• Identification and Response to the Security Risks of Generative AI in China Under the Technological Decoupling
• 2026 Vol. 44 (6): 1121-1128.
• Abstract ( )
• Abstract：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.

• External Tariff Shocks, Manufacturing Firm Governance, and Innovation Resilience
• 2026 Vol. 44 (6): 1129-1143.
• Abstract ( )
• 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.

• Construction of Trusted Data Space: Preconditions, Core Technologies, and Governance Logic
• 2026 Vol. 44 (6): 1144-1153.
• Abstract ( )
• As the infrastructure for realizing market-oriented allocation of data elements, the construction of a trustworthy data space is related to national digital sovereignty and industrial competitiveness. This study is based on the concept, foundation, function, and related theories of trustworthy data space, and constructs a three-dimensional model of "system technology ecology" for the construction of trustworthy data space. By breaking the "trust security value" triple paradox in the flow of data elements, it provides a systematic theoretical tool for the "trustworthiness 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 system, technology, and governance; (2) The core technology focuses on the dual breakthrough of privacy security and trusted verification; (3) At the level of governance logic, it is necessary to establish a mixed framework of multi subject collaborative governance and risk immune system; (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 a trustworthy data space requires the establishment of a system that promotes institutional innovation, technological breakthroughs, and governance experiments in a coordinated manner. Firstly, we need to accelerate the promulgation of the "Measures for the Administration of Data Property Rights Registration". Secondly, we need to establish a national level data space technology laboratory. Thirdly, we need to explore a "regulatory sandbox" mechanism to promote cross-border data pilot projects.

• The breakthrough innovation and high-quality development of enterprises in Low-altitude economy field
• 2026 Vol. 44 (6): 1154-1168.
• Abstract ( )
• 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.

• Development Trends of AI-Enabled Digital Transformation in Manufacturing: A Literature Review
• 2026 Vol. 44 (6): 1169-1177.
• Abstract ( )
• In the context of the deep restructuring of the global manufacturing competition pattern and the convergence of a new round of scientific and technological revolution, AI-enabled digital transformation of the manufacturing industry has become a strategic initiative to break through the bottleneck of industrial upgrading and cultivate new quality productivity. This paper analyzes the global dynamics of AI-enabled manufacturing transformation by systematically combing domestic and international literature to reveal the evolution law, global competition situation and future challenges of AI-enabled manufacturing digital transformation. The study found that: (1) AI technology penetration presents a staged leap characteristic, has evolved from a single point of technology application, local process optimization to the systematic empowerment stage, through the technology penetration - process re-engineering - ecological restructuring of the three-dimensional role of the mechanism, to comprehensively drive the digital transformation of the manufacturing industry. (2) The global situation of AI-enabled manufacturing digitization: the data element has risen to be a strategic asset, the whole chain efficiency has systematically jumped up, the underlying logic of the enterprise has structurally changed, major economies have accelerated the strategic layout, and AI governance has become a global common issue, and China has formed a unique development path relying on the combination of advantages of “technology+market+industry”. (3) There are three challenges in AI-enabled digital transformation of manufacturing industry: data silos and algorithmic black boxes at the technical level, green constraints and standard barriers at the industrial level, and competition for technological dominance and differentiation of governance systems at the international level. The study proposes the research hotspots that should be further focused on: AI underlying logic innovation and restructuring of the global manufacturing landscape, AI-enabled manufacturing digitization and greening coupled to reshape the global path of green mountains and green water, AI-enabled manufacturing international game focus and the trend toward an open and win-win global community of destiny. This paper provides a systematic framework for understanding the strategic value, practical path and global competition pattern of AI-driven digital transformation of manufacturing industry, which is of decision-making reference value for China's development of new-quality productive forces and the construction of a manufacturing powerhouse.

• How does factor agglomeration affect new productivity through ambidextrous innovation?
• 2026 Vol. 44 (6): 1178-1191.
• Abstract ( )
• Innovation factor agglomeration is a key way to integrate scientific and technological innovation resources, and an important way to promote new productivity through exploratory innovation and exploitative innovation. At present, the development of new productivity in 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 productivity by breaking the limitations of traditional innovation models. However, there is still insufficient research on the systematic mechanism of innovation factor agglomeration affecting new productivity. In particular, the synergy of innovation factor agglomeration through ambidextrous innovation is still in the “black box”. How does innovation factor agglomeration affect new productivity through ambidextrous innovation 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 productivity. 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 ambidextrous innovation, 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 productivity around the four action paths formed by the cross combination of two dimensions of network effect and scale effect and ambidextrous innovation. The results show that the agglomeration of innovation factors has a significant role in promoting new productivity, 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 productivity. The agglomeration of innovation elements affects new quality productivity through the ambidextrous innovation mechanism. Although the short-term network effect of exploratory innovation is negative, it significantly promotes new quality productivity after 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 productivity through the scale effect of ambidextrous innovation. 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 productivity is 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 productivity. 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 productivity from the perspective of dual innovation, and enriches the theoretical research on the formation mechanism of new productivity. Secondly, the two dimensions of network effect and scale effect are combined with ambidextrous innovation to form four action paths, and the system mechanism of factor agglomeration to empower new quality productivity is 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 productivity.

• CONSTRUCTION AND APPLICATION OF TECHNOLOGY ROADMAP ENHANCED BY AI
• 2026 Vol. 44 (6): 1192-1205.
• Abstract ( )
• 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 from an Institutional Perspective: Theoretical Exploration and Framework Construction
• 2026 Vol. 44 (6): 1206-1216.
• Abstract ( )
• 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.

• Research on the Iterative Mechanism Between Scientific Research and Technology R&D in Original Innovation.
• 2026 Vol. 44 (6): 1217-1228.
• Abstract ( )
• Against the backdrop of intensifying global competition in foundational and frontier technologies, original innovation has become a core driver of technological self-reliance and industrial upgrading. The internal mechanisms of original innovation, particularly the interaction between scientific research and technology R&D have gained increasing scholarly attention. While prior studies have preliminarily revealed the linkage between science and technology from a knowledge-flow perspective, systematic empirical evidence on how the two form a sustained, bidirectional positive feedback loop within the original innovation system remains scarce. To address this gap, this study focuses on the new energy vehicle industry, analyzing paper and patent data from 42 enterprises between 2015 and 2024. Using dynamic structural equation modeling (DSEM), we examine the bidirectional evolution paths, mediating mechanisms, and boundary conditions between scientific research and technology R&D. The results show, first, a significant bidirectional causal relationship between firms’ scientific research intensity and technology R&D intensity, forming a stable positive feedback cycle and promoting sustainable innovation. Second, scientific research quality and technology R&D breadth play key mediating roles in this dynamic transformation. High-quality scientific research enhances the authority and systematization of propositional knowledge, thereby advancing subsequent technology R&D. Broad technology R&D, in turn, expands the scope and diversity of prescriptive knowledge, helping to refine scientific questions and focus research directions. Third, cooperation intensity exerts significant cross-level moderating effects: technology cooperation strengthens the mediating path from research quality to R&D intensity, while research cooperation enhances the transformation from technology breadth to research intensity. These findings underscore the vital role of innovation collaboration in knowledge complementarity and resource integration. This study offers three main theoretical contributions. It provides the first empirical validation of the positive feedback mechanism between science and technology in the original innovation system, deepening the understanding of innovation sustainability. It also reveals the mediating role of propositional and prescriptive knowledge in the bidirectional transformation, extending the knowledge-based view into dynamic innovation research. Finally, it clarifies the enabling effect of collaboration at the firm level, addressing a literature gap that has long emphasized macro-innovation ecosystems while overlooking micro-level transformation mechanisms.

• Digitalized Innovation Capabilities: Conceptual Dimensions, Measurement, and Performance
• 2026 Vol. 44 (6): 1229-1240.
• Abstract ( )
• Digitalized Innovation Capabilities (DICs), as a core enabler of firms’ sustainable competitive advantage, have become a central concern in both innovation management research and practice. Although mainstream studies have increasingly explored related concepts, connotations, boundaries, and impacts of digital innovation, there remains a lack of clear definition, rigorous measurement, and reliable empirical evidence regarding DICs. This gap poses challenges to the systematic analysis of their antecedents and outcomes. Based on an extensive literature review, this study systematically reviews the theoretical foundations of DICs and defines it as a firm’s capabilities to empower its innovation processes through the development and application of digital technologies. DICs are conceptualized to include three sub-dimensions: Openness Digitalized Innovation Capabilities (O-DICs), Affordance Digitalized Innovation Capabilities (A-DICs), and Generativity Digitalized Innovation Capabilities (G-DICs). Furthermore, drawing on interviews and survey data, the study develops a DICs measurement scale using EFA and CFA, and examines the reliability and validity of the scale. The results further verify the distinct effects of different DICs dimensions on firms’ innovation performance.

• Antecedents of Innovation Talk-Action Gaps in Manufacturing Single Champion Firms
• 2026 Vol. 44 (6): 1241-1251.
• Abstract ( )

• Influencing Factors and Configurational Pathways of Disruptive Green Innovation in Manufacturing Enterprises
• 2026 Vol. 44 (6): 1252-1263.
• Abstract ( )
• 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: collaboration-dominant knowledge-data-driven, competition-dominant knowledge-R&D-driven, coopetition-dominant R&D-data-driven, and capability-dominant pathways, 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 co-opetition 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.

• Does Public Data Openness Promote Technology Transfer in Higher Education Institutions?— Based on a Quasi-Natural Experiment of Government Data Platform Access
• 2026 Vol. 44 (6): 1276-1287.
• Abstract ( )
• 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 resulting 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.

• From "NIMBY Effect" to "Neighborhood-Benefit Project": A Case Study on Socio-Ethical Governance in the Hangzhou Jiufeng Waste-to-Energy Project
• 2026 Vol. 44 (6): 1288-1296.
• Abstract ( )
• 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.

• From ChatGPT to DeepSeek: A Standardized Approach for Intelligent Agent Value Alignment
• 2026 Vol. 44 (6): 1297-1306.
• Abstract ( )
• 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. Currently, intelligent agents have achieved technological transitions from single task processing to multimodal perception, from rule-based 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 fact, the alignment of the value of intelligent agents is both a technical and normative issue. The Third Plenary Session of the 20th Central Committee of the Communist Party of China also emphasized that reform should focus on systematicity, integrity, and synergy. Based on this, it is urgent to adopt a collaborative paradigm of technical regulation, ethical adjustment, and legal governance: at the technical level, build a dynamic value recognition system, a systematic value adjustment mechanism, and a flexible value regulation program; At the ethical level, following the ethical mission of putting people first, formulating scientifically reasonable ethical principles, and designing scenario based ethical rules; At the legal level, determine the value alignment standard, clarify the nature of value alignment, improve the value alignment evaluation method, and accelerate the formation of new quality productivity to promote Chinese path to modernization.

• Mapping the impact of interdisciplinarity on public engagement in citizen science
• 2026 Vol. 44 (6): 1307-1319.
• Abstract ( )
• 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.

• Evolutionary Path and Prospects for Ethical Research on Brain-Computer Interfaces:A Dynamic Analysis of Interdisciplinary Intersections and Risk Evolution
• 2026 Vol. 44 (6): 1320-1330.
• Abstract ( )
• With the breakthrough progress and accelerated industrial expansion of 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 BCI ethics: (1) Publications on BCI ethics 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 fields of subject, 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 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 BCI ethics.

• A Study of Attention and Government Governance in the Perspective of New Quality Productivity
• 2026 Vol. 44 (6): 1331-1344.
• Abstract ( )
• 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.