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.