Key core technologies play a decisive role in specific historical periods and industrial domains. Advancing innovation in these technologies is essential for China's transition to a strong science and technology country. However, Chinese latecomer firms face dual constraints in this process: technological blockades from forerunner countries and insufficient indigenous technological accumulation. How these firms overcome these constraints to achieve innovation has become a critical management issue. Existing research has paid insufficient attention to dynamic process mechanisms under different starting points, particularly failing to incorporate technological accumulation into the research context. Given that theoretical development lags behind China's cutting-edge innovation management practices, this paper employs a single-case exploratory case study method. Taking China Railway Construction Heavy Industry Corporation Limited (CRCHI), a leading enterprise in China's tunnel boring machines industry, as the research object, it explores from a dynamic process perspective: under the dual constraints of technological blockades from forerunner countries and a lack of their own technological accumulation, how do Chinese latecomer firms mobilize complementary innovation entities to collaborate, cobble together multi-source innovation elements, and gradually achieve key core technology innovation? It then aims to extract the dynamic process mechanism of key core technology innovation in latecomer firms. The study finds that key core technology innovation in latecomer firms is a dynamic process wherein they play a leading role, mobilize complementary innovation entities to collaborate, and thereby create value. Specifically, first, under the guidance of entrepreneurial spirit and guaranteed by innovation institutions, latecomer firms do their utmost to invest in basic innovation elements such as talent and funds to fill the gap of lacking technological accumulation. This serves as the foundational starting point for key core technology innovation and lays the groundwork for subsequently leveraging external forces like the government. Second, in the interactive process of government guidance-firm undertaking, latecomer firms convert external support into motivation to mobilize complementary innovation entities for collaboration. Based on exploratory and search-based innovation element bricolage models, they collaborate with complementary entities to gradually cross two major chasms in key core technology innovation. Third, the progressive connection and cyclical evolution of exploratory and search-based bricolage gradually generate economic, technological, and industrial value. This trifecta marks the successful realization of key core technology innovation by latecomer firms, thereby completing a dynamic closed-loop process. The findings contribute to theoretical innovation in three aspects: First, by incorporating technological accumulation as a contextual variable, this study identifies the starting point of key core technology innovation in latecomer firms under the dual constraints of technological blockades from forerunner countries and a lack of their own technological accumulation, addressing the theoretical gap in existing research regarding insufficient attention to innovation processes of firms with different starting points. Second, from the perspective of government-enterprise interaction, it reveals the motivational mechanism for latecomer firms to mobilize complementary innovation entities for collaboration, deconstructing the implementation process of latecomer firms undertaking strategic guidance from the government and mobilizing complementary entities for collaboration. This breaks through the limitation of previous studies that primarily focused on the unilateral perspective of government support. Third, centering on the dynamic process of key core technology innovation, the study proposes differentiated innovation element bricolage models for collaboration between latecomer firms and heterogeneous innovation entities, revealing their progressive connection and cyclical evolution. This breaks through the theoretical cognition that treats innovation entities as “homogenized” units. These findings not only deepen the theory of key core technology innovation but also provide theoretical support and practical insights for governments and enterprises to engage in key core technology innovation activities, holding significant importance for China's strategic goal of achieving self-reliance and strength in science and technology.

Specialized, refined, differential, and innovative (SRDI) firms share typical characteristics of small and medium-sized firms, such as limited scale and resource constraints. They also suffer from higher vulnerability risks due to their specialized core businesses, rendering them more susceptible to concentrated policy shocks and industrial cycle fluctuations. Although existing studies confirm SRDI firms possess superior organizational resilience, the driving factors and formation mechanisms behind such resilience remain underexamined. Most prior research centers on static antecedents including leaders' long-term orientation and digital transformation, or investigates resilience-related outcomes such as new quality productivity and industrial chain resilience, while largely overlooking the intrinsic mechanisms of resource transformation. Specifically, it remains unclear how SRDI firms translate limited resources into resilience potential and what differentiated development models arise from this process. To fill these research gaps, this study draws on the attention-based view to address two core research questions: (1) What key resources enable SRDI firms to develop organizational resilience? (2) How do resource-constrained SRDI firms build organizational resilience? This study selects three firms, namely Zhongzhen, Jiashite and Baiying, as case study samples. All three firms have obtained provincial or national SRDI certification and exhibited prominent organizational resilience, as reflected in their rapid post-crisis recovery and stable operational growth, which makes them ideal research samples. Empirical data are collected via semi-structured in-depth interviews and secondary materials from firms, governmental authorities and industry institutions related to SRDI development. This paper adopts a multiple-case comparative approach and implements a two-stage analytical framework of "within-case analysis followed by cross-case comparison" with iterative refinement between data and theory. The findings reveal that the cultivation of SRDI firms' organizational resilience depends on four core resource dimensions: structural resources, relational resources, cognitive resources and niche resources. Among them, niche resources act as the core distinctive feature of SRDI firms. Furthermore, organizational attention explained by the attention-based view serves as a crucial driving force for resilience building. By coordinating attention focus and systematic attention allocation mechanisms, SRDI firms can effectively convert inherent resource endowments into tangible resilience capabilities. This study identifies three distinct resilience-building models for SRDI firms: the iterative adaptation model, the technology defense model, and the strategic agility model. Under the iterative adaptation model, firms with external attention focus and informal allocation mechanisms continuously monitor market and industrial demands, and dynamically optimize technologies, management systems and operational procedures to cultivate environment-adaptive resilience. Under the technology defense model, firms prioritize internal attention and formal institutional arrangements, invest heavily in technological R&D and process standardization to form technical moats, hedge against external risks, and establish stable anti-shock resilience. Under the strategic agility model, firms integrate external attention focus with formal attention allocation to promptly respond to market fluctuations and policy updates, and achieve agile response resilience through rapid strategic iteration and flexible resource deployment. This study follows the logic of literal replication in multiple-case research by demonstrating that SRDI firms require four types of resources for the construction of organizational resilience, and applies the logic of theoretical replication by identifying three differentiated construction modes of organizational resilience. It reveals the unique resource foundations required for building organizational resilience in SRDI firms and refines the dimensions of attention focus and attention allocation, thereby enriching the theoretical mechanism linking organizational attention to resilience construction. In practice, this study offers actionable implications for SRDI firms. It highlights the necessity of proactive accumulation in routine contexts, rational selection of resilience construction modes matching resource constraints, and consolidation of industrial competitive advantages to mitigate the impacts of adverse events.

As the core carrier of national strategic scientific and technological strength, technology-leading enterprises serve as the main force and leader in breaking through the "bottleneck" constraints of key core technologies and achieving the alternative innovation of key core technologies. Alternative innovation theory provides new theoretical underpinnings for systematically deconstructing the process of domestic substitution from technological breakthroughs to market application. Compared to ordinary enterprises, technology-leading enterprises possess unique advantages such as powerful resource integration capabilities and globally leading innovation R&D systems. Consequently, a deep investigation into the intrinsic mechanism underpinning their leadership in alternative innovation of key core technologies holds significant theoretical and practical implications. From the theoretical perspective of multiple institutional logics and using a case study approach, this research focuses on the practice of alternative innovation in international mobile communication standards led by China Mobile Group, to systematically analyze the mechanisms of alternative innovation for key core technologies led by technology-leading enterprises. The access channels for primary and secondary data are well-established. Primary data were collected from November 2024 to May 2025 through semi-structured in-depth interviews with seven core relevant personnel from China Mobile Group and internal documents, accumulating 390 minutes of interviews and generating 122 000 words of transcribed text. Secondary data encompassed corporate annual reports, official information, media coverage, and industry publications, totaling approximately 525 000 words. A complete chain of evidence was formed through cross-validation between primary interview data and secondary public sources. Data analysis employed the analytical induction method for case study research, integrating theories of substitution-based innovation in key core technologies and multiple institutional logics, sequentially conducting first-order initial coding, second-order thematic categorization, and core theoretical dimension refinement. Simultaneously, research reliability was strengthened through double-blind coding, anonymization processing, and third-party expert evaluation of divergence points, ensuring deep connections among data, phenomena, and theory, as well as transparent theoretical construction processes and reliable research conclusions. The findings reveal that, first, the alternative innovation of key core technologies led by technology-leading enterprises demonstrates characteristics of the new nationwide system, where the state plays a strategic guiding role, research institutions undertake the task of technological breakthroughs in the initial phase, and technology-leading enterprises play a central role in technological upgrading and market substitution. Second, technology-leading enterprises achieve the upgrading and market substitution of key core technologies by aligning with state logic and integrating technological logic with market logic. Third, during the process of alternative innovation for key core technologies, multiple institutional logics demonstrate characteristics of dynamic co-evolution and mutual reinforcement. The theoretical contributions of this paper are as follows: First, it constructs a theoretical model of alternative innovation for key core technologies led by technology-leading enterprises. Second, this research deepens and broadens the theory of alternative innovation while enriching the literature on technology-leading firms and key core technologies. Third, it reveals the mechanisms of dynamic coordination and mutual reinforcement among multiple institutional logics in alternative innovation processes, thereby advancing existing research on multiple institutional logics. To achieve alternative innovation in key core technologies, the practical recommendations are as follows: Government departments should rely on the new nationwide system to refine top-level strategic planning and provide dynamic resource support. Technology-leading enterprises should leverage their core capabilities and endowments to play a leading role in driving innovation of key core technologies. Research institutes, in turn, should focus on national strategic needs, undertake technological breakthrough tasks in the initial phase, and maintain close collaboration with technology-leading enterprises. This study not only expands the explanatory scope of the multiple institutional logics theory, but also clarifies the alternative innovation mechanism for key core technologies led by technology-leading enterprises.

With the continuous refinement of open data platforms, their operational models have evolved from one-way static linear structures to multi-dimensional interactive networks. Ecosystem theory has progressively integrated into the development of data factor theory. The open data ecosystem underpinned by open data platforms is increasingly taking shape, playing a pivotal role in enhancing the efficacy of the national innovation system, driving the market-oriented development of data, and supporting urban governance. Therefore, establishing a coordinated, orderly, and sustainable open data ecosystem that effectively releases the value of innovation is critical not just for unlocking economic value and improving governance transparency, but for advancing China's broader digital transformation. This study follows the logical thread of "ecological transformation-generative logic-value analysis-transformation mechanisms". Grounded in a socio-ecological model, it employs theoretical deduction to analyze the evolutionary trajectory and ecological transformation of data openness. The research deconstructs the generative logic of open data ecosystems from three perspectives: constitutive logic, theoretical logic, and practical logic. Furthermore, by explicating the innovation value of open data ecosystems, it constructs an entity-element framework to reveal the mechanisms through which these ecosystems realize and translate innovation value. Research findings are as follows: First, public data openness has evolved through four distinct phases: initial experimentation, the rise of open data platforms, infrastructure building, and finally, deep ecosystem integration. This evolution reconfigures three core relationships: participants become ecosystem builders, display platforms become interactive communities, and one-way linear models become multi-dimensional networks. Second, the generation logic of the open data ecosystem represents the unity of constitutive logic, theoretical logic, and practical logic. Among these, constitutive logic serves as the foundation, theoretical logic as the extension, and practical logic as the implementation. These three logics are interlinked, forming a complete system. From a theoretical perspective, the open data ecosystem comprises five levels: individual, interpersonal, organizational, community, and policy. While these levels remain relatively independent, they are interwoven and mutually influential. The individual level occupies the core position and is nested within the interpersonal level, which in turn is encompassed by the organizational level. The individual level is encompassed by all levels, and every level is constrained and shaped by the policy level. Third, entity and element are key drivers of innovation value in the open data ecosystem, promoting enterprise innovation, the emergence of new industries, and innovations in digital governance across multiple dimensions. The realization of innovation value in the open data ecosystem is a complex, dynamic, and non-linear co-evolutionary process. It is generated under the combined effects of shared mechanisms, market mechanisms, and incentive mechanisms, emerging across various fields at both micro and macro levels, and promoting value leapfrogging through a continuous spiral of iteration, steady value accretion, and gradual expansion. Fourth, the ecosystem enhances innovation value through three distinct lenses. From the perspective of the entity, it transforms cooperative and competitive inter-organizational relationships, converting external forces into synergistic momentum. From the perspective of factors, it elevates innovation value by optimizing data, empowering traditional factors, and promoting factor integration. Finally, from a dual perspective, the ecosystem boosts innovation value through industrial convergence, feedback coordination, and supply-demand matching. This study combines the theoretical framework for mechanism analysis with the goal of realizing innovation value. The framework encompasses entity collaboration, element linkage, and the interaction between the two. Guided by this approach, we comprehensively connect all components in the open data ecosystem. This connection is achieved through diversified coordination, personalized adaptation, convenient services, and scenario-based deployment. The study designs pathways for realizing innovative value across three dimensions. First, entity collaboration serves as the core. Second, element linkage acts as the support. Third, entity-element interaction functions as the driving force. The ultimate aim is to promote a coordinated, orderly, and sustainably developed open data ecosystem. This approach effectively unleashes innovative value and propels data resources from simple openness toward deep innovation utilization.

Competition among complementors on digital platforms for limited platform resources and user resources has shown a trend toward involution, profoundly impacting their innovation and performance outcomes. This not only concerns individual survival but also the sustainable development of the platforms themselves. According to resource dependence theory, the competitive pressure generated by resource scarcity has an important impact on organizational behavioral choices and performance achievement. However, examine studies on complementor performance primarily examines internal attributes, strategic behaviors, and external environmental factors. In contrast, studies on complementor competition tend to focus on the rivalry over user resources, while largely overlooking competition for platform resources. Overall, there is a notable paucity of research investigating how complementor competition within digital platforms affects their performance, as well as the specific mechanisms through which resource competition affects complementor performance, especially from the perspective of complementor experience. Therefore, drawing on resource dependence theory, the study categorizes resource competition into platform resource competition and user resource competition, and takes iOS store APP in China as the research sample to explore the impact of resource competition on complementor performance, then further explores the mechanism of iterative agility and iterative novelty, then reveals the moderating role of complementor product experience and ecological experience from the perspective of experiential learning, and finally probes into the influence of resource competition on reputational heterogeneity of complementor performance effects. The study constructs a regression model to verify the proposed hypotheses, and then it adopts a two-way fixed effects model for regression analysis, and incorporates both individual fixed effects and time fixed effects. It is found that (1)the more intense the competition among complementors for platform and user resources, the greater the degree of inhibition on their performance. (2)The mechanism of action is found to be that this inhibitory effect is realized by reducing the complementors' iterative agility and iterative novelty. (3)Complementor experience serves as a key contingency condition in how resource competition affects performance. Specifically, product experience diversification does not confer an experiential advantage in coping with resource competition; on the contrary, it amplifies the negative impact of such competition. By engaging in multiple concurrent development projects, complementors with diversified product experience suffer from resource fragmentation, insufficient strategic focus, and poor internal coordination. These challenges have weakened their ability to achieve deep vertical specialization and focus on continuous innovation, thereby exacerbating the negative impact on their performance due to the intensified competition for platform and user resources. In contrast, the richness of ecosystem experience enables complementors to have a deeper understanding of platform rules and manage user relationships more effectively. This enables complements to maintain stable market responsiveness and performance even in the context of intensified user resource competition. (4)Increased resource competition has a stronger dampening effect on the performance of complementors with higher reputations. The main theoretical contributions are as follows: First, the impact of resource competition between complementors entering the platform and other complementors regarding both platform and user dimensions on their performance is explored, which enriches the related literature on the relationship between resource competition and complementor performance. Second, iterative agility and iterative novelty are introduced into the framework of resource competition affecting complementor performance, which elucidates the iterative innovation mechanism by which resource competition affects complementor performance. Third, complementor experience is introduced as a boundary condition, and its moderating effect is explored by dividing it into two dimensions of product experience diversity and ecosystem experience richness in combination with the characteristics of digital platforms, expanding the mechanism of the moderating effect of experience. Fourth, the reputation heterogeneity of the impact of platform resource competition and user resource competition on complementor performance is examined.

In the context of increasing global economic volatility and supply chain disruptions, strengthening urban economic resilience has emerged as a critical development imperative for regional sustainability and competitiveness. While substantial research exists on traditional industrial agglomeration, the specific mechanisms through which industrial co-agglomeration—which refers to the synergistic clustering of interrelated sectors such as manufacturing and producer services—influences economic resilience remain theoretically and empirically underexplored. This study addresses this significant research gap by systematically examining how co-agglomeration affects urban economic resilience, with particular emphasis on comparative analysis between China's resource-based cities (RBCs) and non-resource-based cities (NRBs), and investigates the crucial mediating role of innovation resource allocation efficiency and the moderating effect of industrial structure upgrading. Utilizing balanced panel data from 284 Chinese prefecture-level cities (107 RBCs and 177 NRBs) from 2007 to 2022, this study employs a two-way fixed effects model to control for unobservable city- and time-specific factors, supplemented by rigorous mediation and moderation analyses. Urban economic resilience is operationalized as a composite index capturing shock resistance, adaptation, and recovery. Industrial co-agglomeration is quantified via indices measuring spatial coupling and functional interdependence between manufacturing and producer services, derived from establishment-level employment and geospatial data. The study finds that co-agglomeration significantly strengthens economic resilience, with a notably stronger effect in RBCs. This suggests that spatial synergy among interrelated industries offers a vital pathway for resource dependent cities to diversify and stabilize their economies. Furthermore, this beneficial impact is most pronounced during post-crisis recovery phases, where industrial synergy accelerates economic reorganization and regenerative growth following external shocks. Mechanism analysis reveals that alleviating innovation resource misallocation serves as a key channel through which co-agglomeration improves resilience, as geographic proximity facilitates more efficient matching of talent, knowledge, and capital. By reducing information asymmetries and transaction costs, this enhanced matching efficiency directly boosts urban adaptive innovation capability and technological flexibility. Meanwhile, industrial structure upgrading emerges as a significant negative moderator, particularly in NRBs, indicating that rapid structural transformation may weaken the synergistic networks that underpin resilience. This study contributes to the literature in several important dimensions. Theoretically, it bridges industrial geography with economic resilience theory by explicitly articulating and empirically validating the mechanistic pathways of innovation efficiency enhancement and structural change dynamics through which co-agglomeration influences urban adaptive capacity. Empirically, it advances the field through its nuanced comparative analytical framework that accounts for fundamental differences in urban typology, resource endowment, and developmental stage, moving beyond homogenized conceptualizations of urban economies. Methodologically, it develops refined measures for both co-agglomeration and economic resilience that better capture their multidimensional nature and complex interactions. In terms of policy, the results highlight the critical need for differentiated approaches tailored to specific city contexts. For RBCs pursuing structural transformation, strategically fostering industrial co-agglomeration between manufacturing and advanced producer services should form a cornerstone of economic resilience-building strategies, offering a vital pathway to diversify and stabilize economies constrained by resource dependency. Conversely, for non-resource-based cities, policymakers should adopt more calibrated approaches to industrial upgrading that carefully balance the pursuit of technological advancement with the preservation of resilient inter-industry linkages, avoiding the potential disruption of synergistic networks caused by rapid structural shifts. Furthermore, system-wide reforms aimed at reducing innovation resource misallocation should encompass enhanced intellectual property protection, improved university-industry collaboration mechanisms, and cross-sectoral R&D incentives to maximize the resilience benefits of co-agglomeration and ensure long-term urban adaptability in an increasingly volatile global economy.

In the contemporary competitive landscape characterized by high uncertainty, innovation consortia have emerged as strategic vehicles for transcending organizational boundaries and achieving deep integration of heterogeneous knowledge. These consortia represent an advanced evolutionary form of industry-university collaboration led by enterprises and supported by universities and research institutes to tackle critical core technologies under national strategic imperatives. Unlike traditional bilateral partnerships, these consortia integrate diverse actors including horizontal competitors into complex networks, amplifying coordination costs and governance complexity. While knowledge heterogeneity holds significant innovative potential, it also triggers the "bi-decoupling" dilemma in such multi-actor environments. Bi-decoupling reflects goal-level cognitive misalignment (divergent values creating symbolic absorption) and operational-level behavioral defocus (knowledge-power gaps deflecting collaboration), constituting a governance paradox that constrains knowledge transformation. Existing research has yet to systematically elucidate how knowledge heterogeneity influences cooperative performance within the unique organizational context of innovation consortia, nor has it proposed a governance pathway for bi-decoupling from a systemic synergy perspective. To examine the impact of knowledge heterogeneity and its governance pathways, this study constructs a theoretical model involving knowledge heterogeneity, knowledge integration, and cooperative innovation performance, introducing social trust level and intellectual property protection as moderating variables. Methodologically, this research employed a questionnaire survey targeting mid-to-senior managers of enterprises participating in innovation consortia listed in pilot rosters issued by science and technology authorities in Shandong, Jiangsu, Guangdong provinces, and the Yangtze River Delta region. Focusing on strategic emerging industries characterized by high technological breakthrough difficulty and intensive knowledge resource requirements, specifically biotechnology and new energy sectors, the study rigorously verified consortium membership through screening items confirming enterprise participation in leading-firm-driven consortia targeting key technologies and possessing explicit cooperative agreements. To mitigate common method bias, a two-phase longitudinal data collection approach was implemented with a three-month interval between waves. From April to July 2024, 542 questionnaires were distributed through offline channels and Wenjuanxing (a professional survey platform), yielding 411 valid responses after eliminating incomplete or invariant answers, representing an effective response rate of 75.8%. Multiple regression analysis was employed to test the hypotheses empirically. This research integrates the Knowledge-based View and He Xie Management Theory to establish a comprehensive analytical framework. While the knowledge-based view elucidates the strategic value of heterogeneous knowledge as a critical resource for innovation, HeXie Management theory provides a dual-pathway governance mechanism through its "He Principles" framework. The Synergistic Field, activated through robust social trust levels, enables goal alignment by fostering shared value identification and psychological security among consortium members, thereby reducing cognitive fragmentation at the strategic level. Simultaneously, the Facilitative Field, implemented through rigorous intellectual property protection, establishes clear behavioral expectations and legal safeguards that suppress opportunistic behaviors and coordinate complex knowledge interactions, thus addressing operational dissonance. The empirical results reveal a significant inverted U-shaped relationship between knowledge heterogeneity in innovation consortia and firms' cooperative innovation performance, demonstrating that an optimal level of heterogeneity maximizes cooperative outcomes through enhanced knowledge integration processes. Furthermore, social trust level significantly strengthens the relationship between knowledge heterogeneity and performance by mitigating goal-level decoupling through reinforced value identification among consortium members. Simultaneously, intellectual property protection positively moderates this relationship by reducing operational-level decoupling through effective suppression of opportunistic behaviors and providing stable institutional expectations. Both governance mechanisms work synergistically to facilitate the transformation of knowledge heterogeneity into sustainable cooperative performance, offering a comprehensive solution to the bi-decoupling challenge.

With the rapid development of the digital economy, new ventures face a highly uncertain market environment. How to achieve the continuous iteration of entrepreneurial opportunities through adaptive improvisational behavior has become a critical topic in entrepreneurial management research. Although the two major theories of "opportunity discovery" and "opportunity construction" provide foundational perspectives for understanding entrepreneurial opportunities, their limitations in explaining the dynamic evolution of opportunities are increasingly evident. To address this gap, this study draws on dynamic capability theory and integrates a dualistic perspective to construct a theoretical model of "dual entrepreneurial improvisation-serendipity-entrepreneurial opportunity iteration". It also introduces faultline dynamics as a moderating variable, aiming to reveal how exploratory and exploitative entrepreneurial improvisation drive opportunity iteration through serendipity, and to examine the boundary role of faultline dynamics in this process. This study employs a questionnaire survey method, focusing on manufacturing new ventures in China. Data were collected in multiple stages over five months, yielding 299 valid responses. The sample covers 30 provincial-level administrative regions across the country, ensuring good regional representativeness and industry diversity. All variables were measured using established scales, which were refined through a small-sample pre-test and reliability and validity analyses to form the final questionnaire. Data analysis was conducted using SPSS 26.0, employing hierarchical regression analysis, bootstrap mediation tests, interaction term regression, and subgroup comparisons to systematically test the research hypotheses. The main conclusions of the study are as follows: (1) Both exploratory and exploitative entrepreneurial improvisation positively influence entrepreneurial opportunity iteration, and dual entrepreneurial improvisation has a stronger promoting effect compared to a single type of improvisational behavior, reflecting a spiral evolution logic of "discovery-construction-rediscovery". (2) Serendipity plays a partial mediating role between dual entrepreneurial improvisation and entrepreneurial opportunity iteration, indicating that improvisational behavior does not directly generate serendipity but rather shapes the team's "alertness" and "interpretive flexibility," fostering a "prepared mind for accidental discovery" that transforms incidental clues into systematic opportunities. (3) Faultline dynamics positively moderate the relationship between dual entrepreneurial improvisation and serendipity. A high level of faultline dynamics enhances the team's ability to recognize and transform accidental opportunities during improvisation through psychological safety, cognitive reconstruction, and resource support. The innovations of this study are mainly reflected in the following three aspects: First, it breaks through the long-standing opposition between opportunity discovery theory and opportunity construction theory, proposing dual entrepreneurial improvisation as a behavioral mechanism that integrates the two, revealing the dynamic evolution logic of opportunity iteration. Second, it redefines serendipity from a traditional accidental event to a team-level "capacity for accidental discovery," validating its mediating role between improvisational behavior and opportunity iteration, thereby deepening the understanding of the formation mechanism of serendipity. Third, it introduces faultline dynamics as a contextual variable, expanding the application boundary of organizational emotional capacity in entrepreneurial behavior research and providing a micro-foundation for understanding how improvisational behavior is implemented within organizations. Consequently, the study offers several managerial implications. New ventures should institutionalize the balance between exploration and exploitation by adopting ambidextrous entrepreneurial improvisation. By encouraging teams to conduct low-cost, small-scale exploratory tests around core operations, firms can rapidly identify and capitalize on potential market opportunities while keeping risks under control. Moreover, it is crucial to convert serendipity from random luck into systematic organizational assets through institutionalized serendipity management. Managers need to possess keen insight and establish corresponding mechanisms for identification, evaluation, and conversion, turning "luck" into a manageable and accumulative competitive advantage. Finally, cultivating a bounded tolerance for failure is recommended to create psychological safety for improvisation and serendipity conversion, ensuring the organization maintains stability and resilience amid dynamic adjustments.

Innovative enterprises, with their profound high-tech potential and significant competitive advantages, occupy an important position in building an innovative country, nurturing new quality productive forces, and promoting the construc tion of Chinese-style modernization. However, high-tech potential is often accompanied by high R&D investment and high risks, which means that innovative enterprises face higher operational challenges and market uncertainties, thereby re stricting their R&D output. In the face of such a volatile market environment, innovative enterprises urgently need to ex plore a feasible path to maintain innovation vitality and improve R&D performance. Patient capital, as a form of capital that focuses on long-term value investment and pursues returns across economic cycles, is increasingly highlighting its the oretical research and practical guidance value for the typical high-investment, long-cycle, and highly uncertain R&D activi ties of innovative enterprises. During the critical period when the innovation-driven development strategy is deeply imple mented and China's economy is transitioning from a high-speed growth stage to a high-quality development stage, how to effectively utilize patient capital to activate the R&D vitality of innovative enterprises and cultivate new quality productive forces has become a focus topic of common concern in both the academic and practical circles. Existing practices have shown that opportunity windows and dynamic capabilities are important factors influencing the R&D output of enterpri ses. However, no research has confirmed what mechanism roles they play between patient capital and the R&D perform ance of innovative enterprises. Therefore, this study first clarifies the direct mechanism of patient capital on the R&D per formance of innovative enterprises through theoretical argumentation and mathematical derivation based on the endogenous growth theory; Second, from the perspective of opportunity windows and dynamic capabilities, it explores and verifies the path mechanisms of "patient capital → opportunity window → R&D performance of innovative enterprises" and "patient capital → dynamic capabilities → R&D performance of innovative enterprises". Given that the innovation-driven development strategy proposed in 2012 marks the entry of national innovation policies into an unprecedentedly intensive and coherent phase, this study examines innovative listed companies on Shanghai and Shenzhen A-shares from 2012 to 2024 as observation cases. The study first conducts descriptive statistical analysis to re veal data distribution characteristics. Second, employing a fixed-effects model, the study introduces both linear and quad ratic terms of patient capital into the regression to investigate its impact on R&D performance and the changing trend of marginal returns. Instrumental variable estimation is used for endogeneity and robustness analyses. Finally, based on the mechanism paths of windows of opportunity and dynamic capabilities, the study empirically tests the mediating effects of policy windows, market windows, and dynamic capabilities between patient capital and R&D performance. This study yields the following findings: (1) Patient capital enhances R&D performance of innovative enterprises, with this enhancement exhibiting a marginally diminishing trend, i. e. a conclusion that remains robust after endogeneity and robustness analyses. (2) Patient capital exerts dual, opposing effects on R&D performance, functioning as a "double-edged sword." Specifically, patient capital enhances R&D performance by expanding windows of opportunity (both policy-induced and market-based), while simultaneously undermining R&D performance by eroding dynamic capabilities. (3) The effect of patient capital on R&D performance is contingent upon organizational life cycle and institutional environ ment: significant for enterprises in growth and decline stages, non-state-owned enterprises, and highly competitive mar kets; yet insignificant for mature-stage enterprises, state-owned enterprises, and markets with low competitive intensity. This study contributes to the literature by deepening theoretical understanding of the economic consequences and mechanisms of patient capital, as well as illuminating the contradictory logic of its potential "enabling" versus "eroding" effects. It provides theoretical grounding for comprehensively understanding the complex relationship between patient capital and R&D performance. At the practical level, the research conclusions provide theoretical guidance and empirical ref erences for innovative enterprises to rationally expand patient capital, enhance R&D performance, and thereby promote high-quality economic development in China.

In the era of accelerating digital economy development, efficient knowledge flow within organizations has become the cornerstone for enterprises to maintain core competitiveness and innovation vitality. However, with the widespread application of AI technology in workplaces, employees' work scenarios, career expectations, and psychological contracts have undergone profound transformations. Confronted with this powerful technological impact, employees exhibit significant complexity in their knowledge management behaviors. Existing academic research on AI's influence on employees' knowledge hiding behaviors presents two diametrically opposed theoretical perspectives: "survival threats from automation substitution" and "development opportunities from human-machine collaboration". Most existing studies adopt single-perspective approaches, lacking systematic integration of these dual effects and insufficient exploration of the deep psychological cognitive mechanisms driving employees' differentiated responses, as well as organizational contextual boundary conditions. Therefore, investigating how AI technology application affects employees' knowledge hiding behaviors has become a critical research challenge in organizational behavior and human resource management fields. Building upon the theory of stress cognitive evaluation, this study constructs an integrated, moderated dual-mediation theoretical model to systematically elucidate the internal mechanisms and boundary conditions through which AI technology application is taken as an external environmental stressor and exerts influence on employees' knowledge hiding behaviors. In terms of methodology and data collection, the research targeted employees from enterprises in manufacturing, finance, and service sectors that have extensively adopted AI technologies. A two-stage time-lag questionnaire survey was employed to ensure robustness against common method bias. After rigorous data screening and matching, 379 valid samples were obtained. Subsequently, the SmartPLS software was utilized to conduct reliability and validity testing, path analysis, and hypothesis testing using a partial least squares structural equation model. The research findings reveal the "double-edged sword" effect of AI technology application on employees' knowledge hiding behavior. First, there exists a significant "threat effect" pathway. The introduction of AI technology triggers employees' obstructive cognitive evaluation, significantly intensifying their job insecurity and compelling them to perceive their knowledge as a scarce resource for job security, thereby adopting knowledge hiding as a defensive self-protection strategy. Second, there is a significant "opportunity effect" pathway. AI technology can also stimulate employees' challenging cognitive evaluation, prompting them to perceive new skill learning and career growth opportunities. To better adapt to technological changes and establish interpersonal collaboration networks, employees significantly reduce knowledge hiding behavior. Third, team motivational atmosphere plays a key moderating role. High team performance motivational atmosphere (emphasizing internal competition and social comparison) exerts a "catalytic threat" effect, significantly amplifying the indirect effect of AI positively intensifying knowledge hiding behavior through job insecurity. Conversely, high team mastery motivational atmosphere (focusing on learning growth and mutual trust cooperation) has an "empowering opportunity" effect, significantly strengthening the indirect effect of AI negatively inhibiting knowledge hiding behavior through perceived career growth opportunities. This study establishes a unified theoretical framework that demonstrates the dual mechanisms of AI technology in addressing knowledge silos. It resolves the academic debate over whether AI serves as a "knowledge barrier" or a "knowledge-sharing catalyst", while expanding the application scope of stress cognition evaluation theory in emerging technological contexts. The findings provide actionable insights for enterprises to optimize human resource management amid the AI revolution. The research cautions that while implementing AI strategies, organizations should not focus solely on technological adoption but also adopt tailored psychological interventions for employees. By fostering a learning-oriented and collaborative team culture that emphasizes expertise, enterprises can mitigate internal competition and AI-related risks, unlock organizational empowerment opportunities, and ultimately dismantle knowledge silos within organizations.

Entrepreneurial activity is a key engine of economic growth and job creation, yet sustaining entrepreneurship has become increasingly difficult as uncertainty rises and competitive environments shift rapidly. Entrepreneurial persistence is therefore crucial for venture survival and eventual success. Persistence, however, is not determined only by resources or external conditions. It also depends on how entrepreneurs interpret setbacks, evaluate the desirability and feasibility of continuing, and remain committed when alternative options become salient. In this context, cognitive flexibility is particularly relevant. Cognitive flexibility refers to an individual’s capacity to shift cognitive frames, generate alternative solutions, and adjust strategies as circumstances change. Prior research has mainly emphasized its benefits for opportunity recognition, innovation, and early stage entrepreneurial behaviors, while paying less attention to its role in sustaining entrepreneurial action. Moreover, the capacity to generate alternatives is not always beneficial, because it may also increase attention to non entrepreneurial paths. This study addresses this gap by examining when cognitive flexibility strengthens entrepreneurial persistence and when it undermines it. Drawing upon identity motivation theory, this study develops a theoretical framework that treats entrepreneurial identity centrality as a boundary condition and entrepreneurial goal commitment as the critical psychological mechanism translating cognitive flexibility into persistent entrepreneurial action. Specifically, this study argues that when entrepreneurial identity occupies a central position in the self-concept, cognitive flexibility is harnessed to advance entrepreneurial objectives, thereby fortifying goal commitment and sustained effort. Conversely, when entrepreneurial identity is weakly central, cognitive flexibility may divert attention toward alternative identities and career trajectories, eroding commitment to the focal venture. This study empirically validated this framework using a three-wave time-lagged survey design to minimize common method bias and strengthen causal inference. Following Zahra’s criteria, the investigation delimited samples to firms established within eight years, targeting founders as primary informants. Data were collected from August to October 2024 across Shanghai, Suzhou, and Shenzhen. The initial distribution of 745 questionnaires yielded 287 valid matched responses after excluding unmatched cases and invariant responding patterns. This study further controlled for individual characteristics (gender, age, education, entrepreneurial experience) and firm-level attributes (size, industry, firm age) to isolate the hypothesized relationships. Results show that cognitive flexibility does not exert a stable direct effect on entrepreneurial goal commitment. Instead, its effect depends strongly on entrepreneurial identity centrality. Simple slope analyses reveal a crossover pattern. When entrepreneurial identity centrality is high, cognitive flexibility significantly increases entrepreneurial goal commitment. When identity centrality is low, cognitive flexibility significantly decreases entrepreneurial goal commitment. In addition, entrepreneurial goal commitment significantly promotes entrepreneurial persistence and serves as the key transmission mechanism through which the interaction between cognitive flexibility and identity centrality influences persistence. Bootstrap analyses further indicate that this indirect effect is positive and significant under high identity centrality, but negative and significant under low identity centrality, with confidence intervals excluding zero in both cases. This study contributes by clarifying the double-edged nature of cognitive flexibility in sustaining entrepreneurial action. Cognitive flexibility is not uniformly beneficial; it produces opposite outcomes depending on identity structure. The findings also extend identity motivation theory by showing that identity centrality channels how cognitive resources are deployed and determines whether cognitive flexibility can be effectively converted into persistence. In practice, the results suggest that efforts to develop entrepreneurs’ cognitive flexibility should be goal-anchored rather than treated as universally beneficial. Support programs can strengthen entrepreneurial identity centrality and reinforce goal commitment through role validation, community affiliation, and structured goal setting with progress feedback, thereby improving persistence while reducing unproductive goal switching.

The deep integration of artificial intelligence (AI) has become a core strategic driver for enterprise productivity growth. However, a paradox has emerged at the micro-level within organizations: Contrary to expectations that AI would simplify workflows and enhance efficiency, employees commonly encounter a counterintuitive outcome where increased usage leads to greater exhaustion, ultimately impairing job performance. Existing literature predominantly focuses on the enabling effects of AI in sharing repetitive tasks, largely overlooking the potential psychological costs, cognitive intrusions, and subsequent efficiency declines it may trigger. To address this gap, this study aims to unravel the “black box” of how AI applications influence employee job performance, transitioning the academic focus from a single-effect analysis to a multiple-balance mechanism study. Drawing upon the Job Demands-Resources (JD-R) theory, this study constructs a dual-chain mediation model to comprehensively examine the double-edged sword effect of AI applications. The JD-R framework allows for a dialectical perspective, revealing two distinct pathways. On one hand, AI serves as a job resource by elevating task automation, which theoretically mitigates resource depletion. On the other hand, AI can act as an intrusive job demand, significantly increasing cognitive load through its sheer volume of generated information and the constant need for human verification. Work burnout is conceptualized as the critical psychological mechanism linking these technological pressures to subsequent performance changes. Furthermore, perceived job complexity is introduced as a vital boundary condition that determines whether AI functions as a resource or a demand. To empirically test the proposed hypotheses, this study employed a three-wave longitudinal research design, effectively minimizing common method bias. Data were collected over a three-month period across three distinct time points with one-month intervals. The sample comprised employees from 26 enterprises located in major Chinese economic hubs, including Beijing, Shanghai, Zhejiang, Jiangsu, and Guangdong. These firms operate in AI-intensive sectors such as information technology, high-end manufacturing, financial services, healthcare, and scientific research. After matching across the three waves, a total of 350 valid questionnaires were obtained, representing an 87.3% response rate. The data were systematically analyzed utilizing SPSS 26.0 and AMOS 24.0, while the PROCESS macro and Bootstrap resampling techniques were applied to verify the complex moderated mediation paths. The empirical results reveal that the double-edged sword effect of AI applications on job performance is profoundly contingent upon perceived job complexity. First, when employees perceive a low level of job complexity, AI applications seamlessly align with highly standardized and predictable task requirements. In this context, AI exerts a significant positive impact by acting as a resource; it enhances the level of task automation, effectively alleviates work burnout, and ultimately promotes overall job performance. Conversely, in scenarios of high perceived job complexity, tasks are typically unstructured, dynamic, and uncertain. Under these conditions, AI generates knowledge-intensive outputs that require intense human scrutiny, thereby transforming the technology from an assistive tool into a high-pressure job demand. Consequently, AI applications significantly amplify employees' cognitive load, accelerate the depletion of psychological resources, exacerbate work burnout, and ultimately diminish job performance. This research presents several theoretical innovations. Primarily, it shatters the prevailing linear cognitive assumption of AI's universal empowerment, uncovering a critical “functional reversal” of technological effects across varying task contexts. Additionally, this study successfully extends the traditional JD-R model into the digital era, expanding its theoretical boundaries to clearly define the motivation and depletion mechanisms in human-machine collaborative scenarios. Practically, the findings offer vital guidance for organizations navigating intelligent transformations. Management should abandon “one-size-fits-all” AI deployments, instead implementing differentiated strategies tailored to specific job complexities. Furthermore, organizations must empower employees with technical autonomy, which will potentially grants the right to refuse AI in highly complex tasks, while actively managing “cognitive surplus” and establishing dynamic early-warning systems to mitigate tech-induced burnout before it translates into tangible performance losses.

Against the backdrop of intensifying global S&T competition and the accelerating emergence of disruptive breakthroughs, non-consensus ideas and high-uncertainty exploration are increasingly viewed as a critical source of major original advances, what Chinese policy discourse terms "disruptive innovation" and "imaginative exploration". Yet conventional funding decision making, which centers on peer review and consensus-based scoring, tends to exhibit an endogenous preference for low-risk and predictable projects. This makes forward-looking non-consensus proposals more likely to be institutionally screened out at early decision stages. "Non-consensus" denotes proposals with such extreme exploratory uncertainty that peers cannot agree on their merit, manifesting as either knowledge uncertainty (whether the scientific hypothesis holds) or execution uncertainty (whether the technical approach can succeed). China's scientific and technological development is currently at a pivotal juncture of shifting from quantitative expansion to qualitative upgrading and pursuing high-level self-reliance and strength in science and technology, which makes support for such non-consensus innovation especially urgent. Meanwhile, the Third Plenary Session of the 20th Central Committee of the CPC has explicitly called for “establishing a mechanism for adopting non-consensus projects based on real-name recommendations by experts”, and a number of localities have launched related policy pilots. However, academic research has lagged behind these practices. How to make funding decisions for non-consensus projects centered on real-name recommendations by experts has therefore become a key issue that deserves scholarly attention. To address this gap, this paper develops a decision-making mechanism for funding non-consensus projects via real-name recommendations by experts, designed to be embedded within China's research funding system through a combination of theoretical analysis and international comparison. Specifically, the study first theorizes non-consensus as a structural blind spot of existing funding systems, elaborates the theoretical foundations of real-name recommendations by experts, and conceptualizes project selection as a multi-stage process. On this basis, it proposes a three-stage decision model for funding non-consensus projects: Gate-Triage-Calibration. Second, the study systematically compares six international archetypes of decision models for supporting non-consensus projects: (1) quality-threshold plus partial lottery, (2) tie-break lottery, (3) golden-ticket (champion) nomination, (4) program officer discretion, (5) ARPA-style mission-oriented portfolio management, and (6) Sandpit (idea-lab) mechanisms that generate proposals through intensive workshops. Third, the study distills the key matching rules and framework for selecting among these decision models, namely type of uncertainty, disciplinary paradigm stability, type of mission orientation, and project scale and time horizon. Finally, the study designs a China-oriented mechanism embedded in the existing funding architecture: In the Gate stage, review disagreement automatically flags divergence-type proposals into a broad non-consensus pool. Experts with limited quotas (1—2 per cycle) then conduct real-name nominations from this pool, submitting structured opinions on each project's uncertainty type and paradigm relationship. This produces a curated non-consensus pool with both dissent characteristics and accountable quality assurance, making it ready for track selection. In the Triage stage, appropriate funding tracks are selected according to the existing funding structure and the matching framework. In the Calibration stage, a closed institutional learning loop is established through performance evaluation, reputation scoring, and rule adjustment. This study makes three contributions. First, it constructs a dynamic, end-to-end decision model for funding non-consensus projects, deepening the understanding of process-based governance in research funding and elevating the discussion from isolated peer-review bias to an operational framework of institutional decision processes. Second, by systematically comparing international decision models and examining how they can be embedded within China's existing funding system, this study broadens the explanatory perspective on contextual fit and institutional transfer across different funding tracks. Third, it proposes a China-oriented decision mechanism for funding non-consensus projects that is pilotable, evaluable, and iteratively adjustable, providing design-level support for improving research funding governance and strengthening support for frontier exploration and disruptive innovation.

In China, conservation of resources and protection of the environment are established as fundamental national policies. As the nation strives for sustainable development, green innovation has increasingly become a central focus for enterprises aiming to achieve high-quality growth. In the realm of academic research, previous studies have predominantly adopted a static approach to explore the role of digital technology application in fostering green innovation. However, digital technology can be employed to manage both digital resources and traditional resources. Unlike traditional resources, digital resources possess dynamic characteristics such as fluidity, shareability, and self-growth. For instance, as data flows and spreads, new data is generated, enabling digital resources to continuously iterate and appreciate in value. Given that, previous perspectives often overlook the underlying dynamic mechanisms and the adaptive organizational environments that are crucial for digital technology to effectively drive green innovation from a dynamic viewpoint. Thus, theoretical research urgently needs to consider the role of digital technology in green innovation from a dynamic perspective. Meanwhile, enterprises should leverage digital technology to manage processes dynamically across both digital and traditional resources. To address this gap, grounded in dynamic capability theory and adaptive structuration theory, this study constructs a research framework integrating digital technology application as the independent variable, resource orchestration capability as the mediating variable, organizational alertness as the moderating variable, and green innovation as the dependent variable. To guarantee the high quality and reliability of the data, this study meticulously conducted reliability and validity analyses on the 230 valid questionnaire datasets collected from enterprises in cities such as Wuhan, Shenzhen, Zhengzhou, and Qingdao. Correlation analysis was then employed to explore the relationships among the variables. Based on these preliminary analyses, regression analysis was implemented to rigorously verify the direct effects and assess the presence and strength of the mediating and moderating effects. The empirical analysis reveals key findings. First, digital technology application has a significant positive impact on green innovation. Second, digital technology application has a significant positive effect on resource orchestration capability, and resource orchestration capability also has a positive effect on green innovation. Third, it is found that resource orchestration capability mediates the relationship between digital technology application and green innovation. Fourth, organizational alertness moderates not only the relationship between digital technology application and resource orchestration capability but also the mediating effect of resource orchestration capability. This study addresses the paradoxical challenges in digital technology application by exploring management approaches that enable enterprises to deploy digital technology effectively for green innovation from a dynamic perspective. By analyzing the mechanisms and boundary conditions through which digital technology affects green innovation, this study guides enterprises in leveraging digital advantages while avoiding risks of green innovation failure, ensuring sustainable progress. The study advances theoretical understanding in three ways. First, drawing on dynamic capability theory and adaptive structuration theory, it proposes a theoretical model exploring the relationships between digital technology application, resource orchestration capability, organizational alertness and green innovation. Breaking from existing static perspectives, it adopts a dynamic view to explore digital technology's role in the green innovation process. Second, it reveals the mechanism linking digital technology application and green innovation, demonstrating that resource orchestration capability mediates this relationship, thereby expanding our understanding of how digital technology application influences green innovation. Third, this study explores the relationship between digital technology application and the organizational environment. Empirical findings reveal that organizational alertness not only moderates the relationship between digital technology application and resource orchestration capability but also moderates the mediating role of resource orchestration capability. This contributes to the literature on the boundary conditions of digital technology application affecting green innovation.

Major international infrastructure projects play a critical role in advancing the high-quality development of the Belt and Road Initiative, and value co-creation among project participants is key to achieving shared economic, organizational, and social benefits. However, these projects are typically characterized by multi-actor interactions, resource dependence, complex governance environments, and high levels of uncertainty, which often lead to simultaneous cooperation and competition (coopetition) among stakeholders. Existing research has not sufficiently explained how coopetition influences value co-creation in such complex cross-border project environments, particularly when organizational capabilities and contextual conditions interact dynamically. To address this gap, this study develops a theoretical framework of “relational attributes-capability attributes-transaction attributes” to clarify the multi-dimensional mechanisms underlying value co-creation in international megaprojects. Drawing on institutional theory and ambidextrous innovation theory, this study conceptualizes coopetition along two dimensions: coopetition intensity and coopetition structure. It also incorporates dual innovation capability (exploitation innovation and exploration innovation) to represent the internal dynamic capabilities of project organizations. In addition, project complexity and environmental uncertainty are introduced as transaction attributes to reflect the challenges inherent in cross-border infrastructure delivery. A fuzzy-set qualitative comparative analysis (fsQCA) of 172 international infrastructure projects is conducted to examine how different combinations of relational, capability, and transactional conditions jointly influence value co-creation outcomes. The results reveal three main findings. First, no single factor is sufficient to drive value co-creation. Instead, value co-creation emerges from the configuration of multiple interacting conditions, demonstrating clear equifinality. Second, the study identifies four high value co-creation configurations and four low value co-creation configurations, which are further consolidated into three archetypal patterns: (1) Pioneer-oriented projects with vertical coopetition structures leverage both exploitation innovation and exploration innovation to integrate complementary resources from parent firms while simultaneously adapting to host-country environments, particularly under high environmental uncertainty. (2) Deep-plowing projects embedded in highly complex and uncertain environments adopt horizontal coopetition structures and emphasize exploration-oriented innovation to develop new local knowledge, expand markets, and enhance responsiveness. (3) Conservative projects with lower complexity rely mainly on vertical coopetition and operational coordination, achieving value co-creation without requiring extensive innovation capabilities. Third, the study finds that national cultural context significantly shapes the choice of value co-creation paths. Collectivist cultures promote resource integration by strengthening coordination norms and relational embeddedness, whereas individualist cultures rely more on structural specialization and capability matching to enhance allocation efficiency. Meanwhile, uncertainty avoidance exhibits a differentiated effect: high uncertainty avoidance cultures emphasize rule-based coordination and risk control, whereas low uncertainty avoidance cultures depend more on flexible structures and adaptive adjustment. This study makes several theoretical contributions. It enriches coopetition research by distinguishing its structural and intensity dimensions and demonstrating how their interplay influences outcomes in infrastructure project networks. It advances ambidextrous innovation theory by showing how exploitation and exploration innovation capabilities operate not independently but configurally within cross-border project settings. It also extends value co-creation research by revealing multiple effective pathways rather than a single optimal strategy, highlighting the necessity of context-sensitive decision-making. Finally, by incorporating cultural heterogeneity into the configuration analysis, this study broadens the cross-cultural applicability of coopetition theory in international project management. Overall, this research provides actionable insights for international contractors and policymakers: effective coopetition strategies must align with project complexity, innovation capability development, and cultural environment to achieve sustainable value co-creation in global infrastructure delivery.

With the deep integration and widespread application of artificial intelligence (AI) technologies in organizations, the behavioral patterns and psychological states of employees, as the main agents of innovation, are undergoing profound shifts. While the use of AI technologies enhances work efficiency and optimizes task allocation, it may also trigger new types of work stress and cognitive conflicts, thereby exerting complex influences on employees' innovation motivation and behavior. Although existing research has already paid attention to the "double-edged sword" effect of AI technology use on employees' innovation willingness or innovation behavior, few studies have systematically explored its differentiated impact mechanisms and boundary conditions on employees' innovation inertia. Employees' innovation inertia refers to a latent negative work state manifesting as a psychological mindset and behavioral tendency in which individuals cling to existing work patterns and avoid change and innovation attempts. This inertia not only undermines individuals' innovation self-efficacy but may also lead to the rigidification of an organization's core competencies, posing a long-term obstacle to sustainable development. On the basis of the cognitive appraisal theory of stress, this study constructs a moderated chain mediation model to systematically examine how AI technology use exerts paradoxical effects on employees' innovation inertia. Specifically, the "double-edged sword" refers to the dual pathways through which identical AI use may simultaneously operate: when employees appraise AI as a controllable opportunity for personal growth (challenge appraisal), they adopt problem-focused coping strategies that inhibit innovation inertia; conversely, when they perceive AI as an uncontrollable threat with potential losses (threat appraisal), they resort to emotion-focused coping strategies that promote innovation inertia. This study attempts to answer three core questions: First, does AI technology use simultaneously produce dual effects that both inhibit and promote employees' innovation inertia? Second, how does it exert differentiated impacts through employees' cognitive appraisal and work rumination pathways? Third, how does employees' growth need strength, as a key individual trait, moderate these processes? Clarifying these mechanisms represents an important theoretical contribution at the intersection of organizational behavior and technological innovation management, as well as a critical managerial imperative for human resource practices in the intelligent era. Results indicate that AI technology use exerts both inhibitory and promotive effects on employees' innovation inertia, with the inhibitory effect predominating, resulting in a net reduction of innovation inertia overall. Specifically, when employees form challenge appraisals of AI use, the technology primarily inhibits innovation inertia by fostering problem-solving rumination, which is a pathway more pronounced among employees using assistive AI in technical positions. Conversely, when employees form threat appraisals, AI use strengthens innovation inertia primarily by triggering emotional rumination, with this pathway more salient among employees using autonomous AI in intelligent positions. Furthermore, growth need strength positively moderates the relationship between AI technology use and innovation inertia: employees with higher growth needs benefit more from challenge appraisals and problem-solving rumination, thereby experiencing greater inhibition of innovation inertia. This study extends the cognitive appraisal theory of stress by revealing how AI use triggers divergent rumination pathways leading to opposing behavioral outcomes, with AI type and job nature acting as boundary conditions. Accordingly, organizations should tailor interventions to these contextual factors and provid emotional support for employees facing threat appraisals while framing AI as a developmental tool for those with high growth needs to maximize innovation potential.

Productive entrepreneurship has emerged as a critical policy priority for sustainable growth. Unlike generic entrepreneurship which may merely reallocate existing resources without net value creation, productive entrepreneurship drives aggregate productivity through high growth firms and innovative entrants that expand economic capacity. Consequently, policy attention has increasingly shifted from supporting general startup activity toward cultivating entrepreneurial ecosystems (EEs), understood as systemic configurations of institutional environments, financial infrastructure, knowledge provision, and cultural norms that embed and shape entrepreneurial action. While recent scholarship largely presumes symmetric efficacy across outcome dimensions. However, this symmetry assumption confronts an empirical paradox:ecosystem inputs such as institutional arrangements and entrepreneurial culture exhibit considerable stability and path dependence, while high-growth outputs demonstrate marked volatility and weak temporal persistence. This "stable inputs but volatile outputs" pattern suggests that ecosystems may function primarily as activation mechanisms which lower entry barriers and amplify opportunity recognition, rather than as absorption mechanisms which reliably convert transient high-growth outcomes into stable, reproducible regional capacities. Yet this structural asymmetry remains insufficiently theorized and empirically untested in dynamic terms, leaving a critical gap in understanding the mechanism boundaries of ecosystem influence. Addressing this gap, the study distinguishes productive entrepreneurship into two analytically separable dimensions: prevalence (the rate of productive entry, operationalized as innovative startup density per 10 000 population) and persistence (the temporal stability of such entry, measured as the inverse coefficient of variation over rolling time windows). It constructes a dynamic joint framework linking ecosystem quality with both outcomes and employed panel vector autoregression (PVAR) on panel data covering 23 countries from 2016 to 2024. The PVAR approach offers distinct methodological advantages for this inquiry. First, by treating all variables as endogenous within a simultaneous system, it mitigates endogeneity concerns without requiring a priori assumptions about causal direction. Second, through impulse response functions and multi period lag structures, it captures the temporal sequencing and cumulative effects of mutual influence, particularly the lagged feedback from entrepreneurship to ecosystem restructuring. Third, variance decomposition enables quantification of how shocks to different dimensions propagate through the system, revealing asymmetric feedback intensities. Thus, the study systematically analyzed temporal responses, feedback mechanisms, and lag structures. The results reveal three main findings. First, ecosystem quality and the prevalence of productive entrepreneurship display a significant bidirectional dynamic relationship, with Granger causality tests confirming mutual predictability. In contrast, no significant dynamic association is identified between ecosystem quality and persistence of productive entrepreneurship, suggesting that ecosystems are more effective at activating entry than sustaining long-term stability. Second, impulse response analysis shows that the stimulative effect of ecosystems on entrepreneurial entry peaks in the first period and then gradually declines, forming an inverted V-shaped pattern. Conversely, feedback from productive entrepreneurship to ecosystem quality emerges with a clear time lag, becoming pronounced only after the second period, indicating that ecosystem restructuring requires cumulative institutional absorption. Third, variance decomposition demonstrates that while ecosystem self-shocks dominate short-term fluctuations, productive entrepreneurship increasingly contributes to ecosystem variance over time, stabilizing at approximately 16% by the tenth period. Meanwhile, ecosystem quality accounts for roughly 6% of the long-run variance in entrepreneurial prevalence, underscoring the persistent influence of structural conditions. The practical inspirations of this research are as follows:(1) Given the pronounced short-term activation effect, policymakers may use entry-related indicators, such as venture capital – backed start-ups or gazelle firms, as timely feedback signals for ecosystem adjustment, with one- to two-year policy cycles. When such indicators decline, immediate-response instruments (e. g. , seed funding, streamlined certification) should be prioritized. (2) Recognizing the lagged feedback and limited ecosystem influence on persistence, policies should be temporally differentiated:early-stage interventions should focus on reducing experimentation and entry costs, while long-term institutional arrangements should encourage successful entrepreneurs to reinvest in the ecosystem through angel investment, mentorship, and entrepreneurial education. Policymakers should regard volatility in high-growth entrepreneurship as a structural feature rather than a policy failure and avoid rigid performance targets for sustained high-growth firm counts.

Against the dual backdrop of advancing global sustainable development agendas and the accelerating implementation of China's "dual carbon" strategy,green technology innovation has become a core driver of high-quality corporate development. Existing literature has extensively explored the determinants of ESG performance from two perspectives: external policy drivers and internal governance constraints. However,research on green innovation tends to focus on factor accumulation in terms of innovation intensity,with insufficient attention paid to the strategic logic of corporate green technology path selection. This makes it difficult to effectively explain the practical puzzle of “similar green technology investment intensity but divergent ESG performance”. Meanwhile,firms face a dilemma between “conformity” and “differentiation” in their green technology deployment: following mainstream industry technology paths can secure institutional legitimacy and resource support,while exploring differentiated paths may create first-mover advantages. To clarify this strategic dilemma and its impact mechanism on corporate ESG performance,this study introduces green technology footprint similarity (GTS) as an analytical tool,shifting the research focus from technology stock evaluation to technology structure evaluation,and systematically examines how the alignment between corporate green technology path selection and the industry mainstream affects ESG performance. This study employs data from Chinese A-share listed companies spanning 2009-2024. Drawing on the International Patent Classification (IPC) system,the study identifies the technological domains of corporate green patents and calculates corporate green technology footprint similarity using the cosine similarity algorithm. This measurement approach overcomes the semantic noise problem inherent in traditional text mining methods and achieves comparability of technology structures across firms,time periods,and industries. At the theoretical level,by integrating four core stages of the value creation process,inculding resource accumulation,capability building,risk control,and value realization,this study innovatively constructs the “Green-Intelligence-Resilience-Quality” (GIRQ) four-dimensional mechanism framework,achieving a theoretical breakthrough from “factor accumulation” to “mechanism integration” in the analysis of ESG determinants. The findings are as follows: First,GTS exhibits a significant U-shaped relationship with corporate ESG performance. However,due to measurement implications,the vast majority of sample firms are located in the right-side ascending segment of the U-shaped curve,indicating that a “follow the trend” technology convergence strategy is more conducive to gaining recognition from ESG evaluation systems. Second,mechanism tests confirm the explanatory power of the GIRQ fourdimensional framework: in the Green dimension,higher technology similarity significantly strengthens the corporate green knowledge base by facilitating knowledge sharing and reducing knowledge recombination costs; in the Intelligence dimension,technology path convergence enhances compatibility between firms and industry digital infrastructure,accelerating digital transformation processes; in the Resilience dimension,mainstream technology path selection reduces policy compliance costs and environmental uncertainty,enhancing firms' ability to adapt to external environmental changes; in the Quality dimension,GTS exhibits a U-shaped relationship with firm competitiveness,with high-similarity firms obtaining sustainable competitive advantages through ecological synergy and agglomeration effects. Finally,heterogeneity analysis reveals that the ESG-enhancing effect of GTS is more pronounced in large-scale firms and high-growth firms,with a greater impact on environmentally sensitive industries. This reflects that resource integration capability and strategic flexibility are key conditions for converting technology footprint similarity advantages into ESG performance. The marginal contributions of this study are threefold: First,it introduces GTS into corporate ESG research,enriching the theoretical understanding of the relationship between green technology strategy and sustainable development,and revealing how green technology embeds into ESG governance logic through institutional isomorphism mechanisms. Second,it improves the measurement methodology of GTS by constructing an operable and comparable quantitative indicator based on the IPC classification system and cosine similarity algorithm,enhancing measurement precision. Third,it constructs the GIRQ four-dimensional mechanism framework,systematically integrating ESG determinants and delineating multiple transmission pathways. The research conclusions provide decision-making references for firms in formulating green technology strategies,adapting upward to institutional pressures,and balancing innovation differentiation,while also offering empirical support for governments in constructing precisely classified green governance policy systems.

With the widespread adoption of AI in critical fields such as credit and justice,algorithmic fairness has become a key issue in digital governance. While developers often try to quantify fairness using specific metrics,the selection of these metrics is not merely a neutral technical setting. Rather,it reflects a strategic trade-off where companies should balance compliance costs,efficiency,and social responsibility. This paper analyzes the Impossibility Theorem of Fairness,which demonstrates mathematically that different fairness metrics are often incompatible. This incompatibility forces developers to prioritize certain ethical values over others. Consequently,the selection of a metric becomes the actual source of algorithmic bias or fairness,making legal scrutiny essential. However,this paper identifies significant gaps in the current legal framework when regulating this choice behavior. Through a critical analysis of existing anti-discrimination laws and algorithmic governance regulations,the paper reveals two primary structural dislocations. The first is the dislocation of regulatory focus: current laws predominantly focus on observable discriminatory results while neglecting the upstream process of metric selection. This retrospective focus allows enterprises to engage in regulatory arbitrage and fairness washing,where developers select technically easier or commercially favorable metrics to create a veneer of compliance,thereby evading substantive scrutiny. The second is the dislocation of regulatory standards. There is an inherent conflict between the legal paradigm of individual causal accountability,which seeks specific causation for individual harm,and the algorithmic logic of group statistical correlation,which operates on probability distributions across populations. This mismatch renders traditional legal remedies ineffective,as individual plaintiffs struggle to prove causation against black-box statistical decisions,leading to formalism in algorithmic auditing. In light of these challenges,this paper argues that a mere optimization of result-based metrics is insufficient. Instead,legal regulation must undergo a paradigm shift from Result-Based Regulation to Process Accountability. The law should not attempt to prescribe a single “correct” metric,which is theoretically impossible,but should instead regulate the decision-making process itself. The core thesis is that the act of choosing fairness metrics must be transformed from an opaque technical decision into a legally reviewable,transparent,and accountable procedure. To implement this shift,the paper proposes a comprehensive regulatory framework centered on Procedural Due Diligence and a full-lifecycle Algorithmic Impact Assessment (AIA) system. Specifically,the framework advocates for the establishment of Procedural Due Diligence,where algorithm developers,similar to fiduciaries in corporate law,bear a duty of care. They should document the rationale behind their metric selection,demonstrating that they have weighed potential conflicts,assessed societal impacts,and considered the rights of vulnerable groups. Furthermore,the paper details the design of a robust AIA regime that mandates pre-deployment evaluation to identify data biases and justify the final choice of metrics. To ensure checks and balances,the framework suggests a multi-layered evaluation structure involving internal ethical committees,external independent audits for high-risk algorithms,and regulatory filing. Finally,recognizing the varying risks of different algorithms,the paper advocates for a dynamic grading mechanism to balance regulatory effectiveness with the burden on innovation. By anchoring regulation in the procedural propriety of metric selection,this framework bridges the gap between abstract legal principles and concrete technical implementations. It effectively addresses the issue of fairness washing by making the value trade-offs visible and traceable. Ultimately,this paper contributes to the discourse on algorithmic governance by providing a concrete pathway to embed Process Accountability into the lifecycle of algorithmic development,ensuring that the pursuit of digital efficiency does not come at the expense of substantive social justice.Meanwhile,a process-based accountability framework,while theoretically compelling,is not self-executing. Its operationalization hinges on a constellation of institutional enablers and practical safeguards: regulatory agencies must cultivate the technical capacity to substantively review algorithmic impact assessments; firms must navigate the delicate balance between procedural compliance and innovation incentives; and courts must progressively refine doctrinal standards to treat procedural due diligence as a legally cognizable benchmark for assessing algorithmic legitimacy. These interlocking challenges represent critical frontiers for future scholarship as a roadmap for embedding meaningful accountability into the governance of algorithmic systems.

Artificial Intelligence Agents (AI agents) refer to intelligent entities powered by large-scale models and integrated external tools,and they are capable of autonomously executing complex tasks.As a pivotal frontier in AI industrialization and a cornerstone of China’s “AI+” strategy,AI agents hold transformative potential for production paradigms and productivity.However,high market entry barriers may concentrate market power among a few dominant operators,giving rise to intricate and covert monopoly risks,including collusion among AI agents,collusion between agents and their operators,and abuses of dominant market positions.Given their rapid deployment and deep societal integration in China,such risks which are amplified through complex interactions and cascading effects could escalate into systemic threats to economic and social stability.However,current literature has paid little attention to the governance of monopoly risks posed by artificial intelligence agents,offering neither theoretical analysis of preventive governance nor systematic examination of the requirements for constructing such mechanisms. In response,this paper argues for implementing the directive from the Third Plenary Session of the 20th Central Committee of the Communist Party of China to “establish a monitoring,early-warning,and response system for science and technology security ” by strengthening a preventive governance framework for AI agent-related monopoly risks.This approach aims to overcome the limitations of traditional ex-post antitrust enforcement and fully realize the preventive objectives of the Anti-Monopoly Law.Drawing on Germany's compliance incentive mechanisms and the European Union's regulatory penalties under the Digital Markets Act (DMA),two leading models in digital platform governance,this paper proposes three pillars of preventive governance: (1) tailoring internal compliance and governance requirements to distinct types of monopoly risks (e.g.,collusive agreements,abuse of dominance) to enhance operability during operators' ex-ante R&D phases; (2) refining external regulatory mechanisms to avoid both the pitfalls of purely ex-post supervision,which may cause irreversible harm,and rigid “one-size-fits-all” interventions; and (3) advancing comprehensive risk governance through coordinated multi-stakeholder approaches that account for the diverse application scenarios of AI agents. Institutionally,operators should embed risk-specific compliance obligations throughout the entire lifecycle of AI agent development and deployment,enabling Source-oriented risk mitigation.Meanwhile,enforcement agencies must shift from passive oversight to proactive governance by establishing ex-ante monitoring and early-warning systems,and by calibrating tiered regulatory tools to match the severity and stage of emerging risks.Critically,synergy between internal compliance and external enforcement should be institutionalized through positive incentives,such as regulatory sandboxes,compliance credits,or reduced scrutiny for proactive risk mitigation,and a standardized operator commitment system that clearly defines ex-ante obligations,monitoring protocols,and accountability mechanisms.Such an integrated framework not only aligns corporate behavior with public regulatory goals but also fosters a culture of substantive compliance,thereby ensuring that firms' adherence to antitrust norms is genuine and continuous rather than merely performative. Grounded in the real-world context of rapid technological iteration and widespread adoption of AI agents,this study focuses on the underexplored yet high-stakes issue of monopoly risk governance,and sharpens the scholarly focus from general AI-related risks to the specific antitrust regulation challenges posed by AI agents.The study moves beyond the piecemeal approaches prevalent in existing literature,and proposes an integrated governance model that simultaneously enhances internal compliance and external supervision while emphasizing their bidirectional synergy.This approach facilitates a strategic shift in AI agent monopoly risk governance from reactive enforcement toward proactive prevention and meaningfully advances the foresight,coherence,and operational effectiveness of the regulatory system.Looking ahead,continuous monitoring of global enforcement trends and judicial developments in AI-related antitrust cases will be essential to refine China's governance standards,provide clearer regulatory guidance,and effectively integrate ex-ante risk prevention with ex-post accountability mechanisms.

In the era of the digital economy,innovation has become a core driver for enterprises to cultivate competitive advantages,yet the increasing complexity and dynamics of the business environment pose significant challenges to innovators. Under such conditions,improvisation is defined as the spontaneous and creative integration of available resources through the convergence of planning and execution and it has emerged as a critical capability for innovative employees. Although prior studies have demonstrated the importance of improvisation for creativity and adaptive performance,the role of artificial intelligence (AI) in enabling improvisation remains insufficiently explored. Existing research on AI adoption in organizations is largely divided between substitution and augmentation perspectives. The substitution view highlights potential negative consequences of AI,such as job insecurity and skill obsolescence,whereas the augmentation view emphasizes AI's capacity to enhance employee performance by extending cognitive resources and supporting complex problem solving. While both perspectives offer valuable insights,they provide limited explanations of how AI functions as an actionable resource that supports employees' improvisation in dynamic and unstructured work situations. Moreover,prior studies have tended to focus on relatively static outcomes (e.g.,job satisfaction or creativity),paying less attention to the behavioral mechanisms through which AI adoption translates into real-time adaptive action. Drawing on conservation of resources (COR) theory and signaling theory,this study addresses these gaps by examining how and under what conditions AI adoption stimulates improvisation among innovative employees. From a COR perspective,AI is conceptualized as a novel and high-quality resource that can initiate a resource gain spiral by enhancing employees' perceived career growth opportunities and motivating proactive resource acquisition. Specifically,this study proposes that AI adoption increases employees' perceived career growth opportunities,which in turn encourage engagement in AI-assisted bricolage,an approach involving the creative recombination and utilization of available resources with the support of AI technologies. Through this process,employees become better equipped to engage in improvisation when facing emergent challenges. Furthermore,following the signaling theory,this study introduces leaders' AI symbolization as a key boundary condition,arguing that leaders' symbolic behaviors regarding AI convey salient signals that shape employees' interpretations of AI as either a developmental opportunity or a potential threat. To test the proposed moderated serial mediation model,data were collected using a three-wave,time-lagged survey design from matched pairs of innovative employees and their immediate supervisors across innovation-intensive industries,including software development,marine engineering,pharmaceutical R&D,electronic engineering,and materials science. At Time 1,supervisors assessed AI adoption within the team and their own AI symbolization behaviors. At Time 2,employees reported perceived career growth opportunities and trust in AI (as a control variable). At Time 3,employees evaluated AI-assisted bricolage and improvisation. Demographic variables such as gender,age,education,and organizational tenure were controlled. Structural equation modeling (SEM) with Mplus 8.3 and bootstrap procedures were used to test the hypotheses. The results provide strong support for the proposed model. AI adoption positively influenced employees' perceived career growth opportunities,indicating that AI is not merely viewed as a productivity tool but also as a developmental resource. Perceived career growth opportunities,in turn,significantly promoted AI-assisted bricolage,which further enhanced improvisation,confirming the serial mediating mechanism. In addition,leaders' AI symbolization positively moderated the relationship between AI adoption and perceived career growth opportunities,such that this relationship was stronger when leaders exhibited higher levels of AI symbolization. Moreover,the indirect effect of AI adoption on improvisation through perceived career growth opportunities and AI-assisted bricolage was significant only under conditions of high leader AI symbolization,highlighting its critical boundary role. This study makes several important contributions. Theoretically,it extends COR theory to human– AI interaction contexts by elucidating how AI can trigger a resource gain spiral that culminates in improvisation. It also advances improvisation research by shifting the focus from pressure-driven reactions under resource scarcity to opportunity-driven proactive action enabled by AI. In addition,by highlighting leaders' AI symbolization as a contextual signal,this study enriches the leadership and signaling literature in the AI era.The findings suggest that organizations should not only invest in AI technologies but also foster leadership behaviors that symbolically endorse AI use and help employees perceive AI as a source of career growth. Such efforts can facilitate effective human– AI collaboration and unlock the improvisation potential of innovative employees.

Technology mergers and acquisitions (M&A) are widely used by firms to acquire external knowledge and enhance innovation. However,the failure rate of technology M&A remains high,at approximately 70% in both the U.S. and China. Existing academic research on technology M&A innovation mostly focuses on the integration of technical talent,organizational structure,and resource allocation within acquired firms,or explores the optimization of M&A decision-making from the perspective of resource complementarity and technical matching between the two parties,yet largely overlooks the vital impact of M&A activities on the key inventors of the acquiring firms themselves. These key inventors serve as the core carriers of the acquiring firm's original technical accumulation and the main force for knowledge absorption,integration,and secondary innovation following an M&A; changes in their creativity during the post-M&A stage directly determine the efficiency of benefit transformation and the sustainability of corporate innovation. This study takes the evolution of creativity among key inventors in acquiring firms after technology M&A as its core research object,constructing a dynamic multi-level creativity evaluation model to realize scientific and accurate assessment of their creativity evolution,thereby addressing the limitations of existing single-dimensional and static evaluation methods. By combing the theories of social network,innovation economics and talent creativity,this study builds a multi-dimensional and interrelated evaluation framework from three core levels: collaboration network dynamics,individual inventor characteristics,and M&A context features,which systematically covers the internal and external factors affecting post-M&A creativity. In terms of research methods,this study adopts a two-stage PCA-BP neural network model to solve the problems of high dimensionality and multicollinearity of initial indicators,as well as the non-linear correlation between influencing factors and creativity changes: firstly,Principal Component Analysis (PCA) is used to reduce the dimension of initial evaluation indicators,filter out redundant information and extract core components; secondly,the extracted principal components are input into the Back-Propagation (BP) neural network for training and fitting,so as to build an efficient and accurate non-linear evaluation model. For empirical testing,this study selects key inventors active between and as the research sample,collecting valid patent records with key inventors identified by patent quantity,citation frequency and claim counts to ensure sample representativeness. The sample is divided into training sets and testing sets to verify the model's effectiveness. The test results show that the PCA-BP model has a mean square error (MSE) of only 0.00058 and a prediction accuracy rate of 97%,with its predicted creativity trajectory highly consistent with the actual change trend,demonstrating strong generalization ability and robustness. Comparative analysis further proves that the dynamic multi-level index system and PCA-BP model are significantly superior to traditional static indicators,single BP neural network and random forest model in evaluation accuracy,convergence speed and anti-interference ability. This study realizes innovation in research perspective and method in the field of technology M&A innovation: in theory,it shifts the research focus from acquired firms to the core inventors of acquiring firms,putting forward a systematic “network dynamics-individual characteristics-M&A context” analysis framework and enriching the theoretical research on post-M&A talent creativity and innovation management; methodologically,it integrates PCA and BP neural network,providing a feasible technical path for non-linear evaluation in small-sample and high-dimensional scenarios. In practice,the constructed model can serve as a targeted diagnostic tool for enterprises to monitor the creativity changes of key inventors after M&A,helping enterprises timely identify creativity decline risks,carry out targeted intervention,optimize core human capital management,and further improve the innovation performance and success rate of technology M&A,thus providing important theoretical support and practical reference for M&A decision-making and post-merger integration management.

In an era marked by escalating geopolitical volatility,recurrent exogenous shocks like extreme climate events and public health crises,global supply chains face mounting structural vulnerabilities and operational uncertainties. Consequently,bolstering enterprise-level supply chain resilience has evolved from a mere operational concern into a core strategic imperative—central to safeguarding national economic security,stabilizing the real economy and preserving long-term industrial competitiveness in the global market. Concurrently,the rapid advancement of digital transformation has redefined the role of data in economic development:no longer merely a passive byproduct of business operations,data now functions as an active,strategic core production asset that enables agility,real-time responsiveness,and anticipatory decision-making across interconnected supply ecosystems. Within this evolving data ecosystem,government-collected and publicly accessible data plays a pivotal role: it is comprehensive in coverage,inherently non-rivalrous,broadly representative across various sectors and geographies,and underpinned by solid institutional legitimacy that collectively enhances its utility for systemic risk mitigation and industrial collective learning in supply chain management. Practically speaking,openly available government datasets help fill persistent intelligence gaps in key domains such as scientific demand forecasting,multi-dimensional supplier due diligence,and pre-emptive risk identification and early warning. By integrating these authoritative public resources into daily operational decisions,firms can optimize their inventory holding policies,dynamically recalibrate sourcing and sales networks in near real time,and orchestrate more agile,evidencebased responses during unexpected supply chain disruptions,thereby significantly strengthening both organizational adaptability and broader supply network recovery capabilities. As such,understanding the causal relationship between governmental data transparency and corporate supply chain resilience,and clarifying its internal transmission mechanisms,is not only academically compelling but also critically timely for formulating evidence-informed policymaking to boost industrial chain security. Drawing on a balanced panel dataset of Chinese A-share listed companies spanning 2007–2024,this study takes the launch of local government data open platforms as a quasi-natural experiment,and employs fixed-effects,instrumental-variable approaches and a series of rigorous robustness tests to causally identify the impact of government data opening on enterprise supply chain resilience. Results indicate that government data opening significantly and robustly strengthens enterprise supply chain resilience,with precise supply-demand matching emerging as a key and core mediating mechanism that bridges data openness and resilience enhancement. Moderation analyses further reveal that both enhanced digital infrastructure and a mature and sound data legal framework significantly intensify this positive effect by providing technical support and institutional guarantee respectively. The heterogeneity analysis indicates that in the groups of small and medium-sized enterprises,private enterprises,enterprises with weak market positions,and those in regions with low marketization levels,the enhancing effect of government data opening on the resilience of enterprise supply chains is more significant and prominent,as these entities suffer more from information asymmetry and resource constraints. The expansion analysis shows that the improvement of the overall quality of government data opening,especially in the critical subdivision dimensions of policy guarantee,data quality,and platform construction,all independently and significantly strengthen the resilience of enterprise supply chains,verifying the vital importance of a quality-oriented development model in government data opening practices. This study proposes four targeted and actionable policy recommendations for practical implementation. First,accelerate the construction of government data opening platforms and unify national construction standards to fully activate the value of public data elements. Second,strengthen the digital infrastructure and institutional guarantees for precise supplydemand matching,and vigorously promote the construction of new digital infrastructure and the improvement of the data legal system. Third,build a targeted data capability support system to enhance the data utilization efficiency of information disadvantaged entities such as SMEs and private enterprises. Fourth,continuously improve the quality of government data opening and perfect the supporting system to enhance the reliability,usability and practical value of open government data in an all-round way.

Against the backdrop of continued global climate governance and deepening consensus on the carbon peaking and carbon neutrality(“dual carbon”) goals,green transformation is a key path for enterprises to break through resource and environmental constraints and achieve high-quality economic development. As the core subject of resource consumption and pollution emissions in the industrial system,heavily polluting enterprises have long relied on the traditional development model of high investment and high emissions,which not only exacerbates the pressure on the ecological environment,but also faces the development dilemma of rising resource costs and weakened market competitiveness. Currently,the green transformation of heavily polluting enterprises faces multiple challenges,including insufficient institutional guidance,weak industrial collaboration,and a lack of endogenous motivation. These issues have led to prominent problems such as a lack of transformation drive and misalignment with viable transformation pathways. Therefore,breaking through this development deadlock and enhancing the efficacy of green transformation has become an urgent issue. Green technology innovation serves as a key driver for corporate green transformation,significantly reducing resource consumption and pollution emissions while promoting industrial upgrading and fostering synergistic development of environmental and economic benefits. Existing literature primarily focuses on single-element analysis,either examining institutional environments or investigating the impact of corporate resource endowments on green technology innovation,overlooking the interactive effects among institutions,industries,and enterprises. It remains unclear whether any individual factor constitutes a necessary condition for achieving high levels of green technology innovation. How do the IIE factors (short for Institution,Industry,and Enterprise) couple to foster high green technology innovation What are the mediating mechanisms of green technology innovation Addressing these questions helps managers formulate differentiated green technology innovation and transformation strategies based on the institutional and industrial environments of their regions,combined with the unique characteristics of their enterprises. Drawing on complex systems theory and the “strategic triangle” framework,this study analyzes A-share listed companies in China's heavily polluting industries. Using a complex mediation to explore the driving mechanisms of green technology innovation and enterprise green transformation under the three-dimensional interaction of the “institution-industry-enterprise” framework,the study yields the following findings:(1)No single factor constitutes a necessary condition for achieving high green technology innovation; however,enterprise nature exerts a pervasive influence across various contexts.There are three differentiated driving configuration paths for high green technology innovation in heavily polluting enterprises,namely: industry led enterprise response type,industry enterprise dual wheel drive type,and institutional response type under industry enterprise dual. There are three paths for non-high green technology innovation,all of which include non-high enterprise nature; (2) The complex mediation mechanism test shows that all six configuration paths that generate high and non-high green technology innovation can indirectly promote the green transformation of heavy polluting enterprises through green technology innovation. The industry enterprise dual wheel drive configuration path in the high configuration has a direct promotion mechanism for the green transformation of enterprises,while the three types of configurations that generate non-high green technology innovation have a direct inhibition mechanism for the green transformation. The innovation of this article is as follows:(1) Traditional research often explores the influencing factors of green technology innovation from a single perspective of institutions or resources. This study adopts the “strategic triangle” theory to examine multiple driving paths from an “institution-industry-enterprise” configurational perspective,enriching the research on the influencing factors of green technology innovation in heavily polluting enterprises. (2) Existing literature mostly focuses on the linear mediation mechanism of green technology innovation,and there is a lack of research on complex systems formed by multidimensional and interdependent independent variables. This article constructs a complex mediation model to reveal the complex mechanism of how the IIE ecosystem affects the green transformation of heavily polluting enterprises through green technology innovation within the “strategic triangle” framework. It deepens the contextual application of the complex mediation model and expands the research on the driving mechanism of enterprise green transformation.

As scientific research evolves into an increasingly data-intensive paradigm,the cross-border free flow of scientific data is a crucial catalyst for innovation and a fundamental prerequisite for addressing global challenges. Its promotion has thus become imperative for both scientific advancement and social well-being. The global strategy of enhancing data sovereignty and security has therefore placed greater emphasis on the governance and control of cross-border data flows. However,unlike commercial data flows with clear commercial purposes,scientific data,which embodies the dual nature of a public good and strategic asset,has not been effectively governed. National security concerns are a primary factor influencing countries' decisions regarding the facilitation of data flows. Consequently,understanding how this governance dilemma affects international scientific collaboration has become a core issue for scholars. This study focuses on the bottlenecks caused by value conflicts and regulatory fragmentation and their impact on the efficiency of scientific collaboration. Specifically,it examines how tensions between open science and national security impede scientific data flows and explores whether differentiated governance approaches should be applied to data exchanges with countries at varying levels of strategic trust. Existing studies have shown that different regulatory models exert a significant influence on data flows,yet discussions remain insufficient regarding comprehensive governance mechanisms that balance security and development. Accordingly,this paper conducts in-depth research on this issue based on governance theory,creatively integrating a multi-stakeholder perspective into the field of data flow governance,alongside comparative legal analysis. This study selects the regulatory frameworks of the European Union,the United States,and China as the primary objects to identify and analyze their governance models and legal rules. It ultimately provides a comprehensive analysis of their core legal texts and policy documents. The study verifies the arguments by defining core concepts and constructing a theoretical framework for governance. To further validate the arguments,typical scenarios are used to re-examine practical challenges and analyze the internal mechanism through which regulatory frameworks influence the efficiency of scientific collaboration. The results show that the governance dilemma of cross-border scientific data flows,reflected in value conflicts and regulatory fragmentation,has a significant negative correlation with research efficiency,and governance challenges can hinder the progress of international collaboration. At the macro-level,an excessively broad definition of national security leads to the weakening of the open science principle,which also reduces the significance of collaboration in policymaking. At the micro-and meso-levels,legal uncertainty and high transaction costs exert an increasingly significant negative impact as regulatory requirements become more stringent,which to a certain extent verifies the chilling effect of over-regulation on international scientific activities. This study integrates governance theory,comparative legal analysis,and research findings in the field of scientific collaboration. It takes the governance of cross-border scientific data flows as the research object,enriching research on the security-development nexus in this specific domain. Unlike previous literature,this study introduces a novel tiered cooperation model into the external governance perspective for theoretical and policy analysis,thereby expanding upon classic data governance models. The logical analysis supports the study's conclusions and strengthens the theoretical foundation of data governance research. The findings also carry practical implications for the formulation of data governance policies. Governance should evolve from coarse control to refined management: clarifying legal concepts such as legitimate purpose and public interest in research,establishing granular classification rules for scientific data,optimizing security assessment mechanisms for research projects,and exploring a dual-list management model combining negative lists and exemption lists to provide more stable and predictable legal expectations for scientific activities.

In the digital economy era,platform ecosystems have emerged as pivotal arenas for entrepreneurial innovation,particularly for platform-dependent startups,emerging firms that heavily rely on digital platforms for traffic,users,channels,and infrastructure to conduct business and develop products. These startups face a unique dual-identity dilemma: they act as complementors within the platform ecosystem while striving for independent growth. This tension often leads to innovation barriers,such as resource coordination difficulties,cognitive convergence,and over-reliance on platform rules,which complicate new product development (NPD). Despite the growing importance of ecosystem embeddedness,the process by which these firms integrate into platform networks to share resources and collaborate,existing research has largely overlooked its dynamic impacts on NPD in this context. Prior studies tend to adopt static perspectives,focusing on platform leaders or strong complementors,while neglecting the challenges faced by resource-constrained,peripheral startups. Moreover,they fail to explore nonlinear mechanisms and the role of iterative processes in overcoming uncertainties. This study addresses these gaps by investigating how ecosystem embeddedness influences NPD performance in platform-dependent startups,adopting an opportunity iteration perspective. Opportunity iteration refers to the dynamic,nonlinear evolution of entrepreneurial opportunities through continuous adjustment,optimization,and upgrading based on feedback loops,stakeholder interactions,and environmental changes. Drawing on an integrated theoretical framework that combines embeddedness theory,dynamic capabilities theory,entrepreneurial opportunity theory,and complex adaptive systems theory,the study proposes a nonlinear model of "ecosystem embeddedness—opportunity iteration—NPD performance" This framework posits that embeddedness enables resource sharing and multi-stakeholder collaboration but can turn counterproductive at excessive levels. The study hypothesize an inverted U-shaped relationship between embeddedness and NPD performance,mediated by opportunity iteration,with entrepreneurial learning and entrepreneurial networks as moderators. To test these hypotheses,the study employed an empirical approach using survey data from platform-dependent startups in China,reliant on major digital platforms. Samples were collected via incubators and alumni networks in multiple economic regions,targeting mid-to-senior managers. Variables were measured using established Likert-scale items: ecosystem embeddedness (4 items); NPD performance (5 items); opportunity iteration (5 items); entrepreneurial learning (10 items); and entrepreneurial networks (2 items). Firm age and size served as controls. Reliability and validity were confirmed through standard statistical tests,with common method bias appropriately addressed. The hierarchical regression results are summarized. First,ecosystem embeddedness exhibits a significant inverted Ushaped effect on NPD performance,supporting H1. Moderate embeddedness facilitates resource access and innovation synergies,but over-embeddedness induces path dependence and conflicts. Second,embeddedness similarly affects opportunity iteration in an inverted U-shape,while opportunity iteration positively impacts NPD,confirming H2a and H2b. Opportunity iteration mediates this relationship nonlinearly (H2c),with Bootstrap tests showing significant direct (0.2185) and indirect (0.1740) effects (95% CI excluding zero). Third,entrepreneurial learning moderates the embeddedness-opportunity iteration link,steepening the inverted U-curve,validating H3 by amplifying positive effects through knowledge internalization. However,entrepreneurial networks do not significantly moderate embeddedness-NPD,rejecting H4,possibly due to network paradoxes like redundancy in platform-constrained contexts. Robustness checks affirmed the results. This research extends embeddedness theory to digital platforms by highlighting its nonlinear "double-edged sword" nature in resource-constrained startups,integrating complex adaptive systems to explain self-organization and emergence. By introducing opportunity iteration as a nonlinear mediator,it advances entrepreneurial opportunity theory beyond static views,linking it with dynamic capabilities to elucidate how iterations bridge embeddedness to innovation under uncertainty. The mixed moderating effects refine boundary conditions,challenging network theory's universality and emphasizing contextual heterogeneity. Practically,firms should monitor embeddedness levels,foster iterative systems,and prioritize learning programs. Platform leaders can offer modular interfaces,while policymakers might subsidize multi-platform strategies to mitigate dependencies.

Amid the rapid development of digital technologies,digital innovation ecosystems foster value co-creation by engaging in diverse collaborations with external organizations through openness and sharing. In this process,the blurring of organizational boundaries,the deepening of subject interaction,and the redundancy of resource information triggered by digitization lead to diverse value co-creation tensions. The complexity of these tensions,characterized by multiplicity,continuity,and concurrency,makes their governance a significant challenge. Tension is an unstable factor in the development of digital innovation ecosystems; without scientific governance thinking and means,tensions will intensify and worsen,causing internal dysfunction in system operations,impeding value creation and acquisition,and even resulting in value codestruction. Therefore,actively exploring the governance path of value co-creation tension in digital innovation ecosystems has become an urgent issue. This paper employs the paradoxical theoretical framework of four elements,and integrates the value co-creation process of digital innovation ecosystems,encompassing "strategic goal-subject interaction-resource utilization," with the connotation of value co-creation tension. It deconstructs value co-creation tensions into three core tensions: the tension of openness and closure of digital strategy,the tension of competition and cooperation of digital subjects,and the tension of sharing and protection of digital resources. It selects the incentive mechanism,contract mechanism,and relationship mechanism as the three dimensions of the governance mechanism for digital innovation ecosystems,and adopts the fsQCA method to synergistically combine tensions with governance mechanisms,deeply analyzing how their diversified paths affect value co-creation in digital innovation ecosystems. Data were collected through questionnaire surveys. To ensure questionnaire validity,a pre-survey was first conducted with high-technology enterprises selected through the research team's social network,inviting enterprise managers to complete the questionnaire. After adjustments based on feedback,the final questionnaire was formed. Formal surveys were then conducted from late May to September 2024,covering North China,Central China,and East China,including Beijing,Henan,Hubei,and Anhui provinces. The target respondents were high-technology enterprises that use digital knowledge and information as key production factors,employ digital technology as the core driving force,and engage in collaborative innovation with external partners. Sampling primarily relied on the research team's social network and expanded through snowball sampling. Online distribution was adopted for data collection convenience. A total of questionnaires were distributed,were returned,and valid questionnaires were obtained after eliminating invalid responses,yielding an effective response rate of 74.87%. The findings reveal that single factors such as tensions and governance mechanisms are not necessary conditions for digital innovation ecosystems to achieve high levels of value co-creation; the independent existence of tensions or untargeted governance mechanisms does not promote high value co-creation. Four configurational paths lead to high levels of value co-creation: (1) incentive-relationship co-governance under the competition and cooperation tension scenario; (2) contractrelationship co-governance under the combined openness and closure and sharing and protection tension scenario; (3) incentive-relationship co-governance under the openness and closure tension scenario; and (4) contract governance under the sharing and protection tension scenario. Three configurational paths lead to non-high-level value co-creation,which are asymmetric with the paths generating high-level value co-creation. The theoretical contributions of this paper are threefold. First,it enriches research on value co-creation tension in innovation ecosystems within digital contexts by integrating the value co-creation process of digital innovation ecosystems,comprising "strategic goal-subject interaction-resource utilization",and deconstructing three core tensions: the openness and closure tension of digital strategy,the competition and cooperation tension of digital subjects,and the sharing and protection tension of digital resources. Second,it reveals different driving paths for governing value co-creation tension in digital innovation ecosystems,providing practical and targeted governance strategies. Third,it employs fuzzy-set qualitative comparative analysis to expand methodological diversity in empirical research on value co-creation tension in digital innovation ecosystems.

Amid a persistently sluggish global economy and escalating external uncertainties,China's economy faces multiple pressures. Accelerating the cultivation of urban new quality productive forces is great significance. Digital technologies have emerged as crucial engines for fostering new quality productive forces by reshaping innovation spaces and enabling networked collaboration. Yet digital innovation activities are inherently volatile,uncertain,complex,and ambiguous,rendering them vulnerable to external disruptions. Faced with such shocks,some cities demonstrate strong adaptive capacities,while others struggle to recover and fail to drive new quality productive forces,a divergence attributable to variations in digital innovation resilience. Consequently,unlocking the value of digital innovation resilience for urban new quality productive forces warrants urgent scholarly attention. In this context,this paper uses the panel data of prefecture-level and above cities in China from to 2023,incorporates urban new quality productive forces,digital innovation resilience,entrepreneurial activity,digital divide and government digital attention into a unified research framework,and explores the internal mechanism through which digital innovation resilience affects urban new quality productive forces. Specifically,it examines the mediating role of entrepreneurial activity and the moderating roles of digital divide and government digital attention,thereby providing a comprehensive understanding of the transmission pathway and contextual boundaries of this relationship. The main conclusions are as follows:First,digital innovation resilience has a significant positive impact on urban new quality productive forces,which is further confirmed by robustness tests. Heterogeneity analyses suggest that the positive impact is significant in cities with a high level of digital economy development,service-oriented cities,as well as large and medium-sized cities,whereas it is insignificant in cities with a low level of digital economy,production-oriented cities and small-sized cities. Second,entrepreneurial activity plays a partial mediating role in the impact of digital innovation resilience on urban new quality productive forces. Third,digital divide plays a negative moderating role in the impact of digital innovation resilience on urban new quality productive forces. The moderating role of government digital attention in the impact of digital innovation resilience on urban new quality productive forces is insignificant. This study proposes the following policy implications. First,unified data middle platforms should be established and R&D data resources integrated to underpin the development of urban new quality productive forces driven by digital innovation resilience. Meanwhile,accelerating the construction of digital technology pilot bases and facilitating the penetration of digital technologies into traditional industries will help boost urban new quality productive forces. Furthermore,differentiated strategies for enhancing digital innovation resilience should be tailored to local digital economy development,industrial structure,and urban scale. Second,relevant departments need to optimize the business environment and enhance entrepreneurial vitality. In addition,efforts should be made to strengthen intellectual property protection and improve institutional guarantee for innovation and entrepreneurship,thereby smoothing the pathway for digital innovation resilience to drive urban new quality productive forces. Third,bridging the digital divide requires increased investment in new digital infrastructure to lay a hardware foundation. Open access to digital tools should be promoted to encourage widespread adoption by innovative entities. Moreover,intercity cooperation of digital innovation should be strengthened to inject strong impetus into the development of urban new quality productive forces driven by digital innovation resilience. This study offers three theoretical contributions. First,existing studies have focused on the relationship between digital innovation and new quality productive forces,and lack analyses on the impact of digital innovation resilience on urban new quality productive forces. Based on resilience theory,this study verifies the positive impact of digital innovation resilience on urban new quality productive forces and deepens the research on the impact factor of new quality productive forces. Second,based on technological innovation theory,this study incorporates entrepreneurial activity into the research framework,verifies the mediating role of entrepreneurial activity in the impact of digital innovation resilience on urban new quality productive forces,and clarifies the transmission path through which digital innovation resilience affects urban new quality productive forces. Third,based on regional innovation theory and signaling theory,this study discusses the moderating roles of digital divide and government digital attention in the impact of digital innovation resilience on urban new quality productive forces,and expands the boundary conditions under which digital innovation resilience affects urban new quality productive forces.

As a representative form of new quality productive forces,the rapid development of the low-altitude economy has placed regulators across the globe into the Collingridge Dilemma. Specifically,this manifests in three dimensions: in the temporal dimension,a paradox emerges between the swift deployment of technology and the lag in regulatory response; in the knowledge dimension,both regulators and developers fall into a state of “shared ignorance” due to information asymmetry; and in the power dimension,structural challenges arise from imbalanced allocation of authority and responsibility,along with poorly coordinated governance tools.A vertical comparison with other industries in China reveals that the lowaltitude sector faces more acute governance challenges,intensified by accelerated technological iteration and great power competition. There is an urgent need to systematically address core issues such as disordered values,rigid management structures,and legal gaps. From a horizontal perspective,the EU,the U.S.,and Japan have developed distinct governance models rooted in their respective legal traditions and industrial conditions. The EU adopts a unified legislative framework and proactive regulation,constructing a rigid model that prioritizes safety and rights protection. While this forward-looking regulatory design provides a stable,transparent,and predictable institutional environment,which embodyies the principle of “regional coordination + regulation first”,it also constrains internal innovation vitality. The United States emphasizes market drive and incremental adaptation,adopting a flexible regulatory paradigm characterized by “layered oversight + market competition” to foster technological and industrial advancement. Although this approach grants significant latitude for innovation in early stages,it also introduces regulatory uncertainty and potential conflicts due to legislative delays and fragmented rules. Japan has developed a co-governance model based on national planning and social participation. While this model demonstrates strong strategic impetus and enables rapid resource concentration and technological breakthroughs,it also suffers from insufficient innovation diversity due to deep administrative intervention. Each of these three models presents distinctive strengths and limitations. The manifestation of multidimensional safety risks in the low-altitude economy under the Collingridge Dilemma fundamentally stems from insufficient synergy among ethical values,policy guidance,and legal norms. To resolve this dilemma,China should critically learn from international experience and adopt a holistic approach to construct a trinity governance pathway integrating “ethics-policy-law”: At the ethical level,guided by the holistic view of national security,a shift should be made from pursuing “zero-risk” absolute safety toward managing “tolerable risk”,and from rigid control to a modern paradigm of “resilient governance”. At the policy level,the government should transition toward a service-oriented role,empowering industrial leapfrogging through equitable subsidies,institutional innovation in central-local and civil-military coordination,and the construction of a nationwide integrated digital governance platform. At the legal level,a dualtrack strategy of “interpretation and legislation” should be pursued,which involves adapting the existing legal system while advancing forward-looking legislation,with particular focus on tackling complex issues such as data classification,grading,and cross-border transmission. The trinity synergistic governance pathway proposed in this study not only addresses practical challenges in China’s low-altitude economic development but also offers a systematic and operable Chinese solution to global low-altitude governance. Looking ahead,as technology continues to evolve and application scenarios expand,the governance system must keep evolving dynamically. It is expected that through deep collaboration among ethics,policy,and law,China will be able to foster industrial innovation while ensuring robust safety safeguards. This will ultimately achieve a virtuous cycle between high-quality development and high-level security,thereby establishing the low-altitude economy as a genuine new engine for future economic growth.

The widespread adoption of digital technologies,particularly advances in next-generation information technologies such as cloud computing,big data,and artificial intelligence,has provided robust technical support for the rapid development of digital platforms. As a key component of the platform economy,open innovation platforms transcend temporal and spatial constraints by connecting diverse stakeholders to co-construct collaborative innovation ecosystems,effectively facilitating the free flow of knowledge,technology,and creativity within the system. This,in turn,profoundly reshapes the global competitive landscape. By breaking down organizational boundaries and integrating innovation resources within the ecosystem,open innovation platforms significantly enhance the efficiency and quality of innovation,increasingly becoming a core strategic pathway for enterprises to achieve sustainable development. In this process,platform leaders rely on digital technologies to efficiently connect external innovation entities and design reasonable incentive mechanisms to attract broad participation from complementors,thereby constructing and sustaining a dynamically evolving,value-co-creating open innovation ecosystem. How to incentivize platform complementors to contribute knowledge has become a critical issue for open innovation platforms to ensure their survival and achieve sustainable development. Open innovation platforms bring together diverse innovation entities such as enterprises,users,and research institutions,thereby constructing an open,shared,and collaborative innovation ecosystem. Within this system,the knowledge contribution behavior of complementors not only promotes knowledge flow and collaborative innovation within the ecosystem but also significantly enhances the overall innovation capability and competitive momentum of the platform. However,there remains insufficient research on the motivations,influencing factors,and mechanisms of knowledge contribution by platform complementors. In particular,there is a lack of systematic examination of the structural factors driving complementors' knowledge contribution from the perspective of the platform ecosystem. How do platform reciprocity mechanisms drive platform complementors to contribute knowledge? What impact does the platform innovation environment have on this process? These questions have not yet been fully and clearly addressed theoretically or empirically. This study takes complementors from typical domestic open innovation platforms as the research subjects. Drawing on social exchange theory,it collects data via electronic questionnaires distributed to complementors on multiple representative platforms including Xiaomi Community,Huawei-related communities,and Haier HOPE. The survey was conducted in two waves (January– May and November– December 2025),with questionnaires distributed and returned (89% response rate). After screening,valid questionnaires were obtained,yielding a validity rate of 93.83%. Furthermore,the study systematically examines the pathways through which platform reciprocity mechanisms influence complementors' knowledge contribution behavior,and further explores the moderating effect of the platform innovation environment in this process. The findings are as follows: (1) Platform reciprocity mechanisms transform complementors' knowledge contribution from a unilateral effort into an investment behavior,thereby promoting their knowledge contribution. (2) Reciprocity mechanisms not only function as a social norm but also play a role in trust-building,effectively enhancing complementors' value perception across instrumental,social,and emotional dimensions. (3) Perceived value serves as the motivational foundation for complementors' knowledge contribution,influencing their knowledge contribution behavior. (4) Perceived value mediates the relationship between platform reciprocity mechanisms and knowledge contribution. (5) The platform innovation environment positively moderates the impact of reciprocity mechanisms on perceived value; that is,the stronger the innovation atmosphere,the greater the facilitating effect of reciprocity mechanisms on complementors' value perception. The innovations of this study are as follows: First,it constructs a framework for complementors' knowledge contribution drivers from a platform ecosystem perspective,addressing the gap in demand-side user research by analyzing supplyside complementors' resource and value exchange decisions,thereby expanding open innovation platform research boundaries. Second,this study systematically reveals how platform reciprocity mechanisms influence complementors' knowledge contribution through pathways such as value perception and further identifies the moderating role of the platform innovation environment. This deepens understanding and offers practical guidance for optimizing reciprocity mechanisms to sustain complementor participation.