Innovation serves as the core driving force for organizations to sustain competitive advantages. While organizations increasingly demand innovative breakthroughs, they simultaneously rely on rigid rules and procedures to regulate employee behavior, which may inadvertently suppress promising creative ideas. Bootlegging, defined as employees' covert pursuit of innovation without formal organizational authorization yet potentially beneficial to the organization, has become a key pathway for enhancing corporate innovation capability. Given that organizations cannot openly encourage employees to deviate from regulations, it is theoretically and practically imperative to investigate what leadership approaches can create conditions for the natural emergence of bootlegging, enabling employees to persist in creative exploration despite organizational resistance. Platform leadership, a novel leadership style arising in the digital age, emphasizes the co-development of leaders and employees through building open and inclusive organizational platforms, thereby creating favorable conditions for employee bootlegging. However, existing research has only preliminarily explored the influence of platform leadership on employee bootlegging from perspectives such as social exchange, self-construction, and resource conservation, while neglecting intrinsic motivation as a core driver. Since bootlegging is inherently a high-risk, self-initiated behavior that demands strong internal drive beyond competence and resources, expanding the motivational explanation of how platform leadership fosters bootlegging has become an urgent gap to address. In the context of the above considerations, this study draws upon self-determination theory to construct a moderated mediation model, investigating the mediating role of psychological empowerment in the relationship between platform leadership and employee bootlegging, and the moderating role of proactive personality in this process. Data were collected from full-time employees primarily engaged in technology development, product planning, and manufacturing positions through a three-wave survey design with one-month intervals between waves to mitigate common method bias. Of the initial 404 participants, 264 valid matched responses were ultimately obtained. Based on the survey data, the hypotheses were tested using hierarchical regression analysis, bootstrap methods, and other statistical techniques. The empirical results strongly support all five hypotheses. The results are as follows. First, platform leadership has a significant positive effect on employee psychological empowerment. Second, psychological empowerment has a significant positive effect on employee bootlegging. Third, psychological empowerment serves as a significant mediator in the relationship between platform leadership and bootlegging. Fourth, proactive personality positively moderates the relationship between psychological empowerment and employee bootlegging, such that this positive relationship is stronger for employees with higher levels of proactive personality. Fifth, proactive personality positively moderates the indirect effect of platform leadership on employee bootlegging through psychological empowerment, such that this indirect effect is stronger for employees with higher levels of proactive personality. This study makes two primary theoretical contributions. First, grounded in self-determination theory, this study reveals the mechanism through which platform leadership promotes employee bootlegging from an intrinsic motivation perspective, identifying the mediating role of psychological empowerment in the relationship between platform leadership and bootlegging. This expands the theoretical explanations for the internal mechanisms through which platform leadership influences employee bootlegging. Second, this study identifies proactive personality as an individual-level boundary condition, clarifying the differential effectiveness of psychological empowerment in driving bootlegging across employees with varying levels of proactive personality. This sheds light on the question of which employees are more likely to engage in bootlegging under platform leadership, while also offering a foundation for future research to examine the boundary roles of other individual characteristics. This study proposes three key practical implications based on its empirical findings. First, organizations should encourage leaders to practice the platform leadership style and cultivate a proper and constructive attitude toward employee bootlegging. Second, organizations should enhance employees' psychological empowerment to effectively stimulate their intrinsic work motivation. Third, organizations should focus on selecting and cultivating employees with higher levels of proactive personality and fostering a managerial environment that encourages employee proactivity.

The proposal of China's 'Dual-Carbon' goals has positioned market-oriented environmental rights trading as a pivotal policy instrument for advancing energy conservation and emission reduction. Energy use rights trading and carbon emission rights trading, as two core mechanisms, respectively facilitate optimal resource and environmental allocation through total energy consumption control at the source and aggregate carbon emission management at the output stage, thereby promoting green and low-carbon economic transition. However, given the severity of carbon reduction challenges and the entrenched carbon lock-in arising from the co-evolution of high-carbon technological systems and institutional arrangements, a critical question remains: Can environmental rights trading policies effectively overcome carbon lock-in constraints and fundamentally catalyze green transformation? Existing research has documented significant positive effects of energy use rights and carbon emission rights trading on energy consumption, technological innovation, and industrial structure, yet predominantly examines these policies in isolation. While the role of environmental regulation in carbon unlocking has garnered increasing attention, most studies focus on single driving factors. Consequently, whether the combination of energy use rights and carbon emission rights trading generates synergistic effects exceeding individual policy impacts, and through what mechanisms such synergy operates, remains insufficiently understood. Against the policy backdrop of dual-pilot regions, this study constructs a "source-process-output" analytical framework to examine how these two policies jointly influence carbon lock-in. Leveraging the quasi-natural experimental setting of overlapping pilot regions, the study systematically analyzes the synergistic effects and underlying mechanisms across input, process, and output dimensions. This study selects panel data of Chinese 300 cities from 2010 to 2023 as samples, and uses the difference-in-differences method to conduct an in-depth analysis of the impact of the two types of policy instruments on carbon lock-in. The dataset is primarily derived from China City Statistical Yearbooks and official government reports. To mitigate the potential influence of outliers, all continuous variables are winsorized at the 1% and 99% levels. This rigorous data preprocessing enhances the robustness of our empirical findings regarding the decoupling effects of energy and carbon trading schemes. The research results show that, first, both the energy use rights and carbon emission rights trading schemes have significantly reduced the carbon lock-in level of pilot cities; Second, the analysis of the impact mechanism finds that energy use rights and carbon emission rights trading jointly break carbon lock-in by reducing fossil energy dependence, technological path dependence and high-carbon industry embeddedness; Third, further analysis finds that while breaking carbon lock-in, the energy use rights and carbon emission rights trading schemes do not damage the economic effect, but also help promote high-quality economic development. Compared with the existing literature, the contributions of this study include the following three points: First, it expands the research boundary of existing environmental rights trading policies from the perspective of policy synergy. Second, it constructs a systematic unlocking framework of carbon lock-in of 'source control - end-of-pipe governance', and considers the synergistic effect of energy use rights and carbon emission rights trading in breaking carbon lock-in from a holistic perspective, providing a systematic driving perspective and empirical evidence for breaking carbon lock-in. Third, it comprehensively reveals how energy use rights and carbon emission rights trading break carbon lock-in from the whole-process perspective of 'input-process-output', namely fossil energy dependence, technological path dependence, and high-carbon industry embeddedness. This study provides decision-making references for refining energy use rights and carbon emission rights policies and formulating scientific and effective carbon unlocking measures, as well as accumulating new theoretical and empirical evidence for systematically promoting the green and low-carbon transformation of industries.

Against the backdrop of global technological revolution and industrial transformation, the smart economy driven by artificial intelligence as its core and representing an advanced form of the digital economy has emerged as a central engine for China's high-quality economic development and the construction of a modernized economic system. To promote the deep integration of AI and the real economy, China has implemented the strategic initiative of National New Generation Artificial Intelligence Innovation and Development Pilot Zones (hereinafter referred to as “AI Pilot Zones”). Existing research has rarely directly examined the causal relationship between this pilot policy and the development of the smart economy, nor has it systematically explored the underlying transmission mechanisms, spatial spillover effects, and heterogeneous impacts. Thus, this study evaluates the enabling role of AI Pilot Zones in the smart economy, explores their internal mechanisms and spatial dynamics, and offers empirical insights to optimize AI policy frameworks and promote regional coordinated development. Utilizing provincial-level panel data from 30 provinces (municipalities and autonomous regions) in China spanning the period from 2011 to 2023, this study constructs a comprehensive evaluation index system comprising four dimensions: smart resource foundation, smart factor integration, smart industry application, and smart environment support. The entropy-weighted TOPSIS method is employed to measure the smart economy development level of each province. To identify the net effect of the pilot policy on smart economy development, this study treats the establishment of AI Pilot Zones as a quasi-natural experiment and adopts a time-varying difference-in-differences model as the baseline regression method. A mediation model is further used to examine the underlying mechanisms, and a spatial Durbin difference-in-differences model is applied to investigate spatial spillover effects. To ensure the robustness of the empirical findings, multiple endogeneity tests and robustness checks are conducted, alongside heterogeneity analyses from the perspectives of regional location and the degree of marketization. The empirical results yield the following findings: First, the AI Pilot Zone policy significantly promotes the development of the regional smart economy, a conclusion that remains robust after a series of endogeneity and robustness checks. Moreover, the AI Pilot Zones significantly enhance the development levels of the four sub-dimensions of the smart economy: smart resource foundation, smart factor integration, smart industry application, and smart environment support. Second, the policy exerts its impact through three key mediating channels: enhancing human capital levels, fostering digital industry agglomeration, and mitigating the misallocation of innovation factors. Specifically, the pilot policy attracts high-end talent and improves labor quality, promotes the agglomeration of digital industries leading to economies of scale and knowledge spillovers, and optimizes the allocative efficiency of innovation resources, thereby jointly driving the growth of the smart economy. Third, China's smart economy development exhibits significant positive spatial autocorrelation. The AI Pilot Zone policy generates notable positive spatial spillover effects, not only enhancing the smart economy level of the host locality but also effectively stimulating the development of smart economies in neighboring regions. Fourth, significant heterogeneity characterizes the policy effects: the promotional effect is strongest in the Western region, followed in descending order by the Eastern, Central, and Northeastern regions. The policy dividend is more pronounced in areas with higher degrees of marketization, while the effect is relatively weaker in regions with lower marketization levels. The study links the AI Pilot Zone policy to the smart economy through a quasi-natural experiment, clarifying mechanisms and spatial spillover effects while providing a scientific basis for differentiated policymaking and coordinated regional advancement. The study recommends expanding AI Pilot Zones with resource tilts toward the Central, Western, and Northeastern regions, strengthening talent-industry linkages to optimize innovation allocation, and leveraging spatial radiation effects alongside market-oriented reforms to sustain AI's long-term driving force on the smart economy.

Achieving high-level synergy between digitalization and greenization (dual-transformation synergy) is a critical strategic imperative for manufacturing enterprises to achieve high-quality development and meet China's national "dual carbon" goals. While existing literature has examined the individual effects of technological, organizational, and environmental factors on either digital or green transformation, it has neglected the nonlinear mechanisms through which multidimensional conditions interact to drive dual-transformation synergy. Emerging configurational studies have yet to systematically integrate the core dimensions of the Technology-Organization-Environment (TOE) framework, provide insufficiently nuanced characterizations of diverse driving paths, and focus predominantly on high-level synergy while overlooking the configurational logic behind non-high-level outcomes. To address these research gaps, this study examines the complex causal mechanisms underlying high-level dualization synergy in manufacturing firms, offering theoretical insights and practical guidance for enterprises and policymakers alike. Drawing on the Technology-Organization-Environment (TOE) framework and a configurational perspective, this study uses fuzzy-set qualitative comparative analysis (fsQCA) to analyze survey data from 331 Chinese manufacturing enterprises collected between July and September 2025. Six antecedent conditions are identified: (1) technology dimension: digital-green technological innovation capability, digital-green technology integration capability; (2) organization dimension: top management support for dualization, organizational slack resources; (3) environment dimension: government support for dualization, market competition pressure. The fsQCA method is well-suited for this study because it captures conjunctural causation, equifinality, and asymmetric relationships among conditions, thereby transcending traditional net-effect analyses. Necessity analysis shows that, given the complexity and multiplicity of driving pathways, no single condition is necessary for high-level dualization synergy. Sufficiency analysis identifies five distinct equifinal configurations that lead to high-level dualization synergy: (1) Technology-Environment Driven: technological innovation capabilities complemented by government policy support; (2) Technology Driven: synergistic technological innovation and integration capabilities, coupled with top management support and market competition pressure; (3) Technology-Organization-Environment Synergy Driven: tripartite alignment of technological capabilities, organizational slack resources, and government support; (4) Technology-Organization Driven: strong internal technological and organizational resources compensating for external environmental pressures; (5) Organization-Environment Driven: technological integration capabilities, organizational slack, and government support compensating for relatively weak indigenous innovation capabilities. Additionally, two configurations result in non-high-level dualization synergy: Technology-Organization Deficient and Organization-Environment Deficient. These findings highlight that a lack of organizational support and resources is a common underlying condition for transformation failure. Theoretically, this study moves beyond conventional net-effect analyses by integrating the TOE framework with configurational theory, constructing a systematic analytical framework that clarifies the inherent interconnectedness of dual-transformation synergy. The identification of multiple equifinal pathways challenges the dominant "best practice" view and enriches organizational transformation theory by demonstrating how the "different paths, same destination" phenomenon operates in the digital-green context. The identified substitutive and complementary mechanisms among antecedent conditions advance dual-transformation research from asking whether individual factors matter to elucidating how they combine to exert influence. Meanwhile, the use of fsQCA effectively captures the conjunctural and asymmetric causality inherent in complex organizational transitions, allowing for the identification of multiple causal recipes while retaining case-specific complexity. The findings offer differentiated strategic guidance for enterprise managers to tailor transformation strategies to their unique resource endowments and suggest that policymakers shift from one-size-fits-all approaches to targeted interventions, developing diversified policy toolkits for heterogeneous enterprise contexts.

In the digital economy era, propelled by the dual forces of technological evolution and policy guidance, digitally enabled collaborative innovation has become a pivotal strategic imperative for enterprises to cultivate competitive advantages and achieve sustainable development. Digital technology diversification empowers firms to enhance inter-organizational resource orchestration and opportunity responsiveness through constructing digital technology portfolios, expanding digital-physical collaborative frontiers, and dynamically allocating heterogeneous technological elements, thereby serving as a critical value co-creation source of collaborative innovation in digital context. However, current research has three gaps: first, it ignores the actual organizational behaviors of deploying multiple digital technologies simultaneously; second, it remains unclear that whether digital technology diversification can mitigate the limitations of conventional technological diversification through complementary and synergistic effects of technologies; third, it lacks a systematic analysis of how internal organizational capabilities(absorptive capacity) and external environmental conditions (environmental munificence) contingently shape the enabling potential of digital technology diversification for collaborative innovation. Following technology affordance theory, this study conducts empirical analysis using a sample of A-share listed high-tech firms in China from 2012 to 2023. The study identifies sample firms based on high-tech enterprise certification information disclosed in the CSMAR database, following a rigorous screening process, which yields an unbalanced panel of 3 211 high-tech enterprises comprising 21 059 firm-year observations. Patent data for measuring digital technology diversification and collaborative innovation are sourced from Clarivate Analytics' IncoPat database, while firm-level and industry-level data for absorptive capacity, market munificence, and control variables are obtained from CSMAR. To address unobserved firm-specific factors (e. g., corporate culture) and time-varying factors (e. g., economic cycles), mitigate potential endogeneity, and enhance estimation accuracy, the study employs two-way fixed effects models with both firm and year fixed effects for hypothesis testing. This study finds that digital technology diversification significantly enhances firms' collaborative innovation. The relationship is positively moderated by absorptive capacity, meaning the effect is stronger when firms possess higher capacity to absorb knowledge. Conversely, market munificence negatively moderates the relationship, indicating that the positive impact is weaker in more favorable market environments. These moderating effects are evidenced by significant interaction terms in regression models. To ensure robustness, the study conducts multiple supplementary tests. These include re-measuring digital technology diversification using all patent IPC codes, extending the lag structure to two years, and operationalizing collaborative innovation via the natural logarithm of jointly granted patents. The results across these various specifications consistently support the baseline conclusions regarding the effects of diversification, absorptive capacity, and market munificence. The theoretical contributions of the study are as follows: First, this study extends the research of technological diversification into the digital context by clarifying the concept of digital technology diversification and validating the applicability of technology affordance theory within this domain. Second, this study shifts the research focus from digital technologies as an aggregate or single-dimensional construct to diversified digital technology portfolios and reveals the positive impact of digital technology diversification on collaborative innovation, thereby deepening the understanding of collaborative innovation in digital contexts. Third, this study elucidates how absorptive capacity and market munificence shape the value realization of collaborative innovation empowered by digital technology diversification, advancing the comprehension of the interactive mechanisms among technology, capability and environment. Besides, the findings of this study also provide practical guidance for firms to optimize technology allocation and enhance collaborative innovation capabilities in the context of digital economy. For managers, it is essential to establish a standardized, modular digital technology framework. Strengthening organizational absorptive capacity is also crucial. This can be achieved by forming cross-functional teams, promoting internal knowledge sharing, and building a knowledge base to enhance the efficiency of integrating diverse technologies. Furthermore, strategies must adapt to market conditions. In resource-rich markets, companies should focus R&D on core technologies and deepen collaboration with key partners. Conversely, in resource-constrained environments, firms should increase the diversity of their digital technologies to broaden the scope for collaborative innovation and improve matching capabilities with external partners.

With the rapid development of the digital economy, digital industries have become a crucial engine for promoting urban innovation. As an important organizational form of digital-real economy integration, digital industrial clusters play an important role in enhancing urban innovation vitality and advancing regional economic development. However, existing studies have mainly examined the impact of digital industrial agglomeration on urban innovation from a static perspective. Insufficient attention has been paid to the dynamic evolutionary characteristics of agglomeration, and the boundary role of urban knowledge allocation capacity in shaping the innovation effect of dynamic agglomeration remains underexplored. To address this research gap, this study draws on the perspective of dynamic externalities and incorporates dynamic agglomeration of digital industries, urban knowledge allocation capacity, and urban innovation into a unified analytical framework. This study constructs an unbalanced panel dataset of 3 237 city-year observations across 222 prefecture-level Chinese cities from 2006 to 2022. Urban innovation is proxied by the natural logarithm of one plus the sum of city-level invention and utility model patent applications. To measure digital industrial dynamic agglomeration, this study first identifies digital industries using the National Bureau of Statistics classification and then employs a dartboard approach with 500 Monte Carlo iterations to compute a standardized Z-index capturing deviations from random spatial allocation. The Bai-Perron structural break test is applied to detect regime shifts in each city’s agglomeration trajectory, and segmented trend regressions are estimated to quantify directional momentum, with the resulting slope coefficient serving as the dynamic agglomeration proxy. Urban knowledge allocation capacity is operationalized via betweenness centrality in intercity patent transfer networks (knowledge flow configuration) and patent collaboration networks (knowledge synergy configuration), reflecting a city’s bridging role in cross-regional knowledge circulation. City-level economic development, industrial structure, science-education expenditure, and digital infrastructure are included as controls. The study finds that there is a significant inverted U-shaped relationship between the dynamic agglomeration of digital industries and urban innovation. Moderate dynamic agglomeration can effectively promote urban innovation by generating resource adsorption effects, network restructuring effects, and path renewal effects. However, excessive dynamic agglomeration may inhibit urban innovation because it increases the costs of knowledge transformation, structural adjustment, and factor allocation. This conclusion remains robust after a series of robustness checks. Furthermore, the moderating effect analysis shows that urban knowledge allocation capacity significantly strengthens the inverted U-shaped relationship between the dynamic agglomeration of digital industries and urban innovation. Specifically, cities with stronger knowledge allocation capacity exhibit a steeper inverted U-shaped curve, and the turning point shifts to the left. This indicates that stronger knowledge allocation capacity amplifies both the positive effect of dynamic agglomeration in the low-to-moderate stage and the negative effect in the excessive agglomeration stage. Heterogeneity analysis further reveals that the inverted U-shaped effect is more pronounced in central cities and cities with well-developed digital infrastructure, suggesting that the innovation consequences of dynamic agglomeration vary across cities with different resource endowments and digital foundations. This study makes several marginal contributions. First, it moves beyond the static research paradigm of digital industrial agglomeration by introducing the perspective of cluster dynamics, thereby enriching research on the relationship between digital industrial agglomeration and urban innovation from the lens of dynamic agglomeration. Second, it develops a measurement index for the dynamic agglomeration of digital industries, providing a methodological reference for capturing the evolutionary characteristics of industrial agglomeration in the digital era. Third, it reveals the moderating mechanism of urban knowledge allocation capacity and the heterogeneous effects of urban attributes, thus clarifying the environment-strategy matching logic underlying the innovation effects of digital industrial dynamic agglomeration. Finally, the findings provide theoretical support and policy implications for optimizing the spatial layout of digital industries, promoting the differentiated development of digital industrial clusters, and enhancing urban innovation and high-quality development in the context of the digital economy.

The coupling coordination between digitalization and green development is essentially the dialectical unity of productivity innovation, represented by digital technology, and the transformation of development models oriented towards green development. Digitalization refers to the process of utilizing digital technologies to endow individuals, organizations, or systems with new capabilities and opportunities, thereby achieving efficiency improvements, innovative development, and value creation. The concept of green development takes the harmonious coexistence of humanity and nature as its core, advocating for the minimization of natural resource consumption and ecological damage in economic activities to achieve sustainable economic, social, and ecological development. The coupling coordination between digitalization and green development is manifested in a dynamic collaborative process of "empowerment-feedback". This interaction transcends the industrial perspective of a "digital economy-green economy" dichotomy, upgrading to a cross-system collaboration of "system-technology-culture". To empirically verify the coupling coordination between digitalization and green development, explore their coupling model under the concept of ecological civilization, clarify the pivotal role of digital empowerment in the ecological governance of the Yangtze River Economic Belt, and provide theoretical and empirical support for optimizing basin-wide policies, this study investigates the coupling coordination mechanism between regional digitalization and green development. By comprehensively employing methods such as the coupling coordination model, spatial autocorrelation model, obstacle degree model, and geographical detector, this paper constructs an evaluation indicator system. It analyzes the coupling coordination degree, spatiotemporal differentiation characteristics, and driving mechanisms of digitalization and green development in the Yangtze River Economic Belt from 2012 to 2023. The results show that the coupling coordination degree of digitalization and green development in the Yangtze River Economic Belt has increased year by year, with obvious regional differentiation; there is a significant positive spatial correlation and it exhibits spatiotemporal characteristics of spatial differentiation; the driving factors include ecological protection, environmental restoration, resource utilization, and digital technology application, with certain differences across different years; the driving factors are divided into three categories along the time dimension: rising first then falling, falling first then rising, and continuously declining. In the spatial dimension, the intensity of each driving factor's role shows obvious spatial differentiation, among which industrial structure, urbanization rate, and higher education constitute the core driving factors, while technology circulation represents a common factor with insufficient driving force. To improve the digitalization and green development capability of the Yangtze River Economic Belt, it is necessary to accelerate digital transformation and development to achieve coordinated acceleration of digitalization and green development; break through the constraints of key driving factors to balance digitalization and green development; classify and optimize the effectiveness of driving factors to accurately target and address spatial disparities in driving forces; and promote regional coordinated development to narrow the spatial differentiation of coupling coordination degrees. The innovations of this paper are as follows: First, by incorporating core elements of artificial intelligence including data, computing power, and algorithms, and expanding the connotations of digitalization and green development, this paper explores their development mechanisms and reveals the inherent logic of their coupling coordination. Second, it constructs an evaluation index system for digitalization and green development, and uses panel data from 11 provinces and cities in the Yangtze River Economic Belt from 2012 to 2023 to conduct a coupling coordination analysis. Third, it reveals the spatiotemporal characteristics of the coupling coordination degree and performs a spatial autocorrelation test. Fourth, it analyzes the factors restricting the coupling coordination degree via the obstacle degree model. Fifth, it identifies the influencing driving factors of the coupling coordination degree using the geographical detector.

As corporate ESG performance evolves from a peripheral sustainability agenda to a core value reconfiguration mechanism, firms increasingly grapple with a paradox where heightened disclosure coexists with pervasive ESG greenwashing, eroding stakeholder trust and market integrity. The 2025 Central Economic Work Conference stressed fostering new quality productive forces and comprehensive green transformation, yet many enterprises remain trapped in prioritizing form over substance, favoring cosmetic reporting over strategic ESG integration. Prior research extensively investigates external institutional drivers and board characteristics that shape ESG responsibility fulfillment or greenwashing separately, but it overlooks the cognitive foundation at the organizational apex—executives' green perception, embedded beliefs, values, and knowledge regarding ecological sustainability and social responsibility. Crucially, how this strategic cognition simultaneously shapes genuine ESG responsibility and cosmetic greenwashing, and the mechanisms through which such cognitive orientations translate into firm conduct, remain underexplored. Integrating upper echelons theory and strategic cognition theory, this study proposes a dual-pathway framework of promoting genuine ESG responsibility while curbing greenwashing, hypothesizing that executives' green perception enhances corporate ESG performance by fostering substantive responsibility fulfillment while concurrently inhibiting greenwashing. Using a sample of Chinese A-share listed firms from 2012 to 2023, the study further examines the mediating role of corporate integrity culture, grounded in social norm theory, and the contingent effects of ownership structure and industry pollution attributes. Findings demonstrate that executives' green perception significantly improves overall ESG performance through two mutually reinforcing mechanisms: it fosters proactive ESG responsibility fulfillment and effectively suppresses greenwashing behaviors. Endogeneity concerns are addressed using propensity score matching techniques, and a battery of robustness checks confirms that the findings remain consistent. Heterogeneity analyses reveal that the beneficial impact is more pronounced in non-state-owned enterprises and non-heavily polluting industries. In state-owned enterprises, rigid bureaucratic mandates and multi-layered accountability systems constrain the discretionary enactment of green cognition, whereas in heavily polluting sectors, intense regulatory pressure and short-term profit incentives dilute cognitive-driven authentic ESG improvements. Mechanism tests confirm that corporate integrity culture partially mediates both the positive link with ESG responsibility fulfillment and the negative link with ESG greenwashing. It channels executives' green perception into higher responsibility by instilling ethical norms and long-term stewardship values, while simultaneously strengthening organizational commitments to truthful disclosure and substantive action, thereby reinforcing the suppression of greenwashing. Three implications for managerial practice and policy refinement emerge. First, firms should institutionalize the deep cultivation of executives' green perception by incorporating cognitive psychology and greenwashing case deconstruction into leadership development programs, and by establishing asymmetric incentive schemes where verified absence of greenwashing serves as a precondition for equity-based compensation and bonuses, thereby converting cognitive commitment into genuine ESG authenticity. Second, given the attenuated effects in state-owned and heavily polluting contexts, differentiated strategies are necessary. Non-state firms should capitalize on their strategic flexibility to embed green cognition into core strategies, while heavily polluting firms must implement rigorous internal ESG audit mechanisms and redirect resources from cosmetic green marketing toward substantive clean production upgrades, with support from regulatory sandboxes and targeted green subsidies. Third, integrity culture must be elevated from a peripheral corporate value to an organizational cornerstone. Enterprises should establish independent board-level ESG oversight committees and embed green cognition into codes of conduct and performance evaluation systems, thus transforming ESG from a compliance cost into a value generation engine and ensuring the sustained authenticity of ESG performance. This study offers three primary theoretical contributions. First, it enriches upper echelons and strategic cognition perspectives by constructing a dual-dimension framework of promoting ESG substance and curbing greenwashing, while advancing theory on the enabling and constraining roles of executive green perception. Second, it further identifies ownership type and industry pollution as critical boundary conditions, advancing a contingency view of how cognitive drivers translate into heterogeneous ESG outcomes. Third, it unveils corporate integrity culture as a critical mediating channel grounded in social norm theory, bridging the gap between executive cognition and organizational ESG authenticity. Collectively, these insights move beyond unidimensional ESG research and illuminate the cognitive microfoundations and normative pathways essential for fostering substantive corporate sustainability.

Intelligent manufacturing anchors the strategic integration of digital and green transformation, aimed at fostering new quality productive forces and advancing socialist modernization. Current policies, centered on the dual objectives of "improving quality and efficiency" and "reducing emissions and consumption", provide institutional support and resources for coordinated intelligent and green development through knowledge-sharing platforms, collaborative innovation alliances, and energy-saving technologies. However, this coordination faces challenges such as weak intelligent technology and insufficient knowledge foundations; neglecting knowledge system reconstruction risks a "digital performance trap". Only by breaking away from fragmented knowledge acquisition, reconstructing digital knowledge systems, and leveraging industry-university-research collaboration to cultivate digital talent can enterprises bridge the gap between knowledge acquisition and reconstruction, align with contemporary demands, and build new competitive advantages. Drawing upon knowledge orchestration theory, this study identifies intelligent manufacturing policy as a critical opportunity for digital knowledge acquisition, regards digital knowledge deepening reconstruction and expansion reconstruction as key levers for the exploration and exploitation of knowledge value, while positioning industry-university-research collaboration as a vital foundation for mobilizing that value. Using data from Chinese A-share manufacturing listed companies on the Shanghai and Shenzhen Stock Exchanges from 2010 to 2024, this study employs empirical methods, including multi-period Difference-in-Differences, Synthetic Difference-in-Differences, sensitivity analysis, the entropy weight-composite system synergy model, and placebo tests, to examine the impact of intelligent manufacturing policy on coordinated development of intelligentization and greenization. Furthermore, the study investigates the mediating role of digital knowledge reconstruction and the moderating effect of industry-university-research collaboration. Finally, heterogeneity analyses are conducted across four dimensions: firm size, technological level, environmental protection performance, and ownership structure. The research findings indicate that intelligent manufacturing policies significantly promote coordinated development of intelligentization and greenization. This conclusion remains robust across a series of tests, including sensitivity analysis, heterogeneous treatment effects, dynamic Synthetic Difference-in-Differences (SDID), placebo tests, measurement bias tests, bidirectional causality tests, exogenous shock tests, and anticipation tests. Mechanism testing reveals that intelligent manufacturing policies facilitate the dual coordinated development by driving both the deepening reconstruction and expansion reconstruction of digital knowledge. Moderation analysis shows that industry-university-research collaboration significantly strengthens both the direct impact and the digital knowledge reconstruction mechanisms. Heterogeneity analysis further demonstrates that for small scale enterprises, the digital knowledge expansion reconstruction mechanism only holds under the condition of industry-university-research collaboration. For non-high-tech enterprises and state-owned enterprises (SOEs), only the digital knowledge expansion reconstruction mechanism is significant, though industry-university-research collaboration enhances both types of mechanisms. For large-scale enterprises, high-tech enterprises, non-SOEs, and both high-pollution and non-high-pollution enterprises, the direct impact, both knowledge reconstruction mechanisms, and the moderating effects all exist. Unlike previous studies grounded in Knowledge-Based View, this paper builds upon Knowledge Orchestration Theory by identifying intelligent manufacturing as an opportunity for knowledge acquisition, digital knowledge reconstruction as a method of knowledge bundling, industry-university-research collaboration as the environment for knowledge leveraging, and the coordinated development of intelligentization and greenization as the performance of knowledge shedding, thereby extending the theoretical framework of knowledge orchestration. Furthermore, this study offers three marginal contributions: First, from the perspective of dual coordinated development, it expands the boundary of effect evaluation for intelligent manufacturing policies. Second, from the perspective of digital knowledge reconstruction, it enriches the mechanistic understanding of how intelligent manufacturing policies influence dual coordinated development. Third, from the perspective of industry-university-research collaboration, it deepens the exploration of external drivers affecting the impact of intelligent manufacturing policies on dual coordinated development. This paper provides a theoretical foundation and practical insights for manufacturing enterprises to capitalize on intelligent manufacturing policy dividends, reconstruct digital knowledge systems, engage in industry-university-research collaboration, and promote dual coordinated development.

The rapid growth of AI and the digital economy has made computing power a strategic resource for industrial transformation. China's intelligent computing capacity reached 725 EFLOPS in 2024, yet resources remain unevenly distributed, with eastern cities dominating the top tier. Computing-power technology innovation is emerging as a core pathway to break these constraints, enabling larger-scale and more efficient application of advanced technologies while reshaping inter-industry collaborative development patterns. Unlike conventional digital innovation that clusters in coastal hubs, computing-power innovation is factor-driven and relies on energy, climate, and land endowments, while its geographically transferable loads enable cross-regional transmission. Existing research on computing-power technology innovation has predominantly examined its macroeconomic impacts and enterprise-level applications, leaving the micro-level transmission mechanisms that drive coordinated regional development largely unexplored. This raises critical questions: how does computing-power technology innovation reshape coordinated regional development through spatial spillovers? Through what mechanisms does this influence operate across different geographic scales? Drawing on spatial economic theory and growth-pole analysis, this study examines the effects and mechanisms of computing-power technology innovation on coordinated regional development in China. The study uses panel data from 282 prefecture-level cities (2005 – 2021), and constructs a city-level computing-power technology innovation indicator by expanding a 36-term industry-standard lexicon with BERT and identifying 47 104 computing-power patents through an LLM-based classifier, which are then geocoded and aggregated to the prefectural level. Coordinated regional development is measured at multiple scales using 500-meter VIIRS-calibrated nighttime light data and 1-kilometer gridded population data. The empirical analysis applies single-regime and two-regime Spatial Durbin Models with time-and city-fixed effects, complemented by threshold distance matrices to trace the nonlinear decay of spillovers. The findings indicate that computing power technology innovation produces significant positive spatial spillovers, raising the level of coordinated regional development and narrowing the relative development gap of administrative boundary areas, with marginal effects on boundary townships notably exceeding those on central townships. Regarding mechanisms, inter-industry co-agglomeration and the release of human capital agglomeration dividends serve as important paths, reshaping the spatial distribution into a polycentric and specialized pattern. Heterogeneity analyses show that the coordinating effect strengthens in cities with open public data platforms, whereas high entrepreneurial activity amplifies the siphoning effect. Further analysis shows that the spillovers of computing power innovation growth poles follow nonlinear geographic decay: within a 145-kilometer threshold, growth poles significantly promote neighboring cities' coordinated development, whereas beyond 160 kilometers the backwash effect dominates. The contributions of this paper are reflected in three aspects. First, while most existing studies on digital economy and regional development focus on generic digital technologies or the macro-impacts of digital infrastructure, few studies define and measure computing power technology innovation as a distinct factor-driven paradigm. This study clarifies its connotation, uses LLM techniques to identify computing-power patents from large-scale textual data, and measures city-level computing power technology innovation at fine spatial resolution, thereby providing a new analytical construct and empirical basis for research on computing-power economics. Second, existing research rarely combines multi-scale spatial analysis with the geographically transferable properties of computing power. This study addresses the gap by combining Spatial Durbin Models with nighttime light data and gridded population data, jointly capturing inter-city spillovers and intra-city center and periphery dynamics at prefectural, township, and urban-core scales, thereby opening the black box of how computing power technology innovation affects coordinated regional development through inter-industry co-agglomeration and human capital redistribution. Third, existing research on regional growth poles mainly focuses on conventional innovation hubs, and few studies examine how computing power innovation growth poles shape surrounding cities under different geographic-distance conditions. Aligned with China's strategic orientation toward national supercomputing centers, this study builds a research framework for computing power innovation growth poles and empirically identifies the nonlinear distance-decay structure of their spillovers, providing spatial-planning guidance for national computing infrastructure and evidence-based support for reducing regional development disparities.

Although knowledge spillovers fuel innovation, they are subject to significant distance-decay constraints. As the distance between regions increases, the costs of searching for, accessing, and interpreting external knowledge also rise, making effective cross-regional learning more difficult. While information and communication technologies (ICT) have developed rapidly, the specific impact of enterprise-owned social media on cross-regional knowledge flows is underexplored. By standardizing information release, these platforms may fundamentally reshape the geography of knowledge spillovers. Previous studies have examined the role of ICT in facilitating knowledge diffusion from a macro perspective, but the micro-level mechanisms through which digital disclosure platforms reshape spatial knowledge flows remain unclear. This study, therefore, contextualizes the analysis using WeChat official accounts as a representative enterprise-owned social media platform and investigates their effects on knowledge spillovers. Specifically, this study addresses three core questions: (1) whether establishing a WeChat official account significantly amplifies inter-firm knowledge spillovers; (2) whether such effects are moderated by regional absorptive capacity and multidimensional distance; and (3) how the platform's one-to-many, weakly interactive architecture shapes exploitative versus exploratory innovation. To answer these questions, the study uses patent citation data and constructs a multi-period difference-in-differences model to estimate the impact of firms launching WeChat official accounts on knowledge spillovers. Patent citations serve as a useful proxy for the flow and diffusion of technological knowledge, allowing researchers to trace the spatial pattern of inter-regional knowledge linkages. The analysis further investigates how the effects of enterprise-owned social media vary with geographical distance, cultural barriers, and administrative segmentation, whether these effects are influenced by regional absorptive capacity and local innovation environments, and whether enterprise-owned social media exerts heterogeneous effects on different types of technological innovation. The results show that a firm's patents are significantly more cited after establishing a WeChat official account, indicating a clear knowledge spillover effect. This result holds after placebo tests, robustness checks, and instrumental-variable estimations. However, the effect is spatially uneven. While WeChat official accounts can help reduce the obstacles that geographical distance as well as cultural barriers pose to knowledge diffusion across regions, their ability to alleviate administrative segmentation is limited. This finding suggests that while ICT can relax the time and space constraints in the circulation of information, institutional barriers continue to impose substantial frictions on inter-regional knowledge exchange. The magnitude of knowledge spillovers strongly depends on regional absorptive capacity and the local innovation environment. Cities with stronger related knowledge bases, more diversified industrial structures, or more advantageous positions in the urban hierarchy benefit more from digital knowledge spillovers. From a technological diffusion perspective, enterprise-owned social media platforms are more likely to stimulate exploitative innovation than exploratory innovation. This is because WeChat official accounts operate primarily through one-to-many communication and lack frequent reciprocal interaction; they are more suited to supporting the search, screening, and incremental application of codified or semi-codified knowledge rather than fostering exploratory technological development.

With the rapid advancement of artificial intelligence as a general-purpose technology, innovation processes are increasingly embedded in open, networked, and collaborative ecosystems. Compared with traditional proprietary R&D models, AI innovation is characterized by high knowledge intensity, technological complexity, cumulative learning, and strong interdependence among heterogeneous actors. Open-source communities have therefore become critical organizational infrastructures for AI development. However, although existing research has examined open innovation and digital collaboration, the mechanisms through which different participating actors influence innovation performance in AI-oriented open-source projects remain insufficiently clarified. This study investigates how demand-side user participation and supply-side developer engagement affect innovation performance in open-source AI projects and whether their interaction generates complementary or nonlinear effects. Drawing on open innovation theory and the knowledge production function, this study develops an analytical model linking heterogeneous participant behavior to project-level innovation outcomes. Users enhance problem articulation and market relevance, while developers improve technical depth and iterative efficiency. The study posits that while user-developer interaction fosters synergistic knowledge recombination, excessive heterogeneity may increase coordination costs. Empirically, the study analyzes a panel of 3.98 million GitHub projects (2009 – 2025), identifying AI-related initiatives via domain classification. Innovation performance is measured by star growth, fork activity, and sustained contributions. Core explanatory variables quantify user engagement through issue submissions and feedback interactions, and developer participation through active contributors and commit frequency. The empirical strategy employs fixed-effects regression models to control for unobserved project heterogeneity, complemented by robustness checks incorporating lagged variables and instrumental variable approaches to mitigate potential endogeneity concerns. The empirical results reveal several critical findings. First, both user participation and developer engagement significantly enhance innovation performance, but their functional roles differ markedly: user demand exhibits approximately threefold greater impact than developer supply in AI-specific projects (coefficients: 0.258 vs. 0.066), underscoring the dominance of demand-pull mechanisms in data-driven innovation. Second, the interaction between users and developers exhibits an inverted-U relationship: within moderate ranges, participation generates complementary effects, but when both reach high levels simultaneously, a significant negative interaction emerges, indicating a "crowding effect" where coordination costs outweigh synergistic benefits—a manifestation of the "openness paradox". Third, heterogeneity analysis demonstrates that these effects are attenuated by technical complexity (programming language count), which increases coordination frictions, and moderated by license restrictiveness, which enhances developer incentives but impedes user participation. AI open-source projects show stronger demand-pull and supply-driven effects than other sectors, with demand-pull being more pronounced. NLP and multimodal projects exhibit higher demand sensitivity than computer vision, while developer supply benefits multimodal most, reflecting data-driven and scenario-driven characteristics. This study contributes to the literature in several respects. First, it advances open innovation research by shifting the analytical focus from firm-level boundary management to ecosystem-level actor interaction within the context of AI technological paradigms. Second, by employing large-scale digital trace data, it improves empirical identification and external validity beyond small-sample case studies. Third, it extends the knowledge production framework to digital collaborative environments, demonstrating how heterogeneous inputs interact under conditions of technological complexity. Finally, the findings provide policy implications for digital innovation governance, suggesting that platform operators and policymakers should design institutional mechanisms that balance participation incentives, reduce coordination frictions, and enhance collaborative efficiency in AI ecosystems.

Emerging technologies such as artificial intelligence are reshaping the operating models of various organizations at an unprecedented pace, and their application has become a key driver of organizational innovation and development. In the age of artificial intelligence, intrapreneurship among employees is of great strategic significance for enhancing organizational competitive advantages and facilitating organizational digital transformation. The true value of AI technology lies not only in its deployment at the organizational level, but also in the ability of employees to transform their AI disruption awareness into proactive change. AI disruption awareness refers to employees' perception of threats and concerns when facing AI technology in a digitally intelligent workplace. For enterprises, employee intrapreneurship is an important path to breaking through growth bottlenecks and activating organizational vitality. The practical conversion capability of employees' AI disruption awareness is a key indicator for measuring the effectiveness of an enterprise's intelligent transformation. When enterprises require employees to utilize AI technology for intrapreneurship, AI disruption awareness serves as an important driving force. Additionally, AI application is a key element for public sectors to enhance service efficiency, and they encourage employees to utilize AI for internal innovation. It is evident that AI disruption awareness is a crucial foundation for employees to engage in intrapreneurship, and exploring its conversion mechanisms is of significant importance. Existing research indicates that AI disruption awareness has a bidirectional impact: negative perceptions lead to negative behaviors, while positive aspects can enhance innovation and learning capabilities. However, there are gaps in the understanding of the influence mechanisms, with few studies examining its impact on intrapreneurship behavior. Employee intrapreneurship represents an active transformative behavior; therefore, this study introduces innovative role identity as a mediating variable and incorporates job variety and job autonomy as situational factors grounded in person-environment fit theory. This empirical study utilizes a two-stage data collection procedure. The first stage involved surveying active personnel within government institutions in southwestern China through a combination of electronic and paper questionnaires. The second stage supplemented the sample by recruiting corporate employees via an online platform. Across both stages, 638 questionnaires were distributed, with 485 valid responses ultimately retrieved, yielding a response rate of 76.02%. Data were analyzed using AMOS 26.0 and SPSS 27.0 for confirmatory factor analysis, common method bias assessment, descriptive statistics, and hypothesis testing. This paper concludes the following: (1) there is a significant positive impact of AI disruption awareness on employee intrapreneurship. (2) Creative role identity plays a mediating role between AI disruption awareness and employee intrapreneurship. (3) job variety has a positive moderating effect on the relationship between AI disruption awareness and creative role identity; the higher the level of job variety, the more pronounced is the promotion of AI disruption awareness on creative role identity, and job variety moderates the mediating effect of creative role identity between AI disruption awareness and employee intrapreneurship. (4) job autonomy also positively moderates the relationship between AI disruption awareness and creative role identity, and the higher the level of job autonomy, the more significant the promotion effect of AI disruption awareness on creative role identity. At the same time, job autonomy also moderates the mediating effect of creative role identity between AI disruption awareness and employee intrapreneurship. This study makes three key contributions: First, it extends intrapreneurship research to the public sector, examining how AI disruption awareness promotes such behavior and thereby broadening the theory’s scope. Second, it shifts focus from AI disruption awareness outcomes to its mechanisms, identifying innovative role identity as a mediator and enriching theoretical explanations in the digital context. Third, grounded in person-environment fit theory, it introduces the key contextual element of work characteristics in the digital age, verifying the moderating role of job variety and job autonomy in the influence of AI disruption awareness on employees' intrapreneurship processes. This expands the theoretical understanding of the role of context in the relationship between AI disruption awareness and individual behavior.

In an era marked by rising unilateralism and volatile global supply chains, China faces significant "decoupling" risks in critical manufacturing sectors. While national strategies prioritize digital transformation to enhance industrial chain resilience, the specific mechanisms through which the digital economy impacts resilience remain theoretically ambiguous. This study investigates the complex relationship between digitalization and manufacturing industrial chain resilience, specifically addressing two critical gaps in the existing literature. First, while prior research has extensively examined the role of technological innovation, it remains divided on whether such innovation uniformly enhances resilience or introduces additional risks. To resolve this ambiguity, the study distinguishes between traditional "radical innovation" and "defensive innovation" and examines the potential mediating role of defensive innovation in the digitalization-resilience nexus. Second, existing studies have predominantly focused on thresholds such as internet penetration and digital infrastructure, while largely neglecting the hard constraint of regional economic foundation carrying capacity. This study therefore incorporates economic development levels as a key boundary condition and empirically tests for threshold effects that may determine whether and how digitalization translates into tangible resilience gains. Leveraging panel data from 30 Chinese provinces (2012—2023), the study constructs multidimensional indicators for industrial chain resilience (MFG) and digital economy development (DIG) using the entropy weight method. The MFG index integrates four resilience dimensions: resistance (revenue growth, labor productivity), innovation (R&D intensity, patent output), recovery (risk control, industrial restructuring), and sustainability (green efficiency, pollution control). The DIG index spans digital infrastructure (fiber optics, 5G base stations), industrial integration (e-commerce penetration, software revenue), technological innovation (R&D personnel density), and digital inclusion (financial accessibility). By employing bidirectional fixed-effects models, mediation analysis, and threshold regression, the study identifies three core mechanisms. First, digitization directly enhances resilience (coefficient: 0.137 *) by dismantling information barriers, enabling real-time risk monitoring through industrial IoT, and facilitating multi-supplier collaboration platforms. Second, it indirectly fortifies resilience by stimulating R&D investment (mediation effect: 0.100 *), with innovation pathways pivoting toward risk-defense technologies that preempt disruption risks, including backup systems, flexible production lines, and digital twins. Third, a critical economic development threshold (GDP per capita: 9.3621) governs efficacy: below this level, digitization impact is insignificant (0.038), but beyond it, resilience gains surge (0.216). Regional heterogeneity is pronounced, with effects strongest in Eastern China (0.244) due to advanced infrastructure and talent pools, significant but divergent in Central (0.460) and Western regions (0.164), and absent in Northeast China, where legacy industrial structures, brain drain, and high transition costs impede digital dividends. This research contributes to theory and policy in three key ways. First, it pioneers a resilience framework capturing adaptive capabilities beyond traditional industrial chain robustness, revealing digitization role in redirecting innovation toward risk mitigation. Second, it integrates Upper Echelons logic and positions digital infrastructure as an organizational "cognitive layer" with industrial threshold dynamics to explain nonlinear efficacy leaps. Third, it extends SCP (Structure-Conduct-Performance) theory by demonstrating how regional structural factors (infrastructure, scale, human capital) condition digitization conduct-performance link. Policy implications advocate a tiered approach that prioritizes the integration of infrastructure and synergy. This involves accelerating 5G and industrial IoT deployment within manufacturing clusters while reorienting R&D toward resilience technologies and SME knowledge-sharing platforms. To address regional disparities, the strategy suggests speeding up digital infrastructure in Western China, implementing resilience certification in Eastern hubs, and managing transition costs in the Northeast. Furthermore, cross-regional computing resource sharing, such as the "East Data West Computing" initiative, is identified as critical for establishing joint resilience corridors. The findings affirm the digital economy's pivotal role in mitigating systemic disruption risks, contingent on economic thresholds, innovation realignment, and region-specific structural readiness. This provides a nuanced framework for synchronizing industrial security with sustainable transformation.

Amid China's strategic transition toward high-quality development, the concept of new quality productive forces (NQPF) has emerged as a critical lever for enhancing national innovation capacity and industrial competitiveness. Existing studies have primarily focused on macro-level policies, institutional environments and aggregate structural resource allocations, while limited attention has been paid to the micro-level formation mechanisms and internal governance dynamics of new quality productive forces, particularly how the power of R&D-background executives drives internal innovation transformation through their cognitive heuristics and strategic decision-making advantages. Research on the influence of managerial power on new quality productive forces remains scarce, and a systematic analysis of its underlying transmission mechanisms, governance-related boundary conditions and organizational contingencies is still lacking. To fill these research gaps, this study, grounded in imprinting theory and managerial hegemony theory, conducts an empirical analysis using panel data spanning 2015 to 2023 from 1 972 manufacturing enterprises listed on the Shanghai and Shenzhen A-share markets. This study applies rigorous screening procedures to exclude observations from ST, * ST, and PT firms as well as samples containing missing values, ultimately yielding 8 820 valid firm-year observations. The investigation examines how technological executive authority, which is operationalized through the relative ranking and influence of executives with R&D backgrounds within the top management team, affects the formation of new quality productive forces at the enterprise level. The latter is measured via a comprehensive entropy-weighted index encompassing new quality labor, means of labor including industrial robot penetration rates, and subjects of labor. The analysis further probes the mediating role of intelligent transformation, which is assessed through a three-dimensional entropy approach that captures managerial attention to digital technologies, digital intangible asset investment, and digital patent output. Additionally, the moderating effects of ownership concentration and the proportion of independent directors are explored within corporate governance architectures, while controlling for firm-specific characteristics including leverage, enterprise scale, Tobin's Q values, profit growth rates, board size, and CEO duality. Industry and year fixed effects are incorporated to address potential heterogeneity across sectors and time periods. The empirical findings reveal that (1) R&D-background executive power is significantly and positively associated with the level of firms' NQPF; (2) intelligent transformation serves as a positive mediator in the relationship between R&D-background executive power and NQPF; (3) both ownership concentration and the proportion of independent directors positively moderate this relationship. This study makes important contributions at both theoretical and practical levels. Theoretically, it approaches the formation of new quality productive forces from the perspective of executive characteristics, deepening our understanding of the synergistic interaction among new quality labor, means of labor, and objects of labor. By repositioning executive traits—specifically the power of R&D-background executives—as endogenous drivers of NQPF, this research expands the theoretical boundaries of traditional productivity studies and enhances the explanatory power of top management characteristics within the productivity discourse. Furthermore, by introducing corporate governance structures as boundary conditions, the study reveals how governance configurations, particularly ownership concentration and board independence, moderate the influence of R&D-background executive power. This provides new theoretical anchors for understanding the internal governance mechanisms that shape strategic executive effectiveness. It also extends the resource-based view by identifying governance-related contingency factors that condition the value realization of internal resources in fostering firm-level NQPF. The findings suggest that firms aiming to upgrade their productivity structures should consider empowering executives with R&D backgrounds, enhancing their decision-making authority, promoting intelligent transformation strategies, and optimizing governance mechanisms. These steps ensure better alignment between leadership traits, executive power, and innovation-oriented strategies. Ultimately, this study builds a micro-level theoretical framework for understanding the emergence of new quality productive forces, offering insights for enhancing enterprise innovation capability.

Generative AI has become commonplace in academic research, assisting with ideation, drafting, and language refinement. In response, many institutions now mandate AIGC detection to flag suspected AI-generated content. This technology embodies a reflexivity of human thought: it arises from our need to defend academic integrity against GenAI's challenges, yet its very existence provokes new reflection and "anti-detection" behavior. It cannot be denied that AIGC detection has demonstrated its legitimacy and necessity in defending academic justice, adhering to technical ethics, and following consensus contracts. Among them, academic justice defines the ideal state that the academic community should pursue, which is an academic ecosystem with fair opportunities, transparent rules, and clear contributions. Technical ethics stipulate the rules of action that should be followed. If defective detection tools are used, their use itself is illegitimate. Only AIGC detection technologies that comply with technical ethics are worthy of trust. The contractual commitment establishes the logical relationship between relevant parties in academic research, and the introduction of any detection technology is essentially a confirmation, testing, and reshaping of this series of contractual relationships. The necessity of AIGC detection technology lies in the fact that without this technology, academic justice will be difficult to defend, technical ethics will be difficult to adhere to, and the contractual commitments of the academic community may gradually collapse in the absence of constraints. However, when the AIGC detection system is widely used for determining academic misconduct, reflection has not stopped, and its focus has shifted from evaluating the value of detection technology tools to questioning the detection logic, technical principles, and even human-machine relationships. Among them, the ontological paradox is the original questioning and fundamental logical exploration of the contradictions arising from AIGC detection technology. The logical premise and cognitive framework contain fallacies. That is to say, it attempts to classify based on "humanoid characteristics", which is logically untenable. The technological paradox focuses on the principles and operational methods of technology. It reveals the methodological dilemma of the detection tool itself, which is to use an incomprehensible 'black box' to detect another untraceable 'black box'. The relational paradox shifts the discussion toward interactional dynamics, examining how alienation emerges both among scholars and between scholars and detection technology. That is to say, detection technology should have served academic subjects, but in reality, it has exerted a reverse regulation on scholars' behavior. This study adopts a multi-method approach combining theoretical research, speculative discourse analysis, and case studies to examine the reflexivity inherent in AIGC detection. Through theoretical research, it constructs a conceptual framework encompassing academic justice, technical ethics, and contractual consensus to establish the legitimacy and necessity of detection technology while probing its underlying paradoxes. Speculative discussion is employed to dissect the ontological, technological, and relational paradoxes arising from AIGC detection, tracing how classification logic, black-box methodology, and reverse regulation of scholars generate reflexive loops. Case analysis further grounds these theoretical insights in practical scenarios, illustrating how detection tools function in real academic settings and how scholars respond with evasive strategies. Based on this integrated analysis, the study proposes three pathways to transcend the reflexive loop of AIGC detection. First, it advocates a shift in practical logic from surface representation to origin, moving beyond "human-like feature" detection to assess whether research reflects irreplaceable innovative perspectives of human scholars and demonstrates creative GenAI utilization. Second, it recommends enhancing algorithmic reliability, fairness, and transparency through multi-level algorithm upgrades, full-process supervision technology that shifts control from terminal results to research-process compliance management, and dynamic monitoring mechanisms compatible with technological iteration. Third, it stresses the need to establish clear institutional norms with industry consensus for GenAI use and recognition standards, develop policy documents defining stakeholders' rights and obligations, and improve a multi-party evaluation management system encompassing scholars, academic institutions, technology developers, publishing units, and other relevant entities.

The escalating short-termism in R&D alliances has emerged as a critical barrier to deep technological integration and sustained innovation breakthroughs. While existing literature predominantly attributes alliance instability to competitive learning dynamics, where enterprises participate in R&D alliances primarily to maximize acquisition of partners' knowledge while erecting protection barriers to minimize outbound leakage, this perspective assumes that partner cooperative behavior is fundamentally homogeneous. Such an approach inadequately captures the structural heterogeneity embedded in inter-partner resource endowments and technological capabilities, thereby limiting understanding of why some alliances demonstrate remarkable longevity while others disintegrate prematurely. This study shifts the analytical lens to the dyadic relationship between alliance partners, investigating how asymmetry in patent knowledge absorption capabilities influences sustained cooperative innovation performance. Resource dependence theory posits that inter-firm relationships are characterized by bidirectional resource matching and mutual dependence, which collectively determine collaborative stability. However, asymmetric knowledge absorption of patents between partners generates imbalance in this mutual dependence, precipitating dynamic shifts in alliance motivations and behaviors that critically affect innovation continuity. This study argues that either excessively low or excessively high levels of patent knowledge absorption asymmetry hinder the sustained acquisition and productive application of complementary resources. This study theorizes that patent knowledge absorption asymmetry exhibits an inverted U-shaped relationship with sustained cooperative innovation performance. Specifically, moderate asymmetry optimally facilitates complementary knowledge utilization and generative value creation by balancing learning opportunities with appropriation concerns. Conversely, excessively low asymmetry diminishes knowledge diversity benefits, reducing the potential for novel recombination and technological breakthrough. Excessively high asymmetry triggers value appropriation imbalances that destabilize the collaborative relationship, as the more absorptive partner may perceive diminishing returns from continued cooperation while the less absorptive partner fears technological obsolescence and exploitation. Furthermore, drawing on spatial and organizational governance literature, this study proposes that geographic distance and cooperation scale moderate this curvilinear relationship through their simultaneous effects on knowledge transfer efficiency and relational risk perceptions. This study constructed a panel dataset of 363 publicly listed firms in China's automotive manufacturing sector, an industry characterized by intensive R&D collaborations. Patent knowledge absorption asymmetry was operationalized through measurement of differences in patent citation-based absorptive capacity between dyadic partners, capturing both the direction and magnitude of knowledge flow asymmetries. Sustained cooperative innovation performance was measured using longitudinal patent co-application data tracking repeated collaborations over time. The moderating effects of geographic distance were captured through firm-level locational coordinates, while cooperation scale was measured by the number of partners within each firm's alliance portfolio. Employing panel regression techniques with firm and year fixed effects, this study tested both the main curvilinear hypothesis and the moderating propositions. The empirical findings provide robust support for the hypothesized inverted U-shaped relationship. Both geographic distance and cooperation scale serve as negative moderators that significantly flatten this curvilinear relationship. These results yield several theoretical contributions. First, by moving beyond the single-focal-firm perspective to examine dyadic knowledge dynamics, this study reveals knowledge-based power asymmetry between partners as a fundamental mechanism explaining variance in alliance continuity. By exploring both the generative value creation and the appropriative value capture attributes of patent knowledge absorption, this study integrates resource dependence theory with absorptive capacity research, thereby clarifying the nonlinear mechanism through which partner knowledge asymmetry influences sustained cooperative innovation. Second, our findings challenge the prevailing view that geographic proximity is uniformly beneficial for collaborative innovation, demonstrating instead its dual role in simultaneously facilitating tacit knowledge exchange while exacerbating appropriation concerns and competitive tensions. Third, this study identifies cooperation scale as a critical boundary condition moderating knowledge complementarity effects, suggesting that the classic proposition that scale enhances innovation outcomes requires substantial qualification when considered in conjunction with knowledge absorption asymmetry.

Megaprojects are inherently characterized by high levels of volatility, uncertainty, complexity, and ambiguity (VUCA), which demand that team members engage in improvisational innovation to respond effectively to emergent conditions during project execution. This form of innovation involves not only generating novel ideas under severe time pressure but also translating those ideas into actionable solutions. However, idea generation and idea implementation follow distinct and often conflicting logics, i. e. exploration versus exploitation. This inherent tension raises a central question: How can team members in megaprojects be enabled to effectively engage in both activities? Although prior research has largely followed a "separation" logic by using temporal sequencing or structural isolation to avoid tensions between the two activities, this perspective has limited applicability to the complex context of megaprojects, where the two activities are interwoven and iterative. Drawing on ambidexterity theory and cognitive-affective theory, this study investigates how the dynamic interplay of ambidextrous leadership shapes project team members' engagement in these dual innovation activities. This study operationalizes grounded theory through a multi-stage analytical protocol. The empirical foundation comprises semi-structured interviews with 39 members across 12 megaproject teams, selected via purposive sampling. The analytical trajectory involved: (1) open coding, inductively producing 50 initial subcategories; (2) axial coding, refining these into 25 core categories; and (3) selective coding, linking categories to theoretical constructs. The resultant process model explicates how ambidextrous leadership synergistically influences project team members' improvisational innovation. To explain how teams navigate the paradox without temporal or structural buffers, the study integrates ambidexterity theory with cognitive-affective theory, and proposes an analytical framework that links ambidextrous leadership, individual cognition and emotion, network reconfiguration, and improvisational innovation. This framework deconstructs the intrinsic synergistic mechanisms through which ambidextrous leadership facilitates team members' improvisational innovation in megaprojects. First, during idea generation, ambidextrous leadership dynamically shapes team members' cognitive patterns and emotional states through the strategic integration of "opening" and "closing" behaviors, thereby guiding them to construct differentiated social networks and ultimately achieving synergy between novelty and usefulness. Second, during idea implementation, ambidextrous leadership guides team members to reconfigure their social networks by shaping differentiated goal orientations and work states, thereby addressing the paradoxical tension between innovation quality and innovation speed. Third, throughout the entire improvisational innovation process, ambidextrous leadership resolves the tension between idea generation and implementation through a sequential pathway that integrates behavioral strategy, role cognitive adjustment, network reconfiguration, and innovation synergy. This study offers several theoretical contributions. (1) Moving beyond the "separation" paradigm, it reveals how ambidextrous leadership resolves the paradox of creative idea generation and implementation in megaprojects, providing a novel theoretical explanation for achieving synergy when the two activities are difficult to separate. (2) From a holistic process perspective, this study elucidates the differentiated synergistic pathways of ambidextrous leadership at distinct stages of improvisational innovation and demonstrates how a single leadership behavior may disrupt this synergy, further uncovering how ambidextrous leadership addresses multidimensional paradoxes across the innovation process. (3) By integrating cognitive-affective theory and social network perspective, this study proposes a "cognition-emotion driven network reconfiguration" process model underlying the influence of ambidextrous leadership on improvisational innovation. This study also provides key managerial insights. (1) Megaproject leaders should recognize the cyclical interplay between idea generation and implementation during improvisational innovation and flexibly alternate between opening and closing behavioral strategies according to different situations, thereby fostering the synergistic development of both processes. (2) Leaders should attend to the inherent contradictions in team members' innovation processes and adopt flexible behavioral strategies to reconcile the paradoxes of novelty versus usefulness in idea generation and quality versus speed in idea implementation. (3) Leaders should be cautious about the potential harm that a single behavioral strategy may impose on team members' cognition and emotions.

In the context of accelerated globalization and digitization, the world is experiencing unprecedented changes, and enterprises are facing unprecedented challenges and opportunities. On the one hand, emergencies such as geopolitical risks, economic fluctuations, technological changes and public crises have exacerbated the uncertainty of the enterprise's living environment; On the other hand, market competition is becoming increasingly fierce, and the resource constraints and capacity limitations faced by enterprises are becoming more apparent. In this context, high-tech enterprises, as pioneers in promoting technological progress and industrial transformation, how to enhance their organizational resilience has become a key issue that urgently needs to be addressed in their survival and development. Driven by the digital economy, the platform economy has rapidly emerged as a core operating form, giving rise to the rapid development of numerous innovative platform enterprises. As a result, a new business paradigm characterized by platform ecosystems has been formed, creating new opportunities for enhancing the organizational resilience of high-tech enterprises. Through a review of existing literature, this study finds that current research on the economic benefits of platform ecosystem embedding mainly focuses on aspects such as enterprise value and innovation; The research on the influencing factors of organizational resilience mainly focuses on the cultivation and construction of internal capabilities within enterprises. However, in the context of high-tech enterprises, there is still a lack of in-depth research on the mechanism of how platform ecosystem embedding empowers organizational resilience enhancement from an external perspective. This study argues that high-tech enterprises can effectively draw on the strategic wisdom and resource endowments of external ecosystems by embedding themselves in the platform ecosystem, thereby strengthening their strategic resilience and technological foundation. This can not only help enterprises break through the limitations of traditional resilience building paths, but also promote the formation of a more forward-looking and adaptive resilience building model. Drawing upon a comprehensive sample of Chinese high-tech listed firms traded on the Shenzhen and Shanghai A-shares spanning the period 2010 to 2023, this study operationalizes a dual chain mediation framework that incorporates financing constraints and employment scale expansion as sequential and parallel mediators to empirically investigate the intricate mechanisms through which platform ecosystem embedding shapes organizational resilience in high-technology enterprises. Empirical evidence demonstrates that platform ecosystem embedding exerts a statistically significant and positive influence on the organizational resilience of high-tech firms; specifically, both the alleviation of financing constraints and the expansion of employment scale operate not merely as independent mediating channels but also function as critical links in a serial mediation chain, thereby transmitting the effects of platform embedding on organizational resilience through both parallel and sequential pathways. Subsequent heterogeneity analyses further reveal that strategic embedding and platform embedding generate more pronounced promotional effects on organizational resilience relative to purely ecosystem embedding approaches; additionally, the assimilation of non-state-owned enterprises, mature firms, and those operating within high-uncertainty industrial contexts into platform ecosystems yields significant gains in organizational resilience compared to their state-owned, nascent, or stable-environment counterparts. Descriptive statistics reveal substantial variance in organizational resilience, and Hausman specification tests indicate that fixed-effects estimation significantly outperforms OLS and random-effects alternatives. This study makes two contributions. First, it shifts the resilience research lens from internal capabilities to external ecosystem embedding, revealing an alternative pathway for high-tech firms to build resilience through platform networks. Second, by unpacking the dual mediation mechanisms, the study that platform embedding enhances resilience not only directly through financing relief and workforce expansion, but also sequentially via the "financing-employment" chain. These findings elucidate the platform-empowerment black box and inform collaborative governance strategies in the digital economy.

Against the backdrop of the rise of protectionism and the intensification of global technological competition, innovation is the cornerstone of sustainable economic growth and the core competitiveness of enterprises. Cross-border M&As are an important way for Chinese enterprises, especially latecomers, to acquire advanced and complementary overseas technological resources, achieve technological upgrades and transform from "followers" to "leaders". However, the practical results are mixed, indicating a complex nonlinear relationship between the complementarity of technological resources and the innovation performance after M&As. While extant research has identified this nonlinearity, it leaves three critical mechanisms underexplored: (1) the micro-mechanisms converting complementary resources into innovation capabilities; (2) the differential moderating effects of formal versus informal institutional distances; and (3) the distinction between exploratory and exploitative innovation in post-M&A contexts. This study aims to clarify this relationship by exploring its specific mechanism of action and boundary conditions, and to fill the existing research gap. The study employs an unbalanced panel dataset comprising 195 Chinese A-share listed firms from 2008 to 2021; given the time-lag in innovation output, it measures post-M&A innovation using patent applications filed within three years of deal completion, while extending the sample to 2008 – 2024 in robustness checks to address temporal validity concerns. The empirical analysis is conducted using the negative binomial regression model, which is suitable for count data like patent applications. Key variables, including measures for technological resource complementarity, exploratory/exploitative innovation (based on patent classifications), potential/realized absorptive capacity, and formal/informal institutional distances, are constructed using data from sources like the CSMAR database, patent databases, and World Governance Indicators. Considering the overdispersed count-data properties of the dependent variables, this study employs negative binomial regression with industry and year fixed effects to test the nonlinear impact of technological resource complementarity on dual innovation; the inverted U-shaped relationships are confirmed by examining the sign of quadratic coefficients, boundary slopes, and turning points within the sample range. To address endogeneity, patent data are lagged three years post-acquisition, while Propensity Score Matching (PSM) using 1:4 nearest neighbor and kernel matching is applied to mitigate sample selection bias. Robustness is verified through alternative dependent variable measures, exclusion of the 2008 financial crisis period, and extension of the sample to 2024. Furthermore, the stepwise method is utilized to examine both parallel and serial mediation effects of potential and realized absorptive capacities. The findings indicate that, first, the relationship between technological resource complementarity in cross-border M&As and enterprises' ambidextrous innovation (both exploratory and exploitative) is inverted U-shaped. Moderate complementarity promotes innovation, while excessive complementarity can hinder it due to integration challenges. Second, this relationship is mediated through enterprises' absorptive capacity. Both potential absorptive capacity (the ability to acquire and assimilate external knowledge) and realized absorptive capacity (the ability to transform and exploit that knowledge) act as significant mediators. Moreover, they function in a sequential manner: complementarity first enhances potential capacity, which then strengthens realized capacity, ultimately fostering innovation. Third, institutional distances act as contingent factors. Both formal (e.g., regulatory, political) and informal (e.g., cultural, normative) institutional distances moderate the core inverted U-shaped relationships, highlighting the importance of the institutional context in determining the innovation outcomes of M&As based on technological resource complementarity. This paper offers several contributions. Theoretically, it applies the ambidexterity lens from the springboard theory to explain the non-linear (inverted U-shaped) impact of technological resource complementarity on dual innovation, moving beyond traditional linear assumptions. Mechanistically, it "unpacks the black box" by distinguishing and empirically testing the parallel and sequential mediating roles of potential and realized absorptive capacities, enriching the understanding of micro-level transmission processes. Contextually, it integrates insights from both the traditional and institutional logic views within new institutional theory to differentially analyze the moderating effects of formal and informal institutional distances, capturing their nuanced and potentially diverse impacts rather than viewing them solely as homogeneous barriers.

Research universities serve as a core engine of scientific and technological innovation and constitute a primary source of high-value patents and technology transfer. In China, however, a persistent challenge remains: while invention patent applications from universities continue to grow, the rate of successful industrialization lags significantly behind expectations and international benchmarks. This gap underscores an urgent need to shift the patent management paradigm from a purely volume-driven model toward a framework that prioritizes quality and latent commercial value. National policy initiatives have explicitly called for the effective screening of high-value patents from vast university portfolios. Yet, accurately and efficiently identifying such assets within extensive patent databases remains a formidable methodological undertaking. This study addresses this gap by developing and validating a data-driven identification model tailored to the context of research universities, thereby seeking to offer both theoretical refinement and practical support for more targeted patent management and technology transfer strategies. This study proposes a hybrid modeling approach that integrates Genetic Algorithm (GA), Rough Set Theory (RST), and a Backpropagation Neural Network (BPNN). First, an initial multi-dimensional indicator system comprising 15 features was established, encompassing technical dimensions (e.g., inventor team size, CPC classification count, forward citations, scientific linkage), legal dimensions (e.g., patent validity status, first claim word count, litigation frequency, examination pendency), and economic dimensions (e.g., transaction frequency, patent family size, industry coverage breadth, technology-efficacy phrases, university-industry collaboration intensity). To mitigate feature redundancy and enhance model parsimony, GA was coupled with RST for attribute reduction, which yielded a compact subset of eight core indicators: inventor count, CPC count, forward citations, patent validity, first claim word count, examination pendency, industry coverage breadth, and technology-efficacy count. Subsequently, a GA-BP neural network was constructed wherein GA assists in optimizing the initial weights and thresholds of the BPNN to facilitate convergence. The model was trained and validated using a balanced experimental dataset consisting of patents recognized with prestigious national awards (treated as positive samples) and a corresponding set of un-awarded patents drawn from identical technical domains. Following validation, the model was applied to a sample of invention patents from six representative universities, comprising both research and non-research universities. On the validation set, the GA-BP model demonstrated consistent improvements in classification performance relative to standard BP and PCA-BP benchmark models. While the absolute gains are modest, the pattern of enhanced predictive stability across multiple evaluation metrics suggests that the integration of GA for parameter initialization offers a viable means of improving model generalization. Furthermore, robustness checks under varying noise conditions indicated that the GA-BP framework maintained a more stable performance profile, hinting at its potential utility in handling real-world data fluctuations. Application of the model to the six university samples allowed for the identification of a substantial cohort of high-value patents. A comparative analysis revealed tentative distinctions in innovation patterns. Patents from research universities tended to exhibit characteristics associated with moderate-sized collaborative teams, relatively higher upper-bound technological impact, more efficient examination processes, and broader industry relevance. In contrast, patents from non-research universities more frequently displayed attributes such as more extensive first claim drafting and richer technology-efficacy descriptions, suggesting a greater reliance on textual elaboration and application-oriented narratives to signal value. This study extends the patent valuation discourse by incorporating indicators such as examination pendency and technology-efficacy count, providing empirical evidence for the utility of CPC classifications in capturing application-oriented value. By juxtaposing research and non-research universities, the study illuminates heterogeneous pathways to value creation, suggesting that institutions should tailor strategies to their specific ecosystems. Specifically, while research universities might prioritize interdisciplinary collaboration and procedural optimization, other institutions may find greater value in meticulous claim drafting and articulating practical benefits. Ultimately, this model offers a decision-support mechanism to assist technology transfer professionals and policymakers in triaging patent portfolios, aiming to facilitate the efficient translation of academic research into societal and economic benefits.

The commercialization of state-owned scientific and technological achievements has become a key issue in China's efforts to advance innovation-driven development, improve the efficiency of public R&D investment, and accelerate the formation of new quality productive forces. In practice, however, research institutions and universities commonly face persistent obstacles characterized by reluctance to commercialize, fear of commercialization, and difficulty in bringing achievements into application. The root of the problem lies not merely in procedural inefficiency or inadequate market coordination, but in a structural imbalance between rights and responsibilities. Although the Law of the People's Republic of China on Scientific and Technological Progress grants research institutions autonomous disposal rights over scientific and technological achievements, the current state-owned asset management system still constrains commercialization through rigid requirements such as appraisal, approval, filing, and accountability. As a result, a reverse binding mechanism has emerged: research institutions are formally authorized to make autonomous decisions, yet once commercialization outcomes become uncertain, they still bear substantial administrative and legal risks. The study identifies the root cause of the current predicament as the misidentification of state-owned scientific and technological achievements as ordinary state-owned assets, which subjects them to a static governance logic focused on physical control and book-value preservation. This approach fails to recognize their distinctive nature as knowledge-based, time-sensitive assets whose value depends on circulation speed and market adaptability, causing them to lose market windows and depreciate implicitly under lengthy approval procedures. This misalignment in legal characterization further generates three consequences. First, it gives rise to normative conflict between legal empowerment and administrative regulation. Higher-level laws encourage autonomous disposition, whereas lower-level rules retain strict approval and supervisory mechanisms, thereby weakening the practical effect of statutory authorization. Second, it creates operational blockages throughout the entire commercialization chain, including the hollowing-out of disposition rights, the conflict between dynamic value realization and static value-preservation requirements, and the excessive concentration of liability on individual decision-makers. Third, it leads to systemic failure in the broader commercialization ecosystem, manifested in distorted incentive mechanisms, reinforced bureaucratic governance, and the treatment of scientific and technological achievements as rigidly controlled asset objects rather than as carriers of innovation value. The study adopts a combination of normative legal analysis, institutional analysis, and policy observation. It examines the value conflict between innovation-promoting legislation and risk-control-oriented state-owned asset regulation, and analyzes representative local reform pilots in different regions of China. These pilot practices are categorized into three types of policy instruments: rule-adjustment instruments, property-right incentive instruments, and market-enabling instruments. On this basis, the study evaluates both their reform effects and their institutional limits, and further proposes a systematic rule-of-law path for rebalancing rights and responsibilities in the commercialization of state-owned scientific and technological achievements. To address this structural dilemma, this study proposes a four-dimensional legal framework centered on the concept of "time-limited franchised state-owned assets". This concept is a further refinement of the notion of "exceptional assets". Its core implication is that, although state-owned scientific and technological achievements should remain under state ownership because they are generated through public investment, part of the general rules governing state-owned assets should be expressly exempted by law in light of their intangibility, value volatility, and innovation-promoting policy function. At the legislative level, their special legal status should be confirmed through exclusion clauses in general state-owned asset legislation and through specialized legal norms. At the enforcement level, a regulatory model of "negative list + statutory exemption + ex post filing" should be established to shift from prior approval to bottom-line supervision. At the judicial level, a defense standard centered on due diligence should be constructed so as to move liability determination from result based attribution to conduct-based review. At the supporting institutional level, reforms in property-right registration, revenue distribution, and exit mechanisms should be advanced in a coordinated manner so as to facilitate circulation, strengthen incentives, and tolerate reasonable failure.

In an era marked by intensified global economic volatility, a complex and ever-changing trade environment, and heightened geopolitical risks, establishing an independent and controllable industrial and supply chain has become a strategic priority for promoting high-quality development. Supply chain spillover effects have long been recognized as a crucial force driving corporate innovation. Nevertheless, whether artificial intelligence technology, characterized by its unique synergy and generative nature, can produce such spillover effects, as well as the differentiated mechanisms through which it exerts impacts on upstream suppliers and downstream customers, remains insufficiently explored in existing research. To fill this research gap, this study aims to investigate whether and through which pathways artificial intelligence (AI) technology innovation by core enterprises can act as an external driver for supply chain partners to conduct AI technology innovation activities, so as to provide theoretical support and practical guidance for supply chain collaborative innovation. Based on the theoretical analytical framework of "supplier – core enterprise – customer", this study takes China's A-share listed manufacturing companies from 2007 to 2024 as research samples, and conducts an empirical analysis combined with manually compiled artificial intelligence patent data. Finally, 1 316 supplier – focal firm observations and 1 584 customer – focus firm observations are obtained. In the process of data processing and indicator construction, machine learning methods are employed to systematically develop indicators of AI technology innovation. First, AI-related patents are screened from the Incopat patent database using patent IPC codes and keyword matching, covering core technical fields such as machine learning, natural language processing, computer vision, and intelligent robots. Second, semantic features are extracted from patent texts via the BERT pre-trained language model, and a firm-level overall index of AI technology innovation is constructed by integrating patent citations, the number of patent claims, technological scope and other dimensions. On this basis, a technological disruption identification algorithm is further adopted to classify AI technology innovation into two categories: disruptive innovation and incremental innovation. Specifically, disruptive innovation is defined by dual breakthroughs in technology and market, meaning that patented technologies achieve a significant technological leap on the basis of existing knowledge and can open up new market application scenarios. Incremental innovation is characterized by the optimization and upgrading of existing technologies, focusing on performance improvement, scenario adaptation and process transformation of established AI technologies, so as to systematically compare the heterogeneous spillover pathways and mechanisms of the two types of innovation. The empirical results show that AI technology innovation by core enterprises exerts a significant positive spillover effect on both upstream and downstream enterprises in the supply chain, and this effect is mainly transmitted through incremental innovation, while the spillover effect of disruptive innovation is not verified. Mechanism analysis reveals heterogeneous transmission channels between the two. For upstream suppliers, AI technology innovation by core enterprises forces them to increase AI investment, promote industry-university-research cooperation and optimize the financing ecosystem, forming a "forcing effect". For downstream customers, AI technology innovation by core enterprises stimulates their AI investment, optimizes human resource structure and enhances informatization levels, generating a transmission effect. Heterogeneity tests further indicate that this spillover effect is more pronounced among small-scale suppliers and suppliers located in regions with lower government intervention. This study makes three main contributions. First, it expands the research dimensions and analytical framework of supply chain spillover effects, extending the research on the impacts of AI technology innovation from the single-firm level to inter-organizational dynamic relationships, thus enriching the theoretical perspective of supply chain collaborative innovation. Second, it refines the classification of AI technology innovation types and constructs targeted measurement indicators, addressing the inadequacy of existing literature in examining the details of firm behavioral spillovers. Third, it reveals the differentiated spillover mechanisms of core enterprises on upstream and downstream partners, providing theoretical support for enterprises to formulate precise cooperation strategies and promote supply chain collaborative innovation.

Digital technology has fundamentally changed the modes of inter-organizational interaction, and transformed the competitive paradigm from interfirm rivalry to inter-platform ecosystem competition. Joining or constructing the platform ecosystem has become an inevitable choice for enterprises to adapt to this paradigm shift and achieve value co-creation and sustainable development. However, not all enterprises can expand the depth and breadth of value creation by constructing the platform ecosystem. As value creation becomes more complex, the traditional hierarchical structure of enterprises become unsustainable, driving the evolution of organizational structure toward vertical flattening and horizontal integration, and breaking through single organizational boundaries to exhibit prominent inter-organizational connectedness. Meanwhile, the resource integration of enterprises shifts from the optimal allocation of resource stocks to the dynamic connection and integration of resource nodes. Enterprises need to constantly reconfigure existing resource combinations and create new resource linkages to meet the increasingly complex value creation demands. Therefore, in the construction process of platform ecosystem, enterprises need to coordinate transformation of organizational structure with the evolution of resource actions. However, existing research explores the construction process of platform ecosystem from a single perspective of structure or resource, neglecting the reinforcing effect of the co-evolution of structure and resource on platform ecosystem construction. An adaptive organizational structure facilitates the realization of value from resource action, while the evolution of resource actions drives continuous optimization of the organizational structure. The synergistic effect generated by their co-evolution can accelerate the construction and evolution of platform ecosystem. To address this theoretical gap, this study adopts a co-evolutionary perspective to investigate how the interplay between organizational structure and resources drives the formation of a platform ecosystem. Focusing on Haier as a longitudinal single-case study, the study traces its developmental trajectory from 2005 to the present. Data were collected through multiple sources, including semi-structured interviews with Haier's middle- and senior-level managers and frontline employees, as well as secondary archival materials such as corporate disclosures, media reports, and industry analyses. Through a rigorous process of open, axial, and selective coding, we identify key evolutionary events and emergent theoretical constructs across three distinct developmental stages. This research elucidates the underlying mechanisms and pathways of ecosystem construction, offering practical implications for other enterprises navigating similar transformations. The findings are as follows: (1) The co-evolution of organizational structure and resource actions is the key mechanism for the construction and evolution of the platform ecosystem. Through organizational structure transformation, enterprises reconfigure their logic of resource actions, while the upgrading of the breadth and depth of resource actions drives the adjustment and optimization of organizational structure. Their co-evolution drives the platform ecosystem to evolve along the pathway of internal platform foundation—external platform expansion—platform ecosystem construction. (2) Organizational structure transformation is an adaptive response of enterprises to contextual changes, through transforming into flatter, more networked and ecological organizational structure, enterprises gradually dissolve organizational boundaries and ultimately evolve into boundaryless organization. (3) Organizational structure transformation reshapes the logic of resource actions, shifting resource actions from the individual enterprise level resource management to the multi-agent participatory behavior across platforms and industries. (4) With the evolution of platform ecosystem, the corresponding value creation activities become more complex, achieving a continuous elevation from product value to experience value and further to scenario value.

The integration of the digital economy and green development makes the coordinated advancement of digital and green transformation ("twin transition") a critical imperative for manufacturing enterprises. As a new production factor, data is reshaping resource allocation, organizational decision-making, and value creation. Data factor markets, as institutional arrangements for data circulation, may facilitate twin transitions by improving the accessibility, mobility, and utilization efficiency of heterogeneous data resources. However, existing literature primarily focuses on macroeconomic effects of data markets or discusses digital and green transformations separately, leaving insufficient attention to whether and how data factor market development promotes twin transitions and under what boundary conditions such effects become evident. To address these gaps, this study develops an integrated framework grounded in information ecosystem theory and resource orchestration theory. Information ecosystem theory posits that data circulation is embedded in dynamic systems shaped by institutions, technologies, and multi-actor interactions. Accordingly, data factor market development optimizes the external information environment by reducing transaction frictions, alleviating information asymmetry, and enabling compliant cross-organizational data flows. Resource orchestration theory further explains how firms transform externally acquired data into internal capabilities, emphasizing that data value depends not merely on access but on firms' ability to integrate and leverage such resources across production management, energy utilization, supply chain coordination, and green governance. Accordingly, this study argues that data factor market development can promote the twin transition of manufacturing enterprises by improving the external information ecology and enhancing internal resource allocation efficiency. Using panel data on Chinese A-share listed manufacturing enterprises from 2010 to 2024, this study takes the staggered establishment of data exchanges across provinces and municipalities as a quasi-natural experiment. To measure enterprise' twin transition level, it constructs an evaluation index system covering both digital and green dimensions, and applies the entropy weight method together with the coupling coordination degree model. To identify the causal effect of data factor market construction, the paper combines propensity score matching with a multi-period difference-in-differences model, and further conducts parallel-trend tests and placebo tests to ensure the robustness of the empirical results. The empirical results show that data factor market development significantly promotes the twin transition of manufacturing enterprises. This indicates that the institutionalization of data ownership confirmation, circulation, and trading can effectively activate the synergistic value of data as a production factor, thereby helping enterprises improve digital operating efficiency while embedding green constraints into production and management processes. Mechanism analysis further shows that resource allocation efficiency is an important transmission channel. Specifically, data factor market development enhances enterprise' ability to integrate external data into internal operations, making production processes more transparent, decision-making more agile, and environmental objectives more measurable and controllable. By contrast, alternative explanations such as technological progress and strategic adjustment do not pass the full mechanism identification test, suggesting that resource allocation efficiency is the more robust and convincing channel. Further analysis reveals significant heterogeneity in the policy effect. Compared with enterprise-led and mixed models, government-led data factor market development shows a stronger promoting effect. Moreover, the positive effect is more pronounced among non-heavy-polluting firms, non-high-tech firms, and non-state-owned firms. In addition, the promoting effect becomes stronger when firms have higher R&D intensity, when industry competition is more intense, and when the regional level of informatization is higher. These findings indicate that the effectiveness of data factor market development is influenced by enterprise' absorptive capacity, competitive environment, and regional digital conditions. This study contributes to the literature in three respects. First, it extends research on data factor market development from the macro level of institutional design and economic efficiency to the micro level of enterprise transformation by providing direct empirical evidence based on a quasi-natural experiment. Second, the study provides an integrated framework explaining how these two dimensions of digital transformation and green transformation mutually reinforce each other under the enabling effect of the data factor market. Third, it clarifies the heterogeneous boundaries of the policy effect across ownership structures, industrial characteristics, and regional environments. Overall, the findings provide useful theoretical support and policy implications for promoting the twin transition of manufacturing enterprises through data factor market construction.

In new-venture contexts marked by high uncertainty and pronounced resource constraints, how entrepreneurs accomplish the construction of their initial business model often determines whether the firms can gain an early foothold. Prior research broadly recognizes the importance of industry experience, yet a clear process explanation is still lacking of how differently configured industry experience operates across the stages of business model construction and gives rise to heterogeneous combinations of causation and effectuation logics. Addressing this gap, this study draws on imprinting theory and classifies entrepreneurs' industry experience along two dimensions: insider versus outsider and breadth versus depth. It examines how industry experience, through the sedimentation and subsequent activation of cognitive and capability imprints, shapes entrepreneurs' choice of decision logic at the value proposition formation and value network building stages. This study adopts a multiple-case research design grounded in theoretical sampling and replication logic. Following a 2 × 2 matrix of insider/outsider × breadth/depth, we select four new ventures (LS, DG, CT, and HY), covering all four experience configurations while preserving heterogeneity in industry and context, with every case having traversed the full business model construction process. Data are drawn from multiple rounds of semi-structured in-depth interviews and publicly available secondary materials to ensure triangulation. The analysis follows an inductive path: within-case analysis is conducted first, with structured coding applied in accordance with the Gioia methodology; cross-case comparison and iteration are then undertaken, ultimately distilling an integrative theoretical framework and a set of propositions. The findings show that entrepreneurs can be classified, by industry-experience configuration, into four types: industry deep-dwellers, industry breadth-holders, cross-border leaders, and cross-border pathfinders. Different configurations shape the relative strength of cognitive and capability imprints that entrepreneur carry into the focal industry: depth experience is more conducive to strong cognitive imprints whereas breadth experience corresponds to weaker cognitive imprints; insider experience is more likely to produce strong capability imprints whereas outsider experience corresponds to weaker capability imprints. In the course of business model construction, this imprint combination shapes entrepreneurs' subjective perception of uncertainty and resource constraint, which in turn alters the clarity of opportunity evaluation and the perceived matching of resources, leading entrepreneurs to display differentiated orientations between an imprinting mechanism of experiential enactment and a reflexivity mechanism of interactive bricolage. This is ultimately manifested in distinct combinations of causation and effectuation logics across the value proposition formation and value network building stages; importantly, this logic selection is not a static correspondence to experience type but a process-contingent outcome that unfolds across stages. The contributions of this study are threefold. First, the study develops and substantiates the proposition that experience configuration, through shaping cognitive and capability imprints, gives rise to either an imprinting mechanism or a reflexivity mechanism, which in turn governs entrepreneurs' decision logic across different stages of business model construction, thereby clearly delineating the differentiated pathways through which industry experience operates in the value proposition formation and value network building stages. Second, by incorporating entrepreneurs' subjective perception of environmental uncertainty into the analysis and integrating experiential endowments and situational conditions into a single mechanism chain, the study offers a more explanatory contingent account of the unresolved debate in extant research over the relationship between industry experience and causation/effectuation logics. Third, by introducing reflexivity into the business model construction context as an interactional construction mechanism that operates when prior endowments are insufficient, the study explains how, when the imprinting mechanism is unable to function effectively, entrepreneurs generate judgments of opportunity feasibility and understandings of resource matching through sustained environmental interaction. This process advances business model formation when the imprinting mechanism fails to function effectively, and it strengthens the process- and mechanism-level explanatory power of imprinting theory in this context.

As a key pillar of the national economy, the manufacturing industry faces significant challenges in energy consumption and pollutant emissions during its development. While green innovation serves as a crucial means to drive manufacturers toward greener and lower-carbon development, most manufacturing enterprises in China still rely on resource-intensive production models, and some firms engage only in perfunctory innovation to secure government subsidies and tax incentives, undermining genuine sustainable innovation capabilities and environmental performance. In recent years, the proportion and quality of climate risk disclosure by manufacturing enterprises in China have shown an increasing trend year by year. Climate risk perception helps optimize internal governance structures, enhance corporate green resilience, and improve trust among investors and consumers. Yet, existing research relies heavily on broad samples of listed firms, paying insufficient attention to the manufacturing sector. Mechanism analyses have focused narrowly on environmental management, disclosure practices, and ESG performance, yielding fragmented insights. Critical gaps remain: how executive environmental awareness, financing constraints, and green investment interact, and whether they form a serial mediation chain remains underexplored. Whether government R&D subsidies and media oversight can facilitate the innovative driving effect of corporate climate risk perception also warrants further exploration. Drawing on resource-based view and cognitive-behavioral theory, this study examines panel data from Chinese A-share manufacturing listed companies from 2010 to 2023 to empirically analyze the impact mechanism of climate risk perception on green innovation in manufacturing enterprises. The findings are as follows: (1) Climate risk perception can significantly promote green innovation in manufacturing enterprises, with notable effects on both invention-based and utility-based green innovation, a conclusion that remains valid after a series of robustness tests including the instrumental variable method and propensity score matching method; (2) Heterogeneity tests reveal that the impact of climate risk perception on green innovation in manufacturing enterprises exhibits heterogeneity in terms of ownership nature, pollution levels, industry attributes, and regional location, with more pronounced effects in state-owned enterprises, non-heavy-pollution enterprises, advanced manufacturing, and enterprises in eastern regions; (3) Mechanism tests indicate that climate risk perception primarily enhances green innovation levels in manufacturing enterprises through three pathways: strengthening executives' environmental awareness, alleviating financing constraints, and promoting green investment, with financing constraints having the strongest mediating effect, followed by green investment, while the role of executives' environmental awareness is relatively weaker. Moreover, a chain mediation effect exists among executives' environmental awareness, financing constraints, and green investment, with both government R&D subsidies and media supervision positively moderating the positive impact of climate risk perception on green innovation in manufacturing enterprises. The innovations of this study are as follows: In terms of research subjects, unlike prior studies using broad firm samples, this study concentrates on manufacturing enterprises with the manufacturing sector further subdivided into advanced manufacturing and traditional manufacturing. This disaggregation reveals previously masked heterogeneity in how climate risk perception shapes green innovation, thereby expanding the research scope of the relationship between climate risk perception and green innovation. Regarding mechanism identification, a chain mediation model of “environmental cognition-resource acquisition-green investment” was constructed based on cognitive behavioral theory to reveal the influence mechanism of climate risk perception on the green innovation of manufacturing enterprises. At the theoretical level, it clarifies the cascading mediation mechanism capturing how climate risk perception elevates environmental awareness, facilitates resource access, and catalyzes green investment. At the empirical level, the study employs the Bootstrap chain mediation effect test method to validate the effectiveness of the multi-stage chain transmission path. In terms of research context, from the perspectives of formal and informal institutions, this study investigates institutional environmental factors, such as government R&D subsidies and media supervision, to explore their moderating effects, providing empirical evidence and clear pathways for maximizing the innovative driving effects of climate risk perception.

The application of generative artificial intelligence (GenAI) has become a critical pathway for reshaping enterprise production management structures and enhancing competitiveness. Existing literature highlights its dual role in reducing R&D costs, automating workflows, and facilitating human-machine collaboration. While intelligent technologies are generally expected to drive productivity growth, there is a lack of systematic explanation of how generative artificial intelligence affects enterprise total factor productivity (TFP), with most mechanism analyses remaining at the level of technology-enablement hypotheses. The transmission path of "technology application → factor reorganization → efficiency leap" has yet to be empirically tested, and the "black box" between technological advantage and productivity improvement remains unexplored. Therefore, this study selects Chinese A-share manufacturing companies from 2017 to 2022 as samples and conducts an in-depth investigation into the mechanism of generative artificial intelligence in this critical sector. The study employs BERT intelligent text analysis to construct measurement indicators for GenAI adoption and systematically examines its impact on total factor productivity and underlying mechanisms. Baseline regression reveals that GenAI application significantly improves TFP. The promotion effect essentially reflects comprehensive outcomes of expanded enterprise resource bases, improved key resource allocation, and enhanced resource value release capabilities. Mechanism analysis identifies three critical channels: at the strategic resource level, GenAI optimizes R&D configuration; at the operational resource level, it improves internal management efficiency; and at the production factor resource level, it enhances data element utilization. These three layers collectively drive TFP improvement. Moderation analysis demonstrates that the relationship between GenAI and TFP is contingent on contextual conditions. Specifically, human capital upgrading within enterprise organizations and digital infrastructure improvement outside organizations exert positive moderating effects, amplifying AI's productivity benefits. Heterogeneity analysis reveals significant differences across enterprise characteristics and regional contexts. Regarding enterprise heterogeneity, while firms with different property rights all achieve productivity improvements when applying GenAI, the efficacy strongly depends on contextual features. The effects are more pronounced in non-labor-intensive, non-asset-intensive, and technology-intensive enterprises, whereas relatively limited in asset-intensive and labor-intensive firms. This indicates that GenAI functions as a knowledge-enabling technology that synergizes with flexible production structures and innovation activities, complementing high-skilled human capital and high-intensity innovation activities rather than simply replacing procedural labor. Regarding regional heterogeneity, significant effects in eastern and central regions confirm the supporting role of mature economic ecosystems, while the insignificant effect in the western region warns of potential "lag effects" in technology diffusion. Furthermore, when regions with high innovation capabilities apply GenAI, the impact on manufacturing TFP becomes more significant. Notably, regions with low levels of digital economic development exhibit a "filling effect", providing optimistic practical possibilities and theoretical inspirations for latecomers to achieve "technological leapfrogging" through strategic AI adoption. These findings provide empirical support for understanding how "artificial intelligence +" promotes manufacturing productivity. They verify that GenAI enhances TFP while revealing the internal mechanisms and practical boundaries of its effects. Specifically, GenAI is not a universally applicable and automatically effective "universal technology", but rather a strategic resource that can fully release its efficacy only under appropriate resource bases and environmental conditions. The productivity gains depend on whether AI can form effective coupling with enterprises' internal knowledge resources, organizational resources, and data resources. This conclusion offers explanatory evidence and theoretical inspiration for understanding how GenAI enables the real economy, promotes transformation and upgrading of manufacturing industries, and cultivates new quality productive forces.

With the strategic deployment of doing a good job in the five major areas of sci-tech finance, green finance, inclu sive finance, pension finance and digital finance, the high-quality development of sci-tech finance can optimize the alloca tion of financial resources, accelerate the flow of financial resources to the technology field, ensure the full life cycle fun ding supply of technology-based enterprises, promote the improvement of the quality and efficiency of technology financial services, diversify technology innovation risks, promote technological self-reliance and self-improvement, and promote the construction of a strong technological country. However, the current development process of sci-tech finance still faces problems such as mismatch between supply and demand for financing and imbalance in financing structure, etc. , which make it difficult to fully meet the financing needs of enterprises throughout their entire life cycle, limit the efficiency re lease of the financial system, and hinder the high-quality development of sci-tech finance. In this context, in-depth re search on the high-quality development level and dynamic evolution laws of sci-tech finance, and identifying its obstacle factors, holds significant theoretical and practical value for achieving high-level technological self-reliance and self-im provement and strengthening the foundation of a technological powerhouse. Existing research has largely focused on qualitative and quantitative analyses of sci-tech finance but has yet to system atically construct a scientifically rigorous evaluation index system grounded in high-quality development theory or measure quality levels accordingly. Moreover, existing studies have not specifically explored the dynamic evolution characteristics and barriers to high-quality sci-tech finance development. Addressing these gaps, this study constructs a scientific evalua tion index system for high-quality sci-tech finance development across five dimensions: development environment, policy support, service level, risk control capability, and innovation benefits. It employs comprehensive evaluation methods to systematically analyze quality levels across 30 Chinese provinces from 2011 to 2024, while utilizing kernel density estima tion, Markov chain analysis, and obstacle degree models to investigate dynamic evolution characteristics and hindering factors. The research results indicate that the overall level of high-quality development of sci-tech finance has steadily im proved from 2011 to 2024, but it is still in the early stages of development and has great potential for improvement. In terms of regions, the high-quality development level of sci-tech finance shows a distribution pattern of eastern>central> northeast>western. The dynamic evolution results reflect that the gap in high-quality development of sci-tech finance in the country and the four major regions is gradually widening, and polarization is occurring in the country and western re gions. At the same time, before introducing spatial factors, the high-quality development level of low, relatively low, rel atively high, and high-level types of sci-tech finance maintains a high level of stability and exhibits club convergence char acteristics. After introducing spatial factors, there are spatial spillover effects among different types of high-quality sci tech finance development, among which the positive spatial spillover effect is most significant in high-level regions. The diagnostic results of obstacle factors show that per capita regional GDP is the first obstacle factor for the high-quality de velopment of sci-tech finance in China. The ranking of the main obstacle factors in the four major regions and each prov ince in 2024 is heterogeneous compared to the national ranking. The study combines relevant policy documents and the theoretical connotation of high-quality development of sci-tech finance to construct a five in one evaluation index system for high-quality development of sci-tech finance, which includes "environment, policy, service, risk control, and efficiency", and scientifically measures its development level, filling the quantitative measurement gap of high-quality development level of sci-tech finance. From a high-quality development per spective, this study empirically examines evolution trends and spatial spillover effects in high-quality sci-tech finance devel opment, reveals the underlying mechanisms driving the "polarization phenomenon" and "club convergence" observed in the dynamic evolution process, and thoroughly explores factors hindering high-quality sci-tech finance development, there by providing a foundation for region-specific strategy formulation tailored to local conditions.

In the era of artificial intelligence (AI), computing power has emerged as a fundamental pillar underpinning technological advancement, integrating capabilities in information processing, network transmission, and data storage. As a critical form of digital infrastructure, computing infrastructure, such as supercomputing centers, plays a vital role in fostering enterprise-level AI innovation. However, the mechanisms through which computing infrastructure influences corporate AI technology innovation remain underexplored, particularly from a micro-level perspective. Existing literature has extensively examined the macroeconomic benefits of computing infrastructure and the economic consequences of AI technology innovation, yet the micro-level transmission mechanisms connecting the two remain largely unresolved. This theoretical gap arises partly because prior studies have not adequately distinguished between AI technology innovation and AI technology application, despite their fundamental differences: the effectiveness of AI application depends critically on breakthroughs in underlying R&D, while computing infrastructure serves as the foundational "computing base" for enterprise AI development. Consequently, unlocking the "black box" of how computing infrastructure drives corporate AI technology innovation is essential for informing targeted policy design and corporate strategic decisions. This study addresses this gap by examining the impact of computing infrastructure on AI innovation using the construction of National Supercomputing Centers in China as a quasi-natural experiment. Using panel data from A-share listed companies spanning 2007 to 2023, this study employs a double machine learning (DML) model to mitigate endogeneity and model specification biases. The DML framework effectively handles high-dimensional control variables and captures nonlinear relationships, thereby enhancing the robustness of causal inference. The baseline results indicate that computing infrastructure significantly promotes enterprise AI technology innovation, measured by the number of AI patent applications. Specifically, the presence of a supercomputing center in a firm’s host city leads to an average increase of 16.8% in AI innovation output. To uncover the underlying mechanisms, this study proposes and tests three enabling pathways: information empowerment, labor empowerment, and synergy empowerment. First, computing infrastructure enhances data asset disclosure, which signals firm potential to investors and alleviates financing constraints (information effect). Second, it optimizes the skill structure of the workforce by attracting and cultivating high-skilled labor, thereby strengthening internal innovation capabilities (labor effect). Third, it facilitates deeper collaboration within supply chains by reducing technical and transactional barriers, promoting knowledge sharing and joint innovation (synergy effect). Empirical tests confirm that all three mechanisms are statistically significant and economically meaningful. Heterogeneity analyses reveal that the positive impact of computing infrastructure is more pronounced in small-sized firms, digital economy sectors, and high-tech industries. These findings suggest that computing infrastructure serves as a critical equalizer for resource-constrained firms and a catalyst for innovation in digitally intensive and technology-driven sectors. Beyond AI innovation, this study also explores the green implications of computing infrastructure. Results show that it significantly reduces corporate carbon emissions and enhances green technology innovation, underscoring its role in supporting both digital and sustainable development goals. This study contributes to the literature in several ways. First, it bridges the gap between computing infrastructure and AI innovation at the micro-level, offering a novel theoretical and empirical perspective. Second, it delineates a comprehensive framework of enabling mechanisms, enriching the understanding of how computing resources translate into innovation outcomes. Third, it identifies key boundary conditions for the effectiveness of computing infrastructure, providing nuanced insights for targeted policy design. The findings offer important policy implications. Policymakers should prioritize the equitable spatial distribution of computing infrastructure, especially in underserved regions, and promote inclusive access for small and medium-sized enterprises. Enhancing supporting systems including data disclosure standards, skill development programs, and collaborative platforms can further amplify the enabling effects. Additionally, integrating green criteria into computing infrastructure planning can align digital transformation with national carbon neutrality goals.