in the transitional period of the stochastic network evolving into a scalar-free network. (2) Important center nodes of the cooperation network have come to the fore, exerting important influence and radiation-driven ability; at the same time, the ranking of key intermediary nodes is relatively stable, playing the role of intermediary bridge communication. China is in the core position of leading and spearheading in the BRICS S&T innovation cooperation network, with greater influence. (3) Multi-dimensional proximity factors play different degrees of positive influence in the network, R&D investment proximity is the dominant factor affecting innovation cooperation in the study period, with the highest explanatory power; the influence of geographical proximity gradually decreases and tends to be stable with the advancement of time; and there is a two-factor enhancement interaction between different proximity factors, with different degrees of enhancement effect.
Major technological equipment has significant influence on national security and economic development. Therefore, achieving the domestic substitution of major technological equipment is an inevitable requirement for realizing China's high-level self-reliance and self-strengthening in science and technology. However, major technical equipment has the characteristics of complex technology, large value, and high level of innovation investment, and once the transformation of R&D achievements fails, the ability and enthusiasm of enterprises to innovate will decline significantly. Therefore, resolving the risk of transformation of major technical equipment achievements has become an important part of enhancing the innovation power of China's high-tech manufacturing industry and solving the problem of "stuck neck" in core technology.
Technology insurance is an effective management tool covering all kinds of risks in the whole life cycle of enterprise technological innovation, and plays an important role in supporting enterprise innovation activities. Among them, in view of the risk of application and transformation of technological innovation achievements, the Ministry of Finance of China issued a notice in 2015 to carry out the pilot work of the insurance compensation mechanism for the first set major technical equipment, aiming to resolve the risk of the transformation of the first set product and solve the problem of market promotion of the first set product. The pilot policy of the first set insurance is the latest practice of using technology insurance to resolve the risk of the transformation of the first set product results, but few scholars have conducted in-depth research on its practical effects.
To address this gap, this paper studies the economic effect of the first set insurance from the perspective of innovation investment, and digs deep into its mechanism. At the same time, this paper also examines the moderating effect of the broad subsidy standard on the impact of the first set insurance on the innovation investment of enterprises based on the subsidy measures for the first set insurance issued by some local governments. The results show that the first set insurance has a significant promoting effect on improving the level of enterprise innovation investment, and the effect is more significant in non-state-owned enterprises, large enterprises, enterprises with overseas background and low level of regional legalization. Further analysis shows that the first set insurance can improve the level of enterprise innovation investment by increasing product demand, improving enterprise risk-taking ability and alleviating financing constraints. The broader subsidy standard for the first set insurance enhances the role of the first set insurance in promoting the innovation investment of enterprises; In addition, the first set insurance has increased the level of enterprise value by increasing the investment in enterprise innovation.
The contributions of this paper are as follows: First, this paper examines the impact effect and mechanism of the pilot policy of the first set insurance on the innovation investment of enterprises, and discusses the differences of the first set insurance on the innovation investment of enterprises in different industries in the high-tech industry, which makes up for the shortcomings of the existing research to a certain extent. Second, this paper analyzes and examines the mechanism of the first set insurance to increase the innovation investment of enterprises from three aspects: improving the risk-bearing capacity of enterprises, increasing product demand and alleviating financing constraints, and examines the economic consequences of the first set insurance to increase the innovation investment of enterprises from the perspective of enterprise value, and deepens the relevant research on the first set insurance. In addition, this paper also discusses the moderating effect of the premium subsidy standard of the first set insurance on the impact of the first set insurance on the innovation investment of enterprises, and examines the policy synergy between the central and local governments.
In conclusion, this paper studies the practical effect of the first set insurance pilot policy from the perspective of enterprise innovation investment, and proves its role in solving the problem of market promotion of technological innovation achievements. Future research can further focus on how the differences in government subsidy policies for the first set of insurance affect the innovation behavior of enterprises.
Research institutions serve as a vital pillar of the national strategic science and technology force in science and technology, and their technology transfer performance plays a significant role in the national innovation system. Technology transfer services are an essential catalyst in transforming scientific research outputs into social benefits. However, existing studies, mainly focusing on universities and enterprises, have largely overlooked how the scientific and technological achievement transformation services of research institutions influence technology transfer performance.
The Chinese Academy of Sciences (CAS) is a key research institution in China’s innovation activities, with extensive exploration and practice in technology transfer. CAS is a representative case for research institutions’ technology transfer. Its intellectual property commissioner system plays an important role in the protection, transformation, and utilization of intellectual property by providing professional and efficient talent protection and intellectual property services. Therefore, this study focuses on intellectual property commissioners within the Chinese Academy of Sciences and utilizes data collected through a questionnaire survey. It examines the impact and underlying mechanisms of scientific and technological achievements transformation service capabilities on technology transfer performance, distinguishing between two dimensions: basic value-added service capacity and commercial operation service capacity. It also examines the moderating effect of organizational support on the relationship between these two aspects.
Our findings indicate that both basic value-added service capability and commercial operation service capability have significant positive effects on technology transfer performance, with the effect of commercial operation service capability demonstrating a more pronounced impact. The quality of scientific and technological achievements plays a partial mediating role between both types of service capabilities and technology transfer performance. Organizational support is found to positively moderate the relationships between commercial operation service capabilities and the quality of scientific and technological achievements, as well as technology transfer performance.
We contribute to existing research in the following ways. First, we expand the existing literature on technology transfer by analyzing the impact of technology transfer service capabilities on the technology transfer performance of research institutions, and considering the differentiated effects of different service capabilities. This broadens the micro perspective of technology transfer and provides a theoretical research framework for future studies. Second, we explain the mechanisms through which technology transfer service capabilities affect technology transfer performance, thus expanding the channels through which these capabilities operate in the technology transfer process. Third, we incorporate the organizational support, an organizational factor, into the analytical framework between scientific and technological achievements transformation service capabilities and technology transfer, clarifying the conditions under which scientific and technological achievements transformation service capabilities influence technology transfer performance.
In the context of the national innovation-driven development strategy, these findings not only provide theoretical insights for optimizing the allocation of scientific and technological resources and enhancing the technology transfer performance of research institutions through service capabilities, but also offer empirical evidence for policy-making, which has important theoretical significance and practical value. On one hand, research institutions should strengthen the service capabilities of their intellectual property personnel, especially commercial operation service capabilities. In addition, research institutions should place emphasis on improving the quality of scientific and technological achievements. On the other hand, government and research institutions should optimize organizational support mechanisms to foster an organizational environment conducive to technology transfer.
The promotion of platform transformation in manufacturing enterprises has emerged as a pivotal focus in contemporary theoretical and practical discourse. The rapid evolution of digital technologies has catalyzed a new era of transformation for manufacturing enterprises, with platform-based business models becoming a central theme in both academic research and industrial practice. Platform transformation, characterized by the shift from traditional linear value chains to interconnected, ecosystem-driven models, has become a critical strategy for enhancing competitiveness and fostering innovation. However, despite the growing scholarly and practical interest in this domain, existing studies have predominantly focused on the optimization of digital tools or the technical dimensions of digital empowerment, often neglecting the broader organizational and ethical considerations that underpin high-quality platform transformation. This gap in the literature highlights the necessity for a more comprehensive understanding of the factors that facilitate successful platform transformation in manufacturing enterprises.
To address this gap, this study introduces the concept of Corporate Digital Responsibility (CDR) as a novel theoretical lens, grounded in the principles of technology ethics. CDR refers to the ethical and responsible utilization of digital technologies by enterprises, encompassing key principles such as data privacy, transparency, and accountability. By integrating CDR into the analytical framework, this paper seeks to investigate how digital responsibility, in conjunction with effective data resource management and digital interaction, influences the platform transformation of manufacturing enterprises. Through a structured questionnaire survey, the study empirically examines the relationships between these variables and their impact on platform transformation.
The findings demonstrate that CDR plays a significant and positive role in facilitating platform transformation. Enterprises that prioritize digital responsibility are more likely to establish trust with stakeholders, foster collaborative ecosystems, and generate sustainable value through their platforms. Furthermore, the study identifies two critical mediating mechanisms in this process: digital interaction and data resource integration. Digital interaction, defined as the seamless exchange of information and collaboration among platform participants, enhances the operational efficiency and effectiveness of platform ecosystems. Data resource integration, on the other hand, enables enterprises to leverage their data assets more strategically, driving innovation and informed decision-making. Both mechanisms are shown to amplify the positive effects of CDR on platform transformation. Additionally, the research underscores the moderating role of an enterprise’s culture of "science and technology for good." This cultural orientation, which emphasizes the ethical and socially beneficial application of technology, strengthens the relationship between CDR and platform transformation.
The theoretical contributions of this study are threefold. First, it introduces CDR as a novel research perspective within the context of platform transformation, enriching the literature on digital responsibility and its implications for organizational change. Second, it proposes a comprehensive theoretical framework that elucidates the mechanisms through which CDR influences platform transformation, thereby advancing scholarly understanding in this domain. Third, it highlights the importance of an enterprise’s cultural orientation toward technology ethics, offering new insights into the role of organizational culture in digital transformation. Moreover, the findings emphasize the necessity of robust data resource management and enhanced digital interaction capabilities, which are critical for realizing the full potential of platform-based business models. For policymakers, the study provides actionable recommendations for developing technical standards and regulatory frameworks that support the responsible use of digital technologies, thereby fostering an enabling environment for sustainable platform transformation.
In conclusion, this study addresses a critical gap in the literature by exploring the role of CDR in promoting high-quality platform transformation in manufacturing enterprises. By integrating technology ethics with practical insights, it provides a holistic framework for understanding and advancing platform-based business models in the digital age. Future research could build on these findings by investigating the long-term impacts of CDR on organizational performance and examining its applicability across diverse industries and cultural contexts.
A new technological revolution is reshaping the global innovation landscape, making it increasingly important for firms to increase their research and development (R&D) investments. These investments are pivotal for accelerating technological upgrades and maintaining competitiveness in a globalized economy. It is widely accepted that firms increase R&D investment because of their ample resources and positive performance feedback. However, despite a significant rise in R&D investment by listed firms in recent years, their profitability has not demonstrated a corresponding increase. This discrepancy raises an important question: Has the continuous increase in R&D investment by firms surpassed the positive effects of performance feedback?
Building upon the resource-based view and performance feedback theory, a growing body of literature has focused on how external stakeholders, such as the government and venture capitalists, influence firm's R&D investment decisions. In particular, the actions of other firms within the same field can reshape the business environment and influence the allocation of resources among stakeholders. From the perspective of institutional isomorphism theory, it has been found that, in uncertain environments, firms tend to imitate the behavior of other firms operating in similar contexts, particularly when those firms are embedded within the same organizational field. This imitation helps firms reduce decision-making risks and gain legitimacy, especially in a competitive and resource-constrained environment.
Most existing research has examined the role of imitation pressure in promoting R&D investment within a single organizational field. However, focal firms are not only embedded in one field but often operate within both industry and regional fields simultaneously. The overlap between these two fields generates a new organizational space known as the regional-industry dual field. This dual-field environment creates unique dynamics for firms that are embedded in both fields, and raises several important questions. For example, how do new "same-group" firms, those that are both in the same region and industry, influence one another's R&D investment behavior? Furthermore, what happens when imitation pressures from the two fields conflict? consider the situation where firms in the same region have relatively high levels of R&D investment, but firms within the same industry have lower investment levels. How will the focal firm respond in such a conflicting environment? Existing organizational innovation research has not provided clear answers to these questions, especially when it comes to the simultaneous pressures from industry and regional fields.
To address these gaps, this study applies institutional isomorphism theory to explore the impact of dual-field imitation pressure on firm's R&D investment. Additionally, it examines the moderating effects of imitation pressure conflicts. The analysis investigates the relationship between imitation pressure and R&D investment in firms of different market positions, while also assessing whether dual-field imitation pressure helps firms overcome the manipulation of R&D investment levels. Using a two-way fixed effects model, this study conducts an empirical analysis of A-share manufacturing companies listed on the Shanghai and Shenzhen stock exchanges between 2008 and 2022.
The results of the study indicate that dual-field imitation pressure positively influences firms to increase their R&D investment. Furthermore, when imitation pressures from the two fields conflict, the driving effect on R&D investment is strengthened. Firms with varying market positions align with the imitation pressures from the different fields they are embedded in, and, when facing dual-field pressures, they tend to increase their substantive R&D investment. This study not only enriches the literature on the role of imitation pressure in corporate R&D investment but also contributes to the understanding of how social innovation demands can be translated into corporate behavior. By analyzing the interplay between different organizational fields, this study helps provide new insights into the dynamics of R&D investment and the role of external pressures in shaping corporate innovation strategies.
Small and medium-sized enterprises (SMEs) are the driving force of innovation, entrepreneurship, and economic development in China. In the era of digital economy, with the deep integration of digital technology and the real economy, data resources have become the key for SMEs to gain competitive advantages. In practice, some SMEs have seized the opportunities of the times and achieved rapid development through digital transformation, but more SMEs still face significant challenges in the process of digitization, with phenomena such as reluctance, inability, and fear of digital transformation being widespread. At the same time, in the face of complex and volatile market, technology, and institutional environment, the “specialized, refined, differentiated, and innovative (SRDI)” development path is considered an important strategic pathway for high-quality growth of SMEs in China. Existing studies have attempted to explore the impact and boundary conditions of the digital economy on the growth of “SRDI” SMEs, confirming that the business environment has a synergistic effect on the digital economy to enhance the cultivation effectiveness of “SRDI” SMEs. However, most existing studies focus on the macro-level influence of the digital economy on the development of “SRDI” SMEs, without delving into the micro-level analysis of the role of digital technology application, nor examining the internal mechanisms and boundary conditions by which the digital technology application drives the “SRDI” development of SMEs. Therefore, based on the theory of firm endogenous growth mechanism, this paper constructs a theoretical framework of the process by which the application of digital technology empowers the growth of SMEs, and uses questionnaire survey data for empirical analysis to reveal the internal mechanisms and key contexts of the aforementioned process, namely how the application of digital technology promotes enterprise growth by enhancing the “SRDI” strategic orientation of SMEs, as well as the moderating effect of external market conditions (intensity of market competition) and SMEs’ ability to acquire ecosystem resources (acquisition of ecosystem resource). Empirical analysis reveals that the digital technology application can promote the growth of SMEs, and this promotion is achieved by strengthening their “SRDI” strategic orientation. However, the impact of digital technology application on SMEs’ “SRDI” strategic orientation is moderated by external market conditions and the SMEs’ ability to acquire ecosystem resources. Specifically, the more intense the market competition or the easier the access to ecosystem resources, the weaker the impact of digital technology application on the “SRDI” strategic orientation. The theoretical contributions of this paper mainly include: First, by focusing on the impact of digital technology application on the growth of SMEs, this paper enriches the research on the driving factors of SME growth. Second, from a strategic perspective, this paper reveals the internal mechanism by which digital technology application empowers the growth of SMEs. Lastly, this paper identifies the important situational factors for the promotion of “SRDI” development of SMEs through the application of digital technology. This paper also makes some practical contributions: First, SMEs in the era of digital economy can embark on the path of “SRDI” development by actively applying digital technologies. Second, SMEs that adhere to the “SRDI” strategic orientation should not blindly pursue digital transformation but should consider it strategically.
The 14th Five Year Plan points out to “accelerate the development of services such as research and development design, industrial design, inspection and testing certification”, and “to build an innovation and service complex to form an emerging industry growth engine with complementary advantages and reasonable structure”. It can be seen that building a service chain that matches the industrial innovation chain and driving the fusion of innovation chain and service chain (dual-chain) in emerging industries is crucial for facilitating efficient matching between innovation demands and service supplies and enhancing industrial innovation capability. Digitalization propels changes in dual-chain entities and diversifies the dual-chain fusion models, accelerating the dual-chain fusion process and presenting new evolutionary characteristics and structures. Drawing on the 6C theoretical framework and using the new energy vehicle industry as a vertical case study, this study explores the dual-chain fusion mechanism in emerging industries driven by digitalization. The findings show that the process of dual chain fusion in emerging industries follows a causal logic of external context (driven by digitalization) - fusion behavior (structural adaptability adjustment, supply-demand relationship connection, flexible functional configuration) - fusion result (improvement of industrial innovation capability). The dual-chain fusion has gone through three evolution stages of “fusion exploration→fusion interaction→fusion upgrading” (Change). The dominant elements and combination modes of digital driven context in the three stages of fusion are different, presenting an evolutionary feature of “guided→interactive→pioneering” (Context). Under the drive of digitalization, the dual chain entities undergo strategic updates and sequentially carry out structural adjustments of “digital shaping chain→digital strengthening chain→digital expansion chain” (Construct). As an important support and promotion condition for fusion behavior, it guides the formation and reshaping of the “complementary efficiency cross-border” relationship connection mode (Cooperation). With the evolution of linkage modes, the types and goals of cooperation between dual chain entities have dynamically changed, resulting in the formation of a “single point→modular→ecological” functional configuration for dual chain fusion (Configuration). This not only supports the strategic progression of dual chain entities, but also provides timely feedback for relationship linkage, promoting the optimization of chain network structure. In the continuous deepening of dual chain fusion, emerging industries have successively formed digital driven distributed, fused, and cross-border innovation capabilities (capability), promoting the upgrading of the dual chain fusion network to an ecosystem of “innovation service” cross-border docking and intelligent collaboration, and accelerating the development of new quality productivity. This study uncovers the black box of digital-driving mechanisms and process of dual-chain fusion in emerging industries, to make up for the shortcomings of existing research in exploring the mechanism of dual chain fusion from a static perspective, and the 6C theory is combined with the multi-agent, multi interaction, and multi-stage evolution characteristics of complex systems, providing a suitable framework and inspiration for the evolution research of more types of complex systems in the future. and also provides insights for the strategic planning of dual-chain fusion in industries and the cooperative practices of dual-chain entities.
The evaluation of public policies has been a long-standing concern for both the government and academia. However, most studies either focus on the effectiveness of the text or the outcomes rarely examining the complete chain from policy effectiveness to policy outcomes. This paper, based on the "dual effectiveness" mixed method, takes China's science popularization as an example, and uses panel data from 31 provinces from 2012 to 2022 to conduct empirical analysis, systematically investigating impact of policy effectiveness on policy outcomes and its mechanism. The study finds that policy effectiveness has a significant impact on policy outcomes; the level of policy effectiveness and outcomes significantly depending on the target audience, with the highest performance seen in incentive policies, followed by protective policies, and the weakest in environmental policies; among the four dimensions policy, policy measures have the greatest impact on the effectiveness of policy implementation, followed by policy feedback and policy goals. Therefore, the government should further strengthen the driving of incentive policies, improve the efficiency of incentive policy supply through revision of incentive measures. And improve the overall effectiveness and outcome of the policy by enhancing policy measures, goals, policy strength, and policy feedback. The marginal contributions of this paper are as follows:(1) By adopting a "dual-effect" mixed method, it first quantitatively measures the effectiveness of popular science policies, then analyzes the impact of policy effectiveness on policy outcomes, providing a more objective measurement of the validity of policy texts and their influence on actual implementation outcomes;(2) Starting from multiple factors influencing policy implementation outcomes, it comprehensively considers the impact of policy effectiveness, different categories of policy effectiveness, and various policy dimensions on policy execution results, in order to avoid one-sidedness and subjectivity in policy performance evaluation;(3) Using the established policy effectiveness evaluation model as a tool, a more accurate assessment of the implementation effects of different policy documents and policy instruments can be conducted.