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.

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.

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.

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.

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 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.

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.

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.

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.

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.