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.