The issue of "who should confront key technological challenges" has led policymakers and scholars to increasingly acknowledge that, alongside national strategic S＆T pillars such as national laboratories, premier research institutions, elite universities, and industry leaders,small and medium-sized enterprises（SMEs） serve as an important force. Specifically, SMEs possessing distinctive technical advantages in niche domains can play a crucial role;however, in practice, SMEs often exhibit limited willingness to engage in such efforts due to the inherent characteristics of key technology R＆D: long development cycles, substantial capital requirements, and high levels of uncertainty. Under the dominant market logic of profit maximization, motivating and empowering SMEs to actively participate in key technology innovation has thus become an urgent imperative, necessitating synergistic efforts between the government and strategic S＆T forces such as the leading enterprises.Although existing literature has affirmed the positive role of SMEs in key core technology research, it mainly focuses on strategic scientific and technological forces including core enterprises and scientific research institutions. The behavior mechanism of SMEs participating in key core technology research has not been thoroughly analyzed, which cannot provide sufficient theoretical support for solving the above practical problems. At the same time, most of the existing studies are carried out by inductive deduction and case studies, based on limited sample data and resulting in a weak generalization strength in statistical significance. In view of the practical needs and theoretical gap, employing Meta-UTAUT model integrating perception trust variable and questionnaire data from 283 SMEs, this study integrates PLS-SEM and fsQCA methods to explore the factors influencing the behavioral willingness of SMEs to participate in key technology research under the organizational model of an innovation ecosystem, because innovation ecosystem is an important carrier for SMEs to participate in key technology research. The findings show that antecedent variables including expected performance, effort expectation, social influence, convenience and perceived trust do not directly affect behavioral intention but are fully mediated by attitude. Although attitude exerts a strong linear influence, its absence in one path confirms it is not always necessary. This demonstrates that no single factor alone dictates SME participation. Acknowledging the heterogeneity of SMEs, the study recommends moving beyond a "one-size-fits-all" model and advocates for customized incentive strategies that are tailored to the specific realities and needs of different firms.The theoretical contributions lie in three aspects. First, it shifts the focus from traditional leaders of innovation ecosystems to the pivotal yet underexplored role of SMEs as complementary actors. By constructing an integrated theoretical framework based on an extended Meta-UTAUT model（incorporating trust）, we systematically identify the factors influencing SMEs’ willingness to participate in key core technology research. A central finding is the core mediating role of attitude, through which all other antecedent variables exert their influence. This not only validates but also contextually refines the Meta-UTAUT model, extending its application to the high-stakes domain of key technology breakthroughs and providing novel insights into the complex decision-making mechanisms of SMEs in this critical context. Second, existing studies mainly adopt case analysis or induction and deduction method, thus the generalization power of statistical significance was weak. This study is based on the analysis of 283 valid questionnaire data, which enhances the universality of the study conclusions. Third, this study integrates PLS-SEM with fsQCA method, exploring not only how each factor independently affects the behavioral willingness of SMEs, but also the complex "synergies" and "interactive relationships" among these factors. It is emphasized that these factors can better stimulate the participation willingness of SMEs through different matching.

In recent years, social and ethical issues from the rapid development of AI and genetic technology have driven scholars to focus on social responsibility in technological innovation, and explore ways to balance innovation dividends and risks, thus giving rise to "responsible innovation" which emphasizes that scientific and technological innovation should take benefiting society as its fundamental starting point while pursuing technological advancement and commercial value. The literature review shows that most research on responsible innovation focuses on the meso-macro level. In contrast, studies on employee-level responsible innovation are rare, particularly those considering social responsibility factors. This gap hinders understanding how social responsibility influences employee innovation. From the perspective of systems theory, the internal and external social responsibilities of enterprises have dynamic relevance. When the lack of internal responsibility leads to insufficient employee job security, their innovative attention tends to focus on short-term performance indicators; while when the external responsibility commitment lacks substantive support, employees find it difficult to form a sustainable identification with innovative values. Therefore, this dual disorder may eventually lead to the attenuation of employees’ motivation for responsible innovation, thereby affecting their responsible innovation. Thus, this study aims to explore the impact of the "duality" of corporate social responsibility on employee responsible innovation from the perspective of "inco nsistency between appearance and essence", which has great theoretical value and practical significance.Drawing on the theory of social identity, this article explores the mechanism and boundary conditions of the ’discrepancies both internally and externally’ of corporate social responsibility on employee responsible innovation behavior by analyzing 384 matched questionnaire data collected in two stages. The results show that（1） the "inconsistency both internally and externally" of corporate social responsibility negatively affects employee responsibility innovation.（2） Constructive responsibility perception plays a negative mediating role between the "internal and external inconsistency" of corporate social responsibility and employee responsibility innovation.（3） Participatory leadership not only weakens the negative impact of corporate social responsibility on the perception of constructive responsibility and employee responsibility innovation, but also further weakens the mediating role of constructive responsibility perception.The theoretical contributions of this paper are mainly as follows. First, from the organizational strategy level, this paper enriches the influencing factors of employee responsible innovation. It innovatively proposes that the "inconsistency between appearance and essence" of corporate social responsibility is also a non-negligible influencing factor in the process of employee responsible innovation, and analyzes its mechanism of action on employee responsible innovation. Second, from the perspective of social identity theory, this paper explores the influence mechanism of employee responsible innovation, reveals the mediating role of constructive responsibility perception in the process of the "inconsistency between appearance and essence" of corporate social responsibility affecting employee responsible innovation, and expands the research system of antecedent variables of constructive responsibility perception. Third, from the perspective of connecting leadership style with employee cognitive framework, this paper reveals the internal moderating role of participative leadership in the mechanism that promotes employees to generate constructive responsibility perception, and systematically defines the boundary conditions for the "inconsistency between appearance and essence" of corporate social responsibility to affec t employee responsible innovation.The management implications of this paper are threefold. First, managers should establish a long-term sense of social responsibility and maintain a stable relationship between internal and external stakeholders. They should improve the benefit-sharing system, protect internal stakeholders’ rights, balance internal and external responsibilities, and motivate employees for responsible innovation. Second, companies should focus on cognitive guidance and action empowerment to shape employees’ perception of constructive responsibility. This involves conveying strategic goals, clarifying job responsibilities, and enhancing employees’ awareness and sense of responsibility to foster responsible innovation. Third, enterprises should emphasize participative leadership, establish procedures to select and cultivate such leaders, create a supportive culture, and maximize its positive impact on employee innovation.

With the rise of service-dominant logic and service development paradigms, service-oriented transformation has become a key way for traditional manufacturing firms to build unique competitive advantages. How to improve servitization performance of manufacturing firms to achieve successful service-oriented transformation is a hot topic in current research. Recent studies show that achieving new competitive advantages through such transformation requires manufacturers to update and reshape resources and capabilities while optimizing existing production and manufacturing systems. As two core components of the manufacturing system, product R&D and production processes/integrated innovation are the key to optimizing the system and enhancing servitization performance. Thus, how should manufacturers build this integrated innovation to achieve better performance? Existing literature remains controversial on the impact of product-process integrated innovation on firms' performance. Specifically, for manufacturers' servitization transformation, the mechanisms through which this innovation affects servitization performance have not been deeply explored. Additionally, manufacturing system effectiveness depends on production scale and market conditions. How do these two factors moderate the impact of product-process integrated innovation on servitization performance? This is another important issue requiring attention. To address the above research gaps, this paper, by adopting a panel dataset of 357 listed Chinese manufacturing firms over the period 2010–2023 and applying a multivariate regression model, studies the impact of product-process integrated innovation on manufacturing firms' servitization performance, and the moderating effect of production scale and multi-market contact on the relationship between them. It finds that moderate product-process integrated innovation exerts a positive effect, significantly improving manufacturing firms' servitization performance. However, when product-process integrated innovation exceeds a threshold, the negative effect begins to emerge, which has a negative impact on manufacturing firms' servitization performance. Both production scale and multi-market contact strengthen the inverted U-shaped relationship between product-process integrated innovation and manufacturing firms' servitization performance. As production scale and multi-market contact levels increase, moderate product-process integrated innovation becomes more effective in enhancing manufacturing firms' servitization performance, while excessive innovation further exacerbates its negative impact. Meanwhile, knowledge coupling, as a mediating mechanism, exhibits significantly heterogeneous effects across different levels of production scale and multi-market contact. When production scale and multi-market contact are at an optimal level, moderate product-process integrated innovation, mediated by knowledge coupling, enhances manufacturing firms' servitization performance. When production scale and multi-market contact reach a high level, excessive product-process integrated innovation, mediated by knowledge coupling, inhibits manufacturing firms' servitization performance. The management implications of this paper are as follows: Manufacturing firms should leverage product-process integrated innovation for servitization, avoid blindly escalating innovation efforts and pursue a balanced approach. For example, by establishing cross-functional project teams, they can moderately advance product-process integrated innovation, enabling design and production personnel to collaborate in product development. This fosters tight coordination between design and production phases, ensuring both the feasibility and cost-effectiveness of designs in manufacturing, thereby accumulating collaborative advantages for servitization. Simultaneously, it is critical to guard against excessive integration to prevent negative effects such as process rigidity and technological lock-in, which could inhibit firms' servitization performance. Additionally, during servitization, manufacturing firms should scientifically plan production scales and multi-market contact based on their level of product-process integration.

For a long time, the catch-up development strategy, characterized by the suppression of the prices of factors such as labor and capital, has promoted the rapid growth of China’s economy, but it has also led to the factor market distortions, generating problems such as uneven spatial distribution of factors, poor mobility and price distortions, which in turn triggered the factor allocation distortion. Some studies have shown that when there is no factor allocation distortion, factor allocation optimization can improve China’s economic efficiency by 88.12% on average. At the same time, the digital economy, as an innovative economic form, is digitally penetrating and transforming the real industry, and this process of digital-real integration provides new opportunities for improving factor allocation distortion. Digital-real integration not only promotes the combination of data elements and traditional factors of production, broadens the boundaries of the factor market, promotes cross-domain flows and efficient allocation of various types of factors, but also promotes intelligent producti on and refined management, effectively reducing factor redundancy and mismatch.A large amount of existing literature has explored the impact of the digital economy on factor allocation distortion, but less attention has been paid to the effect of the emerging digital context of digital-real integration on factor allocation distortion. Although individual scholars have explored the relationship between digital-real convergence and factor market distortion, the scope of their research is limited to the provincial level, and they have not conducted in-depth discussions on the mechanisms at play. On the other hand, existing research on the economic effects of digital-real integration mainly focuses on green innovation, Chinese-style modernization, total factor productivity and new quality productivity, etc., yet it rarely specifically explores the impact of digital-real integration on factor allocation distortion, which is profoundly affecting factor flows, factor transactions and factor allocation efficiency, and will inevitably have a far-reaching impact on factor allocation distortion. Thus, this paper utilizes the panel data of 284 cities in China from 2011 to 2023 to deeply explore the impact of digital-real integration on factor allocation distortion and its functioning mechanism from both theoretical and empi rical levels.This paper mainly obtains the following research conclusions: First, the digital-real integration significantly mitigates the factor allocation distortion, which still holds after the endogeneity and robustness test, and when the factor market is subdivided into the labor market and the capital market, the improvement effect still exists significantly. Second, the mechanism test shows that the cost adjustment effect, market integration effect, and technological innovation effect are important mechanisms by which digital-real integration improves factor allocation distortion. Specifically, digital-real integration improves factor allocation distortions by raising the cost of labor use and lowering the opportunity cost of capital, promoting the integration of the labor market and capital market, and enhancing technological innovation. Third, the improvement effect of digital-real integration on factor allocation distortion is more significant in the state of under-allocation of labor and capital at the same time, service-oriented cities, cities with high network size, large cities, eastern cities, and coast al cities.The possible marginal contributions of this paper are as follows: First, aiming at the limitation of the lack of digitalreal integration perspective in the existing research, this paper tries to include the digital-real integration and factor allocation distortion into the same analytical framework, to explore the relationship between the two in depth and provide a new research perspective for the issue of digital-real integration and factor allocation distortion. Second, this paper systematically analyzes the mechanism of the impact of digital-real integration on factor allocation distortion from the economic perspectives of cost adjustment, market integration and technological innovation, which helps to deepen the understanding of the relationship between the two and enrich the theoretical framework in the field of digital-real integration and factor allocation distortion, and provide theoretical support for the subsequent research. Third, this paper further explores the differential impact of digital-real integration on factor allocation distortion under different factor allocation states, industrial bases, network effects, city sizes, and geographic locations, and these differentiated analyses provide empirical evidence for more effectively correcting factor allocation distortion and enhancing the level of digital-real integration.

Currently, China’s economy has entered a new normal after a period of rapid development, with market demand gradually becoming saturated and the limitations of exploitative innovation increasingly evident. The transition from exploitative innovation to exploratory innovation, known as an innovation leap, is considered a crucial strategy for enterprises to escape from existing innovation routines, identify future technological trajectories, and seize R＆D opportunities. If enterprises lack the capability to achieve an innovation leap from exploitative innovation to exploratory innovation, they risk falling into the “exploitative innovation trap” due to their inability to cross the innovation gap in shifting innovation models. Consequently, enterprises urgently need to upgrade and transform their ambidextrous innovation models to break free from the trap. Particularly in the current context of restrictions on key core technologies, enhancing innovation openness has become a critical pathway for enterprises to access external resources and achieve innovation leap. However, previous research has rarely examined the antecedent mechanisms of innovation leap, and there is a lack of exploration into innovation leap in the digital context. To address these research gaps, this study analyzes how innovation openness influences enterprise innovation based on innovation recombination theory.This study constructs a pathway for achieving enterprise innovation leap based on innovation recombination theory. Using a panel data set of 330 Chinese A-share listed companies spanning the period 2007 to 2024, it empirically examines how innovation recombination differences, manifested in knowledge flexibility and knowledge complexity triggered by the breadth and depth of innovation openness, affect enterprises’ innovation leap, as well as the moderating role of enterprise digitalization level in the above relationship.The study results indicate that the innovation openness breadth positively facilitates the innovation leap from exploitative innovation to exploratory innovation by enhancing knowledge flexibility, while the innovation openness depth exhibits an inverted U-shaped effect on the innovation leap due to increased knowledge complexity. Furthermore, enterprises’ digitalization level positively moderates the relationship between innovation openness breadth and innovation leap, and further strengthens the inverted U-shaped relationship between innovation openness depth and innovation leap.The theoretical contributions of this paper are presented as follows. First, departing from previous studies which have narrowly focused on the consequences of enterprise innovation leap, this paper shifts the focus to the influence mechanism of innovation openness on innovation leap, expanding the knowledge-based antecedents of innovation leap, answering the question of “how innovation leap occurs”. Second, based on innovation recombination theory, this paper distinguishes between the breadth and depth of innovation openness and thoroughly investigates their differential effects on the innovation leap through the distinct recombination mechanisms of knowledge flexibility and knowledge complexity. Thereby, it not only provides a novel theoretical perspective for research on innovation leap but also extends the application boundaries of innovation recombination theory. Finally, this paper introduces the moderating role of enterprise digitalization level in the impact of innovation openness on innovation leap. It delves into the mechanism and impact of digitalization level on changes in enterprises’ ambidextrous innovation strategy decisions. The findings not only broaden the research scope of enterprise digitalization but also enrich and deepen the theoretical understanding of ambidextrous innovation strategic management in a digital context.To foster a virtuous cycle between exploitative innovation and exploratory innovation, enterprises should adopt open innovation strategies while balancing the breadth and depth of openness. Expanding openness breadth requires actively collaborating with suppliers, universities, research institutions, and other external partners to integrate diverse knowledge resources, which fuels sustained innovative breakthroughs. For openness depth, enterprises need moderately close, longterm stable partnerships with collaborators to facilitate effective knowledge sharing and conversion. They should avoid over-reliance on narrow knowledge domains to maintain an optimal level. Additionally, advancing digital transformation is critical. Enterprises can leverage cloud computing, big data, and AI to build a flexible, efficient innovation ecosystem. When enterprises strengthen their capabilities in data analysis and opportunity identification, they can achieve innovation leaps at lower transition costs and enhance the scientific rigor and adaptability of their innovation strategy decisions.

Resilience constitutes a core strategic capability that enables enterprises to withstand risks and achieve sustainable development. Whether executive equity incentives can enhance resilience serves as a key indicator of their effectiveness. Using data from Chinese A-share listed companies between 2007 and 2023, this study investigates the impact of executive risk-taking incentives（proxied by Vega） on corporate resilience and its underlying mechanisms. The results demonstrate that such incentives significantly strengthen both risk resistance and recovery capacity, primarily through the channels of strategic resource reallocation and digital-physical integration. Further analysis indicates that environmental uncertainty, complexity, and financial distress positively moderate this relationship. Specifically, risk-taking incentives more markedly improve risk resistance in firms characterized by low long-term debt, small scale, or digital industrialization, whereas they more effectively enhance recovery capabilities in firms with high long-term debt, larger scale, or digital business models. By integrating dynamic capability theory with internal and external crisis perspectives, this study elucidates how executive risk-taking incentives strengthen corporate resilience through enhanced dynamic capabilities, offering impo rtant insights for improving crisis response and corporate governance systems.This study makes the following theoretical contributions:（1） It extends the research on executive risk-taking incentives from conventional contexts of strategic decision-making and financial compliance to resilience cultivation during crises, providing a novel perspective on how governance incentives facilitate dual resilience-building in crisis situations.（2） It enriches dynamic capability theory by not only extending its core concept of resource reconfiguration from a strategic resource-adjustment perspective but also innovatively incorporating digital-physical integration as a responsiveness variable in digital contexts. The study systematically reveals the critical pathway through which governance incentives are transformed into resilience output via capability iteration, thereby bridging theoretical gaps and offering a systematic theoretical explanation for resilience development.（3） While existing literature predominantly relies on static or single-event crisis perspectives, this study integrates three recurrent crisis scenarios into a unified analytical framework. It validates the applicability of principal-agent theory in dynamic environments and provides a theoretical basis for designing differentiated corp orate governance mechanisms.In light of the study’s conclusions, this study offers several managerial implications. Firms should optimize equity incentive contracts to dynamically align executives’ wealth with stock return volatility, thereby enhancing their risk-taking willingness. For instance, incorporating crisis-linked vesting conditions or flexible incentive terms can direct managerial focus toward strategic resource reallocation and digital-physical integration. This approach strengthens dynamic capabilities and transforms external pressures into internal resilience drivers. Moreover, firms should refine resource allocation by establishing thresholds for financial investments and R＆D ratios, thereby channeling resources toward core operations and innovation. Enterprises are also encouraged to advance digital-physical integration: physical firms should adopt digital tools to improve forecasting and risk response, while digital firms ought to develop solutions based on actual industrial needs, enabling risk diversification and value co-creation. Finally, differentiated equity incentives tailored to firm-specific characteristics—such as debt structure, scale, and industry—should be implemented. For example, financially distressed firms may introduce pressure-responsive options to facilitate technological restructuring and light-asset transformation, while digital industrialization enterprises should adopt performance metrics based on patent quality and technological iteration speed, supported by deferred technology dividend payouts. Such customization ensures that incentive mechanisms align with each firm’s stage of dynamic capability development.Building on its findings and limitations, several promising research directions emerge. Future studies could examine the effects of such incentives across diverse institutional and cultural contexts to assess the cross-context generalizability of the findings. Furthermore, introducing more varied internal and external contextual variables would deepen understanding of the boundary conditions influencing incentive effectiveness. Additionally, exploring the interplay between risk-taking incentives and other governance mechanisms, such as board oversight and top management team structure, could contribute to developing a more systematic governance-resilience integrated model. Finally, adopting a micro-level cognitive and behavioral perspective to analyze the psychological processes underlying executive decisions may further illuminate the intrin-sic behavioral mechanisms in the pathway to resilience formation.

Against the backdrop of increasingly severe global climate change, resource scarcity, and environmental pollution, countries and major cities around the world are actively implementing green innovation strategies to break through the limitations of resources and the environment. In recent years, the R＆D personnel and funding investment in the three major regions of China have been continuously increasing, hoping to improve the level of regional technological innovation and green economy development. However, the emissions of industrial wastewater, sulfur dioxide, and solid waste remain high. Optimizing the urban business environment of China’s three major economic circles is crucial for enhancing the efficiency of urban green innovation and achieving coordinated economic, social, and environmental development. However, there are differences in the characteristics of factors such as business market environment, legal environment, government environment, and green innovation level among cities, which may exhibit different characteristics in the green development path. Moreover, optimizing an individual business environment element may not necessarily lead to an improvement in urban green innovation performance.Drawing on the complex system perspective and green innovation chain theory, and employing multi-period fsQCA and three-stage DEA models, this study investigates the dynamic path and trajectory of high-efficiency three-stage green innovation driven by business environment ecology. The study focuses on 49 cities in the Yangtze River Delta, Pearl River Delta, and Beijing-Tianjin-Hebei regions of China from 2018 to 2020. It explores the paths of high-efficiency three-stage green innovation driven by business environment ecology from a dynamic perspective. The results indicate that, firstly, an individualbusiness environment factor is not a necessary condition for achieving high efficiency in the three stages of green innovation. Secondly, green innovation forms distinct high-efficiency configuration paths in each stage. Specifically, in the stage of green knowledge innovation, there are two paths: resource synergy-driven and government-market-driven; in the stage of green technology innovation, there are four paths: innovation-led support, financial-government collaboration, government-market co-promotion, and innovation-resource integration; in the stage of green product innovation, it primarily manifests as a path of financial-human resource integration. Thirdly, the elements of the business environment exhibit three high-configuration trajectories: dominant, buffering-dominant, and turning points. In the stage of green knowledge innovation, the dominant trajectories are the government environment, market environment, innovation environment, human resources, and public services; in the stage of green technology innovation, public services become the dominant trajectory, while the market and innovation environments are turning points; in the stage of green product innovation, the dominant trajectories are the market environment and innovation environment, while public services and human resources are t urning points.The innovations are as follows:（1） Traditional research often regards green innovation activities as a black box or analyzes them based on a two-stage model at the enterprise level. Based on the theory of green innovation chain, this study constructs a three-stage input-output index for urban green innovation and enriches the research on the influencing factors of urban green innovation efficiency from the perspective of business environment.（2） Past studies on business environment configuration have primarily focused on regional traditional innovation or enterprise technological talent, rather than urban green innovation. By adopting a complex systems perspective, this study explores the asymmetric causal relationship between business environment ecology and the efficiency of the three stages of urban green innovation. This enables determining multi-source, heterogeneous improvement paths for the efficiency of each stage of urban green innovation according to local conditions.（3） Previous studies have not considered the impact of time factors on causal relationships. This study adopts a dynamic configuration perspective and employs three years of panel data to identify three movement trajectories of ecological elements in the business environment: dominant trajectory, buffering-dominant trajectory, and turning trajectory. From a dynamic viewpoint, this study enriches the research on the configuration of green innovation ef-ficiency within the business environment.

In the era of the digital economy, data has evolved from an auxiliary information resource into a core production factor driving technological innovation and sustainable development. Understanding how data factor empowers firms to achieve balanced exploitative and explorative innovation,thus enhancing their ambidextrous innovation capability,has become an essential question in both academic research and policy practice. Existing research primarily examines corporate innovation through the lenses of digital economy, digital transformation, and digital technology application, spanning macro, industrial, and digital capability levels. Recent studies have begun to explore the nexus between data factor and corporate innovation, highlighting their promotional effects, potential risks, and relevant theoretical models. However, significant deficiencies persist regarding mechanisms, contextual factors, and theoretical integration. Specifically, research on data factor and corporate ambidextrous innovation exhibits three major gaps: first, a lack of systematic characterization based on micro-level firm data; second, insufficient elaboration on how data factor embeds within the innovation process and exert differential impacts; and third, inadequate empirical testing that incorporates external environmental dynamics and firm heterogeneity, thereby limiting the generalizability of conclusions and practical impl ications.Thus, this study aims to systematically explore the mechanisms and boundary conditions through which the application of data factor influences firms’ innovation performance in both exploitative and explorative dimensions. Grounded in the resource-based view and innovation management theory, this paper constructs a dual-path framework of “stock resource activation” and “incremental resource integration”. It argues that data factor application can stimulate exploitative innovation by identifying and activating slack internal resources, while promoting explorative innovation by strengthening external R＆D collaboration networks. To empirically test these mechanisms, a two-way fixed effects model is established using panel data from 3 076 Chinese A-share listed firms between 2008 and 2023, yielding 29 900 firm-year observations. The level of data factor application is measured through text mining of corporate annual reports across four dimensions: data stock, data development capability, data-driven business applications, and data monetization. Exploitative and explorative innovation are distinguished by patent classification continuity, with slack reso urces and R＆D collaboration as mediating variables.Empirical results show that the application of data factor significantly promotes both exploitative and explorative innovation, with a stronger effect on exploitative innovation, suggesting that data utilization primarily enhances incremental rather than radical innovation. Mechanism analysis confirms two distinct transmission paths: data factor application reduces slack resources by improving resource recognition, allocation, and utilization efficiency, thereby enhancing exploitative innovation; simultaneously, it facilitates explorative innovation by optimizing partner identification, collaboration modes, and knowledge integration efficiency, underscoring the role of data in enabling heterogeneous resource synergy. Heterogeneity analysis shows that these effects are more pronounced under favorable institutional and regional conditions. Specifically, the establishment of national big data pilot zones and the economic advantages of eastern China significantly strengthen the positive relationship between data factor application and dual inno vation outcomes.This study makes three primary contributions. First, it advances micro-level research on data factor by integrating them into the analysis of firm-level innovation behavior, extending the theoretical frontier of data economy studies. Second, it deepens the understanding of how data-driven processes reshape firms’ resource allocation mechanisms, providing empirical evidence that data applications can transform slack resources into innovation assets and foster cross-organizational knowledge recombination. Third, it identifies the heterogeneous effects of institutional environments and regional development levels, offering new insights into how data infrastructure and policy design can enhance firms’ inno vation capability.From a managerial perspective, the findings suggest that firms should strengthen their data management and analytics capabilities to embed data-driven decision-making throughout innovation activities. For exploitative innovation, data can be used to optimize internal processes and accelerate incremental improvement; for explorative innovation, big data and collaborative platforms can help identify emerging opportunities and reduce innovation uncertainty. Policymakers are advised to accelerate data market reforms, promote cross-regional data circulation, and build supportive institutional frameworks,especially in underdeveloped areas,to ensure equitable access to data-driven innovation opportunities.

Amid intensifying global technological competition and the growing demand for high-level technological self-reliance,breakthroughs in key core technologies（KCTs） have become vital to national security,industrial competitiveness,and sustainable development.KCTs combine the public-good attributes of basic research with the strategic importance of defense and industrial lifelines.Yet their innovation process is constrained by high investment,long cycles,and high uncertainty,leading to a persistent gap between high social and low private returns.This market failure discourages continuous R＆D,making enterprise-led collaborative research and development（R＆D） a key strategy to overcome technological bottlenecks and achieve breakthroughs.Enterprises,driven by market competition,are rational actors in KCT breakthroughs but face dual constraints :prohibitive capital demands from long-cycle R＆D,and talent/knowledge gaps from interdisciplinary complexity.These internal limitations,compounded by KCTs’ inherent market failure（high social returns vs.low private returns）,necessitate collaborative R＆D with external partn ers to access resources and overcome bottlenecks.Current literature spans two domains: KCT breakthrough studies emphasize exogenous drivers like policy and infrastructure but lack multi-level theoretical frameworks for enterprise-centered incentives; collaborative R＆D research focuses on micro-level outcomes and regional innovation, overlooking how multi-agent synergies can address KCT-specific chall enges under complex knowledge architectures.This study employs panel data of A-share listed firms in Shanghai and Shenzhen from 2013 to 2023 to examine the impact of collaborative R＆D on KCT breakthroughs. Patent data were obtained from the State Intellectual Property Office of China, and firm-level financial data from the CSMAR database. Abnormal samples（ST and *ST firms） were excluded, and all continuous variables were winsorized at the 1st and 99th percentiles. The empirical framework includes baseline regressions, instrumental variable estimation, robustness checks, and mechanism and heterogeneity analyses. The design integrates theoretical reasoning and econometric testing to reveal how collaborative R＆D models contribute to technolo gical breakthroughs and through which channels they operate.The results show that enterprise collaborative R＆D significantly promotes KCT breakthroughs, and this conclusion remains robust after addressing endogeneity. Collaborative R＆D drives breakthroughs through three mechanisms: easing financial constraints, strengthening talent advantages, and broadening knowledge bases. The effect is heterogeneous—stronger among firms with higher risk tolerance, those in regions with lower innovation resource misallocation, and those operating under stronger intellectual property protection. Both inter-firm and industry–university–research collaborations enhance KCT breakthroughs, while bilateral cooperation proves more efficient than multilateral partnerships.The first key recommendation is to establish a dedicated policy support system by expanding the scope and intensity of policy support for major, frontier, and interdisciplinary fields, integrating universities and research institutes into collaborative networks via co-constructed platforms to strengthen industry-university-research-application synergy, and guiding joint efforts toward industrial and technological bottlenecks through government-organized expert assessments. Second, since collaborative R＆D remedies capital shortfalls critical to KCT progress, central and local governments should increase fiscal support through special funds, R＆D subsidies and tax incentives, and establish robust evaluation mechanisms to adjust policies timely. Third, relevant parties should support the construction of physical and online exchange platforms with funding and venues, and promote deep cooperation between enterprises and universities/research institutes to improve two-way talent exchange and knowledge sharing. Fourth, given that bilateral collaborative innovation drives KCT breakthroughs more significantly than multi-party cooperation and serves as the core of the current innovation system, stakeholders should construct a "bilateral-led, multi-party-coordinated" pattern, prioritize bilateral partnerships through policy guidance and incentives to boost efficiency, and develop multi-party R＆D alliances and industrial innovation communities based on mature bilateral cooperation, with supporting coordinating institutions, governance norms, and bene fit-balancing mechanisms.This study makes several contributions.Theoretically,it identifies enterprise collaborative R＆D as an internal and controllable driver of KCT breakthroughs,extending research on innovation in strategic technologies.Empirically,it clarifies how collaboration alleviates financial,human,and knowledge constraints within a unified analytical framework.Methodologically,it innovates by using the GPT API to annotate large-scale patent data and develop a refined classification of collaborative R＆D types,improving measurement precision.Practically,the findings suggest strengthening policy support for enterprise collaborative R＆D,expanding financial incentives,and fostering talent and knowledge exchange platforms.

Artificial intelligence is increasingly embedded in organizational processes, reshaping work design and employee experiences. While firms seek efficiency and innovation gains, employees often form threat appraisals related to substitution, role reconfiguration, and skill obsolescence. This paper examines how perceived threat in AI-embedded contexts exerts a double-edged influence on thriving at work.Drawing on self-determination theory, the study develops a people-centered account in which self-efficacy and work autonomy act as parallel mediators, and new technology acceptance serves as a boundary condition that shapes both pathways. By delineating these mechanisms, the study explains when perceived threat hinders thriving and when it catalyzes adaptive functioning, offering implications for aligning technological transformation with sustainable employee development.This study employs a two-stage experience sampling design in high-technology organizational settings located across several Chinese cities.In the first stage, person-level assessments of perceived threat, new technology acceptance, and demographics were collected prior to daily data collection. In the second stage, day-level measures were gathered over five consecutive workdays from a matched cohort of employees. Morning surveys assessed self-efficacy and work autonomy from the preceding period, while afternoon surveys assessed same-day thriving at work. All constructs were measured using established and context-adapted instruments. Measurement quality was evaluated, discriminant validity was verified, and potential common method concerns were addressed through procedural and analytical remedies. Hypotheses were tested with multilevel modeling and contemporary mediation and moderation procedures that account for the nested and timeseparated nature of the data. Robustness checks included alternative specifications, additional controls, and sensitivity analyses.The paper validates a moderated dual-mediation framework linking perceived threat to thriving at work. First, perceived threat predicts lower self-efficacy, and self-efficacy promotes thriving at work, yielding a negative indirect pathway. Second, perceived threat predicts higher work autonomy, and work autonomy promotes thriving at work, yielding a positive indirect pathway. Third, new technology acceptance acts as a boundary condition that weakens the adverse linkage between perceived threat and self-efficacy and strengthens the beneficial linkage between perceived threat and work autonomy. Follow-up analyses indicate that these patterns are robust across alternative model specifications and controls. The evidence shows that perceived threat functions as a double-edged appraisal that can either undermine thriving at work by eroding capability beliefs or foster thriving at work by energizing volitional engagement, with the balance contingent on employees’ new technology acceptance.This study advances understanding in four ways. First, it extends self-determination theory to AI-embedded work contexts by clarifying how capability-oriented beliefs and volitional experiences jointly translate perceived threat into divergent work outcomes through self-efficacy and work autonomy. Second, it reconciles mixed findings on AI-related appraisals and thriving at work by specifying parallel mechanisms that operate in offsetting directions—thereby explaining why perceived threat can be both harmful and catalytic. Third, it enriches scholarship on boundary conditions by integrating new technology acceptance as a contextual factor: this factor dampens maladaptive erosion of self-efficacy while amplifying gains in work autonomy, thus refining a situational account of how employees adapt to technological change. Fourth, it makes a methodological contribution by leveraging day-level data and temporal separation to illuminate within-person dynamics, which are often masked in cross-sectional research designs.The findings offer a theory-driven and actionable roadmap for aligning AI adoption with human flourishing at work. Managers can take action on four fronts. First, they should communicate transparently about the goals, decision-making boundaries, and human-AI complementarity of AI initiatives to reduce unwarranted perceived threat. Second, they can build employees’ self-efficacy through targeted upskilling programs, opportunities for mastery experiences, timely performance feedback, and psychologically safe learning environments. Third, they can expand work autonomy by granting employees discretion over work methods, pacing, and problem-solving approaches, as well as by designing workflows that position AI as a resource to empower employees. Fourth, they can enhance new technology acceptance via hands-on pilot programs, peer mentoring schemes, and usability improvements—interventions that increase perceived usefulness and ease of use, thereby shifting the overall balance toward the constructive pathway for thriving at work.

Universities constitute a fundamental pillar of China’s innovation ecosystem, holding approximately one-quarter of the national invention patents. However, their patent commercialization rate remains below 10%, creating a critical disconnect between knowledge generation and productive application. To bridge this "last mile" gap in technology transfer, China introduced the patent open licensing system in its 2020 Patent Law revision—a standardized mechanism enabling patent holders to publicly declare licensing terms and fees. By December 2023, universities had contributed 86% of the 16 065 patents in the national open licensing pool, emerging as the primary drivers of this institutional innovation. Despite significant participation, the micro-level decision mechanisms underlying universities’ choices between open licensing and exclusive protection remain poorly understood. Thus,this study investigates how multidimensional patent quality characteristics influence university patent open licensing decisions, addressing critical gaps in understanding technology transfer strategies. Unlike existing literature focusing on macro-institutional analysis or treating patent quality as unidimensional, this study develops a comprehensive framework examining trade-offs between "monopoly premiums" from exclusive licensing and broader benefits of open licensing. It explores how patents’ technical quality（innovation sophistication and knowledge complexity）, legal quality（protection scope and enforcement strength）, and economic quality（market validati on and commercialization maturity） differentially affect licensing decisions.The theoretical foundation integrates cost-benefit analysis with multidimensional patent quality assessment. Universities face complex optimization problems: exclusive licensing promises monopoly rents but entails substantial transaction costs including partner search, negotiation, and monitoring; conversely, open licensing reduces transaction costs through standardization while potentially sacrificing monopolistic returns. This trade-off becomes nuanced when considering that pate nt quality manifests across multiple dimensions, each potentially exerting differential effects on licensing decisions.The empirical analysis employs large-scale data comprising 473 621 invention patents from 312 Chinese universities filed between 2004 and 2024, with 13 809 patents declared for open licensing during June 2021 to December 2023. Patent quality indicators were constructed using entropy weighting methods based on eleven sub-indicators: technical quality（IPC classifications, inventor teams, citations）, legal quality（claim counts, patent families, international filings）, and economic quality（licensing history, transfer records, pledge activities）. Binary logistic regression models with multilevel moderation effects test hypothesized relationships, supplemented by robustness checks using propensity score matching and complementary log-log models addressing rare event bias.Findings reveal heterogeneity in how different quality dimensions influence licensing decisions. Patents with higher technical and legal quality significantly reduce open licensing probability, as these patents embody greater potential for monopoly premiums through exclusive arrangements. Their technological sophistication and robust legal protection create substantial competitive barriers. Conversely, patents with higher economic quality demonstrate positive association with open licensing, suggesting that once a technology has been market-validated universities are willing to trade monopoly rent for t he transaction-cost savings and network effects of standardized licensing so as to maximize social impact.The study uncovers important contextual dependencies. Regional technology market maturity weakens the positive relationship between economic quality and open licensing, as mature markets provide alternative commercialization channels. Market competitiveness strengthens this relationship, indicating competitive pressure drives universities toward open licensing to establish user bases rapidly. Organizational characteristics reveal another complexity layer: universities with greater public resource support show weakened negative effects of technical and legal quality on open licensing, suggesting well-resourced institutions prioritize social impact over monopoly revenues. Institutional pressure for technology transfer simil arly moderates these relationships.This research makes several theoretical contributions. First, it introduces the monopoly premium concept to systematically explain core trade-offs in patent licensing decisions. Second, it resolves theoretical ambiguity by demonstrating dimension-specific effects of patent quality, challenging unidimensional approaches. Third, it identifies multilevel contextual mechanisms: market environments primarily influence economically valuable patents through efficiency considerations, while organizational characteristics affect technically and legally sophisticated patents through social responsibility channels. The findings also offer practical implications for patent management and policy design.

In recent years,the era’s defining characteristics of volatility,uncertainty,complexity,and ambiguity（VUCA） have become increasingly prominent,plunging enterprises into severe survival challenges.However,some firms have managed to survive,recover,and even thrive through adverse shocks,largely due to their organizational resilience—a critical capability that enables firms to maintain stability and rebound swiftly from unexpected disruptions.This resilience has become indispensable for corporate survival and sustainable development in today’s volatile environment.Meanwhile,the digital wave has swept through,with emerging technologies like artificial intelligence establishing key competitive advantages for enterprises.These technologies are being integrated into daily operations and management models,significantly enhancing stability and recovery capabilities during crises,thereby forming a close connection with organizational resilience.While existing research has examined the determinants of resilience in manufacturing enterprises from various perspectives,it has yet to fully account for the complex interplay among digital technologies,strategic positioning within industrial chains,and internal resource allocation decisions,all of which are crucial in dynamic environments.It is worth noting that the empowering effect of AI technology on enterprise resilience and how to realize it have not been systematically clarified and empirically tested in the existing literature,which will become a key bottleneck to deepen the understanding of the formation of enterprise resilience in the digital era.Therefore,to address the gap,this study draws on resource-based theory and utilizes panel data from China’s listed manufacturing firms（2011-2023）.By applying text analysis to measure AI application intensity based on the frequency of AI-related keywords in corporate disclosures,the study quantifies the development level of AI technology adoption and examine its impact on enterprise resilience.Meanwhile,this study examines the specific impact channels of AI technology application on corporate resilience from the perspective of multi-dimensional effects across the upstream,midstream,and downstream of the industrial chain,and analyzes the differentiated impacts of AI technology application under different scenarios.The research findings indicate that,first,AI technology application significantly enhances enterprise resilience.Simultaneously,AI technology positively strengthens corporate resilience through three key mechanisms :empowering digital innovation in the upstream sector,optimizing digital operations management in the midstream sector,and providing digital marketing services in the downstream sector.Heterogeneity analysis reveals that both external factors（industry competition intensity） and internal resource foundations（digital transformation speed,factor intensity,and fixed asset investment levels） significantly amplify AI’s positive impact on corporate resilience.However,strategic resource allocation patterns show that improved ESG performance creates "resource crowding-out" effects,while excessive resource reserves lead to "resource surplus",thereby diminishing AI’s catalytic role.In summary,the study innovatively constructs an industrial chain collaborative analysis framework,revealing the dynamic law and influence boundary of artificial intelligence technology penetration enhancing enterprise resilience.On the basis of the above research findings,this study proposes the following practical management recommendations for both the Chinese government and enterprises :The government should focus on building an AI technology application support system and strengthening industrial chain coordination policies,with a focus promoting the integrated application of digital technologies represented by AI across the upstream,midstream,and downstream segments of the industrial chain.Enterprises should actively adopt digital technologies represented by AI,optimize industrial chain structures,enhance digital intelligence capabilities,and proactively leverage external environmental pressures to forge their own resilience.This paper contributes to the literature in three innovative ways.First,it introduces an industry-ecosystem perspective to systematically examine how AI application enhances resilience in China’s manufacturing sector,bridging the theoretical gap between AI and organizational resilience.Second,it unpacks the mechanisms through which AI affects resilience via digital innovation,operational optimization,and marketing enhancement,while also highlighting how external environments,internal resources,and strategic allocation patterns shape these effects.Third,it offers a comprehensive theoretical lens for understanding the evolutionary path of AI-enabled resilience,providing actionable insights for both scholars and practitioners in the digital trans-formation era.

In the era of "big science," the scientific research paradigm is undergoing a profound transformation from decentralization to concentration, making scientific teams the core unit for addressing complex challenges and achieving major innovations. In response to the significant demand of China’s "Organized Research Paradigm" strategy for building stable and highly efficient teams, systematically reviewing and integrating the vast body of global research on scientific team structure and innovation effectiveness, and clarifying its developmental trajectory and core themes, it is not only theoretically urgent but also a crucial prerequisite for providing a solid academic foundation for national strategic practice.To this end, this study utilizes bibliometric tools to analyze 4 541 key publications from both domestic and international sources between 2000 and 2024, systematically delineating the development process of global research on scientific teams. The analysis indicates that this field has formed a global research landscape led and dominated by European and American scholars. International research started early, possesses a mature system, and continues to deepen its thematic focus, showing a clear evolution from surface-level characteristics to underlying mechanisms. In contrast, although China has experienced exponential growth since 2015 and become a significant research force, Chinese literature still shows noticeable gaps compared to the international frontier in terms of the systematic nature and depth of topics, as well as journal supp ort. Overall, Chinese research exhibits certain characteristics of lag and fragmentation.Further focusing on the analytical framework of "team identification-structural elements-innovation effectiveness", the study sorts out and reveals research hotspots and findings. In team identification research, the approach has evolved from relying on entity organization lists to using co-authorship networks for identification, forming two main identification paths: entity teams and virtual teams. The former offers reliable information but is difficult to obtain, while the latter is scalable and efficient but susceptible to interference from temporary collaborations. Regarding structural elements, research has expanded from explicit structural elements to implicit structural elements and gradually developed complex model relationships. For instance, implicit elements often act as key mediating variables, regulating the complex mechanism through which diversity affects innovation effectiveness. In terms of innovation effectiveness, research has constructed differentiated evaluation systems: academic teams focus on academic impact, while corporate teams prioritize market value. However, both types of evaluation face the common challenge that traditional quantitative indicators struggle to comprehensively measure the comprehensive contribution of teams in major scientific and technological breakthroughs. This systematic review reveals the development trajectory and core bottlenecks in research methods and theories within this field.Finally, responding to the demands of China’s "organized research paradigm" for stable and highly efficient scientific teams, future research should focus on the following areas: Regarding the focus of research objects, attention should shift from temporary virtual teams to teams with stronger entity attributes, such as those driven by academic leaders or research platforms, to align with the requirement for long-term, stable collaboration in major scientific and technological challenges. Concerning the optimization of structural elements, it is necessary to break through the superficial analysis of existing explicit structures by introducing topological and complex network methods, while simultaneously overcoming the quantitative bottlenecks of implicit structural elements to achieve the transformation from abstract concepts to operable parameters. Pertaining to the evaluation of innovation effectiveness, a multi-dimensional evaluation system aligned with strategic orientations should be constructed, incorporating strategic outputs like national science and technology awards and major engineering breakthroughs into core indicators, and establishing models linking resource input to innovation effectiveness. This will provide solid theoretical support and practical guidance for forging scientific teams capable of undertakin g major national missions.The significance of this study lies in its systematic review of global research hotspots and evolving trends in scientific teams. Building on this foundation, it addresses the new requirements for team building under China’s " Organized Research Paradigm" strategy, integrating cutting-edge international topics with the national strategic context. This research pinpoints critical directions for future investigation,including research subjects, structural elements, and innovation effectiveness,thereby offering targeted insights for constructing a theoretical framework to support scientific teams tasked with major national S＆T missions.

With the rapid development of information network technology, enterprises face growing uncertainty in the digital economic environment. To address this challenge, enterprises’ innovation networks are expanding outward, creating an urgent need to collaborate with other actors in these networks to establish value co-creation mechanisms. Enterprises, as the micro-foundation of economic operation, are the key subjects of digital innovation. However, for traditional enterprises, there may be many barriers to digital innovation. Therefore, many enterprises form digital innovation networks by cooperating with other enterprises, particularly their digital service providers. They integrate digital innovation resources by embedding in digital innovation networks and accelerate the construction of new value creation models. This phenomenon is defined as digital innovation network embeddedness. Digital innovation networks represent a collaborative ecosystem where different participants, including different network participating enterprises, come together to generate shared value using digital innovation resources. Traditional value co-creation theories emphasize dyadic relationships between enterprises and consumers. Yet, the rapid advancement of digital technology has expanded this concept of value co-creation from dyadic interactions to complex networks involving multiple participants. The scope of value co-creation has thus broadened from firm-consumer interactions to inter-firm collaborations. As a result, digital service providers and client enterprises actively exchange and integrate resources, enabling value co-creation among enterprises embedded in digital innovation networks. Simultaneously, the widespread application of digital technologies has raised concerns about corporate digital responsibility（CDR）, such as data privacy and algorithmic ethics. CDR emphasizes the fair and ethical use of digital technologies by organizations when engaging with stakeholders in the digital ecosystem. However, research on CDR remains in its nascent stage, with existing literature primarily comprising conceptual qualitative studies and lacking quantitative investigations into how CDR influences corporate development.Following social network theory, this study divides digital innovation network embeddedness into two dimensions: digital innovation network structural embeddedness and digital innovation network relationship embeddedness. A theoretical framework is constructed to explore how digital innovation network embeddedness facilitates enterprise value co-creation, with CDR introduced as mediating variables to reveal the new mechanisms for multi-stakeholder value co-creation. Through hierarchical regression and Bootstrap test on 317 sets of corporate questionnaire data, the research findings are as follows. First, digital innovation network structural embeddedness and digital innovation network relationship embeddedness not only directly promote value co-creation but also indirectly enhance it by elevating CDR. Digital innovation network structural embeddedness expands firms’ access to heterogeneous digital resources and data elements, strengthening their centrality within the network and thereby fostering resource exchange and risk-sharing. Digital innovation network relational embeddedness improves the transparency of tacit knowledge sharing and reduces collaboration risks through trust and reciprocity mechanisms. Second, CDR partially mediates the relationship between digital innovation network embeddedness and value co-creation. By fulfilling corporate digital responsibility and establishing ethical norms and trust mechanisms, enterprises attract more stakeholders to participate in resource integration and thus achieve value co-creation.This research contributes to the literature in the following ways. First, this study extends research on the impact mechanisms of digital innovation network embeddedness, and extends the focus from single-firm performance to value cocreation among multiple stakeholders. Second, this study deepens the micro-foundations of value co-creation under service-dominant logic and elucidates the mechanisms through which actors embed themselves in digital innovation networks. Third, this study enriches empirical research on CDR and verifies its critical mediating role in how digital innovation network embeddedness empowers value co-creation.At the practical level, this study offers the following insights for enterprises. Enterprises should actively embed themselves in digital innovation networks, occupying structural hole positions and strengthening partnerships to access non-redundant resources and emotional support. Furthermore, they should prior-itize fulfilling CDR and enhance reputation and trust through transparent digital practices.

Against the background of global economic slowdown and geopolitical frictions, enterprises are facing increasing pressure on survival and development. Organizational resilience has become a key capability for firms to cope with adversities and achieve sustainable development, and firms are placing more emphasis on building organizational resilience to ensure survival and long-term growth, yet balancing resilience and cost advantage constitutes a significant dilemma for enterprises. From the perspective of the Resource-Based View（RBV）, enterprises choose strategic alliances mainly due to the lack of key internal resources. However, enterprises with strong organizational resilience can independently adjust resource allocation and respond to crises, which may reduce their dependence on strategic alliances. Although strategic alliances offer advantages such as external resource support and synergy（which can help enterprises maintain cost advantages）, they also pose problems like high governance costs and collaboration risks. Therefore, enterprises with high resilience may tend to rely on internal resource optimization rather than external alliances when weighing costs and benefits.Organizational resilience exerts a two-sided impact on cost advantage. On one hand, it can help enterprises avoid high-cost losses and create cost advantages. On the other hand, cultivating and maintaining resilience requires substantial resource investment, which may increase corporate costs and impact the implementation of cost leadership strategies.Scholars hold divergent views on the relationship between organizational resilience and cost leadership strategy. Additionally, the specific impact of the interaction between organizational resilience and strategic alliances on cost advantage also need s further exploration. Controversies exist in relevant fields, calling for more in-depth research.Therefore,drawing on the resource-based view（RBV）, this study examines the effect of organizational resilience on cost leadership strategy. It also explores the underlying mechanisms using panel data from Chinese A-share listed companies between 2010 and 2022. The results show a significant negative effect of organizational resilience on cost leadership strategy. This reveals a possible “dark side” of resilience. Mechanism analysis shows that highly resilient firms rely less on strategic alliances. This limits their access to external cost-optimizing resources and makes it harder to maintain cost leadership. Further analysis divides alliances into equity-based and contractual forms. Resilience significantly reduces participation in equity-based alliances, which help create cost advantages through capital investment, economies of scale, and resource sharing. In contrast, resilience has no significant effect on contractual alliances, which have limited potential for deep resource integration. Instead, resilient firms focus on internal redundancy and adaptive capacity. While this improves risk resistance, it can reduce resource efficiency and increase operating costs.The study further uses a multi-level contingency framework to explore boundary conditions. At the micro level, managerial myopia strengthens the negative effect of resilience on cost leadership, as short-term oriented managers avoid longterm cost control investments. At the meso level, strategic orientation plays a key moderating role. Growth orientation weakens the negative effect by improving resource acquisition and legitimacy. Profit orientation strengthens it by increasing cost pressures and resource hoarding. At the macro level, market competition weakens the negative effect of resilience on al liance formation. In competitive markets, resilient firms are more likely to form alliances for survival.This study makes three contributions. First, it challenges the idea that resilience is always beneficial by showing its trade-offs with cost leadership. Second, it extends the RBV by examining both the presence and type of strategic alliances as mediators. This shows that even strong firms may forgo alliance-based cost advantages. Third, it enriches the contingency view by showing how managerial cognition, strategic priorities, and market dynamics together shape the effect of resili ence strategies.The findings have practical implications. Firms should avoid over-investing in resilience without considering cost efficiency. They should adopt alliance strategies suited to their resilience level and match resilience investments to their strategic orientation. In competitive markets, balancing internal flexibility with external cooperation is essential for staying competitive without losing efficiency. This study offers a nuanced view of resilience by revealing its trade-offs, boundary conditions, and governance implications, and lays a foundation for future research on how firms can achieve both resilience and efficiency.

Nevertheless, micro-level psychological mechanisms that translate lead users’ innovation traits into actual innovation behaviors remain insufficiently understood. Existing studies tend to isolate individual determinants such as motivation and ability from users’ internal and external emotional mechanisms, and predominantly apply linear models that neglect configurational and interactional drivers of innovation. This paper addresses these gaps by examining how lead users’ motivation-capability traits influence their innovation behaviors through dual emotional pathways—self-identity and platform supp ort—within open innovation platforms.Grounded strictly in the Cognitive-Affective Personality System（CAPS） theory,this study proposes a dual-path model in which four trait dimensions of demand heterogeneity, harmonious innovation passion, innovation self-efficacy and information literacy shape innovation behaviour via two emotional mediators: self-identity, representing the intra-personal route, and platform support, representing the relational route. A mixed-methods design combining structural equation modeling and fuzzy-set qualitative comparative analysis was adopted. SEM was employed to test linear relationships and mediating effects, whereas fsQCA was used to reveal equifinal configurations leading to high innovation behaviour.Data was collected from 343 lead users identified through a hybrid approach involving content analysis and K-means clustering within the MIUI community of Xiaomi Inc. Lead user status was determined by content professionalism and platform influence, both quantified via semantic and behavioural indicators. All measurement items were adapted from validated scales, subjected to tr anslation-back-translation procedures, and demonstrated satisfactory reliability and construct validity.SEM results indicate that harmonious innovation passion exerts the strongest direct effect on both self-identity and platform support, while self-identity displays a significantly stronger influence on innovation behaviour than platform support. Self-identity and platform support partially mediate the relationship between innovation traits and innovation behaviour, with the self-path demonstrating a more pronounced mediating role. These findings underscore the primacy of internal emotional recognition over external validation in driving lead users’ innovative actions. Fs QCA further identifies six configurational paths clustered into five types: dual-core passion-efficacy routes, strong motive-capability interaction, motive-affect synergy, capability-affect interaction and multi-factor synergy. These configurations illustrate that high innovation behaviour can be achieved through multiple trait-emotion combinations, supporting the principle of equifinality. The synergy between demand heterogeneity and platform support consistently emerges as a key driver across both analytical frameworks, whereas platform support alone exhibits a relatively weak effect.Although SEM and fsQCA adopt different analytical logics, the two strands of evidence mutually reinforce one another through convergent findings while offering complementary insights through divergent results. On the one hand, both approaches confirm that platform support exerts a relatively weak direct influence on lead user innovation behaviour and that the joint presence of demand heterogeneity and platform support constitutes a particularly potent driver of innovative activity. On the other hand, the two methods differ in the relative salience of self-identity vis-à-vis platform support and in the precise interplay between harmonious innovation passion and platform support. These convergent and divergent conclusions jointly deepen the understanding of how motivation-capability innovation traits activate lead user innovation behaviour.This study enriches the CAPS theoretical framework and contributes to a deeper understanding of the activation mechanisms through which lead users’ motivation-capability innovation traits influence their innovation behavior. By distinguishing intra-personal and relational emotional routes, the study clarifies how cognitive appraisals of traits are translated into affective responses that ultimately shape behaviour. The mixed-methods approach bridges linear and configurational causality, uncovering alternative pathways that are invisible to traditional regression models.The identification of multiple equifinal pathways provides practical guidance for platform managers to tailor incentive structures, recognition systems and support mechanisms according to distinct trait-emotion profiles of lead user segments. Consequently, platform operators can move beyond one-size-fits-all interventions and cultivate ecosystems that selectively amplify the most effective trait-emotion combinations.

Key industrial technologies are the core areas for cultivating technological competitive advantages and seizing opportunities for industrial development,playing a pivotal role in driving breakthroughs in industrial technology.Strategic emerging industries are knowledge-and technology-intensive sectors rooted in major technological breakthroughs and evolving development needs.Their key technologies are the essential and irreplaceable technologies（or links） that play an important role in the industry,reflecting current technological hotspots,difficulties,or future technological breakthroughs.As China’s technological and industrial competitiveness advances rapidly,Western countries have intensified their technological blockades against China.Strategic emerging industries now face the "small courtyard,high fence" technological dilemma,resulting in unbalanced and inadequate development of key technologies.Against this backdrop,it is essential for China’s strategic emerging industries to focus on enhancing their independent innovation capabilities and breaking technological barriers.Therefore,in the new round of technological revolution and industrial transformation,identifying and clarifying the development direction of key technologies,and making forwardlooking layouts are of great significance for enhancing independent innovation capabilities,achieving high-level technological selfreliance and self-improvement,and accelerating the development of new quality productive forces.Traditional technology identification methods mostly rely on single-source data or static analysis, making it difficult to reveal the cross-disciplinary relevance and dynamic evolution process of key technologies. Furthermore, they lack systematic consideration in data source construction and dynamic analysis, resulting in insufficient accuracy and agility in technology identification. The methods of multi-source data fusion and knowledge association aggregation provide a new direction for solving the above problems. The former can integrate multi-source data and statistical methods into a unified framework, adapting to the intelligence analysis needs for addressing uncertainty and complexity. The latter functions through the reorganization, association, aggregation and presentation of multi-dimensional and multi-granularity information objects（including knowledge association, knowledge aggregation, etc.）.Drawing on a three-stage intelligence analysis model, this study integrates multi-source data fusion, knowledge association and aggregation methods to construct a key technology identification model for strategic emerging industries.Specifically, first, multi-source data fusion provides a comprehensive data foundation for key technology identification;second, knowledge association is applied to reveal potential correlations between technical topics in multi-source data, which in turn helps identify key technological hotspots;and finally, guided by the theme of knowledge aggregation and integration technology, core themes in the development of generic technologies and industrial-specific technologies are extracted, and on this basis, the development direction of key technologies in strategic emerging industries is further explored.To verify the scientific validity of the key technology identification method, this study conducts a horizontal analysis by selecting the BERT terminology model and the Gompertz patent model. Three quantitative indicators for knowledge network topology are adopted: Technology Potential Index（TPI）, Technology Influence Degree（TID）, and Cross-domain Relevance（CDR）. The study finds that incorporating multi-source data fusion and knowledge association aggregation into a unified framework to construct an industry key technology identification model can help identify strategic emerging industry key technologies with uncertainty and complexity. In addition, the effectiveness of the proposed method is verified by comparing it with relevant authoritative documents.The contribution of this paper includes two aspects: Firstly, it enriches the identification methods of emerging technologies. In response to the uncertainty and complexity of key technologies in strategic emerging industries, it integrates multi-source data fusion and knowledge association aggregation into the intelligence analysis model, reduces the difficulty of identifying key technologies in strategic emerging industries, expands the ideas of emerging technology identification, and deepens the identification methods of emerging technologies. Secondly, it reveals key industrial technologies with higher granularity. The study conducts a deep analysis of common and hot technologies in the industry, and compares them with authoritative and public literature to obtain finer grained key technologies. The insights offer theoretical support for government authorities in formulating targeted industrial policies and provide valuable guidance for enterprises to ad-vance their technological innovation strategies.

In an era marked by intensified global economic volatility and unprecedented entrepreneurial challenges, resilience has emerged as a cornerstone capability for venture survival and growth. Despite its theoretical and practical significance, extant research on entrepreneurial resilience remains fragmented, constrained by two critical gaps:（1） the theoretical oversimplification of resilience as a uni-dimensional psychological construct, which fails to capture entrepreneurs’ distinct capacity to balance survival and growth in volatile environments, and（2） the absence of validated measurement tools reflecting the dual adaptive-breakthrough mechanisms inherent to entrepreneurial contexts. Addressing these limitations, this study pioneers a dual-dimensional conceptualization of entrepreneurial resilience through rigorous scale development and validation, offering novel insights into its differential impacts on entrepreneurial behaviors.This study adopts a three-stage research design through systematic theoretical construction and empirical testing. Grounded theory analysis of 20 entrepreneurs’ in-depth interviews identifies 16 initial categories through three-stage coding, culminating in two core dimensions: adaptive resilience（the ability to maintain survival in crisis through stable kernel, experience reuse, and loss control） and breakthrough resilience（the ability to transform crisis into growth momentum through opportunity reconstruction, proactive action, and cognitive leap）. Subsequently, it develops initial items for the scale through interviews, literature review, and expert evaluation. Exploratory factor analysis（EFA） and confirmatory factor analysis（CFA） confirmed an 8-item scale（4 items per dimension）. Notably, the scale diverges from conventional psychological resilience tools by embedding context-specific entrepreneurial actions. Finally, a longitudinal design employing hierarchical regression demonstrates predictive validity, revealing adaptive resilience’s stronger association with entrepreneurial persistence behaviors and breakthrough resilience’s dominance in driving entrepreneurial proactive behaviors.The study offers several key conclusions. Firstly, it systematically deconstructs the core characteristics of entrepreneurial resilience within venture contexts, advancing theoretical construction and clarifying conceptual boundaries at the theoretical level. By transcending ontological debates on entrepreneurial resilience, it pioneers the integration of theoretical divergences among trait theory, capability theory, and process theory. Existing literature predominantly focuses on the adaptive function of entrepreneurial resilience, emphasizing how highly resilient entrepreneurs navigate environmental shifts through business planning and persist through adversity. While existing literature emphasizes adaptation to adversity, this work highlights how breakthrough resilience enables opportunity discovery and dynamic re-equilibration, revealing resilience’s heterogeneous nature.Secondly, this study establishes a dual-dimensional theoretical model of entrepreneurial resilience, offering a novel perspective for resilience research. Although earlier works categorize resilience into recovery and growth dimensions, their theoretical frameworks remain constrained by literature-driven deductions. Through grounded theory and empirical analysis, this research clarifies the adaptive and breakthrough characteristics of resilience in entrepreneurial contexts, deepening these concepts as adaptive resilience and breakthrough resilience. Specifically, adaptive resilience embodies defensive mechanisms for adversity recovery, while breakthrough resilience represents transitional mechanisms for adversity transcendence. Together, they form a continuous spectrum of resilience evolution. Thirdly, it methodologically enriches entrepreneurial resilience research by providing a comprehensive measurement tool. The academic community currently lacks a precise and holistic operational instrument to examine resilience’s critical role in adversity resistance. The dual-dimensional entrepreneurial resilience scale developed in this research accurately captures entrepreneurs’ heterogeneous resilience manifestations and levels during venture processes. This tool addresses the gap in contextualized measurement within the field, offering an effective instrument to advance theoretical and empirical investigations into entrepreneurial resilience mechanisms and patterns. In conclusion, this study constructs a dual dimensional theoretical framework for entrepreneurial resilience and develops corresponding measurement tools, breaking through the limitations of measurement dimensions in existing research and providing innovative methodological support for field research.

In a business environment where VUCA（Volatility, Uncertainty, Complexity, and Ambiguity） has become the norm, entrepreneurs face the harsh reality of multiple shocks compounding and high failure rates. Entrepreneurial resilience, as a vital construct for coping with crises and explaining entrepreneurial behavior, not only helps entrepreneurs maintain positive psychological states and behavioral patterns amid adversity but also serves as the core capability for startups to achieve sustainable development. As a critical capability enabling entrepreneurs to maintain psychological stability, bounce back quickly, and achieve sustained growth amid adversity, entrepreneurial resilience has emerged as a key research topic in entrepreneurship studies. Existing research has identified several influencing factors across dimensions such as individual traits, social support, and institutional environments. However, most studies have been limited to examining single-dimensional factors, with insufficient attention paid to the interactions among multidimensional factors. How multiple factors synergize to form equivalent pathways for resilience enhancement remains unclear. Particularly within China’s unique “guanxi-based” culture and differential social hierarchy, the combinatorial logic of key elements and their underlying mechanisms for fostering resilience remain to be thoroughly elucidated.To address this theoretical gap, this paper adopts a configurational perspective to construct a theoretical model integrating three levels（individual, interpersonal relationships, and environment） to systematically examining the synergistic mechanism of seven key antecedent variables（personal entrepreneurial experience, family entrepreneurial experience, emotional ties, instrumental ties, mixed ties, government support, and sociocultural factors） on entrepreneurial resilience. Employing fuzzy-set qualitative comparative analysis（fsQCA）, this study calibrates and configures variables using questionnaire data from 199 entrepreneurs in 2024 “Thousand Enterprises Survey” project from Shanghai University of Finance and Economics. This study aims to reveal differentiated pathways driving entrepreneurial resilience amid complex mult ifactor interactions.Research reveals that entrepreneurial resilience is not determined by a single antecedent condition, but rather emerges from the complex interaction and joint influence of multiple factors. The study identified five typical pathways driving high entrepreneurial resilience:（1） “Need Driven-Institutional Culture” pathway for individuals with limited personal experience,（2） “Fairness-Personal Relationships” driven type with single-source personal experience,（3） “Fairness-Institutional Culture” driven type with single-source personal experience,（4） “Demand-Fairness” driven relationship-dominant type,and（5） “Experience-Relationship-Environment” triadic synergistic type with dual-source experience. Comparative analysis of these pathways reveals that experiential endowment plays a decisive role in shaping entrepreneurial resilience. Those lacking personal experience primarily rely on the dual-wheel drive of “needs-institutional” factors, leveraging emotional ties for psychological buffering and resource support. Those possessing experience predominantly follow the “experienceequity” logic, achieving efficient resource integration through instrumental ties. Guanxi networks and institutional environments exhibit complementary or substitutive relationships. When external support is strong, multiple relationship types synergize with entrepreneurial resilience. When external support is weak, resilience functions are carried by combinations of instrumental, emotional, or mixed ties. When internal experience and relational capital are highly synergistic, the marginal contribution of institutions correspondingly diminishes. Overall, emotional ties act as “stabilizers”, instrumental ties function as “resource levers”, experience forms the “cognitive engine”, while institutions and culture serve as the s ystem’s “amplifiers”.The theoretical contributions of this paper are reflected in three aspects. First, grounded in China’s unique relational cultural context, this paper systematically distinguishes the operational mechanisms of emotional, instrumental, and mixed ties within entrepreneurial resilience. This reveals the multifaceted functions of guanxi networks in crisis response that extend beyond mere resource acquisition, offering a new localized perspective for understanding how Chinese entrepreneurs navigate crises. Second, this paper overcomes the limitations of traditional single-level analysis by adopting a configurational perspective to integrate individual, interpersonal, and environmental dimensions. It identifies multiple equivalent pathways to high resilience, constructing a theoretical framework for entrepreneurial resilience drivers. Finally, the methodology incorporates fsQCA to effectively capture nonlinear interactions and concurrent causal relationships among ante-cedents, thereby enriching analytical paradigms and interpretive tools for entrepreneurial resilience research.

In recent years, global climate change has becomei ncreasingly severe, with China actively assuming major-countryresponsibilities as one of the principal contributors to worldwide carbon reduction. Under the "Dual Carbon Goals" framework, the government vigorously promotes green transformation in the manufacturing sector, advances new industrialization,and cultivatesi nnovative green development models. To achieve win-win outcomesi n both economic and environm entalperformance, manufacturing enterprises have begun experimenting with business model innovations（BMI）, suchas s ervitization transformation and circular economy practices. Giveni ts strategic and systemic nature, BMI not only provides manufacturing enterprises with more adaptable production methods and operational patterns but also serves as a pote ntialbreakthrough in driving green development and reducing carbon emissions through reconstructing value creation andd elivery processes.E xisting studies predominantly focus on the economic impacts of BMI through questionnaires or case studies, while rarelyinvestigating its rolei n curbing corporate carbon emissions from an enterprise perspective. However, BMI can optimize resource allocation and reduce resource consum ption during value creation and delivery processes, makingi t crucial to expl ore whether BMI effectively inhibits corporatecarbon emission intensity. This study addresses three core questions:（1）Through what pathways does BMI influence carbon emission intensity?（2） How does external environmental regulation a ffect the relationship between BMI and carbon emission intensity? To answer these questions, the study conductes empiricalresearch using data from China’s listed manufacturing enterprises between 2012 and 2022. Grounded in Resource-Based View（RBV） and Dynamic Capability Theory（DCT）, this study develops a composite BMIme asurement system incorporating subjective and objective indicators via the entropy weight method. It further examinesBMI’s impact on carbon emission intensity, while investigating the mediating roles of green innovation and digital transformation, along with the moderating effect ofl ocal environmental regulationi ntensity. Thef indings demonstrate the follo wing:（1） BMI significantly reduces corporate carbon emissioni ntensity, and this conclusion remains valid after a seriesof robustness tests;（2） The emission-reduction effect is more pronounced in ate-owned enterprises, heavily pollutingindus tries, and large-scale firms;（3） Greeni nnovation and digital sttransformation partially mediate the BMI-emission intensityrelationship. Moreover, the mediation role of green innovation is primarily evident in high-quality innovation, namely the output of greeni nvention patents;（4） The moderating effect ofl ocal environmental regulationi ntensity on the relations hip between BMI and firm carbon e missioni ntensityi s context-dependent. This negative moderating effecti s contingen t upon firms’ resource endowments:while statistically insignificant in the full sample, it manifests significantly amongsmall-sized firms or firms with low R＆D investment. In light of these findings, the study derives managerial and policyi mplications as follows. For businesses,i ti s essentialto priori tize the role of business-modeli nnovation on the road to sustainable development byi ntegratingi ti nto strategic dime nsionssuch as carbon-emission governance. Bybuilding ani nnovation matrix of ecological value propositions—intelligentvalue creation— value delivery circulation, enterprises can achieve the co-evolution of economic and environmental benefits.For governments, the taski s to leverage macro-regulatory capacity, balancing the stringency of rules with room for innovation, so as to accelerate corporate green transition and digital upgrading. Policies should be fine-tuned for specifictargets through differentiated supervision, guiding enterprises to convert compliance pressure into long-term drivers of low-c arbo n transformation.Thisstudy contributes theoretically in three dimensions: First, by employing the entropy weight method to measure corpor ateBMI, it enriches interdisciplinary research on BMI and carbon emission drivers at the micro level. Second, this studycon structs the transmission chain of "business model innovation→green innovation/digital transformation→carbon emis sionintensity", and validates the critical mediating role of substantive greeni nnovation. Third, regarding the moderatingeffect of local environmental regulation intensity, this study reveals its pronounced context-dependency. It highlights the central role of enterprises’ resource endowments within theregulatory mechanism triggered by external environmental p ressur es, thereby broadening the theoretical perspective for environmental institutional theory at the level of business modelinnovation.

The digital transformation of entrepreneurial firms constitutes a pivotal impetus for the high-quality advancement of private enterprises. However, they are shackled by resource constraints across technology, talent, and capital dimensions,and confront dual predicaments: the paralysis of wanting to transform yet being unable to execute and the recklessness of blind trend mimicry, both of which stem from the absence of tailored strategies. This necessitates the development of clear, actionable digital transformation frameworks. Extant studies focus on how the introduction of digital technology affects the economic performance of mature enterprises, and little is known about how entrepreneurial firms conduct digitalization to achieve sustainable growth under the dual pressure of “innovation championship” and resource endowment constraints. Based on text mining and identification, this study proposes two types of digitalization strategies: radical digitalization and incremental digitalization. Radical digitalization aims to rapidly achieve deep integration and breakthroughs in efficiency, experience, business operations, and business models through intensive, cross-boundary resource inputs. Its essence lies in seeking to disrupt existing market rules or technological pathways, with the goal of establishing new competitive moats. In contrast, incremental digitalization emphasizes applying digital technologies in a phased and controllable manner within the framework of existing business operations and resource endowments. It prioritizes the optimization and improvement of production, operations, management, and other processes. The core of this strategy is to maintain operational stability while gradually accumulating digital capabilities and adapting to market changes. Drawing upon optimal distinctiveness theory, this study explores the influence of two kinds of digitalization strategies on the sustainable growth of en trepreneurial firms from the perspective of external environment and dynamic capability. By screening the samples of cross-year entrepreneurial firms on GEM and SME board,the study finds that radical and incremental digitalization have no significant impact on short-term profits,but they could improve market value.It indicates that the impact of digitalization on the sustainable growth of entrepreneurial firms is long-term oriented.With the enhancement of market dynamics,the implementation of radical digitalization has a negative impact on the short-term profits of entrepreneurial firms.The higher the degree of regional digitalization is,the stronger the role of incremental digitalization in enhancing the long-term value of entrepreneurial firms.Additional analysis shows that both radical and incremental digitalization can enhance market value of entrepreneurial enterprises by enhancing innovation and absorption capacity.The mediating effect of innovation capability is moderated by market dynamics and regional digitalization degree.The effect of innovation ability on market value is more prominent under high regional digitalization degree and low market dynamics.Heterogeneity analysis reveals that the moderating effect of market dynamism on the mediating role of innovation capability is significantly stronger in non-technology-intensive industries;furthermore, the analysis indicates that the moderating influence of regional digitalization on this mediation is more pronoun ced under the condition of Chair-CIO duality.Its potential marginal contributions are manifold. First, it breaks through the traditional paradigm focused solely on the holistic effects of digitalization. By being the first to systematically compare the differential impacts of radical versus incremental digitalization strategies on both the short-term profitability and long-term value of entrepreneurial firms, this research reveals the time-sequenced accumulation characteristic inherent in digital value creation. This provides a novel explanatory pathway for firm growth research from a behavioral perspective. Second, through uncovering the distinct moderating effects of market dynamism and regional digitalization levels on these two types of digitalization strategies, it clarifies the "environment-strategy" alignment imperative in the digital implementation of entrepreneurial ventures. This offers a theoretical foundation for selecting contextually appropriate digital pathways in specific entrepreneurial situations. Moreover, it advances the understanding of the dynamic capabilities’ action pathways. By elucidating the dual mediating mechanisms of innovation capability and absorptive capacity, the study reveals the intrinsic pathways through which digitalization strategies affect entrepreneurial firm growth. This effectively unpacks the "capability-performance" black box within dynamic capabilities theory, providing new empirical evidence for research on the microfoundations of dynamic capabilities. Finally, the study constructs a tripartite alignment decision-making framework.By dissecting the "capability-environmentstrategy" alignment mechanism, it not only extends the application of the organization-environment fit theory into the digital context but also provides entrepreneurial firms with an actionable decision logic framework to navigate digitalization di-lemmas.

In the current era, the world is experiencing an unprecedented transformation in a century, and the instability of the international situation and the continuous escalation of international trade barriers continue to aggravate the operational risks of the global supply chain, and enterprises around the world are facing the potential risks of blocking, jamming, and breaking the chain due to the interruption of the supply of core raw materials, the blockade of key technologies, and the restriction on the import and export of products. In this context, how to effectively enhance the resilience and stability of the industrial chain supply chain has become a major theoretical and practical problem to be solved. Existing research shows that the diversified layout of supply chain can effectively deal with the risk of enterprise supply chain disruption. With the increasing level of digital industry agglomeration, its impact on supply chain configuration diversification will be more and more prominent.This study takes the data of A-share listed companies in Shanghai and Shenzhen from 2011 to 2023 as research samples, and to examine how the agglomeration of the digital industry affects the supply chain configuration of enterprises, this study first constructs a panel two-way fixed effects model. Subsequently, based on the results of the benchmark regression, a mediating effect model is further introduced to reveal the underlying mechanism of this impact. In order to effectively mitigate endogeneity arising from omitted variables and bidirectional causality, this study further employs the two-stage least squares（2SLS） method to re-evaluate the impact of digital industry agglomeration on the diversification of corp orate supply chain configuration.It is found that digital industrial agglomeration can significantly promote the diversification of corporate supply chain configuration, and this promotion is mainly achieved by enhancing the technological innovation ability of enterprises and improving the quality of corporate information disclosure. Further analysis reveals that there are significant differences in the impact of digital industrial agglomeration on firms’ supply chain configuration, and that such a facilitating effect is more pronounced among non-state-owned firms, firms with relatively low levels of AI application, firms in regions with relatively high degrees of marketization, and firms in coastal regions. Therefore, this study proposes three policy recommendations from spatial layout of the digital industry, agglomeration-efficiency release, and differentiated governance to enable the d igital sector to optimize supply chains and broaden the sharing of agglomeration dividends.Compared to existing research, this paper’s contributions lie in three aspects. First, it expands the research scope of digital industrial agglomeration’s economic effects. Existing literature mainly explores its economic spillovers from perspectives like regional green tech innovation, manufacturing export tech complexity, and corporate resilience, but lacks systematic research on its impact on micro-level enterprises. This paper uses a firm supply chain configuration perspective, builds a theoretical framework for digital industrial agglomeration’s impact on firm supply chain arrangements, and examines these effects systematically. Second, it enriches research on the determinants of firm supply chain configuration. Existing studies focus on micro-firm（e.g., client CEO changes, AI innovation） and macro-policy（e.g., relaxed market access） perspectives, but no research has examined digital industrial agglomeration’s impact—despite its growing prominence. This paper adopts a digital industrial agglomeration perspective to study its impact on firm supply chain configuration, offering a new viewpoint for this field. Third, it reveals the operational mechanisms and differentiated effects of digital industrial agglomeration on firm supply chain configuration, providing new insights for subsequent studies. It examines the mechanisms through the dual lens of technological innovation and information transmission, and explores differential impacts across internal corporate characteristics and external environmental factors. This not only provides theoretical support for understanding supply chain configuration evolution in the digital economy, but also offers decision-making references for improving supply chain efficiency and resilience via optimized digital industry spatial arrangements during the 15th Five-Year Plan period.

The integration capability of complex product systems constitutes the core competence of system integrators,playing a pivotal role in breakthroughs of critical core technologies and the development of complex products.Unlike the technology catchup patterns observed in mass-produced goods,complex product systems exhibit extended technology lifecycles and greater learning difficulties.Although significant research has examined paradigm shifts in systems thinking,technological adaptation,innovation ecosystems,resource orchestration,and organizational models within this context,less attention has been paid to the formation process and mechanisms of integrators’ system integration capabilities—an area that warrants further theoretical investigation.This study adopts complex product system theory to conduct longitudinal exploratory case study on Cell-Bridge Biotech, examining the enterprise’s technological substitution pathway in high-end equipment manufacturing for CGT upstream devices from 2011 to 2023, thereby extracting the enhancement mechanisms of complex product system integration capabilities during domestic substitution. Specifically, prolonged technological breakthrough provides foundational support for domestic substitution. Through upgrading three dimensions of integration capabilities—technological integration, product integration, and system integration—the case enterprise achieved progressive technological substitution, critical module substitution, and ultimately full-process substitution, demonstrating an evolutionary trajectory characterized by gradualism. Technological integration capability refers to the ability to translate underlying technologies into tangible products by organically combining distinct technical components, systems, or software to achieve specific technological objectives. This capability emphasizes the process of technology development and transformation, typically resulting in the conversion of technological principles into laboratory products（product prototypes）. At this stage, products are mostly in the internal testing phase, meeting basic requirements for consistency and stability. Product integration capability refers to the ability to rapidly and efficiently develop new products and bring them to market. This capability emphasizes swift responsiveness to market demands, iterative product refinement, and accelerated delivery to meet differentiated customer needs and maintain competitive advantage in fiercely contested markets. This process involves creating rapid prototypes, enabling user participation in evaluation to identify issues early and make adjustments, followed by iterative product development based on feedback. System integration capability refers to the ability to consolidate different information technologysystems, hardware, and software components into a coordinated system or network, delivering comprehensive solutions to customers. This typically involves applying integrated technologies to unify disparate sub-product systems,applications,and data repositories,supporting business processes and requirements.Furthermore,the research reveals that the construction of complex product system integration capabilities emerges from the interplay between technological logic and market logic.The technological logic encompasses decomposition and linkage mechanisms,describing the interconnections among components,modules,or systems,while the market logic includes mechanisms of current market demand alignment and future demand traction, reflecting the market responsiveness at different stages of complex product development.This study constructs a process model for enhancing the integration capability of complex product systems in domestic substitution.Theoretically,it constructs a dynamic process model for enhancing complex product system integration capabilities during domestic substitution,unveiling the mechanisms and pathways of critical core technology substitution from the complex product system perspective,thereby extending existing theories on both technology domestic substitution and complex product systems.Additionally,it proposes dual interaction logics—technological and market logics—for capability enhancement during substitution,enriching theoretical frameworks concerning complex system product development.Practically,the findings provide guidance for latecomer enterprises in critical technology domains to achieve technological leapfrogging.Continuous improvement of autonomous innovation capabilities through long-term fundamental technology breakthroughs is essential to reduce external dependence on core technologies, thereby establishing the technological foundation for enhancing system integration capabilities and realizing domestic substitution. Simultaneously, complex product system development must consider not only technological di-mensions but also ensure alignment between technological and market dimensions.

Against the backdrop of the accelerated formation of global innovation strategic networks, enterprises increasingly find it difficult to pursue “go-it-alone” innovation. As a vital component of open innovation and an advanced stage of internationalization, R＆D internationalization is crucial for enterprises in emerging economies such as China to enhance innovation performance and international competitiveness. However, when constructing overseas R＆D networks, these enterprises often face the dual challenges of substantial information gaps and high entry barriers, which in turn increase hostcountry organizational adaptation costs, reduce the efficiency of cross-border resource allocation, and lead to redundant R＆D investment. At the same time, digital transformation has become a core strategic initiative for enterprises to capture market opportunities and respond to environmental challenges. This raises several key questions: Can digital transformation help Chinese enterprises leverage digital capabilities to serve as a “springboard” for R＆D internationalization? What heterogeneity characterizes its effects? How can the theoretical mechanism between the two be explained? What roles do host-country information accessibility and market entry level play in this process?Drawing on the new OLI theoretical paradigm, this study employs data on Chinese listed firms and their overseas investments from 2007 to 2023, and applies a fixed-effects panel model to empirically examine the impact of digital transformation on the level of R＆D internationalization. Further, it investigates heterogeneous effects based on ownership（stateowned vs. non-state-owned） and executives’ overseas backgrounds. In addition, mechanism tests are conducted to explore whether digital transformation enhances enterprises’ ability to exert the new ownership–location–internalization（OLI）advantages of open resources, linkage, and integration by improving host-country information accessibility and market entry level, thereby overcoming the inherent barriers to R＆D internationalization. From a theoretical perspective, the new OLI framework provides an important analytical basis and reference for outward international investment in the digital era. Digital transformation strengthens these three advantages, shrinking information gaps and entry barriers, cutting risk/cost and boosting R＆D efficiency so firms expand and refine global R＆D networks. Moreover, host-country information accessibility and market entry level mediate this process: digital tools reduce information asymmetry and allow real-time monitoring of regulatory change, lowering the liability of foreignness and sustaining R＆D internationalization.Empirical results demonstrate that digital transformation significantly enhances the level of R＆D internationalization, and this conclusion remains robust across a series of tests. Heterogeneity analysis further shows that the effect is more pronounced in state-owned enterprises and in firms with executives holding overseas backgrounds. Mechanism testing additionally confirms that digital transformation effectively improves host-country information accessibility and market entry level, which enables enterprises to realize the new OLI advantages, thereby alleviating information asymmetry and entrybarrier problems and ultimately advancing their R＆D internationalization.Building on these findings, this paper proposes several policy recommendations. First, enterprises should accelerate digital transformation, strengthen their capability to embed in global R＆D networks, deploy core digital technologies, and digitally reengineer R＆D processes to enhance real-time insight into host-country markets, technologies, and talent information. Second, differentiated guidance should be implemented: state-owned enterprises should be encouraged to increase digital investment and closely link it to overseas R＆D performance, while small and medium-sized enterprises should receive targeted "diagnosis–assistance" support to address specific challenges such as information asymmetry and shortages of international talent. Policy support should further incorporate the international experience and digital literacy of executive teams into its design.Third, host-country market access and information service guarantees should be reinforced, including building an authoritative and dynamic host-country R＆D environment information database, promoting mutual recognition and institutional alignment in areas such as cross-border data flows and market entry, strengthening cooperation on intellectual property protection, and simplifying procedures for establishing overseas R＆D institutions. In addition, building industry-or region-level digital R＆D collaboration platforms can foster the sharing of innovation resources, reduce information asymmetry, and thus create a global innovation cooperation ecosystem that advances collaborative R＆D.

With the in-depth development of globalization and digitalization, companies face increasingly complex and dynamic competitive environments. Knowledge, as the core resource of modern enterprises, has rendered the innovation of its management model crucial to sustainable development. Technological innovations not only greatly enhance the efficiency of knowledge acquisition, transformation, and application but also drive knowledge management toward intelligence, dynamism, and collaboration. As a result, how companies can drive the innovation of knowledge management models has become a pressing issue for both academia and industry. Existing literature mainly studies knowledge management model innovation from a linear perspective and case studies, but it fails to clarify the causal differences of multiple factors in the coordinated and unified process of achieving knowledge management model innovation, nor does it reveal the driving pathways of knowledge management model innovation. Therefore, exploring the antecedent variables and driving pathways of knowledge management model innovation is of practical value. This paper seeks to answer the following questions: How are the antecedent conditions of knowledge management model innovation interlinked? Which condition among the antecedent f actors contributes the most to knowledge management model innovation?This study adopts actor-network theory（ANT） to construct a research framework for knowledge management model innovation. Taking 202 valid questionnaire responses from high-tech enterprises as the research data, it employs fuzzy-set qualitative comparative analysis（fs QCA）, importance-performance map analysis（IPMA）, and artificial neural network（ANN） analysis to examine the impact of factors at different levels on enterprises’ knowledge management model innovation and the configurational effects among these factors.The Fs QCA identifies conditional combinations between variables and reveals different causal paths, making it suitable for exploring multiple causal relationships in complex systems. IPMA helps identify important influencing factors through visual means and quantitatively analyzes the actual performance of these factors. By training on a large amount of data, ANN can reveal non-linear relationships between variables while predicting future trends and outcomes. This combination not only enhances the depth of causal analysis, but also improves predi ction accuracy, making it suitable for multidimensional research on complex systems.The results of the study show that（1） A single condition cannot fully explain the driving mechanisms of knowledge management model innovation;（2） six antecedent conditions from the levels of technological actors, human actors, and coordinating actors have a synergistic impact on knowledge management model innovation,the paths leading to complementary high-level knowledge management model innovations can be categorized into three types: technology-coordination dual-core, human-machine synergistic interaction, and technology-led self-driven;（3） AI + knowledge application and the digital capabilities of organizational members can substitute for each other under certain conditions;（4） the key preconditions for knowledge management model innovation mainly lie at the technological actor level, with AI + knowledge transform ation being the core condition for achieving high-level knowledge management model innovation. The possible contributions of this study are as follows: Firstly, compared to the previous method of linear path analysis, this study identifies multiple implementation paths for enterprises to achieve high-level knowledge management model innovation under different configuration conditions from a configuration perspective, revealing the driving mechanism of multi factor interaction coupling on knowledge management model innovation. Secondly, different from the TOE framework commonly used in configuration research, the introduction of ANT integrates technical actors, human actors, and collaborative actors into the same analysis network, with a network logic proposed for innovative construction of knowledge management models under human-machine collaboration, providing a new exploration path for the localized application of ANT in management contexts. Finally, this study proposes that technology actors, human actors, and collaborative actors jointly constitute the core driving force for knowledge management model innovation, and further analyzes the substitutability of technology actors and human actors in specific configurations, responding to the lack of in-depth explo-ration of human-machine collaboration in existing research.

In the context of unprecedented global changes, enterprises are confronted with escalating risks and uncertainties, making organizational resilience a critical capability for achieving high-quality and sustainable development. Entrepreneurship—characterized by innovation, opportunity recognition, and risk-taking—serves as a vital internal driver for firms to navigate environmental volatility. At the same time, the ongoing wave of digital-intelligent transformation is fundamentally reshaping operational models and organizational structures, offering essential technological support for enhancing resilience. The current studies have mostly linked entrepreneurship to innovation performance or business development, with less attention paid to the unique role in coping with uncertainty shocks. There is still a lack of systematic analysis on how entrepreneurship affects organizational resilience through strategic behavior. Although existing research has shown the role of digital transformation in reshaping organizational resilience, there is limited research that integrates digital transformation into the chain of entrepreneurship and organizational resilience. Environmental uncertainty, as an important moderating factor for corporate strategic behavior, has not received sufficient attention about its boundary conditions in the effectiveness of entrepreneurship, and related research is still relatively limited. Against this backdrop, investigating how entrepreneurship fosters organizational resilience through the pathway of digital-intelligent transformation contributes to a deeper understanding of their interrelationships and provides both theoretical insights and practical guidance for firms seeking to adapt and thrive in complex and dynamic environments.This study uses data from A-share listed companies in China from 2011 to 2022 as the research sample, and sets organizational resilience as the dependent variable, which is measured by performance growth and financial volatility. As the core independent variable, entrepreneurship is characterized by three aspects of an enterprise: innovation capability, strategic decision-making, and self-employment status. This study defines the mediating variable as the degree of digital-intelligent transformation, and identifies environmental uncertainty as the moderating variable, which is categorized into micro-environmental uncertainty and macro-environmental uncertainty. The study employs a two-way fixed effects model to examine the impact of entrepreneurship on organizational resilience empirically. From the perspective of digital-intelligent transformation, it explores the mediating mechanism through which such transformation enhances organizational resilience. In addition, heterogeneity analyses are conducted based on firm characteristics and macro-level environments, and the moderating role of environmental uncertainty in the relationship between entrepreneurship and organizational resilience is also investigated. The study finds that fostering entrepreneurship contributes to enhancing organizational resilience. Entrepreneurship promotes organizational resilience by driving firms’ digital-intelligent transformation. Furthermore, environmental uncertainty positively moderates the relationship between entrepreneurship and organizational resilience. The impact of entrepreneurship on organizational resilience also varies significantly depending on firms’ factor intensity, ownership type, and life cycle stage, as well as the level of digital economy policy support and entrepreneurial activity in the region where the firm is located. Thus,efforts should be made to improve institutional and incentive mechanisms for fostering entrepreneurship;then, measures should focus on accelerating digital-intelligent transformation through technological R＆D and new infrastructure development;next, differentiated policies should be provided in accordance with enterprise types and regional conditions;lastly, enterprises’ environmental adaptability should be enhanced by stabilizing policies and strengthening supervision. The theoretical contributions of this study are threefold. First, it reveals the positive impact of entrepreneurship on organizational resilience, expanding research and guiding practice for sustainability. Second, it highlights the mediating role of digital-intelligent transformation in the relationship between entrepreneurship and organizational resilience. This not only offers new empirical evidence for the theoretical exploration of digital-intelligent transformation,but also provides scientific guidance for enterprises to promote such transformation in practice. Third, it emphasizes the catalytic role of environmental uncertainty in stimulating and reinforcing entrepreneurship, providing new insights into resilience under var-ying conditions.

Accelerating the development of a spatial network of new quality productive forces is essential for optimizing resource allocation and advancing industrial upgrading. It also represents a crucial mechanism for improving locally tailored institutional arrangements and promoting regional coordinated development. Currently, China’s new quality productive forces exhibit pronounced spatial imbalances. A fully efficient and synergistic inter-provincial development pattern has yet to emerge, with persistent challenges including restricted factor flows and inadequate regional cooperation. These obstacles hinder both the overall advancement of new-quality productive forces and the realization of balanced regional development. In this context, this study addresses two core questions: What structural characteristics define China’s spatial network of new quality productive forces, and what network effects does it produce? Answering these questions is of considerable theoretical and practical significance for optimizing the spatial configuration of new quality productive forces and fostering high-quality regional coordination.Using data from 31 provinces in China from 2012 to 2022, this study constructs an evaluation index system for newquality productive forces across five dimensions: new laborers, new labor means, new labor objects, new technologies, and new production organizations. A composite measure is derived through the entropy weight method. A modified gravity model is then applied to establish a provincial-level spatial network of new quality productive forces, and social network analysis is used to examine its overall and node-level structural features as well as block models. To examine the individual characteristics of the spatial correlation network of new-quality productive forces, this study analyzes three dimensions—degree centrality, betweenness centrality, and closeness centrality—to reveal the status and role of each province as a node in the spatial correlation network. QAP regression is then employed to identify the mechanisms shaping the network, and spati al-econometric models are used to test its effects.The key findings are as follows. First, at the overall network level, the density of China’s spatial network of new quality productive forces continues to increase. At the same time, efficiency declines, small-world characteristics strengthen, and a clear “core-periphery” structure emerges. Although interregional collaboration is improving, it remains below the optimal level. Second, at the node level, eastern provinces such as Beijing, Jiangsu, and Shanghai consistently occupy central positions, exerting strong siphoning and intermediary effects, whereas most western and northeastern provinces remain peripheral, marked by significant spillovers and limited cross-regional collaboration. Third, block model analysis reveals strong intra-block but weak inter-block connections: the eastern and central regions form a “bidirectional spillover” block, while most western and northeastern regions function as “net beneficiaries” or “primary beneficiaries”, reflecting limited integration across regions. Fourth, in terms of formation mechanisms, the spatial associations of new labor objects and new technologies are the primary drivers of network formation, while the contributions of new laborers, new labor means, and new production organizations remain limited. Fifth, regarding network effects, overall network density has a significant positive impact on enhancing the development of new quality productive forces and narrowing regional disparities, whereas the influence of node-level structural features is relatively modest. Moreover, educational development and gove rnment intervention further strengthen collaborative improvements in new quality productive forces.To optimize the spatial network of new quality productive forces and enhance regional synergy, this study proposes several policy recommendations. First, policymakers should refine the national spatial configuration of new quality productive forces, promote cross-regional collaborative innovation platforms, and remove administrative barriers. Second, they should reinforce the radiating and driving functions of core provinces while improving the embeddedness of peripheral regions. Third, cross-block linkage mechanisms should be established to facilitate wider resource flows and sharing. Fourth, policymakers should prioritize critical drivers such as new technologies and new labor objects in building regional collaborative innovation networks. Finally, they should improve the governance framework for the spatial network of new quality productive forces to facilitate cross-regional coordination and institutional integration in areas such as education and governmental regulation.

In the context of innovation-driven development and the construction of an intellectual property powerhouse,the cultivation and transformation of high-value patents have emerged as critical drivers of the deep integration between industrial chain and innovation chains.While a limited number of scholars have conducted firm-level research showing that high-value patents can enhance enterprises’ technological innovation capacity,help break the path dependence of low-end lock-in,and promote high-quality development by alleviating information asymmetry,there remains a significant gap at the regional level.Specifically,systematic analysis and mechanism identification are still lacking regarding how high-value patents exert their economic effects by optimizing the a llocation of innovation factors and activating regional synergy mechanisms.This study fills this gap by constructing a comprehensive analytical framework that connects high-value patents to the coupling and coordination of industrial chain and innovation chain via resource reallocation, knowledge spillovers, and institutional mechanisms. The theoretical foundation outlines two primary pathways through which high-value patents facilitate integration. First, by embedding core technologies in upstream production processes, high-value patents help enterprises overcome bottlenecks in key industrial segments, thereby enhancing self-reliance and technological control. Aligning innovation activities with industrial needs strengthens the synergy between R＆D and production, fosters collaborative innovation networks, and facilitates value chain upgrading. Second, drawing on knowledge spillover theory, high-value patents spread through inter-firm cooperation, talent mobility, and innovation networks, thereby promoting the spatial and functional expansion of the industrial chain around innovation hubs. Compared with ordinary patents, high-value patents demonstrate greater scalability and integration potential, fostering systemic innovation synergy and mitigating the “island effect” in technology development. Furthermore, the effectiveness of high-value patents is conditioned by two critical moderating factors: regional absorptive capacity and the strength of intellectual property protection. These institutional and cognitive factors shape the extent to which external knowledge is absorbed, protected, and transformed into integrated inno vation and industrial outcomes.Empirically, the study utilizes panel data from 30 provinces in China’s mainland spanning 2012 to 2023. High-value patents are identified using a machine learning-enhanced income method, while dual-chain integration is assessed through a coupling coordination model. Fixed-effects regression results, supported by robustness checks and instrumental variable methods, confirm the significant positive impact of high-value patents on dual-chain integration. Heterogeneity analysis further reveals that these effects are more prominent in economically advanced and innovation-intensive regions in eastern and central China. Mediation analysis further demonstrates that high-value patents influence dual-chain integration not only directly, but also indirectly by attracting and concentrating innovation resources, which in turn optimizes the spatial and structural alignment between the industrial chain and innovation chain.This study makes three major contributions. Theoretically, it advances the understanding of high-value patents by framing them as systemic enablers of industrial–innovation synergy and highlights, for the first time, the mediating role of innovation factor agglomeration. Methodologically, it introduces a scalable and data-driven method for identifying patent value and evaluating dual-chain integration. To promote the economic effects of high-value patents, targeted measures should be taken from both enterprise and government perspectives across four key areas. First, enterprises should focus on core technologies to enhance the cultivation and market adaptability of high-value patents, while governments need to implement collaborative intellectual property（IP） application mechanisms and advance the integration of such patents with key industries. Second, enterprises ought to strengthen their capabilities in patent management and transformation, and governments should invest in innovation infrastructure and improve the judicial protection system for IP to foster a sound innovation ecosystem. Third, enterprises should develop patent layout strategies based on local industrial strengths, and governments should provide differentiated policies—accelerating the development of high-value patent-intensive industrial clusters in eastern and central regions, and supporting technology-industry collaboration to tap patent potential in northeastern and western regions. Fourth, efforts should be made to improve IP financial support mechanisms, such as developing diversified financial products, and build a talent team for IP services and transformation, thereby constructing a sys-tem of high-value patent transformation clusters.

Open source communities have become a vital platform for open innovation in the digital economy era, playing a key role in fostering the development of new quality productivity. Their open and collaborative structure facilitates broadbased knowledge sharing and distributed technological development. Within the governance framework of open source, licenses serve not only as legal authorization instruments but also as core mechanisms of intellectual property（IP） governance, significantly shaping organizational collaboration, rights allocation, and innovation outcomes. However, existing studies have primarily focused on the infectivity of licenses, often overlooking their broader institutional attributes and the behavioral consequences these rules may trigger. From the perspective of IP governance, this paper constructs an analytical framework of “institutional structure–behavioral response–innovation performance” based on the characteristics of governance objects, and systematically examines how three structural features of open source licenses—complexity, infectivity, and compatibility—affect project innovation performance. In addition, the paper introduces license enforcement deviation as a key mediating mechanism to reveal how institutional design influences project outcomes through behavioral pathways.License complexity refers to the difficulty of understanding and enforcing the rules;infectivity reflects the extent to which license terms constrain derivative works;compatibility influences the feasibility of cross-project module integration and knowledge reuse.License enforcement deviation refers to situations in which contributors,after modifying or inheriting code,fail to comply with the original license terms or remove original attribution information without permission.In terms of empirical design, the paper constructs a dataset of 900 open source projects from the Git Hub platform using a multi-dimensional stratified random sampling approach. The sample covers a variety of programming languages, creation years, and levels of development activity to ensure representativeness and diversity. Data collected via Git Hub’s API and repository metadata include license types, code evolution trajectories, and community interaction features. Innovation performance is measured through a composite index based on seven key indicators and which is reduced using principal component analysis（PCA）. The empirical analysis proceeds in three stages. First, multiple linear regression is employed to test the main effects of license complexity, infectivity, and compatibility on innovation performance, clarifying their structural impact mechanisms. Second, a mediation model is constructed by introducing license enforcement deviation as an intermediary variable to assess the indirect effects of institutional rules via behavioral pathways. The significance and robustness of the mediating effects are tested using the Sobel test and Bootstrap resampling methods. Third, robustness checks are conducted through variable refinement（subdividing infectivity levels）, sample adjustment（excluding newly created or inactive projects）, the inclusion of additional control variables（institutional affiliation of the project）, and instrumental variable estimation using two-stage least squares（2SLS）, providing a comprehensive evaluation of the empirical fi ndings.The results show that license structure significantly influences project innovation performance. Specifically, higher levels of complexity and infectivity are associated with more pronounced negative effects, while greater compatibility facilitates collaboration and knowledge integration, thus improving innovation outcomes. The mediating role of behavioral mechanisms is also confirmed, indicating that institutional rules shape project performance indirectly through their influence on contributor behavior. Further analysis reveals heterogeneous effects across different project types: software development projects are more sensitive to institutional structure, whereas AI and data science projects exhibit relatively weaker r esponses to license rules. This paper contributes to the open source governance literature by shifting the analytical focus from static legal classification to the structural logic of institutional rules, and by extending the “institution-behavior-performance” paradigm. It highlights the deep interdependence between rule design and behavioral responses. Theoretically, it reveals how structural governance tools affect innovation in open collaboration environments; practically, it proposes policy implications for optimizing license design, implementing stratified governance strategies and strengthening behavioral regulation. Future research may further expand data sources and analytical dimensions by incorporating multi-platform dynamic data, longitudinal tracking, and variables related to informal governance mechanisms. This would help deepen understanding of the interaction among institutional evolution, behavioral adaptation, and collaborative innovation, and contribute to building a more dynamic and pluralistic framework for open source IP governance.

Digital innovation is currently reshaping the economic system for innovative development at an unprecedented speed and scale. It has emerged as a pivotal engine for accelerating the high-quality development of the digital economy and for driving the evolution of new productive forces. However, constrained by inadequate digital resources and innovation capabilities, traditional enterprises are encumbered by such predicaments as ambiguous digital innovation pathways and ineffective realization of digital innovation value. As a result, they find themselves in a “well-intentioned but underpowered” dilemma when pursuing digital innovation.Studies indicate that integrating into digital platform ecosystems and engaging in dynamic interactions with multiple participants constitutes a critical pathway for enterprises to achieve digital innovation. Nevertheless, the existing literature has yet to uncover the specific digital-innovation pathways available to ecosystem-participating enterprises.Grounded in the complex adaptive systems theory, this study integrates micro-level cognitive factors, meso-level organizational capabilities, and macro-level ecosystem attributes to construct a theoretical analytical framework of "managerial cognition-organizational capabilities-ecosystem characteristics". Employing the fuzzy-set qualitative comparative analysis（fsQCA） method, it analyzes 263 sets of data to explore the configurational effects of managerial cognitive flexibility, digital perception capability, digital resource synergy capability, ecosystem richness, and ecosystem innovativeness on digital innovation among ecosystem-participating enterprises, while elucidating the underlying mechanisms and pathways of such innovation.The findings reveal that（1） no single core condition constitutes a necessary prerequisite for high-level digital innovation; however, managerial cognitive flexibility, as a core condition, exerts a relatively universal influence;（2） the coupling of diverse antecedent conditions forms two configurational pathways, corresponding to two types of configurations for achieving high-level corporate digital innovation: "cognition-opportunity driven" and "cognition-ecosystem interactive". Specifically, in the cognition-opportunity driven pathway, managerial cognitive flexibility, digital perception capability, and ecosystem innovativeness are present as core conditions, ecosystem richness is absent as a core condition, and digital resource synergy capability functions as a peripheral condition. In the cognition-ecosystem interactive pathway, managerial cognitive flexibility, digital resource synergy capability, ecosystem richness, and ecosystem innovativeness are present as core conditions, while digital perception capability serves as a peripheral condition;（3） three configurational pathways are identified, forming two types of configurations that lead to non-high-level digital innovation: "cognition-capability deficient" and "cognition deficient". The research conclusions remain valid after robustness tests.This study makes three significant contributions. First, from the perspective of ecosystem participants, it identifies the key antecedents and operational mechanisms of digital innovation within participating enterprises, addressing the insufficient attention given to their digital innovation in existing research. Second, moving beyond the prevalent focus on linear relationships between isolated influencing factors and corporate digital innovation, this study draws on complex adaptive systems theory to innovatively construct a "managerial cognition-organizational capabilities-ecosystem characteristics" analytical framework. Using the fs QCA method, it explores the driving mechanisms of multi-factor linkage and synergy in promoting digital innovation among participating enterprises, thereby filling a gap in the literature regarding the unclear pathways of such innovation. Finally, the study innovatively incorporates the richness and innovativeness of digital platforms into the research framework, extending the analytical perspective to the ecosystem level and offering a new theoretical lens and framework for examining the relationship between platform ecosystem characteristics and corporate innovation capabilities.