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 competitive potential 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 competitive potential 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 gap stems from the first-mover advantages of leading countries, while China demonstrates catch-up potential driven by its market scale and policy support. The study provides a new framework for technological competition analysis and strategic recommendations for enhancing China’s energy technology competitiveness.
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 ethical governance of risky emerging technologies poses significant challenges. Although China has introduced policies such as the Opinions on Strengthening the Governance of Science and Technology Ethics, these norms primarily apply to post-consensus governance stages and fail to effectively address ethical issues during the controversy phase. The Asilomar Conference in the 1970s is regarded as a model for resolving ethical disputes in technology. Existing research mainly attributes its success to the expert warning model, overlooking the underlying consensus-building process.By analyzing three classic consensus models and their limitations, this study identifies the key challenges in forming consensus: addressing uncertainty and determining who should lead and coordinate the process. Through an examination of the Asilomar Conference's consensus-reaching process, the research finds that ethical consensus in technology should be expert-led yet involve multiple stakeholders. Experts should be reflective "owl" scientists, institutional constraints facilitate consensus formation, and the final consensus solution must maintain flexibility.
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.
Abstract: 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.
Small and medium-sized enterprises (SMEs) typically focus on niche areas within industrial chains. They possess the foundational expertise and potential to break through key core technologies in specific fields. However, they generally face constraints in innovation resources. Digital technology, as a new type of factor resource, can help SMEs overcome challenges such as limited R&D resources, insufficient technological collaboration, and restricted innovation search, thereby injecting new momentum into the accelerated breakthrough of key core technologies. Therefore, this study investigates the impact of digital technology on the breakthrough of key core technologies by SMEs and the relevant boundary conditions. The findings reveal that digital technology has a positive effect on the breakthrough of key core technologies by SMEs. However, this positive effect is moderated by firm characteristics. Specifically, the absorptive capacity and market momentum of a firm enhance the positive effect of digital technology, while the firm's size and strength weaken this effect. The conclusions of this study enrich the research on the antecedents and situational factors of key core technology breakthroughs by SMEs and provide practical insights for SMEs to leverage digital technology to accelerate key core technology innovation and, in turn, drive high-quality industrial development.
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 interaction between firms and users. Among these changes, 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 speed, uncertainty, and limited resources make strategic decisions more complex. To fill this gap, 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. Specifically, it examines experimentation as a key mediating mechanism and incorporates latent need identification capability as a critical boundary condition that shapes the impact of user interaction.
Based on a multi-stage survey of digital new ventures in China, this study provides robust empirical evidence of an inverted U-shaped relationship between interaction orientation and innovation performance. Moderate interaction levels help firms capture relevant market feedback, foster agile development, and achieve better innovation performance. However, 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 between interaction orientation and innovation performance. In dynamic and uncertain digital markets, experimentation enables ventures to iteratively test assumptions, validate minimum viable products (MVPs), 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. The findings confirm the mediating role of experimentation, thus unpacking the black box of how user interaction drives innovation success. 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. The inconsistent conclusions regarding the relationship between interaction orientation and innovation may stem from insufficient attention to contextual factors. By focusing on the characteristics of digital entrepreneurship, this study provides both theoretical justification and empirical evidence for the nonlinear (inverted U-shaped) effect of interaction orientation on the innovation performance of digital new ventures. It also opens the black box of this relationship by identifying experimentation as a key mediating pathway. 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 vetures 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.
New quality productive forces represent a pioneering pathway of advanced high-quality development. Driven by technological innovation, they 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 level of marketization while reducing algorithm adoption barriers in technologically disadvantaged areas via national big data hub clusters and cross-regional scheduling networks.
The new qualitative transformation of the service industry is a high-quality service economy form with endogenous knowledge production as the core, achieved through the optimization of the service industry structure and the dynamic balance mechanism between supply and demand, and the reshaping of efficiency models. Based on the logical thinking of "elements product industry", this paper elaborates on the theoretical mechanism of digital technology innovation promoting the new quality development of the service industry. Next, an evaluation index system for the new quality development of the service industry is constructed, which includes five dimensions: technological innovation, knowledge accumulation, quality upgrading, business transformation, and economic contribution. It is 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 various core dimensions, and regional development polarization. Finally, empirical tests were conducted using panel data from 30 provinces in China from 2011 to 2023, and it was found that digital technology innovation can significantly promote the overall and multi-dimensional development of the service industry's new quality. 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.
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 the 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.
Abstract: The construction of “Double First Class” is an important strategic deployment for China’s higher education reform. Since its implementation in 2017, the central and local governments at all levels have provided a large amount of resource support for selected universities, which has had a significant impact on the quantity and quality of scientific research work in universities. Use the double difference research model to compare and analyze the panel data of two equal time periods (2011-2021) before and after the launch of the “Double First Class” construction, and explores the impact of the first round of “Double First Class” construction on the scientific research work of 107 comprehensive and science and engineering universities from the two dimensions of the quantity and quality of scientific research output of universities. The research results indicate that the “Double First Class” policy has achieved significant construction effects in both promoting the quantity and improving the quality of scientific research output in universities; In addition, the study further found that the construction of “first-class disciplines” has formed significant positive spillover effects in the first round of construction through policy demonstration effects and local supporting incentives, especially in quality dimensions such as highly cited papers, top 1% papers, and the Category Normalized Citation Impact. The catching up speed of non-selected universities has exceeded that of experimental group universities. The paper recommended to better leverage the systematic impact and driving role of the “Double First Class” construction on national higher education, strengthen top-level policy design, deepen the reform of higher education evaluation in the new era, and promote the realization of the strategy of building a strong education country at a high level.
