Future energy is a critical arena for national competition, where the growing energy demands driven by artificial intelligence and the need for energy security compel China to evaluate the evolution and competitive landscape of future energy technologies, optimize strategies to enhance global competitiveness, and ensure energy self-sufficiency. Utilizing the S-curve principle of TRIZ, the study analyzes and compares the technological evolution stages of future industries at the national level, constructing a competitiveness evaluation system that includes the Technology Impact Index, Technology Growth Index, Technology Maturity Index, Technology Attention Index, and Market Attraction Potential Index. The CRITIC-Entropy Weight Method is employed to quantify the competitiveness of future industries, with an empirical validation conducted using 13 technologies in the future energy sector across China, the United States, Japan, and the United Kingdom. Results indicate that the United States leads in high-barrier fields such as controlled nuclear fusion and CCUS, with a maturity lead of 10-69 years over China; Japan and the United Kingdom hold localized advantages in virtual power plants and nuclear fission; China ranks first in competitiveness for six technologies, including deep-sea wind energy and high-efficiency photovoltaic cells, but its overall technological influence and maturity remain relatively low. The study provides a new approach for analyzing international competition in future industries and offers strategic recommendations for enhancing the competitiveness of Chinese future energy technologies.

The future industry is driven by breakthrough technological innovation as its core driving force, and its development combines multiple characteristics such as strategic leadership, industrial disruption, and technological route uncertainty. The layout of such industries not only helps to grasp the initiative of the global technological revolution, but also serves as a key path to reconstruct the competitive advantage of the industry. With the deepening development of artificial intelligence, big data, and cloud computing technology, computing power resources are gradually replacing traditional production factors and becoming an important driving force for future industrial innovation. This article calculates the future industrial innovation network based on cooperative patents in 285 cities from 2011 to 2021, and measures urban computing infrastructure through the construction of supercomputing centers. The impact mechanism and effect of supercomputing center construction on urban future industrial joint innovation are tested through a difference in differences model. The following conclusions are reached: (1) The construction of supercomputing centers can positively promote the enhancement of the strength of future industrial innovation networks, and this conclusion has been tested through robustness methods such as adjusting regression samples, adjusting model settings, using dual machine learning models, and endogeneity tests, indicating that the empowerment of computing infrastructure can promote the development of future industrial innovation networks. At the same time, computing infrastructure mainly strengthens the external innovation network connection of cities, and its innovation network reconstruction function is more reflected in the external connection level that breaks through administrative boundaries, but it has limited innovation synergy effects among innovation subjects within cities. (2) The computing infrastructure mainly affects the future development of industrial innovation networks through mechanisms such as knowledge innovation linkage, innovation factor adsorption, and innovation boundary expansion. Computing infrastructure helps to strengthen knowledge cooperation links in the innovation chain, promote the flow and aggregation of urban innovation resources, expand technological boundaries, and thus promote the improvement of future industrial innovation network levels. It is worth noting that the policy combination formed by the construction of computing infrastructure, the optimization of innovation ecology policies, and the smooth flow of data elements can generate positive synergies, further promoting the development of future industrial innovation networks. (3) Heterogeneity testing reveals that in the central and western regions, regions with higher levels of market integration and information costs, the construction of computing infrastructure has a more significant impact on the strength of future industrial innovation network connections. The construction of computing infrastructure has positively promoted the centrality of innovation network nodes, while its negative impact is close to centrality, indicating that it is promoting the formation of an unbalanced development pattern in the future industrial innovation network of cities. The results of the spatial double difference model show that the construction of supercomputing centers promotes the enhancement of collaborative innovation capabilities in neighboring cities through spatial spillover effects, and the spillover effects become stronger with increasing distance. Based on the above conclusions, this article has the following policy implications. The first is that the government should optimize the layout of computing infrastructure and promote the deepening of urban innovation networks. The second is that the government should form a diversified policy combination, creating a synergistic effect between computing power and innovation ecology. Thirdly, the government needs to promote the construction of computing infrastructure according to local conditions and narrow the regional innovation gap.

Amid intensifying global technological competition, the implementation of China’s 14th Five-Year Plan, and the comprehensive push for high-quality regional development, provincial laboratories have emerged as key pillars in the nation’s regional strategic science and technology system. These laboratories are rapidly evolving from single-function research entities into integrated “task-force-style” platforms that combine basic research, technological innovation, and results commercialization. However, on the path toward high-level scientific and technological self-reliance, they continue to face structural challenges, including weak theoretical foundations, insufficient cross-sector collaboration, and inadequate policy support. Based on the strategic framework built around three pillars—dynamic mechanism, ecological networks, and institutional safeguards,this study analyzes the drivers, organizational logic, and strategic priorities underpinning the clustered development of provincial laboratories. Drawing on case studies of leading laboratories such as Zhejiang Zhijiang, Guangdong Songshan Lake Materials, Jiangsu Gusu, and Henan Zhongyuan Graphene, the study outlines a pathway for breakthrough through fostering collaborative innovation ecosystems, enabling cross-disciplinary cooperation, implementing flexible operational models, and enhancing targeted policy support. The findings provide a replicable strategic model and practical guidance for advancing the systematic deployment of China’s regional science and technology capacity as part of its broader national innovation agenda.

The rapid development of emerging technologies is fundamentally redrawing the ethical boundaries of human society, making the achievement of ethical consensus more challenging than ever. While China has prioritized technology ethics through top-level institutional frameworks-such as the Opinions on Strengthening the Governance of Science and Technology Ethics (2022) and the Measures for Ethical Review of Science and Technology (Trial) (2023)-these norms are primarily applicable to the "post-consensus" governance phase. They provide standardized procedures for known risks but often lack efficacy during the "controversy phase," where uncertainty is high and fundamental values clash. Historically, the 1975 Asilomar Conference on recombinant DNA (rDNA) is lauded as the gold standard for resolving such disputes. However, existing scholarship predominantly views it through the lens of the "expert warning" model, overlooking the intricate socio-technical process of building the consensus itself. This study evaluates three traditional consensus modes: the ethical approach of principalism, which often fails in morally pluralistic societies; the technical approach of risk assessment, which is often hamstrung by the Collingridge Dilemma; and the social approach of public participation, which can lead to "over-commitment" or governance chaos. The research identifies that the fundamental difficulties in forming consensus lie in managing technical uncertainty and determining which actors should lead the process.To address these challenges, this research utilizes Actor-Network Theory (ANT) to deconstruct the Asilomar process as a "translation" of interests. Under ANT, humans (scientists, lawyers, and policy-makers) and non-humans (viruses, plasmids, and legal codes) are treated as interconnected "actors" within a heterogeneous network. The formation of consensus began with the transition from individual anxiety to a collective "risk community". Early pioneers like Paul Berg and Robert Pollack identified significant hazards in using the SV40 virus-a known oncogenic agent-within E. coli systems. These technical concerns were not resolved in isolation but were translated into a broader scientific dialogue through the 1973 Asilomar I and Gordon Conferences. A pivotal moment occurred with the publication of the 1974 "moratorium" letter in Science, which called for a temporary halt on specific experiments. This act served as a "problematization" strategy, establishing a "mandatory passage point" for the scientific community. By going public, scientists transformed a localized laboratory concern into a global socio-technical challenge, forcing stakeholders to recognize that the very survival of scientific autonomy depended on addressing public fears regarding biosafety and ethical responsibility.The heart of the consensus process occurred during the 1975 Asilomar II Conference, where technical negotiations initially reached a stalemate. Specialized sub-groups tasked with categorizing risks-focusing on animal viruses, plasmids, and eukaryotes-found that scientific knowledge was insufficient to provide definitive evidence of safety or danger. Scientists were caught in a binary trap: they feared that any admission of risk would lead to permanent government bans, yet they also realized that a failure to self-regulate would invite even harsher external control. The "translation" was ultimately completed when legal experts introduced the "non-human" actor of existing institutional frameworks into the network. Law professor Roger Dworkin clarified that scientists would be held personally and institutionally liable for any laboratory-born harm under existing tort and occupational safety laws. This realization fundamentally altered the interests of the core actors. Scientists understood that "pure autonomy" was no longer a viable option and that a pragmatic, tiered risk-classification system-ranging from minimal to high risk-was the only way to safeguard the future of their research. This flexible consensus allowed for a middle path: prohibiting the most dangerous experiments while providing clear, conditional guidelines for others.The study concludes that the Asilomar consensus was not a static event but a dynamic process that was subsequently institutionalized via the NIH Guidelines for Research Involving Recombinant DNA Molecules (1976). This institutionalization ensured that the consensus remained flexible, with the NIH acting as a "boundary organization" to mediate between science, government, and the public. A key takeaway for contemporary technology governance is the necessity of "expert-led multi-stakeholder participation". This model relies on what Harry Collins describes as "Owl Scientists"-those who possess both the technical proficiency to understand the science and the reflexive capacity to grasp its social and ethical implications. The Asilomar experience demonstrates that institutional constraints are not impediments but necessary conditions for consensus, as they provide the "bottom line" that forces actors toward compromise. Furthermore, the final consensus must remain flexible and dynamic, utilizing categorized management to address scientific uncertainty and allowing for periodic revisions as new knowledge emerges. For China's emerging technology landscape, the lesson is clear: ethical governance must move beyond the mere declaration of abstract principles to the creation of flexible, tiered, and dynamic consensus-building mechanisms that can navigate the profound uncertainties of the controversy phase.

The United States has methodically constructed a remarkably efficient and resilient national innovation system. This sustained endeavor has enabled the nation to successfully transcend its initial position as a technological follower, ultimately achieving and consolidating preeminent global leadership in innovation. Furthermore, the US has consistently reinforced its dominant standing within critical frontier scientific and technological domains on the world stage. A comprehensive longitudinal analysis of the trajectory underpinning the enhancement of this system’s overall functional efficacy reveals four distinct and consequential developmental phases. During the foundational formative stage, innovation activities were overwhelmingly propelled by private initiative and market dynamics, leading organically to the nascent emergence of the national innovation system’s fundamental architecture. The exigencies of World War II precipitated significantly heightened governmental engagement and strategic direction, decisively steering the system’s evolution towards a more organized, centrally coordinated framework aimed at enhancing collective output. Subsequently, spanning the prolonged Cold War era through the dawn of the 21st century, a deliberate and resource-intensive dual-track development strategy—simultaneously advancing military technological supremacy and robust civilian technological advancement—acted as the principal engine driving a comprehensive, multi-faceted elevation of the entire system’s aggregate efficiency and innovative capacity. Entering the 21st century, the strategic paradigm has demonstrably shifted towards an integrated focus on both cutting-edge scientific prowess and sustained economic competitiveness, viewed through the prism of intensifying great power rivalry. This reorientation necessitates and actively drives continuous dynamic adaptation and meticulous, ongoing optimization within the national innovation system, yielding demonstrable, steady gains in its overarching operational efficiency and global impact. This research critically examines the indispensable catalytic role played by governmental entities in orchestrating the construction, refinement, and functional augmentation of the national innovation ecosystem. Our analysis discerns that the demonstrable enhancement of the US system’s overall efficacy is fundamentally characterized by several interconnected pillars: the paramount importance of clear, forward-leaning strategic orientation providing decisive direction; the continuous, deliberate refinement of organizational and institutional architectures to maximize coordination and responsiveness; the systematic cultivation and nurturing of a vibrant, interconnected innovation ecosystem fostering collaboration and knowledge exchange; and the establishment of a robust, multi-layered policy framework offering sustained, foundational support. Furthermore, by scrutinizing emergent trends and strategic recalibrations within the contemporary US innovation landscape and assessing their profound potential ramifications under evolving global dynamics, this study extrapolates pertinent insights and proposes actionable countermeasures for augmenting the holistic efficacy of China’s national innovation system. Key recommendations encompass: intensifying proactive, strategically-aligned foresight exercises coupled with mechanisms for agile policy and resource adjustment; advancing the paradigm of organized, mission-oriented basic research, rigorously aligned with both long-term strategic imperatives and pressing societal/industrial needs; accelerating the development of a high-performance innovation ecosystem distinguished by the seamless, frictionless flow and optimal allocation of critical talent, capital, knowledge, and infrastructural resources; and systematically enhancing the coherence and comprehensiveness of the integrated innovation policy support system to provide holistic, effective underpinning.

The digital society refers to a social form driven by digital technology, a multi-layered and complex system that constantly evolves and generates through technological iterations. Reinterpreting the digital society is of significant necessity and should follow a systematic analytical framework of “core technological devices - social organizational logic - social relational structure - social ethical issues”. Accordingly, from the advent of electronic computers to the popularity of the global Internet, from the rise of artificial intelligence to the emergence of large language models, the digital society has undergone four rounds of social form reconstruction driven by technology. As the “core technological device”, digital technology continually drives the iteration of “social organizational logic” and the reorganization of “social relational structure”, while also giving rise to new and increasingly complex “social ethical issues”. The reinterpreting of the digital society will help better address the social transformations empowered by digital technology and lead toward a smarter and more sustainable digital civilization.

Generative Artificial Intelligence (GAI), while driving industrial transformation and innovation, has introduced unprecedented governance challenges including deepfakes, algorithmic bias, and provenance gaps. In response, China’s Labeling Method for Content Generated by Artificial Intelligence establishes a dual-track regulatory system combining explicit visual identifiers and tamper-resistant implicit watermarks embedding metadata like timestamps and data trails. This framework aims to create an end-to-end oversight chain—embedding identifiers at generation, enabling verification during dissemination, and ensuring traceability upon consumption—adapting copyright law’s publicity principles to shift from "signature presumption" to "technological determination" for AI-generated content. However, implementation faces critical barriers: obligations are structurally misaligned across stakeholders, with model developers lacking mandates to architect foundational watermarking interfaces, service platforms overburdened by metadata validation duties that strain SMEs and end-users operating without risk-tiered responsibilities. Further complicating enforcement, fragmented metadata formats, proprietary/open-standard conflicts, and outdated national standards create interoperability voids that hinder cross-platform verification, particularly as emerging technologies like latent-space video compression outpace regulatory updates. To address these dual challenges of obligation imbalance and technical fragmentation, a tiered governance approach is essential. Upstream, developers must be mandated to preset multimodal implicit labeling interfaces and deploy automated integrity checks, establishing traceability at the architectural source. Midstream obligations should align with platform capabilities, focusing on reliable visible tagging and “notice-and-takedown” mechanisms akin to e-commerce governance, avoiding undue technical burdens. Downstream, user duties require differentiation—minimal labeling for general content creation versus stringent metadata verification for professional outputs like legal analyses, coupled with substantive review for identifier-removal requests to prevent abuse. Concurrently, metadata standardization demands dynamic co-regulation: unifying cross-modal identifiers through universal “AI_Content_ID” fields anchored to blockchain-certified provenance records; establishing agile standard-revision protocols using regulatory sandboxes and unannounced inspections to keep pace with innovations like Parquet columnar storage; and legally recognizing compliant metadata systems as copyright-enforceable “technological measures” to close enforcement gaps. Future policy must prioritize inter-departmental coordination for cross-sectoral standard harmonization, risk-calibrated regulatory pilots in sensitive domains like healthcare, and proactive engagement in global forums such as ISO/IEC JTC1—collectively advancing a governance paradigm that balances innovation incentives with robust risk mitigation, steering GAI from disruptive growth toward responsible deployment.

With the deep integration of information technologies such as big data and artificial intelligence algorithms into the financial services industry, the rapid rise of financial technology has been promoted. From a technical perspective, financial technology can optimise the allocation of financial resources and improve financial efficiency, thereby promoting innovation and development in the financial services industry. However, everything has a two-way nature. When financial technology is deeply embedded in the financial services industry, it also reshapes the traditional financial equilateral trilemma. At this time, financial regulation is difficult to achieve the triple goals of financial innovation, market integrity, and rule simplicity at the same time, forming the asymmetrical trilemma of financial technology. That is, financial technology regulation is difficult to achieve the triple goals of financial technology innovation, risk prevention, and rule simplicity at the same time, and the triple goals have unequal value attributes. In the context of the asymmetrical trilemma of financial technology, through theoretical analysis, it is found that overly strict or complex financial technology regulation suppresses the innovation and development of financial technology, leading to a decrease in the benefits of financial technology innovation; while overly loose or simplistic regulation of financial technology is not conducive to preventing financial technology risks, leading to increased regulatory costs for financial technology. Only by adopting moderate financial technology regulation (such as twin peaks regulation) can the innovation and development of financial technology and the prevention of financial technology risks be achieved. By using the comparative analysis method, it is found that most countries (such as the United Kingdom, the United States, Australia, etc.) explore the twin peaks regulatory system from the perspective of financial technology regulatory concepts, goals, and means, achieving the dual peak goal of optimizing financial technology regulatory effectiveness (achieving innovation and development of financial technology, and preventing financial technology risks), and optimizing the simplicity of financial technology rules: transforming regulatory concepts and adopting moderate regulatory concepts; building a twin peaks regulatory system to achieve the dual peak regulatory goals; enriching regulatory measures and adopting legal regulation and technological regulation. In order to solve the asymmetrical trilemma of financial technology and achieve the dual regulatory goals of promoting financial technology innovation and preventing financial technology risks under the constraint of financial technology regulatory resources, China can combine the local financial technology regulatory situation and foreign regulatory experience, and start from the perspective of financial technology regulatory concepts, goals, and means to construct a dual regulatory system in the financial technology scenario, optimize the simplicity of financial technology rules: Firstly, in terms of regulatory philosophy, there has been a shift from segmented regulation to a twin peaks regulatory approach; Secondly, in terms of regulatory objectives, achieving twin peaks regulatory goals through regulatory sandboxes; Thirdly, in terms of regulatory measures, strengthening the dual approach of legal and technological regulation.

As China continues to make significant breakthroughs in critical and strategic technological domains, the transformation of scientific and technological achievements into practical applications has emerged as a pressing national priority. Effectively overcoming the challenges in the commercialization of such key technologies is increasingly recognized as a central driver for translating scientific progress into productive capacity and national competitiveness. However, the process by which core technological innovations evolve into complex, market-ready systems remains insufficiently understood, particularly in the context of large-scale, high-stakes industrial applications. This study addresses this gap by adopting an iterative innovation perspective and conducting an in-depth case analysis of the“Wellleader+Drilog”system—an exemplar of China’s national strategic equipment developed by China National Offshore Oil Corporation (CNOOC).

Through longitudinal case evidence, this study reveals the underlying mechanisms that shape the transformation of critical core technologies into high-end industrial solutions. First, the process of technology commercialization follows a logic of “iterative foundation-iterative process-iterative outcome,” advancing through a three-stage trajectory: (1) basic function realization, where core technical principles are translated into rudimentary product forms; (2) complex function expansion, in which capabilities are enhanced and diversified to meet operational complexity; and (3) differentiated function leadership, where innovation enables the development of unique product attributes that serve as competitive advantages in strategic scenarios. This dynamic evolution highlights how the iterative nature of innovation supports both technical maturity and functional sophistication over time.

Second, the transformation path is shaped by stage-specific innovation patterns, characterized as “validating iteration-adaptive iteration-collaborative iteration.” These correspond to distinct transformation goals: the initial stage focuses on converting technical feasibility into operational usability; the middle stage emphasizes improving reliability under varying operational conditions; and the final stage aims to achieve optimal fit between technology capabilities and complex industrial application scenarios. This staged approach not only facilitates technological adaptation and refinement but also ensures that products are increasingly embedded within their target environments.

Third, the product development process in the transformation of scientific and technological achievements follows an iterative logic grounded in the framework of “function analysis-behavior construction-structure output.” Specifically, at the functional level, product goals are defined in terms of three core objectives: the realization of basic functions, the correction of functional deviations, and the coordination of functions across complex application scenarios. At the behavioral level, the development process involves exploring the mapping logic between system components, understanding the effects of component interactions, and replicating effective architectural configurations to construct a coherent product system. At the structural level, the process produces outcome-oriented outputs including deviation calibration, standard refinement, and system-level architecture construction. These three dimensions—function, behavior, and structure—form an integrated design logic that is continuously refined through recursive feedback loops at different stages of the development cycle, thereby enabling iterative innovation and progressive product evolution throughout the commercialization process.

By illuminating the iterative innovation mechanisms underpinning the commercialization of a critical core technology in a complex industrial setting, this study contributes to the theoretical advancement of research on science and technology transformation and industrial innovation. It offers practical implications for policymakers, R&D managers, and firms seeking to accelerate the transition from technological invention to scalable, high-performance product systems. In doing so, it provides a structured roadmap for enhancing the effectiveness of national innovation systems and supporting the industrialization of strategic technologies in emerging economies.

The diffusion of technological knowledge across regions is fundamental for enhancing innovation capacity and achieving balanced regional development. While prior research has highlighted the role of technological proximity in facilitating knowledge spillovers, it often neglects the dual nature of inter-city linkages that encompass both competition and complementarity. This paper extends the discussion by introducing the concept of technological complementarity, defined as cities sharing a broad cognitive base but specializing in different sub-fields, thereby creating heterogeneous yet synergistic technological structures. Using patent application and citation data from 284 Chinese cities between 2003 and 2019, we construct a novel index of inter-city technological complementarities based on IPC categories and link it with patent citation flows. The resulting panel of 681,748 city-pair observations enables a systematic empirical assessment of how complementarities shape technological knowledge diffusion.

The empirical results show that technological complementarities significantly promote inter-city technological knowledge diffusion. Cities with higher complementarities are more likely to cite each other’s patents, indicating stronger knowledge flows. A one-unit increase in complementarity is associated with a substantial rise in both the intensity and probability of patent citation. Unlike excessive similarity, which often induces homogeneous competition and technological lock-in, complementarities provide non-redundant knowledge resources, enhancing opportunities for recombination and innovation. Thus, complementarities are revealed as a critical driver of regional knowledge dynamics, contributing to the resilience and efficiency of innovation networks.

Mechanism analyses further reveal that complementarities foster knowledge diffusion through three pathways: collaborative innovation, technology transfer, and talent mobility. Complementary cities are more likely to engage in joint R&D and co-patenting, lowering communication barriers and facilitating tacit knowledge exchange. They also promote technology transfer, which transforms potential externalities into tradable innovation elements and strengthens cross-regional application of knowledge. In addition, complementarities stimulate the mobility of skilled talent, who act as carriers of tacit knowledge and bridge otherwise disconnected networks. These mechanisms collectively establish a robust chain through which complementarities translate into knowledge diffusion.

Finally, contextual and heterogeneity analyses show that the impact of complementarities is contingent on environmental conditions. Cultural distance increases communication costs and reduces complementarities’ effectiveness, whereas institutional proximity enhances cooperation predictability and amplifies diffusion. Transportation connectivity, particularly high-speed rail, mitigates spatial decay and enables complementarities to function over long distances. Moreover, eastern and high-tier cities benefit more strongly from complementarities due to superior absorptive capacity and institutional support, while effects are weaker in central, western, and lower-tier cities.

Overall, this study contributes to innovation geography and regional innovation empirically validating the role of technological complementarities in shaping inter-city knowledge diffusion, extending the theoretical framework beyond technological proximity, and providing policy implications for fostering collaborative innovation platforms, reducing institutional frictions, and facilitating talent and technology flows across regions.

In recent years, China has intensified its focus on achieving breakthroughs in key core technologies across strategic domains. However, there are still gaps in existing research. First, prior studies predominantly concentrate on large enterprises, overlooking the specialized expertise, operational flexibility, and efficiency advantages inherent to small and medium-sized enterprises (SMEs). This oversight obscures the unique role of SMEs in accomplishing significant technological advancements despite their smaller scale. In practice, many SMEs specialize in niche segments of industrial chains, accumulating technical expertise and innovative capabilities over time. Their focused positioning often enables them to outperform larger firms in breakthroughs related to critical materials, components, and manufacturing processes within specific supply chain nodes. Moreover, large enterprises’ efforts to overcome key technological bottlenecks frequently depend on collaborative support from specialized SMEs. Second, while SMEs universally face innovation constraints—including limited R&D resources, insufficient technological collaboration, and narrow innovation search scopes—digital technologies, as a novel factor of production, hold potential to mitigate these barriers and propel critical technological breakthroughs. Existing scholarship has explored pathways, outcomes, and influencing factors of SMEs’ technological advancements but pays inadequate attention to the role of digital technologies. Third, the development of “first-of-a-kind” and “first of batch”, a distinctive policy-driven innovation practice in China, represents a critical yet unexplored frontier for core technology breakthroughs, warranting deeper scholarly investigation.

To address these gaps, this study examines how digital technologies influence SMEs’ breakthroughs in key core technologies and identifies boundary conditions shaping this relationship. Utilizing a sample of SMEs in Hangzhou, Zhejiang Province, and situating the analysis within their engagement in “first-of-a-kind” and w“first of batch” innovation initiatives—a national policy context aimed at accelerating the commercialization of pioneering technologies, we investigate the mechanisms through which digital technologies facilitate of breakthroughs of key core technologies of SMEs. We further analyze the moderating roles of absorptive capacity, organizational scale, and market power.

Our empirical findings demonstrate that digital technologies significantly enhance SMEs’ capacity to achieve breakthroughs of key core technologies, but these effects are contingent on firm-specific characteristics: absorptive capacity and market power amplify the benefits of digital adoption, whereas larger organizational scale paradoxically diminishes its efficacy. This moderating effect of scale suggests that while digital tools democratize access to innovation resources, organizational growth may introduce bureaucratic rigidity that erodes the agility central to SMEs’ competitive advantage.

Our findings enrich theoretical understanding of antecedents and contextual factors in SMEs’ pursuit of key core technology breakthroughs. By challenging conventional scale-centric innovation paradigms, the study reveals how digital technologies can accelerate innovation dynamics to favor specialized, agile firms. Practically, it provides policymakers with evidence-based insights to design targeted intervention, such as enhancing digital infrastructure accessibility, fostering SME-centric innovation ecosystems, and optimizing fiscal incentives for collaborative R&D—that leverage digitalization to amplify SMEs’ disruptive potential. For SMEs, the findings emphasize strategic imperatives: cultivating dynamic capabilities to harness digital resources while maintaining organizational flexibility to avoid scale-induced inefficiencies. By bridging the digital divide and rebalancing innovation ecosystems, this research contributes to strategies that harness SMEs’s technological vitality to drive high-quality industrial development, ultimately advancing strategic goals of technological self-reliance of China.

Playing the role of leading-chain enterprises' traction to achieve the overall breakthroughs of key core technology throughout the industrial chain and supply chain is an important way to guarantee the security of the industrial chain and supply chain. According to existing literature, corporate basic research can generate positive spillover effects, which influence subsequent innovation both within and across industries. So, when a leading-chain enterprise enhances its basic research capabilities, could this generate spillover effects along the industrial chain and supply chain that impact other enterprises in the chain, beyond potentially influencing its own technological breakthroughs? If there is an impact, through what mechanisms does the spillover effect operate? Does this impact vary between upstream and downstream leading-chain enterprises and member enterprises? However, relevant research has not systematically addressed these questions. The dimensions within empowerment theory can be categorized into psychological, resource, and structural empowerment. These dimensions systematically reveal the systemic impact of basic research conducted by leading-chain enterprises on breakthroughs in key core technologies among chain member enterprises.

This study focuses on the bottlenecks faced by chain member enterprises in pursuing key core technological innovations. Guided by empowerment theory, it conducts theoretical analysis centered on whether leading-chain enterprises can effectively empower chain member enterprises to overcome their innovation barriers. Empirical testing is conducted using a large sample of data from listed advanced manufacturing enterprises from 2015 to 2022.

The findings are as follows: (1) when the level of basic research of upstream leading-chain enterprises is high, it can significantly promote key core technology breakthroughs of chain member enterprises, while no such effect exists between downstream leading-chain enterprises and chain member enterprises. (2) Mechanism research shows that upstream leading-chain enterprises promote key core technology breakthroughs of chain member enterprises mainly through three kinds of empowerment effects: psychological empowerment, resource empowerment and structural empowerment. (3) Heterogeneity analysis reveals that the positive effect is more pronounced when: -Chain member firms are non-state-owned; - The industry faces intense competition; - The region exhibits low human capital levels; - Geographical and technological distances between leading-chain and member firms are shorter, while organizational distances are greater (e.g., state-owned leading-chain firms paired with non-state-owned members).

The contributions of this paper are as follows: First, the scope of spillover effects are expanded to include leading-chain and chain-member enterprises classified by their positions within the industrial chain, thereby broadening the scope of research subjects for industrial chain and supply chain spillover effects. Second, through large-sample empirical tests, we find that the spillover effects of upstream and downstream leading-chain enterprises' basic research is different along the industrial chain and supply chain. This provides new empirical evidence on whether leading-chain enterprises can drive the development of chain-member enterprises in innovation fields. Furthermore, guided by the empowerment theory, we have unlocked the inherent “black box” of spillover effects generated by the basic research of leading-chain enterprises. This also expands the application scenarios of the empowerment theory. Finally, this paper also establishes for the first time a methodology for identifying leading-chain enterprises and chain member enterprises. This provides a relatively precise and feasible basis for subsequent research to identify and determine such entities.

With intensifying global technological competition and the emergence of a new wave of industrial revolution, breakthrough innovation has become a critical engine driving national development. Understanding the mechanisms behind breakthrough innovation is therefore an urgent research priority. Innovation entails novel combinations of production factors, and the introduction of new elements such as knowledge and data has enriched this notion of "new combinations." As a core production factor, scientific knowledge plays a pivotal role in breakthrough innovation. Investigating its connection to innovation value carries significant theoretical and policy implications.

Drawing on knowledge recombination theory and using a Tobit model, this study analyzes biotechnology patents granted by the United States Patent and Trademark Office (USPTO) from 2010 to 2021, along with the scientific literature cited therein, to explore the influence of heterogeneous knowledge sources on the value of breakthrough innovation. The main findings are as follows: (1) From the perspective of organizational boundaries, the paper distinguishes the effects of internal and external sources of scientific knowledge. Internal scientific knowledge positively contributes to the value of breakthrough innovation. External scientific knowledge exhibits a U-shaped relationship with innovation value—while diversity in external sources can be beneficial, it also increases integration complexity, necessitating strong absorptive and transformative capacities within the organization. Once an organization’s internal knowledge accumulation surpasses a certain threshold, it is more capable of capitalizing on open innovation, thereby enhancing the value of its breakthrough innovations. This mechanism helps explain why knowledge-intensive organizations often emerge as leaders in open innovation. (2) From the structural perspective of knowledge sources, the study finds that the layered structure of knowledge within university-industry-government collaborations—ranging from basic research to applied development—is conducive to generating breakthrough innovations. This provides micro-level support for the "Triple Helix" model of innovation. (3) From the perspective of knowledge characteristics, the study reveals differentiated impacts of scientific and technological knowledge on innovation value. While greater cognitive distance in technological knowledge tends to enhance breakthrough innovation, an increase in cognitive distance in scientific knowledge may raise the risk and uncertainty of innovation. This diverges from prior literature that suggests broad search and distant recombination of technological knowledge enhance breakthrough innovation. By distinguishing between scientific and technological knowledge, the study deepens the theoretical framework of knowledge recombination.

This research makes three primary contributions: (1) It refines the understanding of how internal versus external scientific knowledge affects breakthrough innovation, clarifying the distinct roles of internal innovation and inter-organizational collaboration; (2) it elucidates the role of scientific knowledge generated through university-industry-government collaborations, shedding light on the deeper value of such partnerships; (3) it reveals the differential mechanisms by which scientific versus technological knowledge contribute to innovation value, thereby enriching knowledge recombination theory.

Based on the above discussion, this paper proposes the following two policy recommendations: First, innovation policies—especially in science-driven industries—should prioritize incentives for internal knowledge accumulation. Second, knowledge exchange and integration across academia, industry, and government should be strengthened by removing institutional barriers and encouraging deeper cross-domain knowledge construction in collaborative innovation.

Digital technologies have brought profound transformations to the entrepreneurial activities, particularly by enabling new forms of firm-user interactions. Interaction orientation—a firm's strategic tendency to interact with and learn from users—has been increasingly recognized as a key enabler of innovation, its effectiveness remains context-dependent and underexplored in digital entrepreneurial settings. Prior research often highlights its linear benefits while overlooking potential drawbacks and the mechanisms through which it operates. This gap is especially salient in the context of digital entrepreneurial, where uncertainty and limited resources make decisions more complex. This study draws upon the Lean Startup Theory and investigates the “double-edged sword” effect of interaction orientation on innovation performance in digital new ventures, examines experimentation as a key mediating mechanism and incorporates latent need identification capability as a boundary condition.

Based on a multi-stage survey of digital new ventures, this study provides empirical evidence of an inverted U-shaped relationship between interaction orientation and innovation performance. When interaction becomes excessive, it generates diminishing returns—leading to information overload, resource strain, and decision-making fatigue due to redundant or conflicting user input. In addition, this study confirms the mediating role of experimentation, thus unpacking the black box of how user interaction drives innovation success. In dynamic and uncertain digital markets, experimentation enables ventures to iteratively test assumptions, validate MVP, and optimize product-market fit. Interaction orientation supplies the raw input—user insights and behavioral signals—while experimentation transforms this input into actionable innovation outcomes. Moreover, this study highlights the moderating effect of latent need identification capability, offering new insights into the conditions under which interaction orientation is most effective. Ventures with strong capabilities to detect unspoken, future-oriented user needs are more adept at filtering valuable signals from user interactions and translating them into focused experiments. The moderation analysis reveals that such ventures reach the optimal point of experimentation at lower levels of interaction orientation, suggesting that latent need identification sharpens the strategic value of firm-user interaction.

Theoretically, this study contributes in three key ways. First, this study clarifies the "double-edged sword" effect and underlying mechanism of interaction orientation on innovation performance in digital entrepreneurship contexts, offering a contextualized explanation to reconcile conflicting findings in existing literature. By focusing on the characteristics of digital entrepreneurship, this study provides both theoretical justification and empirical evidence for the inverted U-shaped effect of interaction orientation on innovation performance of digital new ventures. Second, this study uncovers the inverted U-shaped relationship between interaction orientation and experimentation, addressing the research gap on the antecedents of experimentation from the demand-side perspective. While prior studies have primarily explored the drivers of entrepreneurial experimentation from the supply side, the highly dynamic nature of user needs in digital contexts necessitates timely sensing and response to guide effective experimentation. This study demonstrates the positive effect of interaction orientation on experimentation in digital new ventures, thereby enriching the antecedent research of entrepreneurial experimentation in digital settings. Third, this study highlights the critical contingency role of latent need identification capability in digital entrepreneurship, expanding the boundary condition literature on interaction orientation. Enabled by digital technologies, digital new ventures are increasingly capable of capturing and interpreting user needs. In this context, the capability to identify latent needs serves as a crucial catalyst for realizing the benefits of interaction orientation, yet it remains underexplored in existing research.

Practically, this study highlights key innovation management strategies for digital new ventures. They should leverage digital technologies to interact deeply with users, personalize products and services, and predict latent needs through data analysis. They must remain agile by adjusting innovation directions based on market changes and iterating MVPs rapidly. At the same time, these new ventures should avoid excessive interaction that leads to information overload and resource dispersion. Prioritizing relevant user feedback, integrating information effectively, and allocating resources strategically are crucial for enhancing innovation performance in fast-changing digital environments.

Against the backdrop of the deep integration between global technological innovation waves and institutional reforms, patent thicket has emerged as a quintessential phenomenon in the evolution of intellectual property systems, characterized by property right fragmentation and unbalanced patent allocation. Despite its growing relevance in modern industrial landscapes, existing literature has not fully explored the specific mechanisms through which its structural contradictions influence the high-quality development of enterprises. This research aims to bridge this gap by adopting a multi-theoretical framework integrating resource-based theory, transaction cost theory, and dynamic capability theory, seeking to systematically dissect the complex interplay between patent thicket dynamics and enterprise development trajectories.

The study constructs a two-dimensional analytical model explicitly incorporating two critical dimensions: density-induced fragmentation costs and quality-driven synergistic benefits. Departing from traditional single-dimensional approaches, this model recognizes patent thicket as an interactive system where the quantitative accumulation of patents and their qualitative attributes co-evolve to shape organizational outcomes. Leveraging a comprehensive dataset of A-share listed companies from 2010 to 2023, the research employs panel data fixed-effect models to establish causal relationships and moderating effect tests to explore contextual contingencies. This empirical strategy enables a nuanced examination of how patent thicket operates across diverse organizational and environmental contexts.

Key findings reveal a dualistic influence of patent thicket on enterprise high-quality development. Specifically, patent thicket density demonstrates a significant inhibitory effect, primarily attributed to escalating transaction costs, innovation bottlenecks, and strategic lock-ins arising from fragmented intellectual property rights. Conversely, patent thicket quality—operationalized through indicators including patent originality, technological centrality, and commercial value—exerts a robust promoting effect by facilitating knowledge spillovers, enhancing competitive positioning, and enabling dynamic capability building.

The study further uncovers that environmental dynamism, characterized by rapid technological changes and market fluctuations, and environmental complexity, reflecting stakeholder diversity and regulatory landscapes, act as positive moderators. These factors amplify both the inhibitory effect of density and the promotive effect of quality, indicating that in volatile environments, challenges posed by patent density intensify while advantages of high-quality patents become more pronounced.

Additionally, the research highlights heterogeneous effects across firm characteristics. Small-scale enterprises exhibit heightened sensitivity to patent density, likely due to their limited resource buffers and bargaining power in patent licensing negotiations. In contrast, high-quality patents demonstrate a more significant innovation-driven effect for enterprises in highly competitive industries, where technological superiority serves as a critical differentiator. These findings illuminate how resource endowment, such as firm size, and market structure, such as industry competitiveness, moderate the patent thicket effect, providing insights into context-specific strategic responses.

By reconstructing patent thicket as an interactive system of density and quality, this study transcends conventional paradigms and offers a more nuanced understanding of its operational laws in dynamic environments. Theoretical contributions include integrating fragmented literatures, proposing a novel analytical framework, and identifying contingency mechanisms. Practically, the findings provide strategic guidance for Chinese enterprises to navigate patent thicket challenges, particularly in formulating balanced patent acquisition strategies, enhancing patent quality management, and adapting to environmental uncertainties. This research thus serves as a vital theoretical anchor for promoting high-quality development within China's evolving innovation ecosystem.

New quality productive forces represent a pioneering pathway of advanced high-quality development. Driven by technological innovation, the productive forces embody the productive capacity arising from critical and disruptive technological breakthroughs and serve as a vital means for achieving high-quality development. Recently, artificial intelligence (AI) has emerged as a transformative technology reshaping economic and social operations, as well as driving productivity leaps, assuming an increasingly prominent role in international competition and national development. As an institutional framework, AI policy holds substantial potential to guide and promote AI-enabled development of new quality productive forces. However, the mechanisms through which AI policy empowers new quality productive forces, as well as the contingent conditions governing this empowerment effect, remain under-researched through systematic empirical analysis. Existing evidence on this relationship has relied predominantly on theoretical discussions, while the limited quantitative literature focuses primarily on the firm level, leaving a gap in city-level empirical analysis. Given that cities serve as critical hubs for innovation clustering and industrial transformation, such analysis carries greater policy relevance. Furthermore, prior studies have emphasized the technology itself rather than policy effects. Drawing on an analytical framework encompassing technological innovation, production-factor allocation, and industrial upgrading, this study exploits the gradual implementation of China’s National New Generation Artificial Intelligence Innovation and Development Pilot Zones since 2019 as a quasi-experiment to quantitatively examine the impact of AI policy on multiple dimensions of new quality productive forces, including new workers, new means of production, new objects of labor, new technologies, new production organization, and new data factors. Results from a staggered difference-in-differences (DiD) estimation indicate that AI policy has increased new quality productive forces by at least 5.84%. Mechanism analysis reveals that the policy enhances new quality productive forces through stimulating technological innovation, optimizing production-factor allocation, and catalyzing industrial upgrading. Heterogeneity tests further indicate that the policy’s effect on new quality productive forces is more pronounced in cities with higher marketization levels and stronger technological capacity. This study offers insights into how AI policy can enhance high-quality development and foster the advancement of new quality productive forces. Sustained, AI policy-driven progress in new quality productive forces requires prioritizing technological innovation as the core driver, optimizing production-factor allocation as a supporting pillar, and advancing industrial upgrading as the implementation pathway, while concurrently improving cities’ marketization levels and technological capacity. Policy implementation should first establish a multi-tiered innovation ecosystem, such as creating an integrated support mechanism spanning basic research, applied research and commercialization. Second, efforts should continue toward building a unified national market for data factors, promoting institutional innovation in data ownership confirmation, circulation, trading, and regulation. Third, policies should foster coordinated development between traditional and emerging industries—on one hand, advancing the intelligent transformation of traditional industries, and on the other, cultivating strategic emerging industries and promoting the development of new AI-driven models. Finally, AI policy should enhance targeting precision and equity through guiding market entry cities with lower levels of marketization while reducing algorithm adoption barriers in technologically disadvantaged areas via national big data hub clusters and cross-regional scheduling networks.

From the economic development experience of other countries in the world, it can be seen that the service-oriented stage is a differentiation stage for whether growth can be sustained and whether economic catch-up can be successful. To successfully overcome the middle-income trap and ensure the sustainability of economic growth, it is necessary to apply the theory of new quality productivity to practice, and then promote the development of China's service industry towards high-quality direction, that is, the new quality of service industry. The new quality of the service industry is a high-quality service economic form, characterized by endogenous knowledge production, which reshapes the efficiency model through the optimization of the service industry structure and the dynamic balance mechanism between supply and demand. With the transformation of the new generation of information technology, digital technology innovation has undoubtedly become the most important new quality productivity during the Fourth Industrial Revolution, and is a strategic highland for major economies around the world to compete for layout. So, can and how can digital technology innovation promote the high-quality development of the service industry? This is the core question that this article intends to answer.

The article elaborates on the theoretical mechanism based on the logic of "elements to products and then to industries". Among them, the element level emphasizes that knowledge becomes the core driving force of economic growth; At the product level, there is a service upgrade from standardization to personalization; At the industrial level, it is manifested as the rise of knowledge intensive service industries. Subsequently, the article constructed an evaluation index system for the new quality development level of the service industry, which includes 5 core dimensions, 14 primary indicators, and 23 secondary indicators. The analysis pointed out that the new quality development of China's service industry presents characteristics of overall low-level slow rise, significant stage heterogeneity, uneven development of each core dimension, and regional development polarization. Finally, the article conducted empirical tests using panel data from 30 provinces in China from 2011 to 2023, and found that digital technology innovation can significantly promote the high-quality development of the service industry. This conclusion still holds true after a series of robustness tests. Mechanism testing shows that upgrading supply side factors, driving demand side momentum, and ensuring institutional environment constitute a triple empowerment path, which can more fully unleash the empowering potential of digital technology innovation for the new quality development of the service industry.

The article studies the application of the theory of new quality productivity to the practice of service economy development, providing theoretical support and policy inspiration for China to seize opportunities for digital technology innovation and promote high-quality economic development. Firstly, it is necessary to establish a "dimensional regional" dual balanced governance system to solve the dilemma of uneven development of core dimensions of service quality and significant regional differences. Secondly, both supply and demand sides should work together to build a collaborative innovation ecosystem for digital and service industries, and strengthen the empowerment efficiency of the digital economy. Thirdly, innovate the linkage mechanism between social security and market incentives, and unleash the multiplier effect of institutional environment.

With the continuous development of the economy, China has entered a new era of high-quality development. In the new stage of Chinese development, the main direction is to vigorously develop new quality productive forces driven by innovation based on local conditions. For a large economy like China, the real economy has always been the foundation of the entire economic framework, and industrial production is the main component in the real economy. Therefore, supporting and incentivizing industrial innovation has become a major gain for policy makers in the process of building an innovative country. The government has two methods to incentivize industrial innovation, including explicit and implicit fiscal expenditure of science and technology. Firstly, the government can support industrial innovation by directly injecting fiscal funds into industrial enterprises, which is known as explicit fiscal expenditure of science and technology. Secondly, in the process of market-oriented reforms, enterprises are the main body of innovation, so the expenditure methods for supporting industrial innovation more prefer to the ways of market. Therefore, implicit fiscal expenditures of science and technology in the form of R&D expense additional deduction and high-tech enterprise tax reduction are more applicable in more scenarios. Currently, in China’s Public Finance Model, fiscal expenditure of science and technology for promoting industrial innovation has presented a new feature as follows: the implicit fiscal expenditures of science and technology in the form of tax reductions have greatly exceeded the explicit fiscal expenditures of science and technology.

Direct effect of science and technology expenditure by the public sector in supporting industrial innovation activities is increasing industrial R&D innovation input because enterprises are the main body of innovation as described above. However, its actual economic value is bringing about a significant increase in innovation output. Using data from China’s industrial sectors and taking the number of invention patent applications as an indicator of innovation output, this paper estimates the impact of explicit fiscal expenditure in the form of direct government funding and implicit tax expenditure in the form of R&D expense additional deduction and high-tech enterprise tax reduction on industrial innovation output through various methods. This study finds strong and robust evidence that the two forms of implicit fiscal science and technology expenditure have no significant impact on industrial innovation output, and their combined effect is also not significant. Contrastively, R&D expense additional deduction and high-tech enterprise tax reduction have a significantly positive impact on internal R&D expenditure of industrial sectors, with elasticities of 22.38% and 7.9%, respectively. Therefore, industrial enterprises have a strong tendency of “strategic catering” innovation when facing tax reduction incentives. There is evidence that explicit fiscal science and technology expenditure has a significantly positive impact on industrial innovation output, but it is not robust. Since industrial enterprises do not significantly increase internal R&D expenditure due to government inputs fiscal science and technology funds, there is no incentive for industrial enterprises to engage in “strategic catering” innovative behaviors due to explicit fiscal science and technology expenditure.

Based on the theoretical analyses and empirical tests, to increase the value of the market in the allocation of innovation resources, the form of government support for industrial innovation should prefer to implicit tax subsidies rather than explicit fiscal expenditures. However, the “strategic catering” innovation behavior of market entities for tax reduction is not conducive to the effective utilization of innovation resources. This dilemma is a major obstacle to support industrial innovation by using the fiscal funds. Eliminate this bottleneck requires the integration and coordination among technology policies, industrial policies, and fiscal and tax policies in the future.

Key technology enterprises serve as the carriers, creators, and pioneering users of technologies for elderly care services. They provide the underlying technical framework for the innovation system of elderly care services and drive the evolution of this system. Most of the existing studies examine the innovation system of elderly care services from the perspectives of the macro-architecture and operational mechanisms, and there is insufficient attention paid to the important roles and diverse influences of key technology enterprises.

Based on an investigation of Tencent Silver Hair Technology Laboratory, we derive the evolutionary process of the elderly care service innovation system and the key technology enterprise effects. It reveals that the elderly care service innovation system consists of a technology niche system, a technology competition-cooperation system, and interaction flows between them. Within the technology niche system, key technology enterprises exert demand-driven effects and technology-driven effects. Internal and external linkage coordination effects are performed in the technology competition-cooperation system. During the interaction process, key technology enterprises exert a catalytic effect of connecting and consolidating the social and technological systems through demand linkage, resource integration, and rule improvement.

This study clarifies the evolutionary framework and key technology enterprise effects of the elderly care service innovation system from the perspective of actor interactions, deepens and develops socio-technical systems theory, and provides new research perspectives and practical implications for elderly care service innovation.

The “Double First-Class” initiative represents a major strategic endeavor in the reform of higher education in China. Central and local governments have provided substantial resources to support the selected universities, making it as a policy shock that is exogenous to universities. Using a Difference-in-Differences (DID) model, this study exams the “Double First-Class” impact on the quantity and quality of scientific research output of universities. The results indicate that the “Double First-Class” policy has achieved significant outcomes in both increasing the quantity and enhancing the quality of research output. Specifically, the “First-Class Discipline” policy during the first phase of the initiative demonstrated notable spatial spillover effects. In particular, regarding quality dimensions such as highly cited papers, top 1% leading papers, and Category Normalized Citation Impact (CNCI), non-selected universities surpassed the construction universities, thereby driving an overall improvement in the quality of scientific research output across the higher education system. Based on these findings, the study discusses issues such as the balance between construction-oriented and development-oriented thinking in the “Double First-Class” policy, the focus on institutions versus disciplines, and the tensions between homogenization and diversification. Looking ahead, it is recommended to better leverage the systemic driving role of “Double First-Class” in national higher education, and to vigorously advance the strategy of building a strong education system through mechanisms such as dynamic adjustment and categorized development.