This study aims to establish a framework for identifying the key security risks of generative AI in China, particularly in the context of the ongoing technological decoupling between China and the United States. It seeks to provide theoretical foundations and practical guidance for navigating the increasingly complex global technological environment. Through a layered analysis of foundational, technical, and application levels, this study constructs an analytical framework for evaluating the factors influencing the development of generative AI technology in China. Key technological characteristics such as computing power, data, and algorithms are considered, and expert knowledge is utilized to analyze and assess the identified risk factors. The identified security risks include limitations in computing power, data, and development tools at the foundational level; restrictions on model algorithms, computing architectures, and open-source platforms at the technical level; and challenges in market expansion and security thresholds at the application level. The study recommends establishing a coordinated national strategy to develop a response that ensures foundational autonomy, technical integration, and application-driven needs, thereby securing China's strategic position in global AI competition.
In an increasingly fractious geopolitical landscape, the utilization of tariffs has emerged as a pivotal instrument within the arsenal of international economic conflicts. This strategy, characterized by the uncertainty it injects into the policy framework, exerts a substantial impact on the resilience of corporate innovation. The present research delves into this complex interplay by employing a comprehensive dataset comprising A-share listed manufacturing firms in China. Employing a meticulous approach, the study employs textual analysis to construct key explanatory variables and harnesses the robustness of the difference-in-differences methodology to discern the causal efficacy of tariff shocks on the innovation resilience of firms under scrutiny. The empirical findings elucidate a compelling relationship: tariff shocks, often characterized by the imposition or withdrawal of protective tariffs, have been demonstrated to significantly enhance the innovative capacity of Chinese manufacturing firms. This outcome is in line with the widely accepted "adversity breeds innovation" hypothesis, which posits that exposure to adverse economic conditions can serve as a catalyst for increased innovation. Furthermore, the analysis delves deeper into the underlying mechanisms responsible for this phenomenon, revealing a dual-channel transmission effect. The first channel is rooted in macroeconomic policy uncertainty, which can spur firms to adapt and innovate as they navigate through a volatile regulatory environment. The second channel is grounded in internal corporate governance practices, indicating that effective governance structures can bolster resilience by facilitating informed decision-making and fostering a culture of innovation. An intriguing aspect of this analysis pertains to the role of directorate networks and managerial hubs within the firm. The study discerns that these elements serve as reinforcing agents, amplifying the resilience against the disruptions caused by tariff shocks. Conversely, it identifies that executive team faultlines can detract from this resilience, suggesting that the cohesive nature of leadership within the firm is a critical determinant of its ability to innovate in the face of such shocks. In conclusion, this investigation offers novel empirical insights into the intricate relationship between tariff shocks and innovation resilience within the context of Chinese manufacturing firms. These findings not only provide theoretical underpinnings for understanding the mechanisms through which economic stressors impact innovation but also offer practical guidance for policymakers navigating the treacherous waters of trade disputes. By elucidating the factors that can enhance or diminish resilience, the study contributes to the formulation of policy frameworks that are conducive to the cultivation of an innovative ecosystem within the broader context of international economic relations.
As a new infrastructure for data circulation and utilization, trustworthy data space is an important carrier for building a national integrated data market. The construction of trustworthy data space may become a key path to solve the contradiction between security and circulation in the market-oriented configuration of data elements, providing dual support of “security base” and “circulation hub” for digital transformation. The construction of a trustworthy data space can reconstruct data production relationships through technological integration, providing institutional guarantees and technological infrastructure for efficient allocation of data elements across organizations and industries, thereby achieving innovation in the paradigm of data sovereignty governance, and ultimately realizing the secure release of data value and high-quality development of the digital economy.
Trusted data space is a value exchange network with endogenous security attributes built on the basis of data space technology. By integrating privacy computing, blockchain, and smart contract technology, it forms a full cycle trusted guarantee system for the circulation of data elements. Its five foundations include the construction of key standard systems, breakthroughs in core technology innovation, optimization of infrastructure services, improvement of governance systems and mechanisms, and expansion of international cooperation networks. Its eight functions include identity authentication function, certificate traceability function, resource management function, access control function, privacy computing function, security sandbox function, contract execution function, and value evaluation function.
Based on the concept, foundation, function, and related theories of trusted data space, a three-dimensional model of “system technology ecology” for the construction of trusted data space has been constructed. By breaking the “trust security value” triple paradox in the flow of data elements, a systematic theoretical tool has been provided for the “trust controllability availability” of data elements in complex business environments. Research has found that: (1) The construction of trustworthy data space needs to follow the coordinated development logic of institutional, technological, and governance. Institutions can constrain the ethical boundaries of technological applications, technology can support the efficiency improvement of ecological collaboration, and ecology can practice feedback on the iterative optimization of institutional rules. The organic integration of the three has broken through the limitations of traditional single dimensional research and provided a systematic theoretical tool for the “trustworthiness controllability availability” of data elements in complex business environments; (2) The core technology focuses on the dual breakthrough of privacy security and trusted verification. The collaborative innovation of these two types of technologies can not only solve the “privacy utility” paradox in data sharing, but also establish a technical closed loop for ownership traceability through an undeniable proof mechanism; (3) At the level of governance logic, a hybrid framework of multi subject collaborative governance and risk immunity system needs to be established, and a three-dimensional governance model of “government regulation industry autonomy enterprise self-discipline” should be constructed to achieve the organic integration of technical barriers and institutional safeguards; (4) The realization of the value of a trustworthy data space depends on the iterative adaptation of technical tools and institutional rules.
The high-quality development of the trustworthy data space requires the construction of a system that promotes institutional innovation, technological breakthroughs, and governance experiments in a coordinated manner, in order to solve the triple problems of ownership ambiguity, technological constraints, and cross-border barriers in the circulation of data elements. Firstly, we need to accelerate the implementation of the “Measures for the Administration of Data Property Rights Registration” and clarify the rules and registration process for data ownership separation through legislation; Secondly, it is necessary to establish a national level data space technology laboratory to address the bottleneck problem of core technologies in trustworthy data space, integrate resources from universities, research institutes, and leading enterprises, and establish a national level laboratory; Thirdly, we need to explore the “regulatory sandbox” mechanism to promote cross-border data pilot projects. In response to the conflict of rules and regulatory uncertainty in cross-border data flow, it is recommended to carry out “regulatory sandbox” pilot projects in free trade zones.
As a strategic emerging industry in China, the Low-altitude economy is becoming a key area for cultivating new quality productive forces. With its high technological intensity, cross-industry integration, and strong potential for value creation, this sector is expected to reshape industrial structures and foster new modes of economic growth. Unlike traditional industries that rely on technology introduction and incremental absorption, the Low-altitude economy is distinguished by more pronounced innovation capabilities, thereby accelerating industrial modernization in China. Nevertheless, it also faces significant challenges, including technological immaturity, regulatory uncertainty, and high environmental risks. Against this backdrop, breakthrough innovation is not only essential for securing competitive advantages in global markets but also a key driver of high-quality and sustainable development. However, existing research at the micro-level remains limited, particularly on enterprise innovation strategies, organizational conditions, and their interaction with external environments. To bridge this gap, this study examines the diverse pathways through which breakthrough innovation occurs in the Low-altitude economy, demonstrates its pivotal role in advancing high-quality development, and provides both theoretical contributions and practical implications.
Based on the TOE framework, this paper develops a complex mediation model that integrates QCA with regression analysis to systematically examine the pathways leading to breakthrough innovation and the mechanism of high-quality development in Low-altitude economy enterprises. This integrative approach provides a more comprehensive understanding of how technological, organizational, and environmental factors interact to generate innovation outcomes and foster sustainable development. The research findings are as follows: (1) The formation of breakthrough innovation capabilities follows multiple concurrent causal logics, and there is no single necessary condition. (2) There are five driving paradigms for enterprise innovation: R&D-driven, digital-driven, environment-driven, collaborative-driven, and innovation-trial-and-error-driven. (3) The complex mediation test shows that, except for the innovation-trial-and-error-driven paradigm, the other four paradigms can basically promote the high-quality development of enterprises through breakthrough innovation capabilities. These results not only deepen the understanding of how breakthrough innovation emerges but also indicate its pivotal role in promoting high-quality development.
Beyond the key findings, this study contributes to the literature in several important ways. First, it shifts the analytical focus on the Low-altitude economy from macro-level regional governance to micro-level enterprise strategies, emphasizing how firm-specific technological, organizational, and environmental conditions shape innovation models and development paths. Second, from a methodological perspective, the integration of QCA with regression analysis offers a more nuanced understanding of causal complexity and provides novel insights for the study of complex systems. Third, the study extends the applicability of the TOE framework to dynamic and uncertain industrial contexts, while the identification of multiple effective innovation paradigms underscores the theoretical importance of strategic flexibility in enterprise innovation. From a practical standpoint, the study also offers actionable implications for both enterprises and policymakers. For enterprises, the findings suggest the need to carefully assess technological assets, organizational readiness, and environmental conditions before selecting appropriate innovation paradigms and development strategies. For policymakers, the results highlight the importance of targeted infrastructure investment, improvements in the business environment, and the development of supportive digital platforms to foster enterprise innovation and high-quality growth.
Under the backdrop of the profound restructuring of the global manufacturing competitive landscape and the emergence of a new wave of technological revolution, AI-enabled digital transformation in manufacturing has become a Strategic Initiatives for breaking through industrial upgrading bottlenecks and cultivating new quality productive forces. Through systematic literature review and analysis of global trends in AI-driven manufacturing transformation, this study reveals the evolutionary patterns, global competitive dynamics, and future challenges of AI-empowered digital transformation in manufacturing. Key findings include: (1) AI technology penetration exhibits phased leapfrogging characteristics, evolving from single-point applications and partial process optimization to systemic empowerment. This transformation is driven by a three-dimensional mechanism—technology infiltration, process reengineering, and ecosystem reconstruction—that comprehensively accelerates manufacturing digitalization. (2) The global landscape demonstrates five salient features: data elements becoming strategic assets, systemic enhancement of full-chain efficiency, structural transformation of corporate operational logic, accelerated strategic deployment by major economies, and the emergence of AI governance as a global common agenda. China has developed a distinctive pathway leveraging its "technology-market-industry" synergistic advantages. (3) Three-tier challenges persist: technical barriers including data silos and algorithmic black boxes, industrial constraints involving green transition pressures and standardization gaps, and geopolitical tensions over technological dominance and fragmented governance frameworks. The study proposes future research priorities: innovation in AI foundational logic and its impact on reshaping global manufacturing geography, pathways for AI-enabled green-digital synergy in sustainable manufacturing, and strategies to navigate international AI competition toward an open, win-win global community. This research provides a systematic framework for understanding the strategic value, implementation pathways, and global competition patterns of AI-driven manufacturing digitalization, offering policy implications for developing new quality productive forces and advancing China's manufacturing powerhouse construction.
Innovation factor agglomeration is a key way to integrate scientific and technological innovation resources, and an important way to promote new quality productive forcesthrough exploratory innovation and exploitative innovation. At present, the development of new quality productive forcesin China is facing challenges such as insufficient driving force of scientific and technological innovation, limited breakthrough of key technologies, and difficulty in obtaining innovative resources in underdeveloped areas. The agglomeration of innovative factors promotes the development of new quality productive forcesby breaking the limitations of traditional innovation models. However, there is still insufficient research on the systematic mechanism of innovation factor agglomeration affecting new quality productive forces. In particular, the synergy of innovation factor agglomeration through dualinnovation is still in the "black box". How does innovation factor agglomeration affect new quality productive forces through dualinnovation mechanism? The existing research mainly discusses the agglomeration of innovation factors from the aspects of spatial distribution characteristics, influencing factors and economic effects, but there are few explorations on the different paths that affect the new quality productive forces. Some scholars have made preliminary explorations on talent agglomeration, capital agglomeration and technology agglomeration as important research dimensions, but these studies focus more on the spatial distribution characteristics and agglomeration effect measurement of innovation elements.
From the perspective of dualinnovation, based on the data of 30 provinces in China from 2014 to 2023, this paper systematically explores the influence mechanism of innovation factor agglomeration on new quality productive forcesaround the four action paths formed by the cross combination of two dimensions of network effect and scale effect and dualinnovation. The results show that the agglomeration of innovation factors has a significant role in promoting new quality productive forces, among which the positive effects of innovation technology and platform agglomeration are the most significant. As an important moderating variable, regional innovation environment significantly strengthens the positive impact of innovation factor agglomeration on new quality productive forces. The agglomeration of innovation elements affects new quality productive forcesthrough the dualinnovation mechanism. Although the short-term network effect of exploratory innovation is negative, it significantly promotes new quality productive forcesafter scale, and can be transformed into exploitative innovation to form a continuous driving force. Further analysis from the factor dimension shows that among the four elements of innovative talents, capital, technology and platform, only innovative technology agglomeration and platform agglomeration have a significant positive impact on new quality productive forcesthrough the scale effect of dualinnovation. The former enhances the technological breakthrough ability of innovation subjects by promoting technology spillover and knowledge diffusion, while the latter reduces innovation barriers and enlarges synergy effects as an important hub for the integration of innovative resources. Although talent agglomeration and capital agglomeration also show a positive relationship in some stages, their separate impact on new quality productive forcesis relatively limited, and they may need to be combined with technological innovation and platform construction to give full play to their effectiveness. This shows that in the current development stage, technological innovation and platform construction may be the core bridge connecting innovation elements and new quality productive forces. The heterogeneity test also finds that the effect of innovation factor agglomeration is more significant in regions with higher levels of marketization, digital economy and transportation infrastructure.
The marginal contribution of this paper is that, first, it reveals the mechanism of innovation factor agglomeration on new quality productive forcesfrom the perspective of dual innovation, and enriches the theoretical research on the formation mechanism of new quality productive forces. Secondly, the two dimensions of network effect and scale effect are combined with dualinnovation to form four action paths, and the system mechanism of factor agglomeration to empower new quality productive forcesis constructed. The third is to introduce adjustment effect and heterogeneity analysis to provide empirical basis for differentiated policies. The research conclusions provide theoretical basis and policy enlightenment for optimizing the spatial layout of innovation elements, improving regional innovation efficiency and promoting the formation of new quality productive forces.
The technology roadmap holds significant strategic importance for the development of new quality productive forces in China. However, the existing methods for constructing technology roadmap still need to make trade-offs between ease of use and accuracy, and the intelligence level also needs to be improved. Moreover, the accuracy of mining information in technology roadmap based on network data source still needs to be optimized. As artificial intelligence enters a new stage, how to simultaneously enhance the efficiency, accuracy, and intelligence level of technology roadmap construction method based on AI technology is an urgent issue to be solved today.Therefore, given the existing limitations in current research, this study proposes an AI-enhanced framework for technology roadmap construction. Firstly, conduct theme evolution analysis for the three dimensions of "technology" - "product function" - "market" respectively. In the technology dimension, based on the evaluation index system of frontier technologies, the data set is subject to theme clustering through the screening of patents and academic papers, and the theme evolution of the technology dimension is analyzed in combination with the time dimension. In the product function dimension, the text classification of Large Language Model(LLM) is used to screen product news related to the field, and based on this, information extraction of product categories and functions is carried out, and the product theme evolution is analyzed in combination with the time dimension. In the market dimension, for two types of data sources, namely news, scientific and technological reports, the methods of "generative summary by LLM + clustering" and "TRT semantic analysis" are respectively adopted, and the market theme evolution is analyzed in combination with the time dimension. Secondly, the themes among various dimensions are associated through keywords matching. The technical points and market applications in the product function sentences are extracted based on LLM, and are respectively matched with the theme words of the technology dimension and the market dimension to establish the associations among various dimensions. Finally, based on the Retrieval-Augmented Generation (RAG) technology and the interactive dialogue with LLM, the framework implements systematic prediction of technological development.Utilizing the knowledge database built with futuristic data, through the multi-round interaction with the RAG-enhanced LLM, the future application scenarios are comprehensively predicted, and based on these scenarios, the technological development trends in the future are scientifically forecasted, completing the entire technology roadmap construction process. An empirical analysis is conducted in the field of UAV target identification and tracking, constructing a technology roadmap for this field. Three realized technical routes are identified, including radar detection, computer vision, and laser detection. The analysis reveals three characteristics of future technological development in the field of UAV target identification and tracking, namely "systematization," "intelligence," and "modularity". This study provides strong support for the development of the UAV target identification and tracking field and validates the scientificity and feasibility of the proposed framework, which makes the drawing of the technology roadmap more efficient, accurate, and convenient, and enhances the intelligence level of the node construction and prediction process of the technology roadmap.
Digital transformation (DT) is increasingly recognized as a disruptive force reshaping organizational structures, governance models, and societal paradigms. As businesses and institutions undergo digital transformation, they are confronted with challenges that question the traditional boundaries of management theories. These challenges are not only technological but also deeply institutional, involving tensions between existing institutional norms and the emerging demands of the digital era. This transformation brings with it a complex interplay of institutional pressures and systemic coupling mechanisms that have yet to be fully understood or theorized. Therefore, the need for theoretical deconstruction and paradigm innovation is crucial to comprehending the transformative processes in the digital age. This study, using an institutional perspective, addresses these gaps by applying a systematic literature review (SLR) methodology, coupled with Python and R language analyses, to synthesize the antecedents, processes, and outcomes of digital transformation. The goal is to develop a robust analytical framework that provides both theoretical insights and practical guidance on how organizations can navigate the complexities of digital transformation.
The research constructs an analytical framework that centers on the triadic structure of “driving forces—transformation process—transformation outcomes.” This framework aims to integrate the dynamic relationships between external institutional drivers, the organizational transformation processes they induce, and the resulting organizational outcomes in the context of digital transformation.
Firstly, the study explores how institutional pressures such as policy, legislation, social expectations, competitive forces, best practices, and industry standards shape the digital transformation journey. It analyzes the ways in which these external forces—acting as institutional elements—drive organizational change by compelling firms to adopt digital technologies. The role of institutional pressures in fostering organizational adaptability to new digital practices is critically examined, with particular attention to the mechanisms through which organizations respond to external environmental shifts. The study emphasizes how these institutional drivers contribute to the acceleration of digital transformation across various sectors, compelling businesses to reconsider traditional practices and adopt new digital strategies.
Secondly, the paper delves into the mechanisms of de-institutionalization and re-institutionalization within organizations undergoing digital transformation. It posits that digital transformation is not simply the adoption of new technologies but also involves a cyclical process of dismantling traditional institutional norms (de-institutionalization) and constructing new institutional frameworks (re-institutionalization). This dynamic is central to understanding how organizations must manage the tensions between old and new systems during digital transformation. The study also highlights the evolutionary nature of this process, showing that organizations must constantly adjust and adapt as they encounter new digital technologies, competitive pressures, and regulatory demands.
Finally, the paper discusses the institutional outcomes of digital transformation, focusing on how these transformations lead to shifts in organizational structures and business models. Key outcomes discussed include technology adoption and the realization of new digital functions, servitization and intelligent transformation of products and services, and the reconfiguration of supply chains and organizational collaboration. These outcomes underscore how digital transformation is deeply institutionalized, affecting not only technological adoption but also the very way organizations operate and collaborate across value chains. The study provides a framework for understanding these outcomes through the lens of institutional theory, revealing how institutional environments play a significant role in shaping the trajectory and success of digital transformations.
From a theoretical perspective, this study constructs a multi-level analytical framework for digital transformation, integrating the fragmented research on digital transformation from an institutional perspective. It emphasizes that institutional pressures drive digital transformation through three mechanisms (coercive, normative, and mimetic), and proposes a dual process of de-institutionalization and re-institutionalization. This not only explains the institutional roots of the phenomenon of "digitalization without transformation," but also fills the gap between institutional theory and transformation practices in the context of the digital economy. From a practical perspective, the study highlights that China’s multiple transformation tasks require the external force of institutional environments to drive digital transformation. It suggests strengthening institutional supply and adaptation at the government, industry, and organizational levels to promote the deep integration and sustainable development of digital transformation.
In the era of the new technological revolution and industrial transformation, the core of international competition has migrated toward foundational and frontier scientific domains. As China transitions from a manufacturing powerhouse to an innovation leader, the government has prioritized original innovation as a critical lever for strategic industrial upgrading. Original innovation is driven by major scientific discoveries and breakthrough technological principles, representing a profound integration of propositional knowledge and prescriptive knowledge. While theoretical frameworks suggest that the synergy between scientific research and technology R&D is vital for innovation sustainability, empirical evidence remains limited. Current studies primarily focus on one way knowledge flows from science to technology or static correlations between the two. This leaves several critical research gaps. First, there is a lack of empirical verification regarding the bidirectional and time lagged feedback loop between science and technology within a single innovation system. Second, the internal mechanisms through which specific knowledge attributes such as scientific quality and technological breadth facilitate this transformation are not well understood. Third, the boundary effects of collaboration intensity at the firm level in moderating these iterative paths remain unclear. This study addresses these gaps by exploring the positive feedback mechanisms within the original innovation system of the New Energy Vehicle industry.
To examine these dynamics, this study focuses on the New Energy Vehicle industry because it represents a sector that has achieved rapid growth through the integration of complex scientific research and technological breakthrough. The research sample includes 42 prominent enterprises, encompassing both vehicle manufacturers and suppliers, observed over a ten year period from 2015 to 2024. The dataset consists of 14,395 academic papers and 115,293 patents, which serve as proxies for scientific research and technological R&D respectively. Methodologically, the study employs dynamic structural equation modeling (DSEM) to analyze the longitudinal data. This approach allows for the decomposition of variance into between firm and within firm components, facilitating the assessment of dynamic, time lagged causalities while controlling for auto regressive effects and time trends. Variables in the model include scientific research intensity, technology R&D intensity, scientific research quality measured by high impact publications, technology R&D breadth measured by the diversity of patent classifications, and cooperation intensity reflecting joint outputs with universities and research institutes.
The empirical analysis confirms the existence of a robust positive feedback loop between scientific research and technology R&D within the original innovation system. Firstly, the results demonstrate a significant bidirectional and time lagged causal relationship at the firm level. Specifically, the scientific research intensity of an enterprise in a given year positively predicts its technology R&D intensity in the following year, indicating that scientific accumulation effectively transfers to technological applications. Conversely, technology R&D intensity also positively drives future scientific research intensity as technological development identifies new theoretical gaps that require scientific inquiry. Second, the study reveals the mediating roles of knowledge quality and breadth within this cycle. Scientific research quality, representing theoretical rigor and authority, acts as a bridge for translating discovery into application. Meanwhile, technology R&D breadth facilitates the feedback from technology to science by expanding the diversity of the knowledge base and generating complex, cross domain research questions. Thirdly, the findings highlight the moderating role of innovation collaboration. Technology cooperation intensity reinforces the path from scientific quality to technological output, whereas research cooperation intensity strengthens the transformation of technological diversity into scientific advancement. These results confirm a knowledge complementarity effect through external partnerships.
The current study offers significant contributions to innovation management. Theoretically, it provides the first systematic empirical validation of the bidirectional positive feedback mechanism in the original innovation system, shifting focus from static linkages to dynamic sustainability. By integrating the knowledge based view, it clarifies how qualitative and structural attributes of knowledge function as catalysts for co evolution. Furthermore, it clarifies the micro level enabling role of university industry collaboration in fostering internal R&D iterations. Practically, the study advises managers to prioritize the simultaneous development of basic research and applied technology. Firms should establish dual capability systems where scientific breakthroughs serve as triggers for technology and engineering bottlenecks act as incubators for scientific exploration. Policymakers are encouraged to support collaborative innovation platforms that facilitate the seamless integration of high quality science with diversified technological applications to foster new quality productive forces.
In the rapidly evolving digital economy, Digitalized Innovation Capabilities (DICs) have emerged as a pivotal enabler of sustainable competitive advantage for enterprises, fundamentally reshaping innovation management paradigms. Despite growing scholarly attention to digital innovation concepts, boundaries, and impacts, a significant theoretical and empirical gap persists regarding the precise definition, rigorous measurement, and systematic validation of DICs. This limitation impedes a holistic understanding of their antecedents and outcomes, hindering both academic discourse and practical implementation. To address this critical gap, this study synthesizes insights from Resource-Based View (RBV) and Dynamic Capabilities (DCs) logic to reconceptualize DICs as a firm’s capabilities to empower its innovation processes through the development and application of digital technologies. This integrative framework transcends conventional RBV’s static resource focus and DCs’ process-oriented limitations. Through systematic literature analysis and empirical validation, we deconstruct DICs into three interdependent dimensions: Openness Digitalized Innovation Capabilities (O-DICs), which enhance the depth, breadth, and efficiency of information exchange, expand channels for acquiring external resources, improve organizational learning, and ultimately increase market acceptance of innovations through digital technology development and application. This transcends traditional Open Innovation (OI) by enabling broader participation, deeper insights, and faster knowledge flows; Affordance Digitalized Innovation Capabilities (A-DICs), leveraging digital technologies to provide diverse possibilities and functional support for innovation activities across different contexts. Rooted in the concept of affordances, A-DICs help firms overcome barriers arising from bounded rationality, cognitive diversity, institutional differences, and cultural diversity. They enable context-sensitive adaptation, enhance innovation legitimacy, and expand innovation boundaries by facilitating resource integration across physical and cyber spaces, cross-industry collaboration, and scenario-specific application of technologies; Generativity Digitalized Innovation Capabilities (G-DICs), utilizing digital technologies to stimulate spontaneous, diverse, and unpredictable positive changes within the innovation process, driving it towards greater efficiency and sustainability. Capitalizing on inherent digital generativity, G-DICs foster emergent innovation, accelerate the iteration and diffusion of innovative outputs, and expand the value network by enabling diverse actor participation and collaboration. Methodologically, this research employs a mixed-methods approach to develop and validate the DICs Scale. Initial constructs derived from semi-structured interviews with industry experts and managers underwent exploratory factor analysis(EFA), yielding are fined item pool. Subsequent confirmatory factor analysis(CFA) with large-scale survey data confirmed the scale’s robustness, demonstrating high reliability, and discriminant validity across manufacturing and service sectors. Regression analyses further established that all three DICs dimensions exert statistically significant positive effects on innovation performance. This research makes significant theoretical contributions. It systematically defines the theoretical connotation of DICs and proposes a novel three-dimensional framework (O-DICs, A-DICs, G-DICs), moving beyond the limitations of the DCs framework in explaining how digital technologies empower the innovation process and revealing the micro-foundations of innovation capabilities in the digital context. Crucially, it develops and rigorously validates the DICs Scale, providing a scientifically reliable and effective measurement tool to empirically assess DICs, addressing a critical gap in the field. Furthermore, it empirically verifies the significant positive impact of DICs on innovation performance, offering clear guidance for managers seeking to enhance innovation outcomes and formulate digital innovation strategies. Notwithstanding these contributions, limitations include reliance on cross-sectional data and sectoral concentration. Future research should pursue longitudinal designs to track capability evolution and examine sector-specific moderators.
In the context of China’s push toward high-quality development in the manufacturing sector, the phenomenon of “innovation talk-action discrepancy” has attracted growing attention. While some firms emphasize innovation narratives in their disclosures, their actual innovation outputs often fail to align. This inconsistency can distort resource allocation and undermine the effectiveness of innovation policies. To explore the underlying mechanisms of such discrepancies, this study selects China’s manufacturing “single champion” firms—recognized as exemplary representatives of the “little giant” or specialized and sophisticated enterprises—as its research sample. These firms play a vital role in driving breakthroughs in key technologies, and their innovation behaviors have strong signaling effects in both policy and market environments.
Drawing upon the Motivation-Opportunity-Ability (MOA) framework, we identify six antecedent conditions and employ dynamic Qualitative Comparative Analysis (QCA) based on panel data to examine the complex configurations that lead to either consistency or inconsistency between innovation disclosures (“talk”) and innovation outcomes (“action”). Innovation discrepancy is measured by the residual between textual innovation disclosures in annual reports and actual patent applications. Firms are classified into “talk-action consistent” and “talk-action inconsistent” categories based on the standardized residual range.
Our findings reveal that no single factor alone is necessary to explain the emergence of innovation talk-action discrepancy. Instead, multiple configurations of conditions—representing different behavioral logics—jointly account for the observed patterns. Specifically, three distinct pathways are identified as drivers of “talk-action inconsistency”: (1) policy-driven signaling, wherein firms exaggerate innovation language to align with government incentives; (2) packaging innovation, where firms focus on impression management for investors; and (3) survival-oriented strategies in resource-constrained environments. In contrast, two main configurations lead to “talk-action consistency”: (1) prudent innovation, associated with firms having strong internal capabilities and long-term orientation, and (2) pragmatic innovation, driven by technological strength and strategic clarity.
Although the configurations themselves remain relatively stable over time, our dynamic analysis reveals that the consistency of the “talk-action inconsistent” configurations exhibited sharp fluctuations in 2020, likely linked to the COVID-19 outbreak and subsequent policy shocks. Conversely, the consistency of “talk-action consistent” configurations increased notably in 2022, coinciding with the nationwide reopening and renewed emphasis on industrial upgrading.
The study contributes to the literature in several ways. First, it introduces a unified analytical framework that integrates innovation disclosures and outputs, moving beyond prior studies that often treated them as separate domains or conducted only comparative analyses. Second, it adopts a configurational perspective to examine the interplay among multiple antecedents rather than relying on linear or single-variable approaches. Third, by incorporating temporal dynamics through panel-based QCA, the study addresses the limitations of static cross-sectional designs and provides richer insights into the evolution of innovation behavior under changing external environments.
Practically, the findings offer valuable implications for both policymakers and enterprise managers. For government agencies, the results underscore the importance of refining innovation incentive mechanisms and developing more reliable performance evaluation systems that account for both input and output dimensions. For regulatory bodies and investors, the study highlights the need for more sophisticated disclosure verification tools—such as big data analysis and natural language processing—to detect inflated innovation narratives. For enterprises, especially those in traditional manufacturing industries, the results suggest that building robust internal innovation capabilities and fostering consistent innovation execution can help achieve authentic and sustainable growth.
Overall, this study enriches the theoretical understanding of innovation behavior among “little giant” firms and provides actionable recommendations to promote the high-quality development of China’s specialized and sophisticated enterprises in the manufacturing sector.
Facing escalating environmental challenges and global competition, manufacturing enterprises must adopt disruptive green innovation strategies to achieve sustainable economic and environmental benefits. Disruptive green innovation refers to the process where enterprises introduce green products or services with different performance attributes from those required by mainstream consumers, initially targeting low-end or niche markets, and gradually improving the product to disrupt the mainstream market. Despite its importance in enhancing sustainable competitiveness and meeting the "dual carbon" goals, disruptive green innovation faces challenges due to its complexity, high risks, and uncertainty. This raises an important question: What factors lead to the more effective implementation of disruptive green innovation, and how do these factors interact?
This study adopts a mixed-methods approach by combining case studies of BYD and Luyuan with fuzzy-set qualitative comparative analysis (fsQCA), to explore the factors driving disruptive green innovation. The results of case study identify several key drivers, including innovation ecosystem coopetition, green knowledge acquisition, green R&D investment, environmental resource coordination, and big data analytics capability. Using survey data from 332 manufacturing enterprises in strategic emerging industries, this study applies fsQCA to uncover configurational pathways driving disruptive green innovation. Key findings include: (1) Innovation ecosystem coopetition, green knowledge acquisition, green R&D investment, environmental resource orchestration, and big data analytics capability constitute core antecedents of disruptive green innovation. (2) No single antecedent is a necessary condition for disruptive green innovation. (3) Six configurational paths enabling high disruptive green innovation are categorized into four types: knowledge-data-driven pathway under cooperation dominance, knowledge-R&D-driven pathway under competition dominance, R&D-data-driven pathway under coopetition dominance, capability-led driven pathway, with substitutable relationships among element combinations under specific conditions. (4) Non-high disruptive green innovation configurations fall into two types: collaboration-resource-data-constrained and knowledge-R&D-data-constrained. This research provides theoretical and practical insights into how enterprises achieve disruptive green innovation across diverse contexts.
This study makes several theoretical contributions. First, this study enriches the research on antecedent factors of disruptive green innovation by case study. Currently, research on the drivers of disruptive green innovation is still in its early stages. Through exploratory case studies, this study investigates key antecedents of disruptive green innovation and expands the understanding of this concept. Second, this study also identifies the configuration paths driving disruptive green innovation, which improves and deepens the analytical paradigm of its formation mechanism. Existing studies on the antecedents of disruptive green innovation have rarely focused on the joint effects of multiple factors. Given the complexity and systemic nature of disruptive green innovation, this study applies the fsQCA method from a configurational perspective to explore the co-action of factors such as innovation ecosystem coopetition, green knowledge acquisition, green R&D investment, environmental resource orchestration, and big data analytics capability. By adopting a mixed-methods approach, this study offers a more comprehensive interpretation of the research model, revealing results that might be overlooked by a single method. This approach helps to improve the understanding of how enterprises conduct disruptive green innovation.
From a practical perspective, this study suggests that enterprises should adjust their innovation paths based on their strengths and market demands, optimizing their roles and strategies within the innovation ecosystem, especially when facing international competition. Digital technologies, particularly big data analytics capability, are identified as key drivers for disruptive green innovation. Enterprises should also strengthen coopetition relationships with partners, engage in cross-sector collaboration, and share knowledge to enhance green innovation. Additionally, investing in green R&D and improving resource coordination are essential for achieving sustainable green innovation.
As the core component of the nation's strategic scientific and technological forces, leading technology enterprises bear the critical mission of overcoming key core technologies and achieving high-level self-reliance and self-strengthening in science and technology. However, at present, China's leading technology enterprises still encounter challenges such as a high degree of reliance on foreign core technologies and an imbalance between "scale expansion" and "quality breakthrough." Moreover, the traditional single knowledge search mode struggles to address the nonlinear evolution characteristics of the technical system, which may lead to technical breakthroughs being trapped in the predicament of "inefficient lock-in." In this context, constructing a diversified knowledge search paradigm of "endogenous breakthrough - exogenous activation," integrating internal knowledge reserves with external cross-domain knowledge flows, has become a pivotal pathway to empower the breakthrough of key core technologies for leading technology enterprises.
This study focuses on leading technology enterprises. By referencing relevant policy documents and research to define the research subjects, it systematically investigates the influence mechanism of multi-knowledge search on the breakthroughs of their key core technologies, yielding the following core conclusions:(1) The impact of diversified knowledge search on the breakthrough of key core technologies in leading technology enterprises exhibits differentiated characteristics. Both internal knowledge search and external technical knowledge search demonstrate an inverted U-shaped relationship with breakthroughs in key core technologies. External market knowledge search and talent-embedded knowledge search significantly promote technological breakthroughs. (2) Breakthrough willingness positively moderates the promoting effects of the three external knowledge searches (market, technology, and talent embedding) on technological breakthroughs but negatively moderates internal knowledge searches. This suggests that leading technology enterprises need to avoid excessive path dependence while strengthening the integration of external knowledge. (3) Internal knowledge search promotes breakthroughs by delving deeply into specific fields through technical specialization; external market knowledge search identifies demands and expands technical paths using diversified technologies; external technical knowledge search and talent-embedded knowledge search function simultaneously via dual pathways of technical specialization and diversification.
This study elucidates the systematic mechanism by which multi-knowledge search empowers the breakthrough of core technologies in leading technology enterprises, offering significant insights for optimizing enterprise innovation strategies: On the one hand, it is essential to balance internal and external knowledge resources, optimize knowledge search strategies, integrate internal knowledge bases using technologies like big data and artificial intelligence, and simultaneously expand collaboration with universities and research institutions, dynamically adjusting search strategies to maximize the synergy effect of internal and external knowledge. On the other hand, it is necessary to strengthen talent-embedded knowledge search. Through a diversified talent introduction, cultivation, and evaluation system, a cross-domain knowledge network should be constructed to provide diverse problem-solving ideas for technological breakthroughs. Additionally, it is crucial to build a learning organization and enhance breakthrough willingness.
The research conclusion not only enriches the theory of innovation search but also provides practical guidance for leading technology enterprises to overcome "bottleneck" technologies and achieve high-level self-reliance and self-strengthening in science and technology.
Universities in China have achieved remarkable results in technological research, playing a leading role in constructing an independent knowledge system. However, university technological achievements still face the bottleneck of relatively low rates of industrial transformation. Addressing the challenge of translating university research outcomes requires multi-party collaboration, establishing a public transformation platform involving diverse actors such as universities, enterprises, and technology transfer institutions, and improving the specialized service system for technological achievement transformation. Therefore, public data openness emerges as a novel solution, offering new possibilities for technology transfer. Public data openness refers to the process by which governments integrate public data resources and make them equally accessible to the public. It grants the public the rights to know, query, and utilize data, facilitating better resource discovery and industrial upgrading by societal actors, thereby creating commercial and social value. The transformation of university technological achievements is a “university-enterprise” collaborative innovation process. For enterprises, public data openness provides access to data resources at lower cost. The data elements not only empower enterprise R&D but also reduce the uncertainties they face. For universities, public data openness helps eliminate information barriers between universities and enterprises, and facilitate the coupling of university achievements with market demand. In this regard, investigating whether and how public data openness influences the technology transfer in higher education institutions is of significant importance for enhancing university technological development.
This study employs the launch of government data platforms as a quasi-natural experiment. Based on data covering Chinese university patent transfers from 2009 to 2023, a multi-period difference-in-differences (DID) model is used to investigate the impact of public data openness on the technology transfer of universities. The research findings demonstrate that public data openness positively promotes the transformation of university technological achievements. This conclusion still holds true after passing the parallel trend test, placebo test, double machine learning causality test, psm-did test and removing other policies’ effect. The mechanism test demonstrates that (1) Public data openness facilitates the coupling of university achievements with enterprise demand, promoting transformation through university-industry collaboration. And public data openness increased the number of university-industry jointly applied patents.(2) Public data openness effectively reduces transaction costs between universities and enterprises and alleviates the time lag in technology transfer. And public data openness exerts a stronger promoting effect in regions with protracted conversion delays. Heterogeneity analysis reveals that the impact of public data openness on university achievement transformation is more pronounced in regions with higher data openness quality and better digital infrastructure. Furthermore, its promoting effect is more obvious for invention-type patents and high quality patent transformations. This study bridges a significant gap in the literature by empirically demonstrating that public data openness facilitates university technology transfer.By providing rigorous evidence on how it overcomes the “last-mile barrier” in this process, our findings expand the research horizon on open public data governance.
This study provides the following insights for leveraging public data openness to optimize university technology transfer service systems. At the government level, authorities should accelerate public data disclosure while guiding multi-stakeholder participation in platform development to establish a cross-sectoral “communication bridge”. Differentiated policy support must be implemented through targeted interventions and technical guidance for regions with inadequate digital infrastructure, thereby bridging regional divides in technology commercialization. Universities should proactively deepen industry-academia partnerships through joint R&D initiatives to boost industrial applicability of innovations, while comprehensively showcasing research outputs via dedicated platforms spanning fundamental research to market applications to enhance external visibility. Prioritizing high value patent development tightly aligned with market needs remains critical for overcoming commercialization bottlenecks. For enterprises, dynamically monitoring university research outputs and acquiring relevant technologies through patent transfers can mitigate information asymmetry induced delays. Companies must also proactively articulate technical requirements, budgetary parameters, and collaboration frameworks to establish closed-loop innovation cycles.
The Not-In-My-Backyard (NIMBY) effect, as a typical governance challenge in environmentally sensitive infrastructure projects, fundamentally stems from the structural imbalance between technological rationality and socio-ethical values. From the perspective of socio-ethical governance, this study takes the Hangzhou Jiufeng Waste-to-Energy Project as a case to systematically analyze the internal mechanisms and practical pathways of its transformation from the "NIMBY effect" to a "Neighborhood-Benefit Project," based on field investigations. By adopting a "macro-engineering" philosophy to reconstruct governance paradigms, the Jiufeng Project repositions waste incineration as an organic integration of technological and social engineering, establishing a NIMBY governance mechanism through dual dimensions of technological innovation and social reform. Through tripartite synergies of technological advancement, economic compensation, and social collaboration, the project expands its value space to achieve symbiotic enhancement of environmental and social benefits. A dynamic coordination mechanism is developed via stakeholder analysis to formulate rational risk-sharing and benefit-distribution schemes, thereby promoting sustainable interest balance. Additionally, by standardizing the behaviors of government, enterprises, and the public, the project establishes an iterative socio-ethical governance framework. The governance experience of the Jiufeng Project in transitioning from a "NIMBY effect" to a "Neighborhood-Benefit Project" provides a practical model for resolving similar governance dilemmas in comparable infrastructure projects.
With the implementation of the "Artificial Intelligence+" strategy, ChatGPT and DeepSeek intelligent agents will accelerate their commercialization and provide strong impetus for the development of new quality productivity. Intelligent agents have achieved technological transitions from single task processing to multimodal perception, from rule driven to autonomous learning, and from static response to dynamic programming, which has also led to risks of perception loss, decision-making disorder, and execution disorder. The alignment of values is related to whether intelligent agents can truly serve humanity, and has certain legitimacy, necessity, and feasibility. However, intelligent agents face the practical dilemma of "how to align" and "which values to align with", which is due to the lack of a sound value alignment system. In view of this, it is urgent to adopt a collaborative paradigm of technical regulation, ethical adaptation, and legal governance, that is, to construct a dynamic value recognition system, a systematic value adjustment mechanism, and a flexible value regulation program; Adhering to the ethical mission of putting people first, formulating scientifically reasonable ethical principles, and designing scenario based ethical rules; Determine the standard of value alignment, clarify the nature of value alignment, improve the evaluation method of value alignment, and accelerate the formation of new quality productivity to promote Chinese path to modernization.
Interdisciplinary approaches are widely advocated in global science policy as a key strategy for addressing complex social issues. However, evidence linking interdisciplinary research to its impact on the public remains limited. It is generally believed that interdisciplinary research produces solutions to critical problems by recombining knowledge from multiple fields. Yet the public often struggles to appreciate this recombination, since most individuals are not as specialized as patent examiners. To address this gap, our study introduces a more accessible attribute of interdisciplinary scientific knowledge—that of being a public good—alongside its re-combinatory nature, to explain how interdisciplinary citizen science fosters public engagement. Drawing on classic signaling theory from product management research, as well as social cognition theory and science communication theories, we focus on the signaling system formed by citizen science project teams, the public, and society as a whole. Based on this framework, we develop a theoretical model to examine how interdisciplinarity influences the public’s willingness to participate in citizen science projects. According to social role theory, we also consider how a research team’s identity might moderate the effect of interdisciplinarity on engagement willingness. Our study analyzes 880 citizen science projects hosted on Experiment (experiment.com) and engagement data from 37,867 contributors. We employ text‐mining methods—including Latent Dirichlet Allocation topic modeling and Word2Vec—to quantify three dimensions of interdisciplinarity in project titles and descriptions: diversity, balance, and disparity. Validation through manual checks and algorithmic tests confirms the robustness of our quantification approach, demonstrating that text mining effectively identifies disciplinary topics. Empirical results show that interdisciplinarity in citizen science projects promotes public engagement through two mechanisms. First, there is a direct positive effect: projects spanning diverse disciplines attract both broader and deeper public engagement. Second, there is an indirect positive effect: interdisciplinarity reduces a project’s novelty, thereby mitigating novelty’s negative impact on public engagement willingness; this indirect effect, however, pertains only to public engagement breadth. We find that when the research team is affiliated with academia, the direct positive effect of interdisciplinarity on public engagement breadth is strengthened, although no significant moderation appears for public engagement depth. Furthermore, when disciplinary diversity or balance is high, teams composed of both academic and amateur members negatively moderate interdisciplinarity’s impact on public engagement breadth. Under conditions of high disciplinary balance, such mixed teams also negatively moderate the effect on public engagement depth. In contrast, amateur teams’ identity shows no significant moderating effects. Using the Web of Science classification system and project tags, we classify citizen science projects as either natural science-oriented or humanities and social science-oriented. In natural science-oriented projects, interdisciplinarity enhances both public engagement breadth and depth, and the moderating effects of team identity align with our main findings. However, in humanities and social science-oriented projects, interdisciplinarity negatively affects public engagement breadth, though academic team can offset or even reverse this negative effect. This study enriches understanding of how interdisciplinary research influences public engagement. Practically, it offers guidance for government agencies and scientists in managing interdisciplinary citizen science activities.
With breakthrough advances and accelerated industrial expansion in brain-computer interface (BCI) technology, its ethical implications have increasingly garnered academic attention. Based on scientometrics and combined with interdisciplinary and risk-evolution analytical perspectives, this study examines the landscape, knowledge base, and research hotspots in international research on the ethics of brain-computer interface: (1) Publications in research on the ethics of brain-computer interface demonstrate a three-stage evolutionary trajectory, with fluctuations closely linked to technological cycles, social events, and policy shifts, culminating in 2024 as a window of explosive growth. (2) Bioethics, cognitive neuroscience, and machine ethics form a modular interdisciplinary citation network spanning normative values, technical pathways, and human-machine collaboration frameworks, thereby supporting a multidimensional theoretical foundation. (3) Research hotspots have formed clusters in the domains of subjectivity, technology, and society, highlighting the dynamic characteristics of risk evolution. (4) With the deepening of brain-computer integration, issues concerning the neural rights of integrated subjects and social acceptance urgently require attention. Based on the assessment of the current situation, future research should focus on fostering interdisciplinary empirical collaboration, driving industrial optimization and upgrading, and promoting decentralized network governance, so as to contribute to the improvement and development of global research on the ethics of brain-computer interface.
The Third Plenary Session of the Twentieth Central Committee of the Communist Party of China proposed “improving the institutional mechanisms for developing new quality productive forces in accordance with local conditions,” providing a strategic guideline for advancing the transformation and upgrading of regional productivity. As a new form of productive force characterized by innovation-driven dynamics and efficiency-oriented goals, the development of new quality productive forces is crucial for enhancing regional vitality and restructuring economic systems toward high-quality development. However, the implementation of this national strategy at the local level has revealed significant challenges, particularly the trend of policy convergence and homogeneous industrial planning across regions, which risks undermining local comparative advantages and aggravating inefficient competition.
Drawing on the theory of government attention, which posits that the allocation of attention determines the prioritization of policy resources and institutional efforts, this study investigates how local governments interpret and implement the central directive on new quality productive forces. Specifically, the study collects and analyzes political affairs texts related to new quality productive forces, published on the official websites of thirty provincial-level governments between 2023 and 2024. Using topic modeling based on the BERTopic algorithm, generative artificial intelligence techniques, and machine learning methods such as the random forest algorithm, the study systematically identifies the focus areas of government attention and examines their regional variations.
The findings reveal three major patterns. First, local governments demonstrate a structure of “five-dimensional focus and coexistence of dual tendencies” in their attention, covering five domains: new means of labor, new types of laborers, industrial sectors, application scenarios, and enabling environments. This indicates both differentiated governance strategies and converging policy orientations across regions. Second, policy imitation behavior is prevalent and primarily manifests as “late-developing region catch-up” and “peer-level learning.” This results in a spatial paradox in which industrial policy similarity increases with geographical distance, reflecting a widespread tendency to emulate economically advanced regions regardless of local resource endowments or development conditions. Third, regional heterogeneity in governance strategies is evident. Eastern provinces, which possess strong innovation capabilities and relatively high fiscal autonomy, tend to demonstrate desirable convergence characterized by targeted adaptation. In contrast, central provinces, constrained by weak industrial foundations and fragmented development trajectories, are more prone to undesirable convergence and may fall into a governance dilemma of “resource involution.”
To address the risks of excessive policy convergence and foster truly localized development of new quality productive forces, this study proposes three governance recommendations. First, the central government should strengthen top-level design, promote layered and classified policy guidance, improve regional coordination mechanisms, and align national strategies with the specific conditions and needs of localities. Second, local governments should enhance regional cooperation and policy synergy, avoid blind imitation, and build cross-regional industrial cooperation platforms to promote rational division of labor and resource allocation. Third, governments at all levels should coordinate differentiated innovation strategies in key industries with the collaborative development of related sectors, clarify industrial roles based on regional development stages, and prevent inefficient duplication. These recommendations aim to support a more adaptive, efficient, and coordinated institutional framework for the development of new quality productive forces in China.