Promoting collaborative innovation between industry and academia is a crucial measure to accelerate the transformation of scientific and technological achievements and foster high-quality economic development. Nevertheless, there is still a lack of systematic understanding of the driving factors influencing the formation and evolution of industry-research collaboration networks, particularly the dynamic mechanisms of various proximities. Existing research on cooperative networks primarily focuses on two areas: descriptive analysis of the basic characteristics of cooperative networks and exploration of the impact of proximity on their formation and evolution. Although the multidimensional proximity perspective has expanded our understanding of industry-research cooperation, there are still limitations. First, most studies treat proximity factors as independent variables, ignoring the intrinsic connections between regional (geographic and institutional) and organizational (social and cognitive) levels. Second, research is often based on static scenarios, failing to fully capture the dynamic evolution of networks. Lastly, traditional regression analysis struggles to effectively capture the interdependencies and endogenous mechanisms between network relationships.
Following multidimensional proximity theory, this study explores the evolutionary characteristics and influencing factors of the Chinese Academy of Sciences' industry-research collaboration network using the Exponential Random Graph Model (ERGM). The research examines proximities from two dimensions: organizational-level proximity (social proximity and cognitive proximity) and regional-level proximity (institutional proximity and geographical proximity), and reveals the dynamic changes of different levels of proximity during the stages of industry-research collaboration development.
Utilizing patent co-application data from CAS between 2000 and 2021, this study constructs six periods of CAS collaboration networks at three-year intervals. To gain deeper insights into the changes in the internal driving forces of the industry-research collaboration network development, this study divides the network evolution into three stages: initial development, rapid development, and stable development. Using the R software, the study visualizes the industry-research collaboration networks at the three evolutionary stages and employ ERGM to compare the impact mechanisms of different levels of proximities on the dynamic evolution of the CAS collaboration network.
The results indicate that the evolution of the industry-research collaboration network is jointly driven by organizational-level proximities and regional-level proximities. However, the influence of various proximities exhibits dynamic changes over time. In the initial development stage, organizational-level proximities play a crucial role. This is evidenced by the significant positive effects of social proximity and cognitive proximity on network development. This suggests that, in the earlier stages of industry-research collaboration, the existing collaboration experience and similarity of knowledge bases between organizations are significant factors influencing partner selection. However, as collaboration deepens and the network expands, the importance of organizational-level proximities gradually diminishes, and their positive promotional effects exhibit a declining trend. Conversely, the impact of regional-level proximities is not significant during the initial formation stage of the collaboration network. However, as the network evolves, institutional proximity and geographical proximity become the primary drivers of the network's stable development. This transformation reflects the fact that, with the government's emphasis on industry-research collaboration, industry-research collaboration has become increasingly politicized. Furthermore, the similarity of institutional environments and geographical proximity at the regional level have become increasingly important factors in fostering the continuous and in-depth development of industry-research collaboration.
Thus,the government should effectively leverage spatial planning and institutional coordination to create a supportive policy environment for industry-research collaboration. Then, while focusing on institutional cooperation, the government should also consider organizational-level proximity, such as social and cognitive proximity, as these are crucial for fostering the natural development of industry-research partnerships. Finally, given the impact of multidimensional proximity on collaboration varies at different stages, the government should develop targeted support policies tailored to the specific phases of cooperation development. This study enriches the theoretical understanding of the evolutionary characteristics and driving mechanisms of China's industry-research collaboration network, revealing the ways in which different proximities function and the changes in their influence during the dynamic evolution of the network. It provides new perspectives and insights for promoting the sustainable and healthy development of China's industry-research collaboration network.

Business model innovation (BMI) and technological innovation (TI) constitute the dual innovation system of Chinese enterprises. Traditional manufacturing enterprises and emerging Internet enterprises have different starting points and paths in the construction and evolution of the dual innovation system. On the relationship between BMI and TI, the existing literature focuses on the "static coupling" feature, relying on cross-sectional data to verify the complementary mechanism but ignoring the dynamic mechanism of the two. In terms of research objects, the existing research mainly focuses on the single type of enterprises, overlooking the differences in the innovation mix of different enterprise types.
This study focuses on the phenomenon of "two-way efforts" between BMI and TI in both traditional manufacturing enterprises and emerging Internet enterprises. Drawing on theoretical frameworks such as innovation systems, diminishing returns, and arbitrage, it proposes two sets of hypotheses to explain this phenomenon. By collecting and analyzing data from A-share listed traditional manufacturing enterprises and emerging Internet enterprises between 2013 and 2022, regression analysis demonstrates the "two-way efforts" trend and its internal mechanisms, thereby validating the proposed hypotheses.
Two findings emerge from the study. First, the investment level of BMI of traditional manufacturing enterprises grows faster than that of TI, while the emerging Internet enterprises have the opposite trend. Second, the effect of BMI on corporate performance in traditional manufacturing enterprises is stronger than that of TI, while the opposite pattern appears in emerging Internet enterprises. From the perspective of both natural time and enterprise life cycle, the above two findings are confirmed and the evolution trend is reinforcing the law. It can be believed that when traditional manufacturing enterprises and emerging Internet enterprises are based on the poles of the dual system in the early stage of creation, traditional manufacturing enterprises and emerging Internet enterprises are in a mutually reinforcing relationship by the "two-way efforts". The first finding is the concrete manifestation of the "two-way efforts" of the two types of enterprises, and the second finding is the internal logic that spawned the phenomenon of "two-way efforts". Thus, the research hypotheses have been tested.
According to the research results, it can be speculated that in the future, the dual system of TI and BMI of Chinese enterprises will gradually evolve into a system form similar to the Taiji diagram, leading enterprise organizations to innovation and harmonious development in the yin-yang symbiosis. The research results reveal the evolution law of the dual system of Chinese enterprises, enrich the theoretical system of Chinese innovation management, and provide inspiration for policy makers and enterprise managers to guide the sustainable development of the dual innovation system.
The theoretical contributions are as follows. First, it takes the lead to observe the phenomenon of "two-way efforts" between traditional manufacturing enterprises and emerging Internet enterprises in the construction of the dual system of TI and BMI, and systematically provides evidence to demonstrate the existence of "two-way efforts" phenomenon and the inherent logic of the phenomenon. Second, it reveals the heterogeneity of the evolution of the dual innovation system due to the difference of firm types. Third, it is conducive to predicting the macro trend of the evolution and development of the dual innovation system of Chinese enterprises, and sets up new ideas and new directions for the improvement of enterprises' innovation-driven development strategy and even the proposal of future strategic forms.
Three practical recommendations can be drawn from the study. First, the two types of enterprises should learn from each other, and find an opportunity to accelerate the transformation and upgrading of the innovation system in cross-border cooperation and integration. Second, the traditional manufacturing enterprises and emerging Internet enterprises should promote strength rather than avoid weaknesses, striving to overcome the shortcomings and weaknesses in the dual innovation system, and pursue a two-pronged strategy. Finally, the two types of enterprises should have a clear understanding of their own life cycle stages so as to identify the optimal dual innovation system transformation path and strategy aligned with their development phases.

The global rise of women entrepreneurs and the dawn of the "she" era have firmly established women′s entrepreneurship as a pivotal driver of economic and social progress. However, in practice, women entrepreneurs tend to gravitate towards imitative ventures, with a significantly smaller proportion pursuing innovative opportunities. The gender and entrepreneurial disparities observed across nations are not random; they stem from the intricate interplay of diverse factors. Prior research has predominantly examined the impact of women′s selection of innovative opportunities from either a macro or micro perspective, often neglecting the complex interactions and asymmetries among these factors. Consequently, this paper adopts a configurational approach to investigate the influence of individual perception and the institutional environment on women′s recognition of innovative opportunities, aiming to uncover the underlying reasons why women might avoid innovative opportunities and the factors that encourage them to embrace high-innovation opportunities. This exploration is intended to enhance women′s innovative entrepreneurship and elevate the quality of entrepreneurship by offering theoretical backing and practical guidance.
Employing the fuzzy-set qualitative comparative analysis (fsQCA) method, this study draws on data from women entrepreneurship samples across 125 countries and regions in the Global Entrepreneurship Monitor Database from 2020 to 2023. The analytical framework encompasses three micro-level individual perception elements—perceived capabilities, perceived opportunities, and fear of failure—as well as three dimensions of the macro institutional environment—regulative, normative, and cognitive. The study aims to elucidate the distinct configurational pathways to high and non-high innovative opportunities in women′s entrepreneurship and to identify and address potential influencing factors by examining the factors that deter women from selecting innovative opportunities.
The findings of the study suggest that the influence mechanism of women′s innovative opportunities is a concurrent mechanism involving multiple-factor combinations. Three types of configurational pathways are identified for women′s high innovative opportunities: the "individual-environment" dual high-match type, the "opportunity-fear of failure" dominance type, and the "capability-opportunity-cognition" synergy type. For women′s non-high innovative opportunities, two types of configurational pathways are identified: the cognition-led type and the norm-led type. The pathways leading to women′s innovative opportunities and non-high-innovative opportunities are asymmetrical, indicating that the reasons for non-high innovative opportunities cannot be inferred directly from the inverse of the reasons for high innovative opportunities.
The theoretical contributions of this study are threefold. Firstly, it enriches the research on the influence mechanisms of women′s innovative opportunities by integrating six core elements from both the institutional environment and individual perception, revealing their synergistic role in driving women′s selection of innovative opportunities. This highlights the holistic, interconnected, and systemic nature of the conditions that drive innovative opportunities, deepening our understanding of innovative opportunity identification. Secondly, by analyzing the combinational pathways influencing women′s selection of non-high innovative opportunities, the study contributes to a deeper comprehension of the mechanisms affecting women′s reluctance to choose innovative opportunities, thereby enriching the field of women′s innovative entrepreneurship. Lastly, the study employs QCA to reveal that the pathways leading to women′s non-high innovative opportunities are not merely the inverse of those leading to high innovative opportunities, underscoring the asymmetry inherent in QCA methods. By revealing these diverse pathways, the study provides theoretical support and practical insights for enhancing women′s innovative entrepreneurship and improving the quality of entrepreneurship. It also offers a theoretical foundation for national policies aimed at optimizing the innovation and entrepreneurship environment.

Currently, with the rapid development of digital technology, traditional innovation ecosystem is gradually transitioning to digital innovation ecosystem. When facing external shocks, the high suitability of digital innovation ecosystem can help optimize resource allocation, promote industrial synergy and information flow, thereby dispersing the risks brought by external shocks and improving provincial economic resilience. Existing studies have explored the impact of relevant factors on economic resilience from digital and innovative perspectives, but no study has yet explored the relationship between the suitability of digital innovation ecosystem and provincial economic resilience. In view of this, using the panel data of 30 provinces in the Chinese mainland (Tibet is not included due to incomplete data) from 2013 to 2022, this study discusses the impact of the suitability of digital innovation ecosystem on provincial economic resilience and its regional heterogeneity and spatial spillover effect. At the same time, it analyzes the mediating role of high-tech industry agglomeration and the moderating roles of entrepreneurial activity and artificial intelligence.
The conclusions include three aspects. Firstly, the suitability of digital innovation ecosystem can effectively enhance provincial economic resilience. In the eastern region, the suitability of digital innovation ecosystem has a significant promoting effect on provincial economic resilience, while the promoting effects are not significant in the central and western regions. The impact of the suitability of digital innovation ecosystem on provincial economic resilience has spatial spillover effect. Secondly, the suitability of digital innovation ecosystem has a significant positive impact on provincial economic resilience through high-tech industry agglomeration. Finally, entrepreneurial activity and artificial intelligence can significantly enhance the promoting roles of the suitability of digital innovation ecosystem on provincial economic resilience.
The theoretical contributions are threefold. Firstly, the study of the antecedents on economic resilience is broadened from the suitability of digital innovation ecosystem perspective. Secondly, the path role of high-tech industry agglomeration in the suitability of digital innovation ecosystem affecting provincial economic resilience is clarified. Finally, the contextual roles of entrepreneurial activity and artificial intelligence in the suitability of digital innovation ecosystem affecting provincial economic resilience are verified.
This study provides several suggestions for relevant departments. Firstly, the relevant departments should promote the construction of digital infrastructure, guide and support the exploration and application of digital technology, thereby ensuring steady economic growth. From a regional perspective, in the eastern region, the relevant departments can improve the suitability of digital innovation ecosystem by virtue of the resource endowment and digital infrastructure of each province; in the central and western regions, the relevant departments need to formulate strategies based on the actual conditions of each region to improve the suitability of digital innovation ecosystem in each province, so as to make them significantly contribute to provincial economic resilience. In addition, the relevant departments should strengthen inter-provincial collaboration and improve communication mechanism, and give full play to the spatial spillover effect of the impact of the suitability of digital innovation ecosystem on economic resilience. Secondly, the relevant departments should increase investment in high-tech industry, strengthen the deep integration of the industrial chain and innovation chain, promote high-tech industry agglomeration, and help the provincial economy move forward steadily. Thirdly, the relevant departments should formulate precise entrepreneurial incentive policies and optimize the entrepreneurial environment to ensure that the suitability of digital innovation ecosystem continues to inject strong impetus into provincial economic development under high entrepreneurial activity, thus enhancing provincial economic resilience. Finally, the relevant departments should formulate policies to increase support for artificial intelligence, improve the artificial intelligence development system and basic elements such as arithmetic, data and algorithm, and ensure that the suitability of digital innovation ecosystem plays a promoting role in provincial economic resilience under the rapid development of artificial intelligence.

With the deepening advancement of the national unified large market, all regions should leverage their comparative advantages, seize opportunities for cross-regional allocation of innovation factors, pursue differentiated technological research and development, and establish a development pattern of technological complementarity to promote coordinated regional economic growth. In this process, digital technology complementarity serves as a key driver for corporate digital transformation. By leveraging the synergistic effects of technological innovation and cross-sector collaboration, it effectively breaks down industry barriers, propels continuous product innovation and iteration, and further optimizes corporate resource allocation. Given its pervasive nature, digital technology has deeply integrated into the operational frameworks of diverse organizations, significantly fostering cross-domain exchange and collaboration among different technological entities. Consequently, developing digital technology complementarity has become not only a vital pathway for technological advancement but also a critical factor in promoting coordinated regional development. While existing research has recognized the importance of digital technology complementarity, it lacks analysis of the transformation mechanisms underlying its linkage effects from a network perspective.
Patents, a direct indicator of technological advancement, objectively reflect the technological landscape of regions or enterprises. The "technological complementarity" notion holds that diverse tech categories drive regional complementarity, while homogeneous ones intensify competition. On the basis of the calculations, the study constructs a digital tech complementarity network for 30 Chinese provinces, and calculates provincial-level digital technology complementarity indices based on China's digital technology patent data from 2007 to 2023. It then employs a spatial-varying coefficient panel regression model to quantify regional linkage effects of digital technology complementarity in China and utilizes a temporal exponential random graph model to analyze its transformation mechanisms.
The study finds that between 2007 and 2023, the degree of digital technology complementarity among Chinese provinces demonstrated a significant upward trend, with cross-regional digital technology complementarity becoming increasingly active. In terms of linkage forms, the interaction effects of digital technology complementarity exhibited dynamic changes. The siphon effect was primarily concentrated in the Northeast and Northwest regions, exacerbating inter-provincial disparities in digital technology complementarity. In contrast, the radiation effect was mainly observed in the Yangtze River Delta region, tending to narrow these gaps. The transformation process of these linkage forms was influenced by structural dependence effects, actor-relation effects, and temporal dependence effects, with triadic structures serving as the primary network configuration. High-level digital technology complementarity provinces and their radiation capacity emerged as key nodal attributes. Heterogeneity analysis revealed that the linkage transformation mechanism in the eastern region was predominantly driven by structural dependence effects, with a stronger influence than in the central and western regions. Regarding different levels of digital technology development, the structural dependence effect was only significant in regions with high digital technology development. Furthermore, the influence of the out-degree indicator, digital technology complementarity index, and geographical distance on the formation of the radiation effect was more pronounced in high-level regions.
The research contributions of this paper are primarily reflected in the following aspects: First, it supplements existing provincial-level digital technology research from a complementarity perspective. Although the concept of digital technology is relatively mature, research on its complementarity remains insufficient. This study analyzes the provincial digital technology complementarity landscape in China by constructing an inter-provincial digital technology complementarity index. Second, it explores the linkage patterns and changing characteristics of provincial digital technology complementarity in China from a regional linkage perspective. By integrating a spatial-varying panel regression model, this study identifies the linkage effects of digital technology complementarity between provinces and precisely quantifies the impact of changes in one province's digital technology complementarity level on other provinces. Third, this study investigates the transformation mechanisms of digital technology complementarity linkage forms from a network structure perspective. Employing a temporal exponential random graph model, it examines the dynamic forces driving transformations within digital technology complementarity linkage networks. Robustness tests are conducted using multiple methodologies to validate the findings, providing empirical evidence to better advance coordinated digital technology development across China's provinces.

While artificial intelligence has sparked a technological revolution, it has also driven transformations in urban industrial spaces and innovation models. As a new form of spatial organization that facilitates the efficient exchange of key factors and information across temporal and spatial boundaries, virtual agglomeration has become a critical support for the integrated development of urban industrial and innovation chains. Amid the widespread application of AI technology, a systematic exploration of how virtual agglomeration promotes urban dual-chain integration, coupled with analysis of its internal mechanisms and spatial regularities, provides valuable guidance for cities to enhance their innovation source capabilities, optimize regional radiation patterns, construct modern industrial systems, and facilitate their ascent in global value chains through this new spatial organizational form.
In view of this, this study develops an evaluation index system for the two subsystems of urban industrial chains and innovation chains. The industrial chain subsystem encompasses four dimensions (length, width, correlation, and thickness), which respectively assess the vertical depth and value-added capacity (including structural hierarchy, integration, and value creation efficiency), horizontal breadth and diversification (reflecting participant scope, resource utilization, and industrial complexity), inter-segment interdependence and coordination (captured by information flow and shared resource contribution), and activity density and economic strength (as indicated by industrial scale and synergistic agglomeration).
The innovation chain subsystem selects indicators corresponding to three stages: R&D investment, achievement transformation, and innovation industrialization. Within this framework, the study calculated the level of virtual agglomeration and the level of coupled and coordinated development between urban industrial and innovation chains across 222 prefecture-level cities in China from 2006 to 2022, and characterized the spatial evolution of urban dual-chain integration. Furthermore, this study constructed a spatial Durbin model to empirically examine the impact and spillover effects of virtual agglomeration on dual-chain integration. The findings indicate that the overall level of dual-chain integration in Chinese cities has continued to rise: at the regional scale, it exhibits a hierarchical transition pattern from coastal economic belts to inland hinterlands; at the urban system level, it presents a circular nested structure characterized by a gradual decline from central to peripheral cities.
Further analysis reveals that under the geographic matrix and the nested economic-geographic spatial weight matrix, virtual agglomeration significantly promotes the level of coupled and coordinated development of local cities′ industrial and innovation chains. However, a significant negative spillover effect was also observed, indicating that virtual agglomeration exerts a degree of inhibitory effect on dual-chain integration in neighboring cities. Heterogeneity analysis based on city size shows that virtual agglomeration has a significantly positive impact on the coupled and coordinated development of urban industrial and innovation chains in megacities (e.g., Beijing, Shanghai, Wuhan), large cities (e.g., Xi′an, Nanjing), and medium-sized cities (e.g., Shijiazhuang, Taiyuan), whereas the facilitating effect is not significant for small and medium-sized cities. Mechanism analysis demonstrates that information transmission efficiency and the agglomeration of producer services are key channels through which virtual agglomeration influences the integrated development of urban industrial and innovation chains. Specifically, the information transmission efficiency mechanism serves as the primary channel for its direct facilitating effect, while the producer services agglomeration mechanism acts as the main channel for its negative spillover effect. Additionally, market segmentation undermines the facilitating effect of virtual agglomeration on the coupled and coordinated development of urban industrial and innovation chains.
This study makes three key contributions: First, it incorporates virtual agglomeration into the theoretical framework of industrial and innovation chain integration, analyzes the specific impact of virtual agglomeration on dual-chain integration, and thereby expands the research scope of existing literature on dual-chain integration. Second, it uncovers spatial correlations and spillovers between urban virtual agglomeration and dual-chain integration, tests the moderating role of market segmentation, and identifies constraints from geography and market conditions. Third, it explores the heterogeneity and spillover effects of virtual agglomeration across different city sizes, providing theoretical support and empirical evidence for local governments to formulate targeted industrial policies.

The inherent connectivity, modifiability, and scalability of digital technology have catalyzed a fundamental shift in innovation models, transitioning from a traditional product-oriented approach to one that is more user-demand-oriented. This transition has reduced operational and modification costs, enhanced the efficiency of innovation, and supported the supplementation and development of business processes and activities. The widespread adoption of digital technology has comprehensively restructured traditional business and commercial models of platforms, giving rise to digital platforms with typical organizational structures and business models in the digital economy. However, the development of digital platforms also faces a VUCA (volatile, uncertain, complex, ambiguous) environment. To maintain long-term stability and create unique competitive advantages, digital platforms need to continuously break down barriers and construct flexible combinations of resources, conventions, and structures to achieve rapid iterative innovation.
Existing research inadequately explains the driving mechanisms behind iterative innovation of these platforms. Current literature often fails to integrate multiple driving factors comprehensively and relies predominantly on singular empirical methods, such as regression analysis, which can hardly capture complex causal relationships. To address these gaps, this study begins with the unique characteristics of digital platforms and employs socio-technical systems theory to identify key driving factors. These factors are then integrated into a “socio-technical” synergistic framework. Using fuzzy-set qualitative comparative analysis (fsQCA), the study deeply explores the mechanisms and pathways through which different configurations of factors drive iterative innovation of digital platforms. This approach not only reveals the complex interplay among driving factors but also provides actionable insights for digital platforms to achieve agile and sustained iterative innovation.
This study designed a survey questionnaire referring to existing literature scales and made multiple adjustments to ensure clarity. A total of 68 survey questionnaires were collected through Questionnaire Star with 54 validated responses. Drawing on data from 54 questionnaires collected from Chinese digital platforms, the study is conducted within the 'socio-technical' synergistic effect research framework. Reliability and validity tests were performed on condition variables and outcome variables, including Cronbach′s α, KMO, standard factor loading, CR, and AVE. The results show high reliability and validity of the data. The specific research process also includes necessary condition analysis and configuration analysis. Robustness tests conducted on the analysis results confirm their reliability.
The main conclusions are as follows. First, high iterative innovation of digital platforms is driven by the synergistic effects of multiple antecedent conditions across both technical and social dimensions, rather than by any single condition. Second, there are four configurations that drive iterative innovation of digital platforms: open co-creation under digital orientation, data empowerment under digital orientation, high ‘socio-technical’ coordination, and configurations led by product modularity. These four configurations significantly differ in their conditions for driving iterative innovation of digital platforms, reflecting the diversified driving paths formed by the differentiated matching of technical and social factors on digital platforms. Third, a horizontal comparison of each configuration path reveals that there is a certain substitutability among different antecedent conditions. For instance, technical factors (such as digital orientation and product modularity) and social factors (such as data execution, platform openness, and relationship governance) have complementary relationships in driving the iterative innovation of digital platforms. These factors can also substitute for each other to some extent. For example, when product modularity and data execution are insufficient, the deficiency can be compensated by enhancing the level of relationship governance, thereby shifting from one innovation model to another (e.g., from digital modularity-data-enabled open innovation to digital support-open co-creation innovation).
The practical implications are as follows. Digital platforms can choose the most suitable iterative innovation-driving path according to their own conditions. For digital platforms with a solid foundation in digital technology, adopting digitalization as the core strategic orientation is advisable. This involves increasing investment in digital infrastructure, enhancing the integration of digital technology with business operations, and promoting the flow of information and resources through diverse interaction forms. For highly product-modularized digital platforms that lack openness and data execution capabilities, it is recommended to prioritize improving platform openness or strengthening data execution capabilities to drive iterative innovation.

In the era of escalating environmental challenges and the global transition to sustainable development, the significance of corporate environmental, social, and governance (ESG) performance has grown increasingly prominent. The Chinese government's ambitious "dual carbon" targets—aiming to peak carbon emissions by 2030 and achieve carbon neutrality by 2060—highlight the necessity for corporate actions in this domain. As core actors in the domestic economy, companies' responses to the government's goal of enhancing green development capabilities are crucial to economic progress and the attainment of these environmental goals. Government environmental attention (GEA) reflects government-led initiatives and signals to businesses the importance of aligning their operations with environmental sustainability.
This study explores how GEA influences ESG performance in firms, with a particular focus on the mediating roles of firm innovation and executive team environmental awareness. By utilizing a comprehensive dataset of China's A-share listed companies from 2015 to 2022, the study analyzes the relationship between GEA and corporate ESG performance through a robust empirical approach. It employs text recognition technology to develop an environmental lexicon database, and then determines the government's environmental focus by calculating the relative frequency of environmental terms within the lexicon in official government work reports, thereby creating an index of governmental environmental attention.
The results indicate that an increase in GEA significantly enhances the ESG performance of enterprises and has a substantial impact on their social responsibility. This correlation remained even after various robustness checks were conducted, suggesting that government policies had a genuine and lasting effect on business behavior. The study further examined the mechanisms through which GEA affects ESG performance and found that an elevated environmental awareness among management teams serves as a channel for these improvements. Clearly, executives with a sharp awareness of environmental issues can drive superior ESG performance within their organizations. An important contribution of this study is to identify firm innovation as a key mediating factor in the relationship between environmental performance and ESG performance. The research reveals that the government's concern for the environment promotes the improvement of enterprises' innovation capability, which in turn positively influences enterprises' ESG performance. This mechanism demonstrates that companies proactively enhance their innovation capabilities in response to government influence. Further analysis shows that this mechanism is more pronounced when the enterprise already possesses a strong innovation capability.
In addition, the study emphasizes the heterogeneity of GEA effects based on the nature of corporate ownership. Non-state-owned enterprises are more sensitive to the environmental impact, more responsive to the government's environmental policies, and more active in improving corporate ESG performance. This is in sharp contrast to state-owned enterprises, which have a less marked response to GEA, possibly due to different management structures and policy implementation paths. The differential response of GEA based on the ownership structure provides valuable insights for decision-makers and business leaders to develop targeted strategies that leverage unique strengths to address specific challenges faced by different types of businesses.
The contributions of this study are threefold: Firstly, by drawing on the existing literature on the impact of environmental governance on green innovation and environmental pollution, it further examines the relationship between environmental governance and enterprise ESG performance, and expands the understanding of the impact of the government's environmental concern. Secondly, on the premise of executive environmental awareness and enterprise innovation, this paper discusses the ways in which GEA affects enterprise ESG performance, and enriches the discussion on the mechanism and regulatory role of GEA on enterprise ESG performance. Thirdly, considering the institutional context of Chinese enterprises, it analyzes GEA's varying effects on ESG performance across different ownership structures, offering insights for equity reform and green strategy development.
The study provides empirical evidence of government environmental policies' effectiveness in influencing corporate behavior and offers guidance for aligning corporate strategies with national environmental objectives. It underscores the multifaceted firm responses to such policies, advocating a nuanced approach considering firm diversity. This research enriches the corporate sustainability literature by dissecting the complex interplay between government attention, corporate innovation, and ESG performance while emphasizing the pivotal role of corporate governance in addressing environmental challenges and opportunities.

Led by the US, developed countries have modified supply policies towards China, seeking technological decoupling via a "de-Chinaized" supply chain. In response, China has introduced a “double-cycle” development strategy. Enterprises are the core forces to cope with changes in the trade pattern, and ensuring the resilience of enterprise supply chains and security is the top priority for realizing high-quality economic development. Currently, there are few studies in the academic community that utilize U.S. policy of technology decoupling from China as an empirical target to investigate the resilience of enterprise supply chains. This research gap could potentially hinder China's ability to manage the risks inherent in a volatile global market. Therefore, in the context of China's new round of opening-up policy, it is important to explore how U.S. policy of technology decoupling from China affects the resilience of enterprise supply chains, which provides a basis for how enterprises can enhance supply chain resilience in the complex international situation. This research is of great theoretical significance and practical value for the promotion of high-level opening to the outside world in line with the double-cycle development strategy.
To explore the impact of U.S. policy of technology decoupling from China on the resilience of enterprise supply chains, this study utilizes a sample of A-share listed firms from the period 2010-2022 to investigate this impact. The starting time of the US-China technology decoupling policy is identified as 2018. The impact of the US-China tech-nology decoupling policy on the cooperation between enterprises and their upstream and downstream partners is significant. Specifically, customer sales (Y1) and supplier procurement (Y2) are considered as the dependent variables.
The analysis yields some findings . First, the U.S. policy of technology decoupling from China significantly enhances the resilience of enterprise supply chains, and this finding remains reliable after the robustness test. Second, the heterogeneity analysis shows that the resilience of enterprise supply chains is more significantly positively affected by U.S. technology decoupling from China for the non-state-owned enterprises, eastern region, and high-tech industry categories. Third, further research has found that the U.S. policy of technology decoupling from China promotes corporate supplier procurement volumes by driving supply chain efficiency. Additionally, this policy enhances the resilience of enterprise supply chains by increasing supply chain concentration. However, the U.S. policy of technology decoupling from China also inhibits the improvement of resilience of enterprise supply chains through the complexity of supply chain collaborations.
This article makes novel contributions to the research on enterprise supply chain resilience. Firstly, it offers fresh empirical evidence, a significant advancement since the current literature is heavily reliant on qualitative analyses and lacks empirical studies on the relationship between the U.S. policy of technology decoupling from China and supply chain resilience. By integrating both qualitative and quantitative analysis methods, this paper uncovers this relationship, thereby providing valuable empirical evidence for management science research on supply chain resilience. Secondly, it expands and enriches the theoretical research on U.S. policy of technology decoupling from China and resilience of enterprise supply chains. Most of the existing studies examine this issue from the macro level of industry and national strategy. In this paper, from the micro level of enterprises, we discuss how the enterprise supply chains can cope with the unfavorable impacts brought by the changes in the international situation, so as to enrich the theoretical content of the relevant research. Lastly, the paper provides actionable strategies to bolster the resilience of Chinese enterprise supply chains. Given the frequent occurrence of unforeseen events in science and technology policies and international relations, and the scarcity of studies on the influence mechanisms and operational frameworks behind these uncertainties, this paper elucidates the mechanism of U.S. policy of technology decoupling from China affecting the resilience of enterprise supply chains, and provides effective practical insights for enterprises to enhance the resilience of supply chain.

According to the China Digital Economy Development White Paper (2023), China's digital economy achieved a scale of 50.2 trillion yuan in 2022, with a nominal year-on-year growth of 10.3%. The digital industry and the digitalization of traditional industries reached 9.2 trillion yuan and 41 trillion yuan, respectively, accounting for 18.3% and 81.7% of the digital economy. The 14th Five-Year Plan highlights the strategic importance of digital transformation by devoting a separate chapter to "Accelerating Digital Development and Building a Digital China". However, digital transformation of enterprises is a long-term and high-risk process, characterized by significant organizational changes. The adoption of digital technology, which is at the forefront of this transformation, represents the first step for enterprises to embark on digital transformation. Skipping the analysis of the digital technology adoption process and its antecedents leaves a gap in understanding how this initial stage is carried out and what factors influence it. This gap may lead to missing the actions and decisions of enterprises at this stage and failing to clarify the potential phased outcomes of digital technology adoption, thereby affecting the progress of subsequent processes. Therefore, it is important to focus on the digital technology adoption process of manufacturing enterprises and explore its driving factors, pathways, and phased outcomes.
The antecedents of digital technology adoption by enterprises have been extensively studied, moving beyond the traditional technology acceptance model (TAM) to consider a broader range of factors within and outside the organization. These factors include competitive pressure, support from service providers and partners, changing customer demands, government policies, and the financial performance and constraints of enterprises. However, past studies have largely focused on the "net effect" of single antecedents, neglecting the relationships between multiple factors and their combined effects. Scholars argue that focusing solely on net effects can be misleading, as different configurations of variables may lead to the same outcome. The TOE model, which considers technological, organizational, and environmental factors, has been widely applied to understand the adoption of information technology and disruptive technology. In the context of the digital economy, the adoption of digital technology by manufacturing enterprises requires consideration of environmental uncertainties and the unique characteristics of digital technology, such as connectivity and combinability. These factors highlight the limitations of the original TAM and the need for specialized research on the comprehensive influencing factors of digital technology adoption by manufacturing enterprises.
This study addresses this gap by conducting in-depth field interviews within manufacturing enterprises to gather insights from employees' firsthand experiences regarding the adoption of digital technology. On the basis of the interview data from 13 case enterprises, the study employs grounded theory methods, utilizing a three-level coding process to identify multi-level influencing factors of digital technology adoption, including perceived usefulness, technological advancement, digital strategy orientation, organizational business needs, environmental dynamism, and environmental complexity. Building on this, the study constructs a multi-factor comprehensive influence model using the TOE framework and explores multiple pathways influencing high-level digital technology adoption by combining configurational perspectives, fuzzy set qualitative comparative analysis (fsQCA), and necessity analysis. The study investigates whether there are necessary conditions at different levels for high-level digital technology adoption and what configurations of antecedents lead to such adoption.
The findings reveal that the adoption of digital technology in manufacturing enterprises is the result of the complex interaction of multiple antecedent conditions, with no single necessary condition for high-level digital technology adoption. Three driving modes for high-level digital technology adoption are identified: "technology pull-driven", "technology-organization dual-driven", and "technology-environment-organization synergy-driven". Technological conditions, particularly technological advancement, are the most important driving factors, with organizational and environmental factors also playing significant roles in different paths.
This study enriches the research on digital technology adoption in manufacturing enterprises by integrating grounded theory and fsQCA methods into the TOE framework. It identifies "technological advancement" as a crucial factor, compensating for the limitations of the TAM. The study also highlights the importance of considering multiple factors and their configurations in understanding digital technology adoption. By providing a comprehensive analysis framework, this study deepens the understanding of the mechanisms behind digital technology adoption and enhances the validity of empirical research in this area.

Artificial intelligence (AI) is rapidly reshaping national governance, social operations, and economic structures, while simultaneously generating multifaceted risks relating to safety, privacy, discrimination, and systemic uncertainty. In an era characterized by rapid technological iteration and the wide proliferation of AI application scenarios, constructing a scientific, systematic, and forward-looking risk-based regulatory framework has become an essential legislative task for China. This article conducts a comparative examination of AI regulatory regimes in the European Union, South Korea, Canada, and the United States, analyzing their respective approaches to risk identification, classification models, and the allocation of regulatory obligations, with the aim of informing China′s future AI legislation.
At the comparative level, the European Union adopts a four-tier framework—unacceptable, high, limited, and minimal risk—and imposes stringent, lifecycle-wide compliance obligations on high-risk systems. Although comprehensive, this framework produces excessively heavy compliance burdens and lacks flexibility in responding to technological dynamics. South Korea employs a horizontal regulatory model centered on high-impact AI, characterized by concise provisions and a streamlined structure, yet its taxonomy does not adequately differentiate among distinct categories of risks. Canada distinguishes between biased-output systems and harm-based systems, but ultimately applies uniform obligations to both categories, resulting in a disconnect between risk classification and regulatory practice. The United States adopts the most flexible structure: a dual-track model distinguishing safety-impacting AI from rights-impacting AI. On the basis of a unified minimum compliance baseline, the U.S. model adds differentiated requirements for rights-impacting systems and incorporates deferral, exemption, and dynamic adjustment mechanisms that enhance regulatory adaptability.
Despite differences in legislative traditions and policy objectives, the four jurisdictions share a common governance logic: risk identification as the regulatory starting point, risk classification as the core organizing principle, and differentiated obligations as the primary regulatory tool. The proportionality principle underlies these systems. The U.S. dual-track model distinguishing safety and rights impacts offers notable advantages in proportionality, precision, and institutional flexibility, thereby providing a valuable template for China in building a multi-level governance structure.
In light of these comparative insights, this article proposes that China optimize its AI risk-governance framework along three dimensions. First, China should establish a layered legislative structure combining common rules + sector-specific rules. A national-level Artificial Intelligence Basic Law should articulate overarching governance principles, risk-classification methods, and baseline regulatory obligations. Sectoral regulatory authorities should then develop technical standards and regulatory rules tailored to specific application scenarios, thereby balancing systemic coherence with operational flexibility and avoiding fragmented governance.Second, China should adopt a dual-track classification framework that distinguishes between safety-impacting AI and rights-impacting AI. Systems involving life safety, critical infrastructure, or public security should be categorized as safety-impacting AI, whereas systems that affect fairness, fundamental rights, or vulnerable groups should be regulated as rights-impacting AI. For systems that present hybrid or overlapping risks, a primary-risk identification mechanism should be introduced to classify them according to their dominant risk attributes. In addition, a combined-obligations mechanism should be implemented to allow both sets of obligations to apply where necessary, thereby enhancing the precision of risk identification and strengthening the applicability of regulatory tools.Third, China should develop a comprehensive system of dynamic adjustment and flexible exemptions. Through presumed-strict classification, application-based exemptions, periodic review, and cross-departmental feedback mechanisms, regulatory measures can be dynamically aligned with technological evolution and sector-specific characteristics. Such mechanisms help prevent regulatory rigidity and excessive compliance burdens, ensuring that governance tools remain adaptive to emerging risks and evolving industrial practices.
In sum, through comparative analysis of foreign regulatory models and the construction of a localized governance pathway, this article argues that the core of China′s AI risk-governance framework lies in risk-based classification as its organizing principle, a layered legislative structure as its institutional foundation, a dual-path classification model as its methodological approach, and dynamic adjustment mechanisms as its regulatory toolset. A governance system guided by unified national principles, supported by differentiated regulatory rules, and coordinated between central and sectoral authorities can achieve a dynamic balance between safeguarding safety and fostering innovation, thereby forming a Chinese model of AI risk governance capable of addressing the complexities of the digital era.

As a vital component of data resources, public data is increasingly recognized for its strategic role in driving innovation. Governments at various levels in China have actively advanced platform development, initially establishing a two-tier public data openness system at the provincial and municipal levels. However, objective disparities exist between these two levels in terms of data coverage, data quality, and policy enforcement. Compared to independent innovation, enterprise joint innovation relies more heavily on external data resources and can enhance the efficiency of cross-organizational resource integration, thereby fostering the growth of new quality productive forces.
Existing literature primarily focuses on the overall effects of public data openness on enterprise innovation and digital transformation, with limited attention paid to its role in promoting joint innovation. In particular, there is a lack of systematic analysis regarding the differences in policy effects between provincial and municipal platforms. Therefore, this study extends the analytical perspective to explore how two-level public data openness facilitates enterprise joint innovation, with a particular focus on the variation in policy effectiveness across different administrative levels.
To this end, this paper employs a staggered difference-in-differences(DID) model to examine three core relationships: the impact of provincial and municipal public data openness on enterprise joint innovation, the influencing factors behind policy effect differences between the two levels and the mediating role of enterprise digital technology application. Using panel data of A-share listed companies in China from 2010 to 2023, the study ultimately obtains 26 564 firm-year observations, covering 2 185 listed firms after data preprocessing. The conclusions are validated through a series of robustness tests, including parallel trend tests, endogeneity checks, and placebo tests. Additionally, the study further analyzes heterogeneity in municipal-level platforms in terms of openness, R&D investment, and government digital attention, and conducts subgroup analyses based on region, industry, and firm characteristics.
The empirical findings show that provincial-level public data openness significantly promotes enterprise joint innovation, whereas municipal-level openness, constrained by limited platform capabilities, does not exhibit significant enabling effects overall. However, increased investment in scientific research and heightened government digital attention can effectively mitigate this limitation. Further analysis reveals that provincial-level public data openness facilitates joint innovation mainly by enhancing the breadth and depth of enterprise digital technology application. Heterogeneity analysis shows that the effect is more pronounced in innovative cities, digital economy industries, technology-intensive enterprises, and growth-stage firms.
On the basis of these findings, this paper proposes(1) strengthening institutional design and scenario-based orientation of provincial platforms;(2) increasing resource support for municipal platform construction, operation, and maintenance;(3) enhancing fiscal support for science and technology;(4) improving municipal institutional frameworks and data governance capacity;(5) implementing differentiated data support mechanisms by firm type—providing technology standards and research outputs to technology-intensive firms while offering market trends and strategic financing data to growth-stage firms—to enhance joint R&D efficiency and multi-stakeholder resource synergy; and (6) promoting the orderly development of enterprise digital technology application capabilities to strengthen data-to-innovation conversion.
The marginal contributions of this paper are as follows:Firstly, it expands the theoretical perspective on enterprise joint innovation. By positioning public data as a key production factor, this study analyzes its value in enterprise joint innovation, enriching the resource-based theoretical framework of joint innovation.Secondly, it reveals policy effect differences between two levels of public data openness. While prior studies mainly evaluate the overall effects of public data openness from a macro perspective, this study emphasizes the differentiated impact of provincial and municipal platforms, thereby deepening the theoretical foundation for refined, regionally tailored policy implementation.Thirdly, it clarifies the mechanism of public data openness and highlights the mediating role of digital technology application. From the perspective of internal firm capabilities, this study examines how the breadth and depth of digital technology application mediate the relationship between public data openness and joint innovation, thus advancing the understanding of how data elements generate value.

Against the backdrop of intensifying VUCA characteristics in the global economy, manufacturing enterprises are confronted with multiple nonlinear shocks , including geopolitical risks and the reconstruction of industrial supply chains. The traditional model reliant on economies of scale and static resource allocation has fallen into the high-entropy trap due to strategic inertia, exposing the deep-seated contradiction between resource redundancy and insufficient agile response. Lean digitalization reconstructs the underlying logic of the value stream through data flow, integrating the essence of lean management with industrial Internet technology. This approach facilitates dynamic resource allocation, enabling a transition from merely eliminating waste to actively preventing entropy increase, thereby transforming organizational resilience from passive repair to proactive evolution. Although lean digitalization is regarded as the key path to break the predicament of the manufacturing industry, there is still a theoretical decoupling predicament in its interaction mechanism with organizational resilience. At present, both research and practice encounter core bottlenecks such as an absence of collaborative mechanisms between lean concepts and digital technologies, along with an inadequacy in dynamic capability quantitative evaluation systems. Ultimately, this leads to systemic challenges where data empowerment struggles to be effectively translated into gains in resilience. Therefore, elucidating the mechanism by which lean digitalization empowers organizational resilience is not only essential for addressing risks in the VUCA era but also serves as a pivotal proposition for bridging gaps at their theoretical intersection.
In this study, four typical and representative Chinese manufacturing enterprises—FAW Car Co., Ltd., Weichai Power Co., Ltd., Huaxing Textile Group Co., Ltd., and TZME (Tianjin) Co., Ltd.—are selected as samples. The aim is to elucidate the fundamental logic and mechanisms through which lean digitalization influences the development of organizational resilience in manufacturing firms. The research findings indicate that the formation of organizational resilience is a continuous iterative process characterized by three stages: resilience activation, resilience adjustment, and resilience performance. Drawing on the theory of dissipative structures, the study concludes that enterprises engage with negative entropy factors through a sustained influx of positive entropy flow, thereby facilitating the periodic establishment and reconstruction of dynamic equilibrium. Furthermore, under critical threshold triggers, the nonlinear synergy effect becomes exponentially amplified, enabling enterprises to generate driving forces for adaptive evolution within their systems. When external disturbances surpass threshold boundaries, fluctuations among internal elements activate self-organizing mechanisms, ultimately leading to a transformative leap from disorder to order within the enterprises.
The findings reveal three evolutionary stages: (1) In the resistance-absorption stage, short-term economic resilience is built via a dynamic path of environmental impact response (X1) → resource reconfiguration (Y1) → interest alignment (Z1) during crisis response. (2) In the adjustment-recovery stage, mid-term structural resilience emerges through a progressive path of situational recognition (X2) → technological chain integration (Y2) → benefit expansion (Z2) in crisis rebound. (3) In the recovery-growth stage, long-term strategic resilience is achieved via a spiral path of strategic recalibration (X3) → leadership cognition advancement (Y3) → responsibility-driven transformation (Z3) for crisis reversal. Furthermore, the incorporation of lean digitalization offers an innovative shortcut for fostering organizational resilience within enterprises, thereby enhancing both recovery and rebound speed when faced with adverse factors. These mechanisms demonstrate how lean digitalization facilitates structural optimization from disorder to hierarchical order through entropy evolution, enabling the generation of resilience via standardized foundations, self-activated responsiveness, and equilibrium-driven evolution.
From the perspective of dissipative structure theory, this paper employs a multi-case study methodology to delve into the mechanism through which lean digitalization enhances the organizational resilience of manufacturing enterprises. Research findings uncover the bidirectional co-construction logic between technology- imbued management and management-reshaped technology. Moreover, the study offers a replicable operational framework for the manufacturing sector to escape the high-entropy trap and realize a resilient closed loop of shock resistance, rapid recovery, and stable growth. These outcomes not only furnish a theoretical foundation but also supply practical insights for elucidating the intrinsic link between the achievement of intelligent manufacturing upgrades and the bolstering of organizational resilience within Chinese manufacturing enterprises.

“There is no permanent winner, only the constant innovation.”In the Internet era, the interaction between enterprises is more and more frequent, promoting the innovation from the enterprise-oriented linear mode to the iterative innovation mode centered on users and the market. However, existing research largely focuses on empirical analysis and examines iterative innovation from a single dimension. Therefore, exploring the effects of iterative innovation and its impact on organizations is crucial for promoting iterative strategies and overcoming market challenges. Grounded in grounded theory, this study examines the key factors influencing iterative innovation and its value creation logic within organizations and among users, with Tencent WeChat's iterative process as a case study.
The study adopts a qualitative methodology combining grounded theory and case study, and uses data crawler tools to collect data from multiple channels over an extended period from May 2023 to June 2024, covering the iterative innovation process of the organization. This comprehensive dataset includes public materials, news articles, and interview videos from the enterprise's official website, with a particular focus on R&D activities, system upgrades, and user feedback. Using Nvivo and Excel software, the study applied open coding, axial coding, and selective coding to identify four key influencing factors: user orientation, technical support, executive cognition, and performance feedback. These factors lead to four outcomes: redefining the enterprise role, establishing an innovation paradigm, pursuing technological excellence, and creating customer value. Within the 'influencing factors-iterative innovation-action path' framework, the study highlights the panorama of iterative innovation and constructs a fusion model that integrates these elements.
The dimension analysis of influencing factors shows that, in the implementation of iterative innovation, several key factors play crucial roles: (1)user orientation guides the trajectory of enterprise iterative innovation, acting as an external stimulus of iterative innovation; (2) technical support provides the necessary technological capabilities, reflecting the alignment between enterprise iterative needs and technological resources, and represents the core driving force behind iterative innovation activities;(3) executive cognition extends the scope and depth of iterations, offering internal support for iterative innovation; (4) while performance feedback reflects the effectiveness of iterative innovation and inspires subsequent iterations, thus serving as an auxiliary force. Furthermore, in the whole process of iterative innovation, user demand and technical level constitute the starting point of the iterative innovation activities, and on the basis of executives’ insight into the market, the decision-making approach and innovation strategies are refined. Then, performance feedback is utilized for real-time tracking of the iterative outcomes, which in turn offers a precise benchmark for future iterations. This process establishes a logical and closed-loop system that ensures continuous improvement and adaptation.The consequences dimension analysis reveals that the core goal of iterative innovation is to realize the enterprise role change and customer value creation.
Specifically, through a series of iterative innovation activities, a relatively stable innovation paradigm can be established. This paradigm ensures the agility of iterative innovation, allowing the organization to transcend technological barriers and achieve leaps in technical capability. The combined effect of iterative innovation and technological catch-up drives both the transformation of the enterprise role and the creation of customer value, forming a path that integrates technological catch-up, innovation paradigm support, enterprise role replacement, and customer value creation.
The research conclusion promotes the episodic and dynamic analysis of the research of the iterative innovation, contributes the chain research perspective of the iterative innovation, and provides a new theoretical target for eliminating the phased horizon bias in the subject research of the iterative innovation. Moreover, this study offers valuable insights for enterprises implementing iterative innovation strategies. Specifically, enterprises should involve users in the iterative process, closely track user feedback on innovation outcomes, and conduct targeted innovations. Additionally, executives should focus on building an enterprise innovation culture to achieve product upgrades and comprehensive counterattack transformations.

Strategic entrepreneurship is recognized as a pivotal force for firms to gain a competitive edge in the dynamic and complex business environment. This study delves into the intricate dynamics that propel strategic entrepreneurship by constructing a theoretical model that incorporates network orientation and slack resources as independent variables, while entrepreneurial orientation as a mediating variable and competitive intensity as a moderating variable, offering a comprehensive lens through which to examine the driving mechanism of strategic entrepreneurship. The study adopts a robust methodological approach,employing both hierarchical regression and fuzzy set qualitative comparative analysis (fsQCA) to analyze data collected from 325 manufacturing enterprises in China. These methods allow for a nuanced understanding of the complex causal pathways that lead to strategic entrepreneurship, moving beyond simple correlation by the traditional regression methodology to explore the configuration of conditions that enable it.
The findings contribute to the literature by revealing the nuanced relationship between network orientation, slack resources, and strategic entrepreneurship. Specifically, the study uncovers an inverted U-shaped relationship, suggesting that while moderate levels of network orientation and slack resources are beneficial for strategic entrepreneurship, extreme levels may become detrimental. This underscores the importance of balance and suggests that firms must be judicious in their management of these resources. In other words, network orientation is an important channel for enterprises to access external resources, while slack resources provide a material basis for innovative activities from within the enterprise. As a resource-consuming organizational behavior, strategic entrepreneurship needs the support of network orientation and slack resources. However, excessive network orientation or excess slack resources can lead to management problems such as rising costs and path dependence, which in turn negatively affect entrepreneurial orientation or strategic entrepreneurship. Moreover, the study highlights the mediating role of entrepreneurial orientation, demonstrating how it links network orientation and slack resources to strategic entrepreneurship. This finding emphasizes the centrality of entrepreneurial orientation in the process of strategic entrepreneurship, suggesting that firms must cultivate an entrepreneurial mindset to effectively leverage their resources and seize opportunities. Meanwhile, this study has identified the moderating effect of competitive intensity, showing that competitive intensity enhances the relationship between entrepreneurial orientation and strategic entrepreneurship, as well as between network orientation and strategic entrepreneurship. This suggests that in highly competitive markets, firms may need to intensify their entrepreneurial activities, and properly manage their network engagements to achieve strategic entrepreneurship. However, the study also finds that competitive intensity does not significantly moderate the inverted U-shaped relationship between slack resources and strategic entrepreneurship. This means that, when properly managed, slack resources have a more direct impact on strategic entrepreneurship and may not change significantly depending on the level of competition in the market.
Through fsQCA analysis, the study identifies two distinct paths to high-level strategic entrepreneurship: the resource-driven path and the opportunity-driven path. The resource-driven path suggests that firms can achieve strategic entrepreneurship by integrating slack resources and entrepreneurial orientation in low-competition markets. The opportunity-driven path indicates that strategic entrepreneurship can be achieved through the synergistic effect of opportunities and resources from both internal and external sources. At the same time, the study also identifies two paths leading to non-high-level strategic entrepreneurship, characterized by entrepreneurial motivation deficiency and resource constraints. These paths highlight the challenges that firms may face in their pursuit of strategic entrepreneurship and suggest areas that require attention and improvement. These findings reaffirm that strategic entrepreneurship is a dynamic and complex process shaped by the interaction of multiple factors.
In conclusion, this study provides a comprehensive examination of the driving mechanism of strategic entrepreneurship, contributing to both theory and practice. The findings offer valuable insights for firms seeking to navigate the complex landscape of strategic entrepreneurship and highlight the importance of balancing various factors to achieve sustainable competitive advantage. By understanding the complex interplay among network orientation, slack resources, entrepreneurial orientation, and competitive intensity, firms can more effectively chart their path toward strategic entrepreneurship.

Technology M&A constitute one of the most crucial approaches to enterprise open innovation. It not only exhibits a booming development trend in practice but also receives strong support from the government, thus emerging as an important strategic measure for promoting the innovative development and enhancing the competitiveness of enterprises. According to different motives, technology M&A can be classified into two types: technology-related M&A and technology-diversified M&A. Technology-related M&A enable enterprises to acquire and internalize the knowledge base of target enterprises, effectively enhancing their core competitiveness. However, it also has the drawback of limited exploration space in new research fields. Technology-diversified M&A bring enterprises opportunities to apply new technologies, but they also face challenges in technology integration. In this context, clarifying the complex driving mechanisms behind enterprises' technology M&A decisions has become an important research issue.
Existing research often uses empirical methods, focusing on specific factors without comprehensively exploring multi-dimensional influences or considering internal and external perspectives. Additionally, most studies do not classify M&A samples by motives, leading to inconsistent or contradictory conclusions. This study employs the TOE framework and fsQCA method to analyze the multi-dimensional factors and their interactive mechanisms in technology-related and technology-diversified M&A decisions, aiming to provide a more holistic understanding of the driving forces behind these strategic choices.
The results indicate that first, technology M&A decisions are influenced by three dimensions of conditional variables: technology, organization, and environment. The condition combination of technology-related M&A is not the reverse condition combination of technology-diversified M&A. Second, the study identifies seven distinct pathways for technology-related M&A: (1) technology-driven, (2) dual-wheel driven by patent quantity and institutional factors, influenced by profit-seeking executives, (3) dual-wheel driven by technology and market factors, (4) dual-wheel driven by technology and market factors, influenced by profit-seeking executives, (5) dual-wheel driven by patent quality and environmental factors, influenced by profit-seeking executives, (6) dual-wheel driven by technology and organizational factors, and (7) a "desperate" type driven by urgent needs. Similarly, seven pathways are identified for technology-diversified M&A: (1) driven by internal and external challenges, (2) driven by enterprise scale and profit-seeking executives, (3) precautionary-driven, (4) driven by executive decisions and environmental factors, (5) driven by executive decisions and market factors, (6) imitation-driven, and (7) institutionally driven. These pathways highlight the diverse and complex mechanisms underlying M&A decisions, reflecting the interplay of internal and external factors as well as strategic motivations. Finally, a comparison of the driving paths of technology-related M&A and technology-diversified M&A reveals that enterprises with robust technological foundations are more likely to engage in technology-related M&A, whereas those with weaker technological foundations tend to pursue technology-diversified M&A. State-owned enterprises and large-scale enterprises show a greater inclination towards technology-diversified M&A. Technology-related M&A typically occur when executives are profit-driven and the external environment is favorable, while technology-diversified M&A often occur in situations where executives are cautious and the external environment is favorable.
This study presents potential contributions as follows. Theoretically, by adopting the fsQCA research method which is adept at handling multiple concurrent causation, this study comprehensively examines the driving factors of technology M&A decisions from three aspects: technology, organization and environment, thereby presenting the multi-dimensional influencing factors and their interaction and matching mechanisms in technology M&A decisions. It deepens the academic community's understanding of the antecedents of technology M&A and helps scholars of consequence research to fundamentally explain the core question of "why enterprises achieve different performance in technology M&A". Practically, the study provides several actionable insights. First, it emphasizes the importance of analyzing M&A outcomes from a multi-dimensional perspective rather than attributing success or failure to a single factor. Second, through a comparative analysis of pathways, the study comprehensively outlines the driving paths for both technology-related and technology-diversified M&A. This enables stakeholders, including governments and shareholders, to better understand the motives behind M&A activities, facilitating targeted management strategies and improving the success rate of technology M&A. Finally, the study suggests that enterprises can use these driving paths to anticipate competitors' M&A decisions, and take preventive and responsive measures in advance. Similarly, governments can leverage these insights to assess the future development of enterprises and provide more effective guidance and support.

In the context of the rapid advancement of new technological revolution and industrial transformation,key core technologies serve as a decisive force in enhancing national competitiveness and ensuring national security, directly influencing a country′s ability to seize the initiative in international strategic competition. Unlike general technologies, key core technologies encompass a series of continuously evolving innovation processes, including knowledge innovation, technology incubation, and the development of key products. The complexity and dynamics of these technologies have led to a lack of consensus in research concerning their definition and identification. This paper thus focuses on the issue of innovation breakthroughs in key core technologies from multidimensional perspectives, including knowledge, technology, and industry. Existing literature primarily emphasizes single-dimensional analyses of knowledge, technology, or industry, without fully developing a comprehensive theoretical framework that encapsulates the intricate dynamics of an integrated innovation system. Moreover, the dynamic and complex coupling interaction mechanisms between system innovation actors during technological breakthroughs, as well as the laws of technological evolution, remain areas in need of further investigation. Therefore, this paper, from the perspective of multi-layer coupled networks, explores the characteristics and evolution of the "knowledge-technology-industry" (KTI) coupled innovation system for key core technologies.
This study first defines the concept of key core technologies. Next, it delves into the analysis of the KTI coupled innovation system through the lens of a multi-layered coupled network approach, which reveals the core logic of innovation. This logic is structured around a sequence of stages: starting with fundamental knowledge innovation, proceeding to breakthroughs in technological principles, and culminating in the development of new technologies. On the basis of the definition of key core technologies and system characteristics, a method for identifying such technologies based on multi-layer coupled innovation network analysis is proposed. This method integrates complex network topology feature measurements, higher-order invariant models, and multiple PageRank centrality algorithms. Finally, the study explores the evolutionary characteristics and critical conditions of the KTI coupled innovation system at different stages of its lifecycle and proposes future research directions to promote breakthroughs in key core technologies.
The results show that the multi-layer coupled innovation network of key core technologies exhibits both multi-layeredness and coupling, with corresponding nodes displaying dynamic core and critical properties. To refine the concept of key core technologies, it is essential to adopt a holistic approach that transcends the examination of individual technologies or isolated layers. The process should initiate with a thorough evaluation of the foundational technological knowledge, the underlying structural framework, and the platforms facilitating transformational applications.A significant focus should be on the (KTI) coupled innovation system, which is central to understanding the complex interactions and couplings between knowledge, technology, and industry subsystems. By analyzing the joint effects within and between layers of knowledge, technology, and collaborative R&D networks in the multi-layer coupled network, key core technologies can be identified. The pursuit of breakthroughs in key core technologies is a dynamic and long-term systemic challenge. By analyzing the stage-specific characteristics and critical conditions of the KTI coupled innovation system′s lifecycle and designing network governance mechanisms, the effectiveness and resilience of system innovation can be enhanced, facilitating breakthroughs in key core technologies.
This paper introduces innovation system theory and complex system theory, providing new research perspectives and directions for the multidimensional exploration of the KTI coupled innovation system. It also offers new methods for exploring the dynamic and complex coupling interaction mechanisms among actors within the innovation system. On the basis of the evolution of the KTI coupled innovation system, the paper investigates the evolution patterns and breakthrough development directions of key core technologies. The paper provides theoretical and methodological support for systematically understanding the essence and evolution of key core technologies, guiding innovation actors in overcoming technological bottlenecks. The findings are crucial for promoting the effective coupling of innovation resources and R&D needs, formulating technological innovation policies and strategic plans by governments and management departments, achieving independent and controllable key core technologies, and ensuring national security and international competitiveness.

In the digital economy era, the traditional boundaries between enterprises and the market are gradually being dismantled. The deep integration of digital technology and production operations has become a crucial driver of innovation and development. By further stimulating group wisdom and deepening open innovation through this integration, manufacturing enterprises can gain a competitive edge. However, Chinese manufacturing enterprises currently face challenges in leveraging digital technology to promote open innovation. There is a lack of scientific and systematic theoretical methods to guide their efforts. Additionally, research on the composition of digital technology affordance and its mechanisms in promoting open innovation is still limited. This gap makes it difficult for enterprises to effectively implement open innovation strategies that are well-adapted to the complex and dynamic environment of the digital economy.
Drawing to technology affordance theory, this study delves into how the accumulative and variable affordances of digital technology drive open innovation in manufacturing enterprises. To ensure sample representativeness, the study selects four types of manufacturing enterprises undergoing digital transformation, namely "digital transport equipment manufacturing", "digital general and special-purpose equipment manufacturing", "digital electrical machinery,apparatus and instrument manufacturing", and "other smart manufacturing", based on the "Classification of Digital Economy and Its Core Industries (2021)" released by the National Bureau of Statistics. It also selects samples from enterprises in regions with different levels of digital development. The study employs a combination of paper-based and e-mail questionnaires to survey middle- and senior-level managers in relevant enterprises, using a multi-time-point data collection method to reduce common method bias and better capture the relationships between variables.
By empirically examining data from a sample of 312 manufacturing firms, the study finds that (1) both accumulation affordance and variation affordance have a significant positive impact on open innovation in manufacturing firms; (2) resource reorganization and resource relocation are key links connecting the affordance of digital technologies and open innovation in manufacturing firms, playing a partly mediating role; (3) managerial digital cognition is an important internal contextual factor in the process of enterprise digital transformation, which can enhance the effect of cumulative affordance on resource relocation, and at the same time improve the effect of variable affordance on resource reorganization; while it does not play a significant moderating role in the relationship between cumulative affordance and resource reorganization, and variable affordance and resource relocation.
This study makes both theoretical and methodological contributions as follows. First, it develops a novel theoretical analytical framework of 'digital technology affordance-resource reconfiguration-open innovation', which remedies the insufficient explanatory power of existing theories regarding the impact mechanism of digital transformation on open innovation in manufacturing enterprises. Second, it empirically verifies the role of managerial digital cognition in the transmission pathway through which digital technology affordance affects open innovation, thus further clarifying the key boundary conditions for effective open innovation in the context of manufacturing digital transformation.
From a practical perspective, the findings imply that manufacturing firms should take business needs as the guide and advance open innovation by integrating accumulative and variable affordances in a coordinated manner. Meanwhile, enterprises need to leverage the "multiplier effect" of digital resources and expand the scope of open innovation by utilizing market data to accurately capture customer demands and potential business opportunities, dynamically reshaping resource systems to enhance organizational agility, and adopting data analytics to explore new approaches to resource utilization and optimize targeted resource reallocation. In addition, organizations should actively upgrade managerial digital cognition to provide core strategic support for the formulation and implementation of innovation-driven development strategies in the digital era. This can be achieved by regularly conducting training programs on digital technology applications and data element utilization, as well as by strengthening managers' understanding and mastery of the underlying logical framework and architecture of digital operation systems.

The transformation to intelligent manufacturing is a crucial pathway for Chinese enterprises to achieve high-quality development and serves as a key strategy for advancing new industrialization and enhancing new productive forces. However, existing literature predominantly focuses on the benefits of smart manufacturing transformation for individual enterprises, including aspects such as total factor productivity, energy efficiency, and cost management, with limited attention to the disparities in transformation progress across enterprises or the spillover effects on upstream and downstream segments of the supply chain.
To address these gaps in the literature,this study empirically examines the supply-chainspillover effects of smart manufacturing transformation using supply-chain relationship data from Chinese A-share listed manufacturing companies between 2009 and 2022, and analyzes the underlying mechanisms driving these effects. First, it develops a theoretical framework centered on "vertical leadership" and "peer group effects". It posits that the transformation to intelligent manufacturing is not only characterized by technological innovation and network externalities, but also generates a closed-loop, synergistic diffusion effect throughout the supply chain via demonstration effects, knowledge sharing, and technology transfer. Empirical results indicate that the intelligent manufacturing capability of focal firms has a significant positive impact on their supply-chain partners; specifically, an increase of one standard deviation in the focal firm′s intelligent manufacturing level is associated with a 7-percentage-point increase in the intelligent manufacturing level of linked firms. Further robustness checks—using first-difference model, controls for industry and regional trends, instrumental variable methods, and difference-in-differences approaches—consistently confirm the robustness and significance of this positive spillover effect.
Then, the mechanism test reveals that key leading firms, particularly those in core supply-chain positions and state-owned enterprises, are more likely to exercise vertical leadership, thereby significantly enhancing the spillover effects of intelligent manufacturing transformation. Moreover, when supply-chain firms share common shareholders, the resultant peer group effect effectively facilitates the transmission of intelligent manufacturing expertise and technological information from focal firms, accelerating the coordinated transformation process among upstream and downstream enterprises.
Moreover, by analyzing the heterogeneity in the propagation direction of supply-chain spillover effects, this study finds that the intelligent manufacturing transformation of focal firms predominantly spills over to upstream suppliers, creating a "reverse pressure mechanism" that compels suppliers to proactively enhance their intelligent manufacturing capabilities to meet the transformation demands of the focal firms. In contrast, the effect on downstream customer firms is not significant, indicating that upstream supply-chain partners face greater pressure and driving forces during the transformation process. Regarding the learning behaviors of supply-chain firms, further empirical tests reveal that when firms are at a disadvantage in intelligent manufacturing within their industry or region, their transformation largely relies on conservative learning strategies—characterized by passive imitation and following—in order to mitigate market risks and swiftly narrow the gaps with more advanced peers; conversely, when conditions for proactive learning exist, this positive effect is not significant.
In summary, this paper provides an in-depth investigation of intelligent manufacturing transformation from the perspective of supply-chain spillovers, thereby supplementing and extending the existing literature on collaborative effects in firm transformation. The study not only reveals the significant positive spillover effect of focal firms′ intelligent manufacturing transformation on their supply-chain partners, but also elucidates the crucial role of core enterprises—particularly those exercising vertical leadership through their dominant positions and state ownership—in promoting overall supply-chain digitalization. Furthermore, the paper highlights that supply-chain firms generally adopt conservative and passive imitation strategies, which offers new insights for small and medium-sized enterprises confronting high-risk transformations. Drawing on these insights, the paper recommends strengthening systematic policy support for intelligent manufacturing transformation, fully leveraging the leading role of flagship and state-owned enterprises, and promoting deep inter-connectivity and information sharing among all supply-chain participants through demonstration projects and cross-sector collaborations, thereby achieving coordinated enhancement and high-quality transformation across the entire industrial chain.

In the context of global digital transformation, innovation has become a key driver of economic and social development.This is particularly evident in the manufacturing sector, where the rise of industrial Internet platforms is advancing the platform economy, providing strong support for the digital transformation of enterprises and the enhancement of their innovation capabilities.To better understand how industrial Internet platforms drive enterprise innovation, this paper delves into the mechanisms at play.
As a new type of digital infrastructure, the industrial Internet platform connects the platform itself, the various entities within its ecosystem, and external resources. It not only fosters synergies between production and innovation processes but also offers new momentum and opportunities for enterprise innovation. Based on the theories of resource orchestration and open innovation, this paper focuses on Chinese manufacturing enterprises. It adopts a hierarchical framework of "platform—platform ecosystem—participating enterprises" to explore the relationship between the key capabilities of industrial Internet platforms, platform ecological embedding, and enterprise innovation performance, while also analyzing the role of platform network relationships (including strong ties and bridging ties) within this framework.
The key capabilities of the industrial Internet platform, such as industry foundational capabilities, technical capabilities, policy grasp capabilities, and social resource integration capabilities, form the essential foundation for promoting platform ecological embedding and significantly improving the innovation performance of participating enterprises. Platform ecological embedding, which refers to the interaction and resource flow between the platform and its participating enterprises, enhances the technological progress and innovation outcomes of participating enterprises through mechanisms like knowledge transfer, technology spillovers, and innovation collaboration. Building on the concept of platform ecological embedding, platform network relationships (strong ties and bridging ties) play distinct roles in the relationship between platform ecological embedding and enterprise innovation performance. Therefore, platform network relationships not only directly contribute to the improvement of innovation performance but also moderate the relationship between platform ecological embedding and innovation performance.
Manufacturing enterprises constitute the core user base of industrial Internet platforms, possessing complex and diverse demands for such platforms. The industry characteristics of these enterprises align closely with the capabilities offered by the platforms. Investigating the application scenarios of manufacturing enterprises can provide valuable insights into the pivotal role that industrial Internet platforms play in fostering enterprise innovation. Thus, this study targets manufacturing enterprises located in the Yangtze River Delta and the Pearl River Delta regions. Initially, the research team conducted in-depth interviews with 23 enterprise representatives, and formed the initial questionnaire based on the feedback obtained from these interviews. The questionnaire survey was carried out from October 2023 to August 2024, and was designed in two stages to mitigate common method bias while maintaining the feasibility of the research. Additionally, the paper verified the moderating role of platform network relationships in the relationship between platform ecological embedding and enterprise innovation performance. The research findings indicate that the key capabilities of industrial Internet platforms significantly enhance the innovation performance of participating enterprises, with platform ecological embedding acting as a mediator. Furthermore, platform network relationships (strong ties and bridging ties) not only directly improve innovation performance but also positively moderate the relationship between platform ecological embedding and enterprise innovation performance.
This study integrates research in the fields of resource orchestration theory, open innovation theory, and enterprise performance, focusing on the relationship between industrial Internet platforms and enterprise innovation performance in specific contexts. In contrast to previous literature, this paper provides an innovative contribution by simplifying the complex concept of the industrial Internet platform by relating it to the traditional understanding of a digital platform. It delves into the unique key capabilities of industrial Internet platforms, the internal mechanisms linking platform ecological embedding to enterprise innovation performance, and expands the application of resource orchestration and open innovation theories. The empirical analysis supports the conclusions, offering a new perspective for evaluating the value of industrial Internet platforms and providing theoretical insights for Chinese manufacturing enterprises on how to effectively allocate resources and foster innovation within the platform ecosystem.

Small and medium-sized enterprises (SMEs), as a vital engine for driving high-tech industries, play an essential role in bolstering China's economic resilience and promoting high-quality economic development. “Specialized, refined, unique, and innovative” (SRUI) SMEs exhibit strong innovation capabilities and distinctive competitive advantages. Understanding the mechanisms that underpin the high-quality development of SRUI SMEs is crucial for devising scientifically grounded policy measures, optimizing resource allocation, and enhancing firms' competitiveness. However, theoretical research on SRUI SME development remains underdeveloped compared to practical advancements, with limited quantitative studies and a predominance of qualitative analyses. Notably, the existing literature has primarily concentrated on external environmental factors that facilitate the high-quality development of SRUI SMEs, while relatively little attention has been paid to the role of firms' internal technological capabilities. Technological capability, defined as the accumulation of internal knowledge and innovation practices within a firm, serves as a repository of technological expertise that is fundamental to the development and enhancement of products and processes. It constitutes a critical driver for sustaining competitive advantages, improving performance, and advancing high-quality development. Technological complexity and technological knowledge integration capability represent two core dimensions of firms' technological capabilities.
Drawing on endogenous growth theory, economic complexity theory, path dependency theory, and innovation diffusion theory, this paper proposes an inverted U-shaped relationship between technological complexity and the quality of SRUI SME development. Furthermore, grounded in knowledge-based theory, this study incorporates firms' knowledge integration capabilities into the analytical framework to examine how organizational capabilities influence and reshape the relationship between technological complexity and SRUI development quality. The sample selection encompasses those featured in the five consecutive annual lists from 2019 to 2023, resulting in a robust sample of 1 738 nationally acclaimed SRUI-listed companies for the analysis. The quality of enterprise development is assessed across four key dimensions: specialization, refinement, differentiation, and innovation. Relevant data and control variables are drawn from the CSMAR and CNRDS databases. Technological complexity and knowledge integration capability are quantified using firms' IPC classification codes, with data sourced from the National Intellectual Property Administration (NIPA) and the PatSnap database.
The results indicate the following: (1) There is an inverted U-shaped relationship between technological complexity and the high-quality development of SRUI SMEs. When a firm's technological complexity remains below the threshold of 218.766, its increase significantly enhances development quality. However, surpassing this threshold may lead to capability traps, path dependence, and barriers to technology diffusion, which inhibit development quality. (2) Both the depth and breadth of firms' knowledge integration positively moderate the relationship between technological complexity and high-quality development, facilitating the achievement and amplification of the peak benefits of technological complexity. Compared to knowledge integration depth, integration breadth enables the peak positive effect of technological complexity on development quality to be achieved more quickly while significantly enhancing the maximum level of development quality. (3) The impact of technological complexity on development quality is multidimensional, exhibiting significant heterogeneity depending on firm-specific characteristics, industry attributes, and regional factors.
Building on the above findings, this study advocates for firms to optimize the allocation of technological resources, strategically enhance technological complexity, and mitigate the risks of capability traps and resource misallocation stemming from excessive complexity. Firms should systematically advance their knowledge integration capabilities. By efficiently utilizing their technological resources and established organizational practices, they can maximize the untapped value of their current technological assets. Additionally, firms are encouraged to expedite the development of digital infrastructure to strengthen their capacity for managing, organizing, and reutilizing intricate technological combinations. Moreover, governments should formulate nuanced and targeted support policies that align with firms' industry-specific attributes and regional contexts, fostering balanced and sustainable high-quality development on a national scale. This study offers critical theoretical insights into the boundary conditions for innovation and economic growth within economic systems, while also providing actionable guidance for firms in the formulation of innovation management strategies and technological policy frameworks.

Innovation has become the first engine of China′s economic development,and as the mainstay of the private economy continues to gain prominence, the family firms have become the key to promoting the development of innovation in China. However, China′s family firms are not innovative enough, which is not only detrimental to the development of the firm itself, but also negatively affects the transformation and upgrading of the social economy.Introducing non-family shareholders and achieving equity diversification are important ways for family firms to improve competitiveness and achieve sustainable development. Currently, in China′s listed family firms, the non-family shareholders, both in terms of shareholding proportion and appointed director proportion, have reached a certain scale and have become important participators. In this context, whether the involvement of non-family shareholders can promote innovation in family firms, and how to better achieve this promotion, have become common concerns for both academics and practitioners.
On the basis of resource base theory, principal agent theory, and socio emotional wealth theory, this paper analyzes the impact and mechanism of non-family shareholders′ involvement on family firms′ innovation. To achieve this analysis, data on non-family shareholders' shareholdings and appointed directors is manually collected and organized from the annual reports of listed family firms in China from 2006 to 2023. The data is then matched with the firms' patent information. By setting variables and constructing a panel fixed-effects model, the impact and mechanism of non-family shareholder involvement on family firms' innovation are empirically examined. Additionally, heterogeneity analysis is conducted in the context of rich family firm characteristics. Furthermore, from a practical perspective, the optimal shareholding interval for non-family shareholders is quantitatively measured to better promote the innovation of family firms, and the impact of non-family shareholders′ over-appointment of directors on family firms′ innovation is tested based on the logic of non-reciprocal allocation of equity and control.
The study finds that both ownership involvement and control involvement of non-family shareholders significantly promote the innovation of family firms, and the conclusions still hold after a series of robustness tests. Mechanism analysis suggests that non-family shareholders involvement promotes innovation of family firms by advancing the process of professionalization of firm management and improving the efficiency of firm resource integration. Heterogeneity tests indicates that when clan culture is strong, the actual controllers of family firms tend to exhibit stronger corporate control preferences, which weakens the positive impact of non-family shareholders' involvement on family firms' innovation. However, a stringent external regulatory environment can constrain the profit distribution and decision-making preferences of family firms that are oriented by kinship ties. Under such circumstances, in order to meet regulatory requirements, family firms may further clarify property rights and improve corporate governance mechanisms. Clarifying property rights can mitigate the adverse effects of market failures on corporate innovation. Therefore, the more robust the external regulatory environment, the more pronounced the positive impact of non-family shareholders' involvement on the innovation of family firms is likely to be. It is further found that when non-family shareholders′ shareholding is between 20% and 40%, the promotion of family firms′ innovation is the greatest; and the over-appointment of directors by non-family shareholders significantly promotes family firms′ innovation.
This paper contributes to the literature on family firms and innovation in several ways. First, it extends existing research by identifying new mechanisms through which non-family shareholders promote innovation: improving resource integration efficiency and enhancing firm management professionalization. These findings complement prior studies focused on risk-bearing capacity and resource redundancy. Second, the paper quantifies the optimal non-family shareholding range for maximizing innovation, offering a basis for optimizing equity structure. Third, it examines the impact of non-family shareholders' over-appointment on boards using the logic of non-reciprocal allocation of equity and control. Finally, the paper adopts a cautious approach to shareholder relationships, enhancing the rigor and reliability of empirical results. Overall, it expands research on firm innovation and governance and provides practical insights for family firms to embrace non-family shareholders and improve board governance.

Effective investment promotion can facilitate the formation of urban agglomeration economies, driving innovation development among enterprises and cities and exerting a long-term impact on urban economic growth. Under the acceleration of the innovation-driven development strategy, China has set higher standards for innovation quality. However, higher quality implies higher barriers to innovation, leading enterprises to seek more resources and reduced risks alongside increased efficiency and quality through collaborative innovation. Investment opportunely provides a foundation of trust and resource conditions for such innovative collaboration. Due to spatial differences in the allocation of factor resources, these factors often need to flow across regions. Therefore, transportation infrastructure, exemplified by high-speed rail, can effectively lower the cost of capital and innovative factor flows, playing a pivotal role in promoting the linkage between investment-driven innovation factors among enterprises and cities. This study defines factor linkage as the flow of one factor leading to the connection of other factors and explores the impact of high-speed rail on the relationship between corporate urban investment and innovation from the perspective of factor linkage, providing significant insights for optimizing factor resource allocation and achieving regional integration in China.
In terms of research methodology, this paper primarily employs a continuous difference-in-differences (DID) model and incorporates multidimensional fixed effects to control for the influence of other confounding factors. It focuses on analyzing the channels through which high-speed rail affects the linkage between investment and innovation, specifically: the innovation quality channel, the investment sustainability channel, and the investment spillover channel. The study manually collects and compiles a panel dataset covering domestic investments and cross-regional collaborative innovations by A-share listed companies from 2005 to 2022, and constructs an indicator to measure the integration between investment and innovation. To capture intercity high-speed rail connectivity, the analysis uses continuous variables such as service frequency instead of binary indicators of mere connectivity, thereby more accurately reflecting the intensity of intercity linkages.
The study finds that high-speed rail connectivity can promote the increase in the number of cooperative patents in recipient cities driven by enterprises' off-site investments, triggering the linkage effect between investment and innovative factors. Moreover, after a series of robustness checks including endogeneity tests, parallel trend tests, and placebo tests, the conclusions remain robust. Mechanism tests demonstrate that high-speed rail connectivity can strengthen the driving effect of investment on innovation through the innovation quality pathway, the investment sustainability pathway, and the investment spillover pathway. High-speed rail can enhance the driving effect of investment on high-quality innovation; the higher the investment sustainability, the more high-speed rail can promote the driving effect of investment on innovative factors; and investment can also drive the linkage of urban innovative factors through inter-industry spillover and inter-firm spillover, among other avenues. Heterogeneity analysis proves that when capital factors flow to central cities, larger cities, or transport hubs, or when they move within urban agglomerations, the driving effect of high-speed rail connectivity on the investment-innovation factor linkage is greater. Thus, the study proposes to enhance the high-speed rail network, maximize spillover effects of transportation hubs, support long-term investment in innovation, and encourage enterprises to pursue technological breakthroughs through diversified pathways.
This study makes marginal contributions from two aspects. First, regarding the research perspective, by focusing on the linkage between investment and innovative elements as the primary research angle, this paper expands the research boundaries concerning the flow of factors and the causal impact among them. Furthermore, it investigates the role of high-speed rail in driving the effect of capital elements on innovative elements, as well as the pathways through which high-speed rail influences this process. Second, concerning the construction of indicator models, this study constructs a scientifically measured indicator that gauges the driving effect of enterprises' cross-regional investments on urban cooperative innovation. The indicator takes into account the lagging relationship between the two, which breaks away from the conventional measurement approaches used in existing research for constructing multi-factor indices.

In the context of China's transformation toward an innovation-driven economy, collaborative innovation has become a key strategy to enhance national competitiveness. Innovation consortia—collaborative alliances among enterprises, universities, research institutes, and governments—are emerging as vital platforms to aggregate innovation resources, facilitate knowledge flows, and drive breakthroughs in key technologies. However, the construction levels of such innovation consortia vary significantly across regions and industries in China, and the mechanisms for achieving high-level development remain insufficiently explored. This study aims to identify and analyze the configuration paths that lead to high-level construction of innovation consortia in China, providing theoretical insight and practical guidance for future policy-making and institutional design.
This study builds upon the "strategy tripod perspective" to conceptualize the key factors influencing innovation consortium development. Methodologically, the study adopts dynamic qualitative comparative analysis (QCA), which is well-suited for exploring causal complexity in social phenomena. By leveraging dynamic QCA, the study examines how different combinations (configurations) of internal and external conditions lead to high or non-high levels of innovation consortium construction.The empirical analysis is based on data collected from 370 innovation consortia in China. These consortia span a diverse range of industries and regions and have been evaluated based on a multidimensional assessment framework encompassing innovation input, resource agglomeration, collaborative output, governance mechanisms, and institutional support. Variables selected for the configurational analysis include innovation resource integration capability, talent gathering capability, technological achievement transformation capacity, institutional guarantee capability, and industrial development level.
The results of the dynamic QCA reveal four distinct configuration paths leading to high construction levels of innovation consortia.(1)Institutional coordination and resource integration-driven type: This configuration is characterized by the absence of mature industrial bases and large-scale participants but relies heavily on strong institutional support and effective innovation resource integration. It is typically seen in emerging sectors requiring strategic coordination and initial resource mobilization.(2)Industry-oriented transformation type: This path features the presence of leading enterprises and a pressing need for industrial upgrading. It emphasizes the role of industrial drivers and organizational leadership in guiding collaborative innovation and transforming traditional sectors.(3)Technology talent-led type: In regions or industries with limited resource endowment and industrial maturity, this configuration leverages high-end scientific and technological talents to compensate for systemic shortcomings. The participation of top researchers and flexible talent mechanisms plays a crucial role in enhancing innovation capacity.(4)Multi-dimensional advantage resource empowerment type: This type thrives in areas with established industrial systems, abundant innovation resources, and comprehensive institutional support.
These findings suggest that there is no single optimal path for innovation consortium development. Rather, multiple equifinal paths exist that depend on the interplay of resource availability, industrial context, and institutional arrangements. The study offers several theoretical contributions. First, it extends the application of the strategy tripod perspective to the domain of innovation governance, demonstrating its suitability for explaining the multi-faceted nature of collaborative innovation. Second, it enriches the empirical literature on innovation consortia by identifying configuration types that reflect China's diverse innovation landscape.
Moreover, the study has important practical implications. Policymakers should tailor their support strategies based on the specific configuration types of innovation consortia. For instance, resource integration and institutional innovation are crucial in nascent sectors; industry-led consortia require regulatory incentives and supply chain alignment; talent-driven models benefit from flexible talent policies and achievement transformation mechanisms; and mature regional systems should focus on deepening cross-domain collaboration and market-based innovation resource allocation. In terms of methodological contribution, the adoption of dynamic QCA allows for a nuanced understanding of how multiple conditions interact to produce outcomes. This approach moves beyond linear causality and embraces configurational thinking, which is especially relevant for complex policy systems such as innovation governance.
In conclusion, this study provides a novel analytical perspective and robust empirical evidence on the paths to high-level innovation consortium construction in China. It highlights the importance of context-specific strategies, coordinated governance, and multi-actor collaboration in building a more effective and inclusive national innovation system.

Amid the intensifying global geopolitical risks and the in-depth evolution of the technological revolution, China encounters formidable challenges, including the containment of its scientific and technological innovation resources by Western nations and the heavy dependence on crucial technologies and resources. Consequently, accelerating the breakthroughs of core technologies in key fields has become an imperative for China to secure a competitive advantage in the global technological landscape. The "Jie Bang Gua Shuai" mechanism, which refers to an open competition mechanism for selecting the best candidates to lead key research projects,with chief technology officers assuming responsibilities. It has emerged as a novel paradigm in scientific research organization, bearing substantial significance in optimizing the allocation of scientific and technological resources and expediting the conquest of key core technologies.
The principal aim of this study is to investigate the manner in which the "Jie Bang Gua Shuai" mechanism propels the resolution of core technical conundrums. In conjunction with the resource allocation theory, this study undertakes a meticulous dissection of the operational mechanism of the "Jie Bang Gua Shuai" mechanism within the context of facilitating the research and development of core technologies in key fields. Special emphasis is placed on the functions of core innovation elements, resource allocation entities, and the central mechanism of resource allocation therein. This study uses the coal industry in Guizhou Province as a case study. The industry fully cooperates with universities and integrates resources in line with national strategic needs, adopting the "Jie Bang Gua Shuai" mechanism to address the core technological challenges of intelligent mining in coal mines. The study examines how to effectively optimize resource allocation from three perspectives: the "information-talent-funding" innovation element joint mechanism, the "government-industry-university" innovation subject collaborative mechanism, and the "active government effective market" resource allocation coordination mechanism. Furthermore, this study explores how to effectively optimize resource allocation in the process of analyzing cooperation with universities and deeply analyzes the process of Guizhou Province's "Jie Bang Gua Shuai" mechanism to promote core technology research and development.
The research findings unequivocally demonstrate that the "Jie Bang Gua Shuai" mechanism augments the efficiency of resource allocation and fosters the research and development of core technologies through pivotal innovation elements such as open and equitable bilateral information, open and collaborative scientific and technological talents, and diverse and flexible scientific research funds. Guizhou Province's practice serves as a paradigmatic example of the efficacy of this mechanism, with its process mechanism encompassing the joint mechanism of innovation elements like information, talent, and funds; the collaborative mechanism of innovation entities such as the government, industry, and universities, and the coordination mechanism of resource allocation between the "active government" and the "efficient market". In accordance with these insights, three pathways for promoting R&D of core technologies are proposed: first, by clarifying project requirements and target frameworks, establishing an effective talent team selection mechanism, and building a sustained and stable funding support system, breakthroughs in core technologies can be effectively promoted, driving technological innovation and industrial upgrading; second, it is crucial to strengthen cross-departmental and cross-regional cooperation among various innovation entities; thirdly, it is essential to coordinate the allocation of resources between the government and the market. This coordination should not only leverage the government's strategic, service, and reform guidance mechanisms to provide policy support and directional guidance for innovation entities but also fully utilize the market's decisive role in the allocation of scientific and technological resources.
This study plugs the research void in the process of "Jie Bang Gua Shuai" and stimulates theoretical dialogue, thereby making substantial and valuable contributions to the academic domain. It also proffers invaluable theoretical and practical implications for optimizing the allocation of scientific and technological resources and promoting the research and development of core technologies. Future research endeavors could expand the ambit of case studies and adopt quantitative analytical methodologies to provide more robust data support and a scientific foundation for policy formulation and refinement.

With the highly frequent iteration of science and technology and the rapid development of the economy, market competition is becoming increasingly fierce. It is no longer sustainable to rely on factors and investment to drive the development of enterprises, and innovation has become the requirement of the times. However, every benefit will beget a disadvantage. While innovation brings social progress, its potential negative impact may harm the public and society at some point. By integrating ethics and social expectations into the innovation process, responsible innovation provides a good paradigm for solving the negative externalities of innovation. However, it is inevitable for employees to make mistakes in the innovation process due to the long cycle of responsible innovation, multiple constraints and complex uncontrollable factors. So, how can employees maintain the willingness and vitality of responsible innovation with high trial and error costs? An organizational fault-tolerant atmosphere may provide a solution. But does organizational fault-tolerant atmosphere only promote responsible innovation in a positive way? Is there a possible inhibitory effect?
Through the review of existing literature, this study finds that most of the current studies on organizational fault-tolerant perception start from the positive side, while few studies focus on its negative side. The most common view is that the fault-tolerant atmosphere of the organization provides employees with error flexibility and trial and error space to enhance their sense of psychological security and sense of innovation self-efficacy, so as to stimulate more responsible innovation behaviors. However, as the old saying goes, one cannot lead an army with a merciful hear, this study questions whether organizational fault-tolerant perceptions serve as a "sword of honor" or a "charter of pardon". Specifically, it examines if the moral excuse that "mistakes are permissible" fosters a sense of immunity to failure among employees. Such indulgence might lead to repetitive errors, thereby significantly reducing the likelihood of responsible innovation.
However, this study examines whether organizational fault-tolerant perception consistently impacts employees' responsible innovation through two opposite paths: innovation self-efficacy and moral disengagement. Is it different in different situations In order to answer the above questions, this thesis introduces socially responsible human resource management and discusses these two paths in this context. Integrating the discussion above, this thesis builds a double-edged sword model and systematically discusses the mechanism of organizational fault-tolerant climate on responsible innovation. On the basis of the empirical study of 317 self-filled questionnaires from employees in China, a moderated mediation analysis is conducted using SPSS and Mplus software to verify the hypothesis in this thesis.
The conclusions are drawn in four aspects. (1) Organizational fault-tolerant perception has a double-edged sword effect on employees' responsibility innovation. (2) Innovation self-efficacy plays a mediating role between organizational fault-tolerant perception and employees' responsible innovation. Moral disengagement plays a mediating role between organizational fault-tolerant atmosphere and employees' responsibility innovation. (3) Socially responsible human resource management perception (SRHRMP) can strengthen the relationship between organizational fault-tolerant perception and innovation self-efficacy, and weaken the relationship between organizational fault-tolerant perception and moral disengagement. The higher the level of SRHRMP, the stronger the positive influence of organizational fault-tolerant perception on employees' sense of innovation self-efficacy. SRHRMP plays a moderating role in the positive relationship between organizational fault-tolerant perception and employee moral disengagement. The higher the level of socially responsible human resource management, the weaker the positive influence of organizational fault-tolerant perception on employee moral disengagement. (4) SRHRMP can strengthen the sense of organizational fault tolerance and influence the intermediary path of employee's responsible innovation through the sense of innovation self-efficacy, weaken the sense of organizational tolerance and affect the intermediary path of employee's responsible innovation through moral disengagement. The higher the level of SRHRMP, the stronger the indirect effect of innovation self-efficacy. SRHRMP moderates the indirect effect of employee moral disengagement between organizational fault-tolerant perception and employees' responsible innovation. The higher the level of SRHRMP, the weaker the indirect effect of moral disengagement.

The Chinese government has set ambitious goals of peaking carbon emissions by 2030 and achieving carbon neutrality by 2060. In the context of the dual carbon goals, enterprises, as the core subjects of economic activities, are not only key contributors to economic growth but also the primary drivers of carbon reduction. Their green transformation is crucial for survival and development. Green technology innovation is essential for enterprises to achieve this transformation, offering a pathway to balance economic benefits with environmental protection. However, green technology innovation is characterized by dual externalities and faces significant challenges, including long development cycles, substantial investments, and high risks. Moreover, the current institutional and market environments require further improvement. The implementation of corporate green development strategies largely depends on the cognition and capabilities of the executive team. Therefore, exploring how technology-expert executives can empower green technology innovation and achieve carbon reduction with synergistic efficiency is of great significance.
The existing literature has extensively explored the driving factors of green technology innovation, particularly focusing on the role of technology expert executives in enhancing various aspects of enterprise performance. These studies highlight how such executives contribute to improving technological efficiency, promoting technological capital accumulation, elevating innovation levels, increasing option value, and ultimately boosting overall enterprise performance. However, there is little literature regarding the role of technology expert executives in promoting enterprise carbon reduction and achieving synergistic effects. Against the strategic backdrop of green and high-quality development, how to realize the win-win situation of economic performance and environmental performance of enterprises has become an important research issue. Using the data of China's A-share industrial listed enterprises from 2012 to 2023, this paper constructs a theoretical framework of technical expert executives, green technology innovation and the synergy of carbon emission reduction and corporate performance improvement, and discusses the impact of technical expert executives on corporate carbon reduction synergies and its mechanism.
On the basis of theoretical analysis and empirical tests, it is concluded that technical expert executives have a significant positive impact on the synergy of carbon emission reduction and corporate performance improvement. Technical expert executives can effectively reduce the carbon emission intensity of enterprises, improve the financial performance of enterprises, and enhance the coupling and coordination degree of carbon reduction and performance improvement of enterprises;while green technology innovation plays an intermediary role between technical expert executives and the synergy of carbon emission reduction and corporate performance improvement. Technical expert executives have preference for green technology innovation and unique technical expertise, which helps to enhance the willingness and ability of enterprises to innovate green technology and improve the level of enterprises' green technology innovation. Green technology innovation can not only improve the efficiency of energy use, reduce resource consumption, strengthen the end pollution control and reduce the carbon emission intensity of enterprises; it can also help enterprises reduce costs and increase performance, enhance the green core competitiveness of enterprises and improve the financial performance of enterprises, so as to achieve the coordinated promotion of carbon reduction and performance improvement of enterprises.
Further research finds that the stability of the senior management team and good internal governance can provide a strong organizational guarantee for technical expert executives, while the short-sighted management will weaken the positive impact of technical expert executives on enterprises. The positive effect of technical expert executives in promoting the synergy of carbon emission reduction and corporate performance improvement is more significant in enterprises with high internal governance level, strong stability of senior management team and low short-sightedness of management. From the heterogeneity of external environment, the positive effect of technical expert executives in promoting the synergy of carbon emission reduction and corporate performance improvement is more significant in enterprises subject to strong environmental regulations, fierce market competition, and more government subsidies and tax incentives.

The digital innovation ecosystem (DIE) serves as an effective structural framework for driving industrial and organizational development. It integrates multiple stakeholders from industry-university-research, spans horizontal and vertical relationships across chains, and harnesses interdisciplinary resources to continuously deliver innovative solutions. However, existing studies inadequately explain the criteria for defining DIE boundaries, the phenomenon of boundary ambiguity, the interactive relationships between DIE and its environment, which severely hinders the foundational construction and analytical interpretation of DIE. The autopoietic system is the third generation of system theory, which emphasizes the structure and boundary of the system in the output elements of maintaining the system movement. To understand the autopoietic movement of DIE, the autopoietic system theory that can reflect the origin characteristics of output movement is used to construct and explain the digital innovation ecosystem, and further explore the formation and evolution process of digital innovation ecosystem
As a third-generation system theory, the autopoietic system has gained broad application and exhibited unique advantages over first and second generation theories (e.g., dynamic equilibrium, self-organization). However, domestic scholars have remained relatively underexplored in this field compared to their international counterparts. Current research on autopoietic system primarily focuses on sociology, management, psychology, computer science, environmental science, and the history and philosophy of science. This study adopts an integrated connotation-structure-characteristics-evolution framework to investigate the endogenous motion mechanisms of DIE through the lens of autopoietic system theory. From this perspective, DIE is conceptualized as a composite unitary entity formed by the coupling of innovation actors such as industry, academia, research, and application sectors. Fusion interfaces constitute its elemental components, spontaneously generating phase-space boundaries during system output-driven motion. DIE has typical characteristics of self-discipline, self-individuality, self-differentiation and mutual penetration. The generation of digital innovation ecosystem involves the agglomeration of factor-complex relationship and the integration between element unit and compound unit. Specifically, elements required for digital innovation ecosystems aggregate through self-organization by innovation agents (e.g., enterprises) or hetero-organization by institutions (e.g., governments); and in the integration phase, digital innovation ecosystems begin self-demarcation through productive movements of constituent elements, evolving into composite unit entities. Innovative entities such as industry, academia, research, and application also detach from the elemental parts of dynamic equilibrium systems or self-organizing system complexes in the form of autopoietic system units. Meanwhile, macroenvironments such as the economy and politics also emerge in the form of unit entities. This study constructs a galaxy model to depict the productive movements and inter-penetrative interactions of self-generating systems. It posits that the self-generative dynamics of digital innovation ecosystems correlate with positive factors (e.g., shared coding, mutual trust, common values) and negative factors (e.g., cognitive biases, information asymmetry) at fusion interfaces, while also being influenced by permeation resources provided by environmental unit entities like innovation agents
From the perspective of autopoietic system, this paper provides theoretical support for the construction of digital innovation ecosystem and the continuous development. Firstly, it is essential to establish a sense of autopoiesis among innovation entities, including production, learning, research, and application, as well as third-party platforms such as online industrial Internet platforms and offline industrial innovation alliances. These entities should develop autopoietic consciousness by clarifying their vision and mission from an autopoietic perspective, aligning with the goals of the autopoietic system, encouraging autonomy, and promoting internal self-organization. Secondly, an equal interaction mechanism should be designed to fully reflect the mutual penetration between innovation entities and third-party platforms. This mechanism should consider the self-development of innovation entities and ensure mutual respect among all parties. All innovation entities, regardless of size, should be treated as equal units, and smaller nodes in the innovation network should not be overlooked. Finally, the efficiency of interface integration should be enhanced. This includes reducing information asymmetry among innovation entities, improving knowledge search efficiency at the fusion interface, and strengthening trust between innovation entities and the fusion interface organization. Additionally, establishing common values for an independent and integration-oriented digital innovation ecosystem and improving the common coding efficiency of the interface are crucial steps.

Enterprise networks play a pivotal role in providing firms with access to vital information and resources for innovation. Within this complex network of inter-organizational connections, enterprises are simultaneously embedded in multiple types of networks, each with distinct structural characteristics, network contents and functional implications. Among these, the shareholder relationship network—formed through common shareholders among public firms—exhibits low communication frequency and high turnover in relational ties, making it a weak tie network. In contrast, the interlocking directorate network, wherein firms are connected through shared directors, fosters intensive interactions and long-term relationships, thereby constituting a strong tie network. While prior studies have extensively examined the individual effects of network structures on firm performance, limited attention has been devoted to the interplay between multiple overlapping networks and their combined impact on corporate technological innovation outcomes.
Grounded in enterprise social capital theory and the embeddedness theoretical perspective, this study explores the dual network embedding of firms by analyzing the network position and structural roles of firms within shareholder relationship networks and interlocking directorate networks. The study selects 338 publicly listed firms in China′s biopharmaceutical sector from 2011 to 2023, and makes a comprehensive dataset which includes information on 29 145 shareholders, 35 597 directors, corporate innovation outputs, and firm attributes.It then constructs detailed representations of these networks. Social network analysis and panel data regression models are employed to examine how firms′ structural positions in both networks influence technological innovation.
The study verifies the existence of the embedding paradox and the dark side mechanisms in the context of corporate dual network embedding, enriching our understanding of the relationship between networks and technological innovation. The empirical findings yield several key insights. First, the structural embedding within the interlocking directorate network does not exert a significant influence on corporate technological innovation, suggesting that tightly knit, high-frequency interactions among directors may lead to redundant information rather than fostering innovation. Conversely, in the shareholder relationship network, the study identifies a non-linear, inverted U-shaped relationship among corporate innovation, network centrality and structural hole positions. This implies that while a moderate level of network embedding enhances access to diverse knowledge and financial oversight, excessive involvement may induce information redundancy, network lock-in, and cognitive inertia, ultimately stifling innovation.
Furthermore, the study uncovers a substitution effect between the two networks: the interaction term of structural embedding across the dual networks exhibits a significant negative impact on technological innovation. This suggests that firms deeply embedded in both networks may experience diminishing returns due to overlapping governance mechanisms, constrained strategic autonomy, and excessive internal coordination costs. These findings empirically substantiate the paradox of embeddedness in the context of dual network structures, highlighting the inherent trade-offs between network benefits and network constraints.
This study contributes to the literature in several ways. First, it extends network embeddedness theory by incorporating a dual-network perspective, illustrating how different relational structures interact to shape innovation outcomes. Second, it enriches the concept of the embedding paradox, demonstrating that while social capital derived from network ties and structure enhances innovation under optimal conditions, excessive embedding can introduce structural rigidity and curtail exploratory innovation. Third, by revealing the dark side of social capital, this research advances the understanding of how inter-organizational relationships can simultaneously facilitate and inhibit technological advancement.
From a managerial standpoint, these findings offer strategic implications for firms seeking to leverage network positions for innovation. While firms should actively maintain connections within shareholder and directorate networks to harness informational and governance advantages, they must also exercise caution to avoid over-embeddedness, which may lead to diminishing innovation returns. Effective governance mechanisms should balance network centrality and structural flexibility, ensuring that firms maximize the benefits of network ties while mitigating negative spillovers from excessive interdependencies.

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 needs 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 long-term 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 alliance 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 resilience 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.

Amid the intensifying global competition in science and technology, breakthrough technological innovation holds the potential to transform industrial technology architectures and disrupt markets. It is a critical strategy for overcoming bottleneck challenges in key technologies and fostering new quality productive forces. Breakthrough innovation involves developing new businesses or production lines by leveraging novel theoretical principles or technological routes, thereby reconstructing existing knowledge and technology to deliver significant economic, social, and ecological benefits. Given its high-risk, high-investment, and complex nature, breakthrough innovation demands substantial resources and collaboration, often beyond the capacity of a single enterprise. This necessitates the formation of innovation consortia to pool complementary resources and capabilities.
Innovation consortia, as carriers of complementary resources, emphasize the leadership role of leading enterprises. They focus on addressing bottleneck technical challenges and conducting original research aligned with national strategic needs. Meanwhile,the governance mechanisms of these consortia can significantly impact the success of breakthrough innovation. The inherent complexity and uncertainty of breakthrough innovation, combined with potential spillover effects, can lead to collective action problems and hidden risks among consortium partners. This "cooperation paradox" often hinders effective collaboration and the realization of key technological breakthroughs. It is also believed that the appropriate governance mechanism chosen by joint research organizations is the key to the success of the alliance , which can ensure the operation of the alliance and realize the value-added effect of cooperation, and help the alliance to carry out breakthrough technological innovation. Therefore, the influence mechanism of the governance mechanism of innovation consortia on breakthrough technology innovation still needs to be further studied. In addition,the government's support for the innovation consortia, from the beginning of the establishment of the innovation consortia, has an impact on the direction of technological research of the innovation consortia, and encourages it to combine its own advantages to perform resource coordination. There is a close relationship between resource coordination ability and government support, both of which are important influencing factors for innovation consortia to carry out breakthrough technological innovation. Therefore, this paper focuses on exploring the mediating effect of resource collaboration and the moderating effect of government support on the relationship between the governance mechanism of innovation consortia and breakthrough technology innovation.
This study collected relevant data on innovation consortia in the Jiangsu region through a questionnaire survey. Based on the pilot list of innovation consortia proposed by the Jiangsu Provincial Department of Science and Technology for the period of 2022—2023, as well as lists released by science and technology departments in various cities within Jiangsu, 40 innovation consortia were selected. These consortia primarily cover key areas such as integrated circuits, high-end equipment manufacturing, new materials, and green environmental protection. The respondents included the chief scientists of each consortium, representatives from participating units, and the heads of the human resources departments of the leading units. This selection ensured the accuracy and authority of the collected data. Ultimately, 268 valid questionnaires were obtained, yielding a response validity rate of 67%.
This paper, for the first time, approaches the topic from a governance perspective, combining transaction cost theory and relational exchange theory to examine the impact mechanism of the innovation consortia on resource orchestration and breakthrough technological innovation. The empirical analysis on the moderating effect of government support indicates that both innovation consortia with contractual governance and those with relational governance contribute to promoting breakthrough technological innovation performance, with relational governance having a greater effect than contractual governance. Resource orchestration partially mediates the relationship between governance mechanism of the innovation consortia and breakthrough technological innovation. Government support positively moderates the relationship between both types of governance within Innovation Consortia and resource orchestration, with a more significant moderating effect on relational governance.

Innovation is the first driving force for development. China's continuous improvement of its scientific and technological innovation system has led to the continuous improvement of its innovation-driven development capabilities, with innovative achievements in cutting-edge fields such as artificial intelligence and quantum technology emerging, and the turnover of technology contracts increasing by nearly 30%. While previous research has thoroughly examined the varied effects of innovation policies on fostering innovation, it has predominantly concentrated on the mechanisms through which individual policies influence innovation performance, particularly within the realm of enterprise innovation. There is a noticeable gap in understanding how comprehensive national innovation policies can bolster regional innovation performance, and a scarcity of studies that delve into the mechanisms linking these policies to regional innovation outcomes. Furthermore, as a form of macroeconomic policy, the influence of national innovation policies on regional innovation systems is multifaceted and intricate. Variations in implementation across different regions, sectors, and stages may lead to divergent impacts on regional innovation systems. However, research on innovation policies and regional innovation that accounts for regional disparities and investigates cross-level impact mechanisms using panel data is sparse. As an important part of the national innovation system, regional innovation plays an important role in promoting economic and social development, enhancing regional competitiveness, and implementing new development concepts. Regional innovation capacity is not only affected by innovation resources within the region, but also regulated by national innovation policies. Therefore, it is of great theoretical and practical significance to explore the impact of national innovation policy on regional innovation performance and its internal mechanism.
This paper focuses on the diversity and complexity of the impact of national innovation policy on regional innovation systems.As a macro-control policy, these policies may yield varying outcomes across different regions, fields, and stages of implementation. Therefore, in order to investigate the impact of innovation policies at the national level on innovation performance at the regional level, the mediating role of innovation subject cooperation intensity in this process, and the moderating role of regional technology diversification in this process, this paper constructs a cross-level model without confusion based on the panel data of 30 provinces in China. The study adopts regional innovation performance as the dependent variable, with the number of invention patent applications serving as the metric for assessing this performance. It approaches the quantification of innovation policies from the perspective of policy intensity, utilizing national innovation policies as the explanatory variables. The study identifies cooperation intensity as a mediating variable and technological diversification as a moderating variable. Control variables encompass factors such as education level, industrial structure, the scale of high-tech enterprises, road density, and the density of mobile phone switches.The study focuses on the intra-group effect and inter-group effect to empirically test the cross-level impact mechanism of national innovation policy on regional innovation performance.
It is found that (1) national innovation policy has a significant impact on regional innovation performance; (2) cooperation intensity plays a mediating role between national innovation policies and regional innovation performance; (3) cooperation intensity contributes to the improvement of regional innovation performance, and technological diversification plays a moderating role in it.
In order to further improve regional innovation performance and strengthen the impact of innovation policies on regional innovation performance, this paper puts forward corresponding countermeasures and suggestions from the aspects of improving the pertinence of innovation policies, strengthening cooperation intensity among innovation entities, and promoting the diversified development of regional technologies.
This paper clarifies the influencing mechanisms of national innovation policies, regional innovation performance, cooperation intensity of innovation subjects, and technology diversification, expands the previous research on the impact of innovation policies on regional innovation, and enriches the impact model of regional innovation performance. Furthermore, it provides recommendations for optimizing innovation policies and enhancing regional innovation performance in light of empirical analysis results. These contributions provide beneficial supplements to existing research and contribute to the realization of high-quality economic development in China.

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, this study employs multi-period fsQCA and three-stage DEA models to investigate 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 turning 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 efficiency within the business environment.

The manufacturing industry is the backbone of the real economy and the foundation of strengthening the country. At present, China's manufacturing industry is confronted with significant risks and uncertainties that are severely impeding its high-quality development. These challenges include the "bottleneck" issue of key technologies, the insufficient support from the innovation chain to the industrial chain upgrade, and the increasing risk of decoupling and disruption within the industrial chain, among other pressing problems. As a result, as a local exploration of significant national strategy, the Chain Chief System came into being. The local government proposed to take the Chain Chief System as a crucial tool to advance the deep integration of industrial chain and innovation chain.
Since 2018, the Chain Chief System has been gradually implemented in multiple Chinese provinces. A number of supporting policies have also been introduced and have yielded some initial results. With its ongoing development, the Chain Chief System has emerged as a crucial tool for attracting foreign investment and talent, improving the business environment, and advancing the industrial upgrading of local governments. However, there are issues such as weak synergies with the implementation of the Chain Chief System in various provinces that need to be resolved. Especially, the policy role that drives the deep integration of industrial chain and innovation chain has not yet been fully realized. How to give full play to the role of the Chain Chief System in driving the integration of industrial chain and innovation chain and accelerating the optimization and upgrading of the industrial chain has become a urgent theoretical and practical problem that needs to be addressed. Therefore, investigating the mechanism and efficient path of the Chain Chief System to promote the integration of industrial chain and innovation chain, to support the high-quality development of the manufacturing industry, and to expedite the formation of new quality productivity is of great theoretical and practical significance.
In this paper, the LDA thematic model and content analysis method are utilized to sift through and identify the policy tools of the Chain Chief System that facilitate the integration of industrial chain and innovation chain. Additionally, a policy mechanism model of the Chain Chief System to drive the integration of industrial chain and innovation chain is constructed from the perspectives of responsible government and effective market. The fuzzy set qualitative comparative analysis (fsQCA) method is employed to identify the policy configuration path that the Chain Chief System can take to drive the deep integration of manufacturing industrial chain and innovation chain. The results highlight that (1) the mechanism model of the Chain Chief System driving the integration of industrial chain and innovation chain, which is a series of processes of Chain Chief System-multiple policy tools (responsible government and effective market organic combination)-optimal allocation of resources-achievement of policy objectives-deep integration of industrial chain and innovation chain; (2) the five policy tools of enterprise cultivation, industry chain investment promotion, government work, fiscal, tax and financial policies, and collaborative innovation do not individually constitute the necessary conditions for the deep integration of manufacturing industrial chain and innovation chain. rather, it is the combined effect of multiple policy tools under the Chain Chief System that jointly impacts this integration; (3) the effective combination of the Chain Chief System's multiple policy tools leads to the integration of manufacturing industrial chain and innovation chain, known as “All Paths Lead to the Same Conclusion”. Three driving paths, which include overall consideration type, acting in response to the situation type, and mutually complementing type are involved in the deep integration of manufacturing industrial chain and innovation chain. To maximize the effectiveness of the Chain Chief System, it is essential for various provinces to tailor the application of its policy tools according to the specific regional conditions. This approach will help address the differentiated needs of different regions and enhance the synergistic effects of these policies. The findings of this paper offer valuable decision-making references for the government to promote the deep integration of industrial chain and innovation chain.

In the context of profound adjustments in the global economic landscape and the intertwining of "de-globalization" trends, the resilience of industrial chains has become a crucial issue for coordinating development and security.The convergence of multiple risks, including Sino-U.S. trade frictions, geopolitical conflicts, great power competition, and technological blockades, has significantly heightened the threats of "disruption" and "bottlenecks" in industrial chains, further emphasizing the vulnerabilities of the traditional division of labor system.The report of the 20th National Congress clearly emphasizes that "efforts should be made to enhance the resilience and security of industrial and supply chains", considering industrial chain resilience as a key lever for building a modern industrial system.Industrial chain resilience not only addresses the practical need to withstand external shocks and ensure stable economic operation, but also acts as a strategic pivot for cultivating new productive forces, facilitating the economic circulation of major countries, and seizing the high ground of the global value chain.The enhancement of industrial chain resilience depends on the collaboration and innovation capabilities of regional industrial systems, while innovative industrial clusters function as the core carriers of regional industrial collaboration and innovation. Through the efficient integration of innovative elements and networked collaboration, they promote resource sharing and risk-sharing among enterprises, becoming a key support for enhancing industrial chain resilience.Existing studies have extensively examined innovation agglomeration and industrial clustering; however, research on innovative industrial cluster,ie.a complex entity closely related to both innovation and industrial agglomeration,remains relatively scarce.Therefore, this paper builds upon the connotation of industrial chain resilience and considers innovative industrial cluster pilot policies as quasi-natural experiments, analyzing the transmission mechanisms through which innovative industrial clusters influence industrial chain resilience.
This paper draws on panel data from 261 cities in China from 2010 to 2022 and employs a multi-period difference-in-differences model to evaluate the impact effects and mechanisms of innovative industrial cluster development on urban industrial chain resilience from the perspective of collaborative innovation.The study finds that the pilot policies for innovative industrial clusters significantly enhance the resilience level of urban industrial chains.Mechanism tests indicate that the pilot policies promote the enhancement of industrial chain resilience through four pathways: driving innovation and innovation agglomeration, optimizing industrial structure, increasing efficiency of industrial agglomeration, and upgrading the employment market.Further research finds that the policy effects exhibit significant spatial differences: in large cities with a permanent population of over one million, non-old industrial base regions, and areas with higher cultivation of new productive forces, the promotion effect of innovative industrial clusters on industrial chain resilience is more significant.
On the basis of the research findings, this paper offers the following policy recommendations:First, the government should deepen the coordinated development of innovation agglomeration and industrial clustering by promoting close integration among research institutions, universities, and industrial chain enterprises to facilitate technology spillovers and knowledge sharing, strengthen the integration of production, education, research, and application, and accelerate the transformation of innovation achievements. Second, efforts should be made to optimize and upgrade the industrial structure by supporting the development of high-tech and high value-added industries, promoting the transformation of traditional industries, and enhancing the cultivation and introduction of innovative talents to improve labor skills and innovation capabilities. Third, industrial cluster construction should be advanced in accordance with local conditions: the eastern and central regions should focus on improving cluster quality and attracting high-end talents, while the western region should develop characteristic industries aligned with local resource endowments to promote economic diversification. Finally, digital infrastructure construction should be strengthened by accelerating the deployment of 5G, industrial internet, and big data applications to enhance the digitalization level of industrial chains and improve intelligent collaboration and risk resistance capabilities.

In the context of global technological competition, supply chain realignments, and the rapid rise of modern industrial systems, core technologies in key fields have emerged as a crucial driver for scientific progress and a key to achieving national self-reliance in science and technology. These technologies hold significant strategic and economic value. Facing a complex external environment, enterprises need to innovate in core technologies to gain and expand competitive advantages. However, independent innovation often involves high costs, elevated risks, and long development cycles. Therefore, cooperation among enterprises, governments, and research institutions is essential. Strategic alliances, which enable enterprises to pool resources, mitigate risks, and leverage complementary capabilities, have become a vital mechanism for addressing capability gaps and fostering sustainable competitive advantages. This collaborative approach has consequently become an increasingly vital mechanism for enterprises to achieve the innovation of core technologies in key fields.
　In order to investigate the impact of strategic alliances on the innovation of core technologies in key fields, this study collects the strategic alliance announcements issued by Chinese A-share listed companies from 2009 to 2023, and uses relevant financial and patent data to conduct empirical tests. It employs the dual fixed effect model to analyze the potential impact of strategic alliance cooperation on the innovation of core technologies in key fields. Then ,by building on the strategic gap theory, the study reveals the transmission path of strategic alliance to promote the innovation of core technologies in key fields by attracting patient capital and restraining managerial myopia.Finally, it comprehensively examines the impact of different conditions and types of strategic alliances on the innovation of core technologies in key fields, which is helpful to further understand the relationship between them.
　The conclusions are as follows: Firstly, the participation of enterprises in strategic alliance is conducive to promoting the improvement of the innovation of core technologies in key fields, and this conclusion still holds after the endogeneity test and the robustness test. Secondly, attracting patient capital and restraining managerial myopia are the mechanisms of strategic alliance to promote the innovation of core technologies in key fields. From the external perspective, strategic alliance is conducive to attracting patient capital, easing the financing constraints, and promoting the innovation of core technologies in key fields; from the internal perspective, strategic alliance can restrain the managerial myopia, optimize the innovation decision, and promote the innovation of core technologies. Thirdly, when considering the influence of other factors, the role of strategic alliances in promoting innovation of core technologies within key fields becomes more nuanced. From an external perspective, strategic alliances formed by enterprises that place a strong emphasis on cooperative culture and have a top management team with a high proportion of academic background demonstrate a more pronounced effect in driving such innovation. Internally, equity-based strategic alliances, as well as those with universities, research institutions, and enterprises as cooperative partners, exhibit a more significant impact on fostering innovation of core technologies in key areas.
This study enriches the literature in the field of strategic alliance and core technologies in key fields, and provides theoretical basis and empirical evidence support for enterprises to participate in strategic alliances to achieve the innovation of core technologies in key fields. Enterprises are advised to actively engage in strategic alliances to boost the innovation of core technologies in key fields. Internally, they should leverage strategic alliances to attract patient capital, securing financial backing for technological innovation. Externally, through collaboration within strategic alliances, they can mitigate managerial myopia, enhance tolerance for innovation failures, and foster a stable environment for innovation. Government departments should incentivize enterprises to engage in strategic alliances, increase financial investment in strategic alliances, establish special funds to guide patient capital toward the innovation of core technologies in key fields, and create a favorable policy environment for the sustainable development of strategic alliances through policy guidance and legal safeguards. In this way, the significant role of strategic alliances in promoting the innovation of core technologies in key fields can be fully realized.

For little giant firms that deeply cultivate key technology fields and attach importance to basic research, delving into scientific knowledge and developing scientific research can not only guide the direction of technological upgrading and clarify the strategic layout of the firm, but also provide more advanced technological theoretical support to help firms achieve major technological breakthroughs. Due to the high risks and high costs involved in conducting scientific research by firms, it is increasingly difficult to rely solely on their own technology and knowledge for scientific research. In addition, relying solely on R&D cooperation in a single discipline within the firm makes it difficult to achieve complex scientific research and high-quality innovation. The interdisciplinary integration of fields has thus made interdisciplinary scientific collaboration an inevitable trend for firms. Enhancing ambidextrous innovation in specialized, sophisticated, differential and innovative (SSDI) little giant firms through interdisciplinary scientific collaboration is a research proposition of considerable practical significance.
However, current research mainly examines how knowledge combinations within a firm's own field affect innovation, but overlooks the impact of interdisciplinary knowledge. On one hand, interdisciplinary scientific collaboration transcends the boundaries of single-disciplinary knowledge, enabling the integration and penetration of knowledge from different disciplines, expanding the exchange of knowledge within and across firms’ domains, facilitating the combination of knowledge production and application, and the integration of scientific theory and social practice, thereby stimulating scientific creativity in firms. On the other hand, interdisciplinary collaboration involves innovative entities such as firms, universities, and research institutions, forming a development system centered on knowledge production, spanning from basic research to the development and application of technological products, within a knowledge innovation value chain. The collaboration among these entities reduces the overall risk borne by each party. Due to their focus on specific fields or technologies, little giant firms often have limitations in their professional knowledge and skills. Additionally, their fixed R&D and innovation models are prone to encountering "innovation dilemmas". Moreover, ambidextrous innovation typically involves knowledge restructuring across multiple fields. Therefore, engaging in interdisciplinary scientific collaboration is crucial for little giant firms to achieve ambidextrous innovation.
Following the theory of knowledge recombination, this study takes 2 542 SSDI little giant firms from 2000 to 2023 as samples and constructs a dataset containing academic papers published by firms and universities over the years for empirical analysis. Previous research has shown that exploratory innovation and exploitative innovation are relatively opposing in nature. This study takes these two types of innovation as the dependent variables, with interdisciplinary scientific collaboration as the independent variable and the substitutability of firms' knowledge as the mediating variable. Considering the availability of data and the experience of existing research, this paper sets control variables at both the micro and macro levels of the firm's environment and employs a fixed-effects panel negative binomial regression model for hypothesis testing. The research findings indicate that interdisciplinary scientific collaboration has a significant positive effect on both exploratory innovation and exploitative innovation; interdisciplinary scientific collaboration has a significant positive impact on knowledge substitution; knowledge substitution plays a partial mediating role between interdisciplinary scientific collaboration and firms' ambidextrous innovation.
The study reveals the mechanism of interdisciplinary scientific collaboration on firms' ambidextrous innovation, offering practical insights for the innovation management of little giant firms. It highlights that interdisciplinary collaboration significantly impacts firm innovation activities by facilitating cross-disciplinary cooperation between firms and universities. This allows firms to access a broader range of disciplinary knowledge and ideas, promoting the intersection and integration of knowledge. The findings complement existing research on scientific collaboration and provide evidence for a deeper understanding of the value of firm scientific collaboration.

The report of the 20th National Congress of the Communist Party of China highlighted the importance of “adhering to sustainable development”, raising critical questions for enterprises about how to achieve sustainable development and foster sustainable development. Traditional industries in China often grapple with issues of high input, high consumption, and high pollution. Thus, a pressing challenge is how to leverage green innovation in the process of digital transformation to reduce environmental pollution, improve ecological quality, and achieve sustainable corporate development. Green innovation not only enhances corporate core competitiveness and economic performance but also effectively reduces pollution, thereby strengthening corporate capability for sustainable development. By embedding green innovation within digital transformation frameworks, enterprises can forge a direct nexus between technological progress and ecological responsibility. Analyzing the interplay among digital transformation, green innovation, and corporate sustainable development, with a particular focus on the mediating role of green innovation in the relationship between digital transformation and sustainable development, offers valuable insights and empirical support for understanding how digital transformation can facilitate sustainable corporate growth. Such research is crucial for advancing China′s digital infrastructure and fostering high-quality, sustainable economic and social development.
Thus, using data from A-share listed companies in China's Shanghai and Shenzhen stock exchanges from 2012 to 2023, this study employs text analysis and machine learning techniques to measure the level of corporate digital transformation. It further constructs a theoretical framework and an empirical model to examine the relationships among digital transformation, green technological innovation, green management innovation, and corporate sustainable development. Specifically, the study investigates the impact of digital transformation on corporate sustainability, the mediating effects of green technological and green management innovations, as well as the synergistic interaction between the two types of innovation.
Findings reveal that digital transformation significantly promotes corporate sustainable development. Digital transformation elevates organizational performance by strategically digitizing five critical domains: organizational management, market operations, product development, manufacturing processes, and supply chain management. This integration optimizes economic efficiency, strengthens social responsibility, and reduces environmental impact, collectively driving sustainable growth. In addition, firm size and firm age have a positive effect on sustainability, while a higher asset-liability ratio exerts a negative impact.Further analysis reveals that digital transformation accelerates the process of green technological innovation through the application of digital technologies and improves innovation efficiency. At the same time, green management innovation contributes to sustainability by enhancing corporate environmental responsibility, building social trust, and encouraging employee participation in green initiatives. Digital transformation thus fosters sustainable development by improving both green technological and green management innovation. Empirical results confirm the theoretical predictions: both green technological and green management innovations serve as significant mediators in the relationship between digital transformation and corporate sustainability.Regarding the synergistic effect between green technological and green management innovations, green technological innovation provides firms with environmentally friendly solutions, while green management innovation strategically supports their implementation. Their mutual reinforcement enhances corporate adaptability in complex markets,and supports the development of long-term environmental strategies. Empirical analysis validates the existence of this synergy and its positive impact on corporate sustainability.
The main contributions of this paper are threefold. First, this study systematically investigates the impact of digital transformation on corporate sustainability. Second, it disaggregates the broad concept of corporate green innovation into two distinct dimensions of green technological innovation and green management innovation and examines both the mediating effects of each type of innovation and their synergistic interaction. This offers a new perspective for understanding the relationship between digital transformation and sustainability. Third, by exploring the interconnections among digital transformation, green innovation, and corporate sustainable development, and analyzing the mediating role of green innovation, the study not only provides a mechanism-based explanation and empirical evidence for how digital transformation can drive sustainable development, but also offers theoretical insights and policy implications to support the development of China′s digital infrastructure and the pursuit of high-quality, sustainable economic and social progress.

Manufacturing enterprises represent a key focus area for developing new quality productive forces in China. In response to the urgent need for profound transformation in production methods, the development of digital finance provides a scientific framework and practical solutions to address the pivotal challenge of understanding and fostering new quality productive forces. For manufacturing enterprises, digital finance, characterized by its innovative, technological, and inclusive nature, generates significant technological spillover effects. It has increasingly emerged as a vital driver for developing new quality productive forces in the manufacturing sector, offering robust support for their transformation and upgrading efforts.
Existing literature primarily explores perspectives such as financial agglomeration and patient capital. However, studies focusing on the relationship between digital finance in micro-manufacturing enterprises and new quality productive forces remain relatively scarce.Amid challenges such as high external dependency on core technologies, manufacturing firms urgently need to innovate rapidly to develop new quality productive forces. This requires these enterprises to possess advanced innovative thinking and a strong willingness to innovate, with the level of risk-taking playing a crucial role in this process. The risk-taking level of manufacturing enterprises directly impacts organizational efficiency, productivity, and creativity. Digital finance, with its financial accelerator function, can effectively alter management's risk aversion tendencies, encouraging them to invest in high-risk, high-return projects, thus continuously improving technological innovation levels and fostering the ongoing emergence and enhancement of new quality productive forces. Therefore, risk-taking likely plays a channeling role in the relationship between digital finance and new quality productive forces in manufacturing enterprises. Furthermore, manufacturing enterprises' decision-making is constrained by internal resource endowments and influenced by external market conditions. Environmental uncertainties can exacerbate information asymmetry, underscoring the importance of digital finance in addressing these challenges. However, current research has not yet integrated risk-taking and environmental uncertainty into the framework to examine the relationship between digital finance and new quality productive forces, leaving their underlying mechanisms unexplored.
This paper uses a sample of Chinese listed manufacturing enterprises from 2013 to 2022 to examine the impact of digital finance on the development of new quality productive forces in the manufacturing sector. The findings reveal that digital finance significantly enhances the new quality productive forces of manufacturing enterprises. Digital finance increases enterprises' risk-taking levels, with risk-taking serving as a mediating mechanism between digital finance and new quality productive forces. Environmental uncertainty positively moderates the relationship between digital finance and enterprise risk-taking, amplifying the effects of digital finance on new quality productive forces. Heterogeneity analysis shows that digital finance consistently enhances the development of new quality productive forces in manufacturing enterprises, regardless of whether they are heavily polluting or non-heavily polluting, or whether they belong to traditional industries or strategic emerging industries. However, this promotion effect is more pronounced in non-heavily polluting enterprises and within the context of traditional industries.
This study investigates the impact of digital finance on the new quality productive forces in manufacturing enterprises, offering some novel insights. First, unlike previous research that primarily explored the impact of digital finance on new quality productive forces at a macro level, this study adopts a micro-level perspective to investigate how digital finance influences the new quality productive forces of manufacturing enterprises. This approach deepens the understanding of the underlying mechanisms and extends the application of financial development theory in this context. Second, the study identifies risk-taking as a key mediator in the relationship between digital finance and new quality productive forces. It clarifies how digital finance empowers manufacturing enterprises by influencing their willingness to undertake high-risk, high-return projects. Third, it empirically examines the moderating mechanism of environmental uncertainty in the relationship between digital finance and risk-taking in manufacturing enterprises. The findings shed light on the boundary conditions under which digital finance influences enterprise risk-taking and enriches the theoretical exploration of contingency factors and functional mechanisms in the interplay between digital finance and risk-taking.Fourth, the study provides nuanced insights into the heterogeneous effects of digital finance across enterprises with different pollution characteristics and between traditional and strategic emerging industries.

Firms conducting basic research can expand their influence, but at the same time, they also face the risk of knowledge spillover. However, there is little research discussing how different technological features of knowledge spillovers affect source firm′s market performance. Specifically, there are two main gaps first, insufficient attention has been paid to the flow of knowledge between science and technology, lacking a perspective on the dynamic evolution of knowledge; second, there is a lack of differentiation among the technical elements generated by knowledge spillover, overlooking the distinct technical characteristics that the spillover of basic research exhibits in subsequent technological fields. This study delves into the dual effects of knowledge spillover in firms' basic research, focusing on how different technological characteristics emerging from such research impact firms' market performance.
This article presents a statistical analysis of the knowledge spillover from the publication of papers by 216 listed firms in China and subsequent patent citations by other enterprises, and constructs an imbalanced panel data between 2000 and 2022. The study uses two technical characteristics of basic research (technology diffusivity and technology expandability) to analyze the impact mechanism of knowledge spillovers on the market performance of firms. The findings demonstrate how these two dimensions of technology derived from basic research influence a firm′s market performance, both positively and negatively. This study contributes to understanding the specific mechanisms through which knowledge spillover affects firm innovation output and market positioning.
The key findings reveal a contrasting impact of the two technological characteristic mechanisms. Technological diffusivity tends to have a negative impact on market performance, suggesting that when firms' basic research is easily replicable, it diminishes a firm′s competitive edge. Conversely, technological expandability is associated with a positive market impact, indicating that research that can be expanded and built upon stimulates market performance by fostering innovation that is more scalable. The paper also discusses how team member mobility within firms can influence these effects. High team member mobility tends to mitigate the positive impacts of technological scalability on a firm′s market performance while exacerbating the negative impacts of technological diffusivity. This introduces a complex interplay between internal firm dynamics and the external market effects of basic research. Moreover, the research contextualizes these findings within different external market environments, suggesting that the dual effects of knowledge spillover are modulated by external market conditions. This aspect adds a layer of depth to the strategic decisions firms must make regarding their engagement in basic research.
This article helps the academic community understand the behavioral motivations of enterprises in basic research phenomena and their strategic choices in technical elements of basic research. In terms of research innovation, this study contributes to the academic discourse by providing a nuanced analysis of the specific technological characteristics of basic research and their distinct impacts on market performance. It extends the literature on innovation management by highlighting the strategic importance of managing not just the output of basic research but also its inherent characteristics that influence knowledge spillover.
In conclusion, this article enriches the understanding of how firms' basic research impacts market performance through the lens of knowledge spillover. It offers valuable insights for firm strategists and policymakers on optimizing the benefits of basic research while mitigating its risks. The findings advocate for a strategic approach to managing the expandability and diffusivity of technological innovations derived from basic research, ensuring that firms can leverage these for competitive advantage in a dynamic market environment. This comprehensive analysis not only advances academic knowledge but also provides practical frameworks for enhancing firm innovation strategies in the face of global competitive pressures. The nuanced understanding of the dual effects of knowledge spillover as presented in this study is crucial for firms aiming to sustain and enhance their market positions through strategic innovation management.