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.